The current plethora of imaging technologies such as magnetic resonance imaging (MR), computed tomography (CT), position emission tomography (PET), optical coherence tomography (OCT), and ultrasound provide great insight into the different anatomical and functional processes of the human body.
Computer vision is the science and technology of teaching a computer to interpret images and video as well as a typical human. Technically, computer vision encompasses the fields of image/video processing, pattern recognition, biological vision, artificial intelligence, augmented reality, mathematical modeling, statistics, probability, optimization, 2D sensors, and photography.
Extracting information from a digital image often depends on first identifying desired objects or breaking down the image into homogenous regions (a process called 'segmentation') and then assigning these objects to particular classes (a process called 'classification'). This is a fundamental part of computer vision, combining image processing and pattern recognition techniques.
The VIP lab has a particularly extensive history with multiresolution methods, and a significant number of research students have explored this theme. Multiresolution methods are very broad, essentially meaning than an image or video is modeled, represented, or features extracted on more than one scale, somehow allowing both local and non-local phenomena.
Remote sensing, or the science of capturing data of the earth from airplanes or satellites, enables regular monitoring of land, ocean, and atmosphere expanses, representing data that cannot be captured using any other means. A vast amount of information is generated by remote sensing platforms and there is an obvious need to analyze the data accurately and efficiently.
Scientific Imaging refers to working on two- or three-dimensional imagery taken for a scientific purpose, in most cases acquired either through a microscope or remotely-sensed images taken at a distance.
In many image processing, computer vision, and pattern recognition applications, there is often a large degree of uncertainty associated with factors such as the appearance of the underlying scene within the acquired data, the location and trajectory of the object of interest, the physical appearance (e.g., size, shape, color, etc.) of the objects being detected, etc.
Video analysis is a field within computer vision that involves the automatic interpretation of digital video using computer algorithms. Although humans are readily able to interpret digital video, developing algorithms for the computer to perform the same task has been highly evasive and is now an active research field.
Evolutionary Deep Intelligence
Deep learning has shown considerable promise in recent years, producing tremendous results and significantly improving the accuracy of a variety of challenging problems when compared to other machine learning methods.
Radiomics, which involves the high-throughput extraction and analysis of a large amount of quantitative features from medical imaging data to characterize tumor phenotype in a quantitative manner, is ushering in a new era of imaging-driven quantitative personalized cancer decision support and management.
Sports Analytics is a growing field in computer vision that analyzes visual cues from images to provide statistical data on players, teams, and games. Want to know how a player's technique improves the quality of the team? Can a team, based on their defensive position, increase their chances to the finals? These are a few out of a plethora of questions that are answered in sports analytics.
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What is Digital Image Processing?
Digital image processing is the process of using computer algorithms to perform image processing on digital images. Latest topics in digital image processing for research and thesis are based on these algorithms. Being a subcategory of digital signal processing, digital image processing is better and carries many advantages over analog image processing. It permits to apply multiple algorithms to the input data and does not cause the problems such as the build-up of noise and signal distortion while processing. As images are defined over two or more dimensions that make digital image processing “a model of multidimensional systems”. The history of digital image processing dates back to early 1920s when the first application of digital image processing came into news. Many students are going for this field for their m tech thesis as well as for Ph.D. thesis. There are various thesis topics in digital image processing for M.Tech, M.Phil and Ph.D. students. The list of thesis topics in image processing is listed here. Before going into topics in image processing , you should have some basic knowledge of image processing.
Latest research topics in image processing for research scholars:
- The hybrid classification scheme for plant disease detection in image processing
- The edge detection scheme in image processing using ant and bee colony optimization
- To improve PNLM filtering scheme to denoise MRI images
- The classification method for the brain tumor detection
- The CNN approach for the lung cancer detection in image processing
- The neural network method for the diabetic retinopathy detection
- The copy-move forgery detection approach using textual feature extraction method
- Design face spoof detection method based on eigen feature extraction and classification
- The classification and segmentation method for the number plate detection
- Find the link at the end to download the latest thesis and research topics in Digital Image Processing
Formation of Digital Images
Firstly, the image is captured by a camera using sunlight as the source of energy. For the acquisition of the image, a sensor array is used. These sensors sense the amount of light reflected by the object when light falls on that object. A continuous voltage signal is generated when the data is being sensed. The data collected is converted into a digital format to create digital images. For this process, sampling and quantization methods are applied. This will create a 2-dimensional array of numbers which will be a digital image.
Why is Image Processing Required?
- Image Processing serves the following main purpose:
- Visualization of the hidden objects in the image.
- Enhancement of the image through sharpening and restoration.
- Seek valuable information from the images.
- Measuring different patterns of objects in the image.
- Distinguishing different objects in the image.
Applications of Digital Image Processing
- There are various applications of digital image processing which can also be a good topic for the thesis in image processing. Following are the main applications of image processing:
- Image Processing is used to enhance the image quality through techniques like image sharpening and restoration. The images can be altered to achieve the desired results.
- Digital Image Processing finds its application in the medical field for gamma-ray imaging, PET Scan, X-ray imaging, UV imaging.
- It is used for transmission and encoding.
- It is used in color processing in which processing of colored images is done using different color spaces.
- Image Processing finds its application in machine learning for pattern recognition.
List of topics in image processing for thesis and research
- There are various in digital image processing for thesis and research. Here is the list of latest thesis and research topics in digital image processing:
- Image Acquisition
- Image Enhancement
- Image Restoration
- Color Image Processing
- Wavelets and Multi Resolution Processing
- Morphological Processing
- Representation and Description
- Object recognition
- Knowledge Base
1. Image Acquisition:
Image Acquisition is the first and important step of the digital image of processing . Its style is very simple just like being given an image which is already in digital form and it involves preprocessing such as scaling etc. It starts with the capturing of an image by the sensor (such as a monochrome or color TV camera) and digitized. In case, the output of the camera or sensor is not in digital form then an analog-to-digital converter (ADC) digitizes it. If the image is not properly acquired, then you will not be able to achieve tasks that you want to. Customized hardware is used for advanced image acquisition techniques and methods. 3D image acquisition is one such advanced method image acquisition method. Students can go for this method for their master’s thesis and research.
2. Image Enhancement:
Image enhancement is one of the easiest and the most important areas of digital image processing. The core idea behind image enhancement is to find out information that is obscured or to highlight specific features according to the requirements of an image. Such as changing brightness & contrast etc. Basically, it involves manipulation of an image to get the desired image than original for specific applications. Many algorithms have been designed for the purpose of image enhancement in image processing to change an image’s contrast, brightness, and various other such things. Image Enhancement aims to change the human perception of the images. Image Enhancement techniques are of two types: Spatial domain and Frequency domain.
3. Image Restoration:
Image restoration involves improving the appearance of an image. In comparison to image enhancement which is subjective, image restoration is completely objective which makes the sense that restoration techniques are based on probabilistic or mathematical models of image degradation. Image restoration removes any form of a blur, noise from images to produce a clean and original image. It can be a good choice for the M.Tech thesis on image processing. The image information lost during blurring is restored through a reversal process. This process is different from the image enhancement method. Deconvolution technique is used and is performed in the frequency domain. The main defects that degrade an image are restored here.
4. Color Image Processing:
Color image processing has been proved to be of great interest because of the significant increase in the use of digital images on the Internet. It includes color modeling and processing in a digital domain etc. There are various color models which are used to specify a color using a 3D coordinate system. These models are RGB Model, CMY Model, HSI Model, YIQ Model. The color image processing is done as humans can perceive thousands of colors. There are two areas of color image processing full-color processing and pseudo color processing. In full-color processing, the image is processed in full colors while in pseudo color processing the grayscale images are converted to colored images. It is an interesting topic in image processing.
Important Digital Image Processing Terminologies
- Stereo Vision and Super Resolution
- Multi-Spectral Remote Sensing and Imaging
- Digital Photography and Imaging
- Acoustic Imaging and Holographic Imaging
- Computer Vision and Graphics
- Image Manipulation and Retrieval
- Quality Enrichment in Volumetric Imaging
- Color Imaging and Bio-Medical Imaging
- Pattern Recognition and Analysis
- Imaging Software Tools, Technologies and Languages
- Image Acquisition and Compression Techniques
- Mathematical Morphological Image Segmentation
Image Processing Algorithms
In general, image processing techniques/methods are used to perform certain actions over the input images, and according to that, the desired information is extracted in it. For that, input is an image, and the result is an improved/expected image associated with their task. It is essential to find that the algorithms for image processing play a crucial role in current real-time applications. Various algorithms are used for various purposes as follows,
- Digital Image Detection
- Image Reconstruction
- Image Restoration
- Image Enhancement
- Image Quality Estimation
- Spectral Image Estimation
- Image Data Compression
For the above image processing tasks, algorithms are customized for the number of training and testing samples and also can be used for real-time/online processing. Till now, filtering techniques are used for image processing and enhancement, and their main functions are as follows,
- Brightness Correction
- Contrast Enhancement
- Resolution and Noise Level of Image
- Contouring and Image Sharpening
- Blurring, Edge Detection and Embossing
Some of the commonly used techniques for image processing can be classified into the following,
- Medium Level Image Processing Techniques – Binarization and Compression
- Higher Level Image Processing Techniques – Image Segmentation
- Low-Level Image Processing Techniques – Noise Elimination and Color Contrast Enhancement
- Recognition and Detection Image Processing Algorithms – Semantic Analysis
Next, let’s see about some of the traditional image processing algorithms for your information. Our research team will guide in handpicking apt solutions for research problems . If there is a need, we are also ready to design own hybrid algorithms and techniques for sorting out complicated model .
Types of Digital Image Processing Algorithms
- Hough Transform Algorithm
- Canny Edge Detector Algorithm
- Scale-Invariant Feature Transform (SIFT) Algorithm
- Generalized Hough Transform Algorithm
- Speeded Up Robust Features (SURF) Algorithm
- Marr–Hildreth Algorithm
- Connected-component labeling algorithm: Identify and classify the disconnected areas
- Histogram equalization algorithm: Enhance the contrast of image by utilizing the histogram
- Adaptive histogram equalization algorithm: Perform slight alteration in contrast for the equalization of the histogram
- Error Diffusion Algorithm
- Ordered Dithering Algorithm
- Floyd–Steinberg Dithering Algorithm
- Riemersma Dithering Algorithm
- Richardson–Lucy deconvolution algorithm : It is also known as a deblurring algorithm, which removes the misrepresentation of the image to recover the original image
- Seam carving algorithm : Differentiate the edge based on the image background information and also known as content-aware image resizing algorithm
- Region Growing Algorithm
- GrowCut Algorithm
- Watershed Transformation Algorithm
- Random Walker Algorithm
- Elser difference-map algorithm: It is a search based algorithm primarily used for X-Ray diffraction microscopy to solve the general constraint satisfaction problems
- Blind deconvolution algorithm : It is similar to Richardson–Lucy deconvolution to reconstruct the sharp point of blur image. In other words, it’s the process of deblurring the image.
Nowadays, various industries are also utilizing digital image processing by developing customizing procedures to satisfy their requirements. It may be achieved either from scratch or hybrid algorithmic functions . As a result, it is clear that image processing is revolutionary developed in many information technology sectors and applications.
Digital Image Processing Techniques
- In order to smooth the image, substitutes neighbor median / common value in the place of the actual pixel value. Whereas it is performed in the case of weak edge sharpness and blur image effect.
- Eliminate the distortion in an image by scaling, wrapping, translation, and rotation process
- Differentiate the in-depth image content to figure out the original hidden data or to convert the color image into a gray-scale image
- Breaking up of image into multiple forms based on certain constraints. For instance: foreground, background
- Enhance the image display through pixel-based threshold operation
- Reduce the noise in an image by the average of diverse quality multiple images
- Sharpening the image by improving the pixel value in the edge
- Extract the specific feature for removal of noise in an image
- Perform arithmetic operations (add, sub, divide and multiply) to identify the variation in between the images
Beyond this, this field will give you numerous Digital Image Processing Project Topics for current and upcoming scholars . Below, we have mentioned some research ideas that help you to classify analysis, represent and display the images or particular characteristics of an image.
Latest 11 Interesting Digital Image Processing Project Topics
- Acoustic and Color Image Processing
- Digital Video and Signal Processing
- Multi-spectral and Laser Polarimetric Imaging
- Image Processing and Sensing Techniques
- Super-resolution Imaging and Applications
- Passive and Active Remote Sensing
- Time-Frequency Signal Processing and Analysis
- 3-D Surface Reconstruction using Remote Sensed Image
- Digital Image based Steganalysis and Steganography
- Radar Image Processing for Remote Sensing Applications
- Adaptive Clustering Algorithms for Image processing
Moreover, if you want to know more about Digital Image Processing Project Topics for your research, then communicate with our team. We will give detailed information on current trends, future developments, and real-time challenges in the research grounds of Digital Image Processing.
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Digital Image Processing Thesis Topics
Digital Image Processing Thesis Topics is our amazing service that helps you at right time. Our expert team provides timely solutions for all the problems you never have from other service providers. Over the last ten decades, image processing is rapidly growing in a wide range of application and industrial fields. We offer a training program for our students to get much information about image processing. Our experts are truly paramount of knowledge with potential power who offers quick responses for students. A thesis in digital image processing is a huge task. First, you plan to identify an area of interest within the field of Image processing.
Our top experts guide you to choose a realistic topic/research problem for your final year projects. Before writing your thesis, we provide you a well-defined research plan with composed research work. This makes you as my choice was the best than others. To help you, contact us for your topic selection and thesis writing.
Image Processing Thesis Topics
Digital Image Processing Thesis Topics is our domain research service created for students with collaborative effort of our top professionals. Our current trend updated technical team expert in the various sub-fields of digital image processing includes imaging, digital photography, and also computer graphics and simulation. We offer you manageable, unique, and well-researched thesis topics so you can choose any one of the specific research topics. We have completed 5000+ Digital Image Processing Thesis Projects worldwide. Our image processing experts are specialized in operations, digital imaging, applications, techniques, and methods. Get come closer to our experts for your Digital Image Processing also in Thesis Topics. If we go and work towards in-depth research, we can find the New Oceans. Here’s we have provided the list of image processing software.
Image Processing Software
Categories of Software:
- 3D graphics software: Image Studio Lite, Image SXM, AutoCAD, also in 3D animation software, free 3D graphics software, RenderMan, Global Illumination software and also Shading languages.
- Computer vision software: OpenCV, Bing Audio, Bing Vision, Dlib, Avizo, AVM navigator, Animal, Insight Segmentation and also registration Toolkit and Softwarp.
- Neuro imaging software: Amira, Anayze, AIR, Caret, CONN, Cambridge brain analysis, Dextroscope, Fiji, RapidMiner, and also Mango, MindRDR, LONI pipeline and Spinal Cord Toolbox
- Bioimaging Software : 3D Slicer, DICOM, FindFace, DeepFace, Heather Dewey-Hagborg, Othanc, and also Drishti, Gimias, Ginkgo CADx, ImageJ, Invesalius, ITK-SNAP, Voreen, and Xebra
Digital Image Processing Ideas
- Image Enhancement using point operations
- Data Compression
- Simple Dictionary Compression
- Image Blur and Calibration
- Color and Contrast Enhancement
- Image Denoising
- Image inpainting
- Images Comparison
- Optical Flow
- Satellite imaging
- Edges and segmentation
- Vision through Turbulence
- Color correction
- 2D Fourier Transform and Convolution
- Linear filtering
- Image Rotation and Sampling
- Noise Reduction
- High Dynamic Range Imaging
- Image Compositing
- Mathematical Morphology also for Image Processing
Latest Digital Image Processing Thesis Topics
- Uncorrelated component analysis also based hashing in digital image processing
- Glacial lake outlines in tablet plateau also based on Landsat 8 imagery and Google earth engine
- An efficient method also for fast multi exposure image fusion
- Microfluidic PCB enabled digital signal processing also for on-chip fluorescence detection
- Remote sensing image denoising also using parallel nonlocal means algorithm on Intel Xeon Phi Platform
- Screen content pictures quality assessment also using Matlab in Image Processing
- Uncertainty Aware Evaluator and also Local Consistency Aware Retriever for Blind Image Quality Assessment
- Support vector machine classification also for retinal blood vessel segmentation
- Accelerated cover selection steganography also using digital image processing techniques
- High accuracy tidal flat digital evaluation model construction based on TanDEM-X Science Phase Data
- Least two significant bits based adaptive tri-pixel unit steganography algorithm
- Real time non local means also based despeckling using digital image processing
- Unseen visible watermarking improved also for copyright protection of digital images
- Image to sensor also based comparative study of PRNU multiple estimation schemes for sensors identification from NIR iris images
These are the topics that are currently working by our top experts, and they can give you guidance in deciding where to begin your preliminary research. We hope you feel satisfied with this information. For further information, you can visit our other articles. You can also visit our experts online 24/7.
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- Digital Image Processing Topics
Learn more about Image Processing here, we have listed latest list of digital image processing topics for thesis and research. Digital image processing furnishes the well-established platform for employing complex approaches for processing digital images to enhance image interpretation and representation. And, it can even perform operations that are hard to work with analog processing methods. Hence, it is widely used in many research fields that deal with image processing areas.
What are the Types of Image Processing?
In general, there are two major categories of techniques for image processing. And they are analog image processing and digital image processing . Analog image processing takes place in 2D analog signals, which are used for photographic films and printouts. Here, image analysts use various chemical components to develop and visualize the image. But in the case of digital image processing, it is processed through intelligent algorithms and approaches in the digital computer where the output is also in the digital image.
We provide the following descriptions for any digital image processing topics,
- Different Digital Image Processing Technologies
- Fundamental Theories and Perception
- Various Research Areas that are currently evolved
- Related Conceptual Concepts / Schemes
- Problem Solving Approaches and Procedures
- Mathematical and Numerical Functions Equation / Formulae
- Own Algorithm Pseudocode for Complex Problem
- Overall System Architecture, UML Diagrams, and Data Flow Chart
- Comparative Study of Different Scenarios and Datasets
- System Performance Evaluation and Analysis
- Information about the Simulation Blocks and Test Results
- Final Discussion on Experimental Outcome
This page is about the emerging Digital Image Processing Topics and current research areas with future advances!!!
Image processing is an extensive research area that can be recognized in all the research fields in some aspects. That is to say; it spreads its footprints in all the dimensions of the real-world environment, which ranges from leaf identification to patient disorder prediction and analysis. For your reference,
here we have listed few important image processing applications .
- Vision-assisted Robotic System.
- Bar Code and QR Code Scanner / Reader
- Social Websites and Apps Development (For instance: Instagram and Snapchat)
- iPhone’s Face Recognition and Unlock System
- Digital-based Computational photography
- Automated assembly based on Sensor for Object Detection
- Advance Processing of Astronomical or Planetary images (For instance: Images of Space Probe and Hubble Telescope Pictures)
- Remote Sensing and Visual Interpretation (For instance: Satellite and Aerial Image)
- Automated Optical or Handwritten Character Recognition (For instance: License Plate and Zip Code)
- Industrial Manufacturing Applications (For instance: Product Optical Sorting and Assessment)
- Biometric Authentication Technologies (For instance: Face, Iris and Finger Print Identification)
- Bio-Medical Imaging and Processing (For instance: Blood Cell Microscope Images and Chest Radiograph Interpretation)
Then, our research team has given you the algorithms that are broadly used in digital image processing projects . Each algorithm has different nature to support various needs of the image processing operations. Here, we have listed some algorithms with their usage based on common categories.
What are the Important Algorithms in Image Processing?
- Classification Ensembles Purpose : Improve the predictive performance and image classification
- Algorithms : Random forest algorithms, random subspace learning, and boosting (Catboost, XGboost)
- Purpose: Search and categorize the pattern
- Algorithms: KD tree-based K nearest neighbors classification algorithm
- Purpose : Analyze various elements to forecast the multi-class probability
- Algorithms : Multinomial and Gaussian naive Bayes algorithms
- Purpose: Classify into multiple different classes
- Algorithms: Binary decision trees
- Purpose: Interpretable model is made up of univariate and bivariate shape methods used to stop the overfitting issue
- Algorithms: Binary classification
- Purpose: Detect hidden relations between attributed
- Algorithms: Binary-Neural networks and multiclass-Neural network classifications
- Purpose: Normalizing and minimizing the dimension
- Algorithms: Quadratic / linear discriminant analysis algorithms
- Purpose: Assess the performance of flowing data
- Algorithms : Fit classification model
- Purpose: Distinguish the labeled and unlabeled data
- Algorithms: Self-training learning and Graph-based learning algorithms
- Purpose: Incorporate decision plane method for analyzing and classify the data
- Algorithms: Multi-class and binary SVM classification
For your benefit , our resource team has shared some advanced research dip project ideas that create an incredibly positive impact on future image and video processing projects . Here, we have given only a few digital image processing topics for your awareness; more than this, we have a massive number of futuristic research topics. You can make a bond with us to know other technical advancements.
Latest Digital Image Processing Topics
- Applications: Advanced Optimization Methods for Multi-variable problems
- Object Detection in Real-time Video Streaming
- DL assisted Video Analysis
- Learning-based Object Identification
- Object Classification Using Learning Techniques
- Bag-of-Features Method for Image Classification
- Medical Fusion Methods for Image enhancements
- Custom based Bag of Features / Models for Image Retrieval
For illustration purposes, here we have taken the one real-time sample application as “Root Analysis in Precision Agriculture.” Here, we itemized the implementation plan for the computing root volume for analyzing the productivity of the plant. It helps to increase plant growth and production . In addition, it can also be extended in other soil crops and hydroponics.
Root Analysis in Precision Agriculture Applications
- Step 1 – Convert the RGB image to a Gray-scale image
- Step 2 – Enhance the Contrast of the image
- Step 3 – Perform Binarize to avoid background noise
- Step 4 – Employ the suitable filter and mask techniques
- Minimize the noise in the image
- Delimit area of interest (mask)
- Step 5 – Implement “AND” operation among mask and processed image
Next, we can see the development of technologies and tools for digital image processing projects . Each tool has special functionalities and different characteristics. So, when you handpick the tool, consider the features and think about which tool gives accurate results for the proposed topic .
Digital Image Processing Tools
- And also many more
Below, our research team has given the dataset that is popularly used while practically implementing the Digital Image Processing Topics . Our developers also help you in selecting datasets since datasets are more important for processing and analysis.
Famous Digital Image Processing Datasets
Waymo Open Dataset
- Used for training self-driving vehicles.
- Contains driving videos with marked objects and followings,
- 3,000 driving videos
- 600,000 frames
- 22 million 2D object boundaries
- 25 million 3D object boundaries
- Intended to exclude the video uniformity issue
- Include several video processing options: lighting, construction sites, weather, cyclists, and pedestrians
- Different kinds of data will eventually increase the model’s generalization capability
- Used for simplification purposes by using neural networks
- Include real-world unlabeled sketches and labeled images
- Include dynamically changing content with the followings,
- 1000 classes (nearly)
- 23700 designs (nearly)
- Used for totaling objects present in the drone images
- It contains 441642 objects and 31,064 images with object class and boundaries
- Include 15532 RGB drone shots and infrared shots for each and every image
If you are interested to know new updates on current Digital Image Processing Topics , then contact our team. Further, we also help you in developing your own novel research ideas.
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Research Topics of Document Image processing
Research Area/ Research Interest: Document Image processing
Research Paper Topics for: Masters and PhD Thesis and publication
- Document Image Processing- A Review
- Hyperspectral document image processing: Applications, challenges and future prospects
- Image processing methods for document image analysis
- Complex image processing with less data—Document image binarization by integrating multiple pre-trained U-Net modules
- Document image analysis
- Document image binarization: Evaluation of algorithms
- An integrated system for handwritten document image processing
- ICDAR 2013 document image binarization contest (DIBCO 2013)
- A prototype document image analysis system for technical journals
- Adaptive document image binarization
- Adaptive degraded document image binarization
- AdOtsu: An adaptive and parameterless generalization of Otsu’s method for document image binarization
- ICDAR 2009 document image binarization contest (DIBCO 2009)
- Document image analysis: A primer
- Structured document image analysis
- Document image processing for paper side communications
- Document image noises and removal methods
- An effective document image deblurring algorithm
- Document image binarization based on texture features
- Document Image Understanding.
- Document image processing based on enhanced border following algorithm
- A rule-based system for document image segmentation
- Robust document image binarization technique for degraded document images
- Document image quality assessment: A brief survey
- Multiresolution morphological approach to document image analysis
- Document image processing—new light on an old problem
- A document image retrieval system
- Document image defect models
- H-DIBCO 2010-handwritten document image binarization competition
- Progress in camera-based document image analysis
- Document Image Processing for Scanning and Printing
- ICDAR2017 competition on document image binarization (DIBCO 2017)
- Combination of document image binarization techniques
- Document image binarization with fully convolutional neural networks
- ICFHR2014 competition on handwritten document image binarization (H-DIBCO 2014)
- Document image analysis for World War II personal records
- The SCRIBO module of the Olena platform: a free software framework for document image analysis
- Adaptive document image thresholding using foreground and background clustering
- Web-based document image processing
- Twenty years of document image analysis in PAMI
- A survey on document image processing methods useful for assistive technology for the blind
- Restoration of degraded historical document image
- GRPOLY-DB: An old Greek polytonic document image database
- Hierarchical content classification and script determination for automatic document image processing
- Page segmentation and content classification for automatic document image processing
- Document image mosaicing
- Convolutional neural networks for document image classification
- Shirorekha extraction in Character Segmentation for printed devanagri text in Document Image Processing
- Signature detection and matching for document image retrieval
- Document image processing
- Adaptive thresholding technique for document image analysis
- ICFHR 2012 competition on handwritten document image binarization (H-DIBCO 2012)
- ICFHR2016 handwritten document image binarization contest (H-DIBCO 2016)
- Insights on the use of convolutional neural networks for document image binarization
- A cylindrical surface model to rectify the bound document image
- Document image understanding: Geometric and logical layout
- Visual appearance based document image classification
- Conversion of PDF documents into HTML: a case study of document image analysis
- Learning document image binarization from data
- Digital libraries and document image analysis
- Retrieval from document image collections
- Document image binarization using lstm: A sequence learning approach
- i DocChip: A Configurable Hardware Architecture for Historical Document Image Processing
- A deep learning approach to document image quality assessment
- The development of a general framework for intelligent document image retrieval
- The detection of duplicates in document image databases
- Difficult and urgent open problems in document image analysis for libraries
- Improved document image binarization by using a combination of multiple binarization techniques and adapted edge information
- A rule learning method for academic document image processing
- iDocChip: A Configurable Hardware Accelerator for an End-to-End Historical Document Image Processing
- Document image binarization using background estimation and stroke edges
- An objective evaluation methodology for document image binarization techniques
- Digipaper: A versatile color document image representation
- Performance evaluation methodology for historical document image binarization
- DIBCO 2009: document image binarization contest
- Hidden tree Markov models for document image classification
- An algorithm for the skew normalization of document image
- State estimation in a document image and its application in text block identification and text line extraction
- Correcting document image warping based on regression of curved text lines
- Adaptive binarization method for document image analysis
- Document image recognition based on template matching of component block projections
- A selectional auto-encoder approach for document image binarization
- Treatment of diagrams in document image analysis
- An enhanced algorithm for Character Segmentation in document image processing
- Text extraction and document image segmentation using matched wavelets and MRF model
- Document image layout comparison and classification
- Document image enhancement using directional wavelet
- Handwritten document image segmentation into text lines and words
- High quality document image compression with
- Document image binarisation using a supervised neural network
- Performance evaluation of document image algorithms
- FAIR: a fast algorithm for document image restoration
- Document image dewarping using robust estimation of curled text lines
- Document image binarisation using markov field model
- Document image compression and analysis
- Document image processing for hospital information systems
- Document image binarization using recurrent neural networks
- Document image decoding using Markov source models
- Cascading modular u-nets for document image binarization
- Document image retrieval: An overview
- Document image binarization based on NFCM
- Morphological degradation models and their use in document image restoration
- Improved binarization algorithm for document image by histogram and edge detection
- Text identification for document image analysis using a neural network
- Logo matching for document image retrieval
- Document image analysis with OCRopus
- Table detection from document image using vertical arrangement of text blocks
- Information retrieval in document image databases
- Document image skew detection: Survey and annotated bibliography
- Document image binarization based on topographic analysis using a water flow model
- Historical document image binarization: A review
- Real-time document image retrieval for a 10 million pages database with a memory efficient and stability improved llah
- Low quality document image modeling and enhancement
- Image processing for document reproduction
- Binarization of document image using optimum threshold modification
- Learning surrogate models of document image quality metrics for automated document image processing
- idocchip-a configurable hardware architecture for historical document image processing: Text line extraction
- Sdk reinvented: Document image analysis methods as restful web services
- Msdb-nmf: Multispectral document image binarization framework via non-negative matrix factorization approach
- Dewarping of document image by global optimization
- Robust document image dewarping method using text-lines and line segments
- Visual and textual deep feature fusion for document image classification
- Document image segmentation using wavelet scale-space features
- Groundtruth generation and document image degradation
- Guest Editor’s Introduction: Document Image Analysis Systems
- Using convolutional encoder-decoder for document image binarization
- PCA-initialized deep neural networks applied to document image analysis
- User-assisted archive document image analysis for digital library construction
- Document image retrieval based on density distribution feature and key block feature
- A methodology for document image dewarping techniques performance evaluation
- Real-time document image retrieval on a smartphone
- Document image classification: Progress over two decades
- Madonne: document image analysis techniques for cultural heritage documents
- Fast and efficient document image clean up and binarization based on retinex theory
- Document image clean-up and binarization
- Cross-Modal Deep Networks For Document Image Classification
- U-Net-bin: hacking the document image binarization contest
- Learning 2d morphological network for old document image binarization
- DivaServices—A RESTful web service for Document Image Analysis methods
- Document image de-warping for text/graphics recognition
- Web document image retrieval system based on word spotting
- Image processing and pattern recognition: fundamentals and techniques
- The state of the art of document image degradation modelling
- Comparison and Analysis of Several Document Image Binarization Algorithms
- Composition of a dewarped and enhanced document image from two view images
- A survey of document image classification: problem statement, classifier architecture and performance evaluation
- Large scale parallel document image processing
- A proposal on digital watermark in document image communication and its application to realizing a signature
- Evaluation of deep convolutional nets for document image classification and retrieval
- Handwritten document image segmentation and analysis
- Farsi document image recognition system using word layout signature
- MAP-MRF approach for binarization of degraded document image
- Document Image Analysis: Current Trends and Challenges in Graphics Recognition
- Automatic document image binarization using bayesian optimization
- A method for combining complementary techniques for document image segmentation
- Neural networks for document image preprocessing: state of the art
- Document image matching based on component blocks
- Document image retrieval using signatures as queries
- Efficient transcript mapping to ease the creation of document image segmentation ground truth with text-image alignment
- Signature-based document image retrieval
- Deep networks for degraded document image binarization through pyramid reconstruction
- Camera based document image retrieval with more time and memory efficient LLAH
- Document image retrieval through word shape coding
- Image enhancement and image restoration for old document image using genetic algorithm
- A Scanned Document Image Processing Model for Information System
- Using morphology in document image processing
- Document image binarization using a discriminative structural classifier
- Use of document image processing in cancer registration: how and why?
- A wavelet approach to double-sided document image pair processing
- Unsupervised neural domain adaptation for document image binarization
- A robust skew detection algorithm for grayscale document image
- idocchip: A configurable hardware architecture for historical document image processing: Percentile based binarization
- A survey on document image binarization techniques
- Document image database retrieval and browsing using texture analysis
- Document image binarization techniques, developments and related issues: a review
- A proposed optimum threshold level for document image binarization
- Handwritten document image binarization: An adaptive K-means based approach
- Degraded document image binarization based on combination of two complementary algorithms
- Document image quality assessment based on improved gradient magnitude similarity deviation
- Rectifying the bound document image captured by the camera: A model based approach
- Document image retrieval with local feature sequences
- Bag–of–colors for biomedical document image classification
- Image and document processing techniques for the RightPages electronic library system
- An improved scene text and document image binarization scheme
- Document image data hiding technique using character spacing width sequence coding
- Fast document image binarization based on an improved adaptive Otsu’s method and destination word accumulation
- Word-level access to document image datasets
- The integration of document image processing and text retrieval principles
- Document image matching and retrieval with multiple distortion-invariant descriptors
- Restoration based Contourlet Transform for historical document image binarization
- Document image characterization using a multiresolution analysis of the texture: application to old documents
- Improved document image segmentation algorithm using multiresolution morphology
- An efficient method for the skew normalization of a document image
- Document image processing methods for active forms
- Text region extraction in a document image based on the Delaunay tessellation
- Effective document image deblurring via gradient histogram preservation
- Weighted PCA for improving Document Image Retrieval System based on keyword spotting accuracy
- Example-based single document image super-resolution: a global map approach with outlier rejection
- Document image quality assessment using discriminative sparse representation
- A review on document image binarization technique for degraded document images
- AGORA: the interactive document image analysis tool of the BVH project
- Word-level multi-script Indic document image dataset and baseline results on script identification
- Selection criteria for a document image processing system for the ELINOR Electronic Library project
- First maurdor 2013 evaluation campaign in scanned document image processing
- DP-LinkNet: A convolutional network for historical document image binarization
- Mechanism for Structuring the Data from a Generic Identity Document Image using Semantic Analysis
- Adaptive deblurring for camera-based document image processing
- Blind document image quality prediction based on modification of quality aware clustering method integrating a patch selection strategy
- Document image mosaicing with mobile phones
- Identification of Kashmiri script in a bilingual document image
- Quality assurance for document image collections in digital preservation
- An Entropy-Based Binarization Method to Separate Foreground from Background in Document Image Processing.
- Document image retrieval to support reading Mokkans
- Directional Wavelet Approach to Remove Document Image Interference.
- Influence of color-to-gray conversion on the performance of document image binarization: Toward a novel optimization problem
- Semi-structured document image matching and recognition
- Scanner-model-based document image improvement
- Multimodal document image classification
- Two stream deep network for document image classification
- Various document image mosaicing method in image processing: A survey
- Embedded textual content for document image classification with convolutional neural networks
- Logo and seal based administrative document image retrieval: a survey
- Document image OCR accuracy prediction via latent Dirichlet allocation
- A learning framework for degraded document image binarization using Markov random field
- Modeling adaptive degraded document image binarization and optical character system
- An improved adaptive document image binarization method
- Complex and degraded color document image binarization
- Gabor filters for degraded document image binarization
- A noise attribute thresholding method for document image binarization
- Review on extraction techniques for images, textlines and keywords from document image
- Fingerprint-based document image retrieval
- Document image retrieval system using character candidates generated by character recognition process
- Historical document image binarization using background estimation and energy minimization
- Document image matching using a maximal grid approach
- Historical document image restoration using multispectral imaging system
- Document image binarization based on stroke enhancement
- Camera-based document image mosaicing
- Binarizing document image using coplanar prefilter
- Hangul document image retrieval system using rank-based recognition
- Cutting the error by half: Investigation of very deep cnn and advanced training strategies for document image classification
- A survey on document image analysis and retrieval system
- A deep transfer learning approach to document image quality assessment
- Study on the model of document image processing
- Contrast enhancement of degraded document image using partitioning based genetic algorithm
- Degraded document image enhancement
- Content-based document image retrieval in complex document collections
- A review on document image analysis techniques directly in the compressed domain
- An improved binarization algorithm based on a water flow model for document image with inhomogeneous backgrounds
- Document image binarization using threshold segmentation
- An automated generation of an electronic library based on document image understanding
- Exploiting state-of-the-art deep learning methods for document image analysis
- The wordometer–estimating the number of words read using document image retrieval and mobile eye tracking
- Document image summarization without OCR
- Ancient degraded document image binarization based on texture features
- A comparative study of different texture features for document image retrieval
- Degraded document image binarization combining local statistics
- A document image segmentation system using analysis of connected components
- No-reference document image quality assessment based on high order image statistics
- Watershed based document image analysis
- Document image applications
- Writer identification in a handwritten document image using texture features
- Performance evaluation methodology for document image dewarping techniques
- Degraded document image binarization using structural symmetry of strokes
- Javanese character image segmentation of document image of Hamong Tani
- A document image model and estimation algorithm for optimized JPEG decompression
- A proposal of a document image reading-life log based on document image retrieval and eyetracking
- Palm leaf manuscript/color document image enhancement by using improved adaptive binarization method
- Direct Processing of Run Length Compressed Document Image for Segmentation and Characterization of a Specified Block
- An object attribute thresholding algorithm for document image binarization
- Towards a structured-document-image utility
- Document image retrieval using deep features
- Script identification of document image analysis
- Image processing based degraded camera captured document enhancement for improved OCR accuracy
- Augment document image binarization by learning
- Document image analysis for active reading
- Camera-based document image mosaicing using LLAH
- Multi-spectral document image binarization using image fusion and background subtraction techniques
- Wavelet based co-occurrence histogram features for texture classification with an application to script identification in a document image
- Gabor filter-based texture for ancient degraded document image binarization
- Document image preprocessing based on optimal Boolean filters
- Duplicate detection for quality assurance of document image collections
- Document image template matching based on component block list
- Planting, growing, and pruning trees: Connected filters applied to document image analysis
- Document image binarization based on texture analysis
- Document image binarization with cascaded generators of conditional generative adversarial networks
- Document Image Retrieval Based on Layout Structural Similarity.
- Introductory digital image processing: a remote sensing perspective
- CMATERdb1: a database of unconstrained handwritten Bangla and Bangla–English mixed script document image
- Versatile document image content extraction
- Document image matching and annotation lifting
- Document image defect models and their uses
- Document image segmentation using deep features
- Document image analysis and verification using cursive signature
- Edge-preserving prefiltering for document image binarization
- Parameter tuning for document image binarization using a racing algorithm
- List of Journals on Digital image Processing, Usability and Vision
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- Research Topics Ideas of Natural language processing
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- Research Topics statistical signal processing
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Research Topics of Document Image processing · High quality document image compression with · Document image binarisation using a supervised neural network
Digital Image Processing Phd Topics Areas · DSP systems · Medical Image processing · Wireless sensor networks · LTE networks · Advanced Robotics and Machine vision
3-D Volumetric Image Processing · Segment Lungs from 3-D Chest Scan · Register Multimodal 3-D Medical Images · 3-D Brain Tumor Segmentation Using Deep Learning
1) detecting whether two pictures contain the same object from different directions, say a house or car. 2) figuring out a way to compensate for time of day (
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