Flag segmentation, feature extraction & identification using support vector m...R M Shahidul Islam Shahed
Develop a system that can identify flags embedded in photos of natural scenes.
Develop a system that can segment a flag portion automatically accurately.
Reduce the identification time and produce a good result.
Apply Support Vector Machine(SVM) to generate the correct Result.
Comparative study on image segmentation techniquesgmidhubala
This document discusses various image processing and analysis techniques. It describes image segmentation as separating an image into meaningful parts to facilitate analysis. Common segmentation techniques mentioned include thresholding, edge detection, color-based segmentation, and histograms. Thresholding involves separating foreground and background using a threshold value. Edge detection finds edges and contours. Color segmentation extracts information based on color. Histograms locate clusters of pixels to distinguish regions. The document provides examples of applying these techniques and concludes that segmentation partitions an image into homogeneous regions to extract high-level information.
AN IMPLEMENTATION OF ADAPTIVE PROPAGATION-BASED COLOR SAMPLING FOR IMAGE MATT...ijiert bestjournal
Natural image matting refers to the problem of an e xtracting the region of interest such as foreground object from an image based on the user i nputs like scribbles or trimap. The proposed algorithm combines propagation and color s ampling methods. Unlike previous propagation-based approaches that used either local or non local propagation method,the proposed framework adaptively uses both local and n on local processes according to the detection result of the different region in the ima ge. The proposed color sampling strategy,which is based on the characteristic of super pixel uses a simple sample selection criterion and requires significantly less computational cost. Proposed method used another method to convert original image to trimap image,which is ba sed on selection process. That use roipoly tool to select a polygonal region of interest withi n the image,it can use as a mask for masked filtering. In which used the Chan-Vese algorithm fo r image segmentation
An Edge Detection Method for Hexagonal ImagesCSCJournals
This paper presents a morphological image processing operation for hexagonally sampled images and proposes a new edge detection method for these images by using a grayscale morphology. This is achieved by applying morphological gradient operators and multiscale top-hat transformations (white and black top-hat transformations) to hexagonal images. The proposed study includes a method for converting hexagonally sampled images as well as the processing and subsequent display of images on a hexagonal grid. Performance evaluation were performed to assess the proposed method. The proposed study shows that a method of edge enhancement by applying three by three hexagonal structuring element achieves results superior to those of a rectangular images. The results indicated that the proposed edge detection algorithms improved substantially after implementation of the edge enhancement method.
A Probabilistic U-Net for Segmentation of Ambiguous ImagesSeunghyun Hwang
Review : A Probabilistic U-Net for Segmentation of Ambiguous Images
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
Design and optimization of compact freeform lens array for laser beam splitti...Milan Maksimovic
"Design and optimization of compact freeform lens array for laser beam splitting: a case study in optimal surface representation", in Optical Modelling and Design III, Frank Wyrowski; John T. Sheridan; Jani Tervo; Youri Meuret, Editors, Proceedings of SPIE Vol. 9131 (SPIE, Bellingham, WA 2014), 913107.
Template matching is a technique used to classify objects by comparing portions of images against templates. It involves moving a template image across a larger source image to find the best match based on pixel-by-pixel comparisons of brightness levels. For gray-level images, the difference in brightness levels at each pixel location is used rather than a simple yes/no match. Template matching is commonly used to identify simple objects like printed characters. Matlab examples demonstrate template matching on sample data sets and correlation maps show the strength of matches across the source images.
Modeling and optimization of high index contrast gratings with aperiodic topo...Milan Maksimovic
"Modeling and optimization of high index contrast gratings with aperiodic topologies", in Modeling Aspects in Optical Metrology IV, Bernd Bodermann, Editors, Proceedings of SPIE Vol. 8789 (SPIE, Bellingham, WA 2013), 87890L
Flag segmentation, feature extraction & identification using support vector m...R M Shahidul Islam Shahed
Develop a system that can identify flags embedded in photos of natural scenes.
Develop a system that can segment a flag portion automatically accurately.
Reduce the identification time and produce a good result.
Apply Support Vector Machine(SVM) to generate the correct Result.
Comparative study on image segmentation techniquesgmidhubala
This document discusses various image processing and analysis techniques. It describes image segmentation as separating an image into meaningful parts to facilitate analysis. Common segmentation techniques mentioned include thresholding, edge detection, color-based segmentation, and histograms. Thresholding involves separating foreground and background using a threshold value. Edge detection finds edges and contours. Color segmentation extracts information based on color. Histograms locate clusters of pixels to distinguish regions. The document provides examples of applying these techniques and concludes that segmentation partitions an image into homogeneous regions to extract high-level information.
AN IMPLEMENTATION OF ADAPTIVE PROPAGATION-BASED COLOR SAMPLING FOR IMAGE MATT...ijiert bestjournal
Natural image matting refers to the problem of an e xtracting the region of interest such as foreground object from an image based on the user i nputs like scribbles or trimap. The proposed algorithm combines propagation and color s ampling methods. Unlike previous propagation-based approaches that used either local or non local propagation method,the proposed framework adaptively uses both local and n on local processes according to the detection result of the different region in the ima ge. The proposed color sampling strategy,which is based on the characteristic of super pixel uses a simple sample selection criterion and requires significantly less computational cost. Proposed method used another method to convert original image to trimap image,which is ba sed on selection process. That use roipoly tool to select a polygonal region of interest withi n the image,it can use as a mask for masked filtering. In which used the Chan-Vese algorithm fo r image segmentation
An Edge Detection Method for Hexagonal ImagesCSCJournals
This paper presents a morphological image processing operation for hexagonally sampled images and proposes a new edge detection method for these images by using a grayscale morphology. This is achieved by applying morphological gradient operators and multiscale top-hat transformations (white and black top-hat transformations) to hexagonal images. The proposed study includes a method for converting hexagonally sampled images as well as the processing and subsequent display of images on a hexagonal grid. Performance evaluation were performed to assess the proposed method. The proposed study shows that a method of edge enhancement by applying three by three hexagonal structuring element achieves results superior to those of a rectangular images. The results indicated that the proposed edge detection algorithms improved substantially after implementation of the edge enhancement method.
A Probabilistic U-Net for Segmentation of Ambiguous ImagesSeunghyun Hwang
Review : A Probabilistic U-Net for Segmentation of Ambiguous Images
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
Design and optimization of compact freeform lens array for laser beam splitti...Milan Maksimovic
"Design and optimization of compact freeform lens array for laser beam splitting: a case study in optimal surface representation", in Optical Modelling and Design III, Frank Wyrowski; John T. Sheridan; Jani Tervo; Youri Meuret, Editors, Proceedings of SPIE Vol. 9131 (SPIE, Bellingham, WA 2014), 913107.
Template matching is a technique used to classify objects by comparing portions of images against templates. It involves moving a template image across a larger source image to find the best match based on pixel-by-pixel comparisons of brightness levels. For gray-level images, the difference in brightness levels at each pixel location is used rather than a simple yes/no match. Template matching is commonly used to identify simple objects like printed characters. Matlab examples demonstrate template matching on sample data sets and correlation maps show the strength of matches across the source images.
Modeling and optimization of high index contrast gratings with aperiodic topo...Milan Maksimovic
"Modeling and optimization of high index contrast gratings with aperiodic topologies", in Modeling Aspects in Optical Metrology IV, Bernd Bodermann, Editors, Proceedings of SPIE Vol. 8789 (SPIE, Bellingham, WA 2013), 87890L
SINGLE IMAGE SUPER RESOLUTION IN SPATIAL AND WAVELET DOMAINijma
This document presents a single image super resolution algorithm that uses both spatial and wavelet domains. The algorithm takes advantage of both domains by upsampling the image in the spatial domain using bicubic interpolation, then refining the high frequency subbands in the wavelet domain. It is iterative, using back projection to minimize reconstruction error between the original low resolution image and the downsampled output. Wavelet-based denoising is applied to the high frequency subband to remove noise before reconstruction. Experimental results on various test images show the proposed algorithm achieves similar or better PSNR than other compared methods.
Abstract Image Segmentation plays a vital role in image processing. The research in this area is still relevant due to its wide applications. Image segmentation is a process of assigning a label to every pixel in an image such that pixels with same label share certain visual characteristics. Sometimes it becomes necessary to calculate the total number of colors from the given RGB image to quantize the image, to detect cancer and brain tumour. The goal of this paper is to provide the best algorithm for image segmentation. Keywords: Image segmentation, RGB
The document describes a proposed ear identification system that uses Gaussian mixture models to segment ear images into color slices, extracts SIFT keypoints from each slice, and fuses the keypoints using concatenation or Dempster-Shafer theory. Experimental results on the IIT Kanpur ear database show identification rates of 92.5% prior to segmentation and 94.75-98.25% after segmentation and fusion. The system provides an efficient approach for ear biometrics by modeling ear color, extracting robust features from slices, and fusing matches across slices.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
This document summarizes an automatic left ventricle segmentation technique using iterative thresholding and an active contour model adapted for short-axis cardiac MRI images. It begins with background on image segmentation and its applications. Then, it reviews related work on cardiac segmentation techniques and their limitations. The proposed method segments the endocardium using iterative thresholding and the epicardium using an active contour model. It estimates blood and myocardial intensities, applies region growing to segment the endocardium in each slice, and propagates the segmentation to remaining slices. Finally, it measures left ventricle volume and compares the results to manual segmentation.
This document describes an object video tracking system that uses a pan-tilt-zoom (PTZ) camera mounted on a rotating platform to track objects in video frames. Three tracking algorithms were implemented and compared: template matching, contour matching, and optical flow. The system was able to successfully track rigid objects up to 400x300 pixels at 30 degrees/second pan and 15 degrees/second tilt rotation. Tracking was stable with rotation and scaling but faults occurred with occlusion or fast target motion.
4D AUTOMATIC LIP-READING FOR SPEAKER'S FACE IDENTIFCATIONcscpconf
A novel based a trajectory-guided, concatenating approach for synthesizing high-quality image real sample renders video is proposed . The lips reading automated is seeking for modeled the closest real image sample sequence preserve in the library under the data video to the HMM predicted trajectory. The object trajectory is modeled obtained by projecting the face patterns into an KDA feature space is estimated. The approach for speaker's face identification by using synthesise the identity surface of a subject face from a small sample of patterns which sparsely each the view sphere. An KDA algorithm use to the Lip-reading image is discrimination, after that work consisted of in the low dimensional for the fundamental lip features vector is reduced by using the 2D-DCT.The mouth of the set area dimensionality is ordered by a normally reduction base on the PCA to obtain the Eigen lips approach, their proposed approach by[33]. The subjective performance results of the cost function under the automatic lips reading modeled , which wasn’t illustrate the superior performance of the
method.
How useful is self-supervised pretraining for Visual tasks?Seunghyun Hwang
Review : How useful is self-supervised pretraining for Visual tasks?
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
This document proposes a remote sensing image fusion approach that combines the Brovey transform and wavelet transforms. The Brovey transform is used first to reduce spectral distortion, followed by a wavelet transform to reduce spatial distortion. The approach was tested on MODIS and SPOT data as well as ETM+ and SPOT data. Statistical analysis showed the proposed technique performed better than traditional fusion techniques like IHS, PCA, and the Brovey transform alone in terms of metrics like correlation coefficient, entropy, and structural similarity. Future work will focus on improving the technique and applying fused images to classification tasks.
This document discusses image fusion techniques for medical diagnostic images. It describes how computed tomography (CT) and magnetic resonance imaging (MRI) provide different but complementary information about tissues. Image fusion combines CT and MRI scans into a single image to leverage the advantages of both modalities. The document outlines a specific fusion method using discrete wavelet transform for decomposition and self-organizing feature mapping neural network for feature recognition and extraction from the decomposed images. The advantages of this method are discussed as well as one drawback.
Further Improvements of CFA 3.0 by Combining Inpainting and Pansharpening Tec...sipij
Color Filter Array (CFA) has been widely used in digital cameras. There are many variants of CFAs in the
literature. Recently, a new CFA known as CFA 3.0 was proposed by us and has been shown to yield
reasonable performance as compared to some standard ones. In this paper, we investigate the use of
inpainting algorithms to further improve the demosaicing performance of CFA 3.0. Six conventional and
deep learning based inpainting algorithms were compared. Extensive experiments demonstrated that one
algorithm improved over other approaches
To get this project in ONLINE or through TRAINING Sessions,
Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: jpinfotechprojects@gmail.com, web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com
Multispectral Satellite Color Image Segmentation Using Fuzzy Based Innovative...Dibya Jyoti Bora
Multispectral satellite color images need special treatment for object-based classification like segmentation.
Traditional algorithms are not efficient enough for performing segmentation of such high-resolution images as
they often result in a serious problem: over-segmentation. So, an innovative approach for segmentation of
multispectral color images is proposed in this paper to tackle the same. The proposed approach consists of two
phases. In the first phase, the pre-processing of the selected bands is conducted for noise removal and contrast
enhancement of the input multispectral satellite color image on the HSV color space. In the second phase, fuzzy
segmentation of the enhanced version of the image obtained in the first phase is carried out by FCM algorithm
through optimal parameter passing. Final shifting from HSV to RGB color space presents the segmentation
result by separating different regions of interest with proper and distinguished color labeling. The results found
are quite promising and comparatively better than the other state of the art algorithms.
Object Elimination and Reconstruction Using an Effective Inpainting MethodIOSR Journals
Abstract: Three major problems have been found in the existing algorithms of image inpainting:
Reconstruction of large regions, Preference of filling-in and Choice of best exemplars to synthesize the missing
region. The proposed algorithm introduces two ideas that deal with these problems preserving edge continuity
along with decrease in error propagation. The proposed algorithm introduces a modified priority computation
in order to generate better edges in the omitted region and to reduce the transmission of errors in the resultant
image a novel way to find optimal exemplar has been proposed. This proposal optimizes the reconstruction
process and increases the accuracy. The proposed algorithm removes blurness and builds edges efficiently
while reconstructing large target region.
Keywords: Image inpainting, texture synthesis, Image Completion, exemplar-based method
FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stoch...Seunghyun Hwang
FickleNet is a method for weakly and semi-supervised semantic image segmentation that generates multiple localization maps from a single image using random combinations of hidden units. It aggregates these maps to discover relationships between object locations. This allows it to expand activated regions beyond just discriminative parts. Experiments on PASCAL VOC 2012 show it achieves state-of-the-art performance in both weakly and semi-supervised settings. Key techniques include feature map expansion for efficient inference and center-preserving dropout to relate kernel centers to other locations.
This document proposes a new method called multi-surface fitting for enhancing the resolution of digital images. The method fits multiple surfaces, with one surface fitted for each low-resolution pixel, and then fuses the multi-sampling values from these surfaces using maximum a posteriori estimation. This allows more low-resolution pixel information to be utilized to reconstruct the high-resolution image compared to other interpolation-based methods. The method is shown to effectively preserve image details without requiring assumptions about the image prior, as iterative techniques do. It provides error-free high resolution for test images.
The document describes a modified CAMSHIFT algorithm for people tracking via video. It begins with an introduction and problem statement regarding object tracking across multiple frames. It then discusses common tracking algorithms like mean shift and CAMSHIFT tracking. The document proposes extending CAMSHIFT tracking with optical flow to incorporate motion information. It provides implementation details using OpenCV with background modeling, object detection and tracking. In conclusion, the modified CAMSHIFT approach aims to robustly track objects in video frames in real-time.
This document discusses restoration of images using super resolution (SR) based image inpainting techniques. It begins with an abstract that outlines how inpainting can be used to restore damaged old photos by filling in missing or distorted regions. The document then reviews two main categories of inpainting methods: diffusion-based and exemplar-based. It proposes a combination approach using exemplar-based inpainting followed by single-image SR to provide a refined restoration. The paper evaluates this combined approach on several test images and finds it can effectively restore large missing regions without blurring, completing the restoration in under a minute.
Large Scale GAN Training for High Fidelity Natural Image SynthesisSeunghyun Hwang
Review : Large Scale GAN Training for High Fidelity Natural Image Synthesis
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
This document provides an overview of various image enhancement techniques. It begins with an introduction to image enhancement and its objectives. It then outlines and describes several categories of enhancement methods, including spatial-frequency domain methods, point operations, histogram operations, spatial operations, and transform operations. Specific techniques discussed in detail include contrast stretching, clipping, thresholding, median filtering, unsharp masking, and principal component analysis for multispectral images. The document also covers color image enhancement and techniques for pseudocoloring.
SINGLE IMAGE SUPER RESOLUTION IN SPATIAL AND WAVELET DOMAINijma
This document presents a single image super resolution algorithm that uses both spatial and wavelet domains. The algorithm takes advantage of both domains by upsampling the image in the spatial domain using bicubic interpolation, then refining the high frequency subbands in the wavelet domain. It is iterative, using back projection to minimize reconstruction error between the original low resolution image and the downsampled output. Wavelet-based denoising is applied to the high frequency subband to remove noise before reconstruction. Experimental results on various test images show the proposed algorithm achieves similar or better PSNR than other compared methods.
Abstract Image Segmentation plays a vital role in image processing. The research in this area is still relevant due to its wide applications. Image segmentation is a process of assigning a label to every pixel in an image such that pixels with same label share certain visual characteristics. Sometimes it becomes necessary to calculate the total number of colors from the given RGB image to quantize the image, to detect cancer and brain tumour. The goal of this paper is to provide the best algorithm for image segmentation. Keywords: Image segmentation, RGB
The document describes a proposed ear identification system that uses Gaussian mixture models to segment ear images into color slices, extracts SIFT keypoints from each slice, and fuses the keypoints using concatenation or Dempster-Shafer theory. Experimental results on the IIT Kanpur ear database show identification rates of 92.5% prior to segmentation and 94.75-98.25% after segmentation and fusion. The system provides an efficient approach for ear biometrics by modeling ear color, extracting robust features from slices, and fusing matches across slices.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
This document summarizes an automatic left ventricle segmentation technique using iterative thresholding and an active contour model adapted for short-axis cardiac MRI images. It begins with background on image segmentation and its applications. Then, it reviews related work on cardiac segmentation techniques and their limitations. The proposed method segments the endocardium using iterative thresholding and the epicardium using an active contour model. It estimates blood and myocardial intensities, applies region growing to segment the endocardium in each slice, and propagates the segmentation to remaining slices. Finally, it measures left ventricle volume and compares the results to manual segmentation.
This document describes an object video tracking system that uses a pan-tilt-zoom (PTZ) camera mounted on a rotating platform to track objects in video frames. Three tracking algorithms were implemented and compared: template matching, contour matching, and optical flow. The system was able to successfully track rigid objects up to 400x300 pixels at 30 degrees/second pan and 15 degrees/second tilt rotation. Tracking was stable with rotation and scaling but faults occurred with occlusion or fast target motion.
4D AUTOMATIC LIP-READING FOR SPEAKER'S FACE IDENTIFCATIONcscpconf
A novel based a trajectory-guided, concatenating approach for synthesizing high-quality image real sample renders video is proposed . The lips reading automated is seeking for modeled the closest real image sample sequence preserve in the library under the data video to the HMM predicted trajectory. The object trajectory is modeled obtained by projecting the face patterns into an KDA feature space is estimated. The approach for speaker's face identification by using synthesise the identity surface of a subject face from a small sample of patterns which sparsely each the view sphere. An KDA algorithm use to the Lip-reading image is discrimination, after that work consisted of in the low dimensional for the fundamental lip features vector is reduced by using the 2D-DCT.The mouth of the set area dimensionality is ordered by a normally reduction base on the PCA to obtain the Eigen lips approach, their proposed approach by[33]. The subjective performance results of the cost function under the automatic lips reading modeled , which wasn’t illustrate the superior performance of the
method.
How useful is self-supervised pretraining for Visual tasks?Seunghyun Hwang
Review : How useful is self-supervised pretraining for Visual tasks?
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
This document proposes a remote sensing image fusion approach that combines the Brovey transform and wavelet transforms. The Brovey transform is used first to reduce spectral distortion, followed by a wavelet transform to reduce spatial distortion. The approach was tested on MODIS and SPOT data as well as ETM+ and SPOT data. Statistical analysis showed the proposed technique performed better than traditional fusion techniques like IHS, PCA, and the Brovey transform alone in terms of metrics like correlation coefficient, entropy, and structural similarity. Future work will focus on improving the technique and applying fused images to classification tasks.
This document discusses image fusion techniques for medical diagnostic images. It describes how computed tomography (CT) and magnetic resonance imaging (MRI) provide different but complementary information about tissues. Image fusion combines CT and MRI scans into a single image to leverage the advantages of both modalities. The document outlines a specific fusion method using discrete wavelet transform for decomposition and self-organizing feature mapping neural network for feature recognition and extraction from the decomposed images. The advantages of this method are discussed as well as one drawback.
Further Improvements of CFA 3.0 by Combining Inpainting and Pansharpening Tec...sipij
Color Filter Array (CFA) has been widely used in digital cameras. There are many variants of CFAs in the
literature. Recently, a new CFA known as CFA 3.0 was proposed by us and has been shown to yield
reasonable performance as compared to some standard ones. In this paper, we investigate the use of
inpainting algorithms to further improve the demosaicing performance of CFA 3.0. Six conventional and
deep learning based inpainting algorithms were compared. Extensive experiments demonstrated that one
algorithm improved over other approaches
To get this project in ONLINE or through TRAINING Sessions,
Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: jpinfotechprojects@gmail.com, web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com
Multispectral Satellite Color Image Segmentation Using Fuzzy Based Innovative...Dibya Jyoti Bora
Multispectral satellite color images need special treatment for object-based classification like segmentation.
Traditional algorithms are not efficient enough for performing segmentation of such high-resolution images as
they often result in a serious problem: over-segmentation. So, an innovative approach for segmentation of
multispectral color images is proposed in this paper to tackle the same. The proposed approach consists of two
phases. In the first phase, the pre-processing of the selected bands is conducted for noise removal and contrast
enhancement of the input multispectral satellite color image on the HSV color space. In the second phase, fuzzy
segmentation of the enhanced version of the image obtained in the first phase is carried out by FCM algorithm
through optimal parameter passing. Final shifting from HSV to RGB color space presents the segmentation
result by separating different regions of interest with proper and distinguished color labeling. The results found
are quite promising and comparatively better than the other state of the art algorithms.
Object Elimination and Reconstruction Using an Effective Inpainting MethodIOSR Journals
Abstract: Three major problems have been found in the existing algorithms of image inpainting:
Reconstruction of large regions, Preference of filling-in and Choice of best exemplars to synthesize the missing
region. The proposed algorithm introduces two ideas that deal with these problems preserving edge continuity
along with decrease in error propagation. The proposed algorithm introduces a modified priority computation
in order to generate better edges in the omitted region and to reduce the transmission of errors in the resultant
image a novel way to find optimal exemplar has been proposed. This proposal optimizes the reconstruction
process and increases the accuracy. The proposed algorithm removes blurness and builds edges efficiently
while reconstructing large target region.
Keywords: Image inpainting, texture synthesis, Image Completion, exemplar-based method
FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stoch...Seunghyun Hwang
FickleNet is a method for weakly and semi-supervised semantic image segmentation that generates multiple localization maps from a single image using random combinations of hidden units. It aggregates these maps to discover relationships between object locations. This allows it to expand activated regions beyond just discriminative parts. Experiments on PASCAL VOC 2012 show it achieves state-of-the-art performance in both weakly and semi-supervised settings. Key techniques include feature map expansion for efficient inference and center-preserving dropout to relate kernel centers to other locations.
This document proposes a new method called multi-surface fitting for enhancing the resolution of digital images. The method fits multiple surfaces, with one surface fitted for each low-resolution pixel, and then fuses the multi-sampling values from these surfaces using maximum a posteriori estimation. This allows more low-resolution pixel information to be utilized to reconstruct the high-resolution image compared to other interpolation-based methods. The method is shown to effectively preserve image details without requiring assumptions about the image prior, as iterative techniques do. It provides error-free high resolution for test images.
The document describes a modified CAMSHIFT algorithm for people tracking via video. It begins with an introduction and problem statement regarding object tracking across multiple frames. It then discusses common tracking algorithms like mean shift and CAMSHIFT tracking. The document proposes extending CAMSHIFT tracking with optical flow to incorporate motion information. It provides implementation details using OpenCV with background modeling, object detection and tracking. In conclusion, the modified CAMSHIFT approach aims to robustly track objects in video frames in real-time.
This document discusses restoration of images using super resolution (SR) based image inpainting techniques. It begins with an abstract that outlines how inpainting can be used to restore damaged old photos by filling in missing or distorted regions. The document then reviews two main categories of inpainting methods: diffusion-based and exemplar-based. It proposes a combination approach using exemplar-based inpainting followed by single-image SR to provide a refined restoration. The paper evaluates this combined approach on several test images and finds it can effectively restore large missing regions without blurring, completing the restoration in under a minute.
Large Scale GAN Training for High Fidelity Natural Image SynthesisSeunghyun Hwang
Review : Large Scale GAN Training for High Fidelity Natural Image Synthesis
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
This document provides an overview of various image enhancement techniques. It begins with an introduction to image enhancement and its objectives. It then outlines and describes several categories of enhancement methods, including spatial-frequency domain methods, point operations, histogram operations, spatial operations, and transform operations. Specific techniques discussed in detail include contrast stretching, clipping, thresholding, median filtering, unsharp masking, and principal component analysis for multispectral images. The document also covers color image enhancement and techniques for pseudocoloring.
This document discusses landmark based image registration using thin plate spline with feature matching. It summarizes that thin plate spline is used for non-rigid deformation of an input image based on corresponding landmark points. SIFT feature matching is then used to match feature points between the original and deformed images, allowing the deformed image to be registered to the original image. The key steps of SIFT involve scale-space keypoint detection, orientation assignment, and creating 128-dimensional descriptors for matching. Together, thin plate spline and SIFT provide a method for image registration that is illumination independent and works across different object positions.
This document discusses research towards developing algorithms to enable robots to autonomously right themselves. It notes that proprioceptive joint and body orientation data is available but sensory data is limited. It aims to maximize robot self-righting abilities, improve performance, and transition analysis from 2D to 3D. Exhaustive search strategies are infeasible due to computational complexity. The document proposes using probabilistic roadmap and rapidly exploring random tree algorithms to simplify the search problem. It also suggests applying white box testing concepts to leverage program limits to identify optimal path plans more efficiently than exhaustive searches.
Detecting Boundaries for Image Segmentation and Object RecognitionIRJET Journal
This document proposes improvements to image edge detection methods. It summarizes previous approaches that have limitations like poor localization of edges, inability to remove noise, and high computational time. The proposed hybrid approach uses principal component analysis and Canny edge detection in parallel across multiple processors. This achieves faster and more efficient edge detection than prior methods. However, the document suggests edge detection quality could be further improved by using an improved wavelet transformation instead of PCA. It recommends a proposal based on wavelet transformations and Canny detection with operator fusion to first apply wavelet noise cancellation and smoothing before edge detection.
This document summarizes an analysis of iris recognition based on false acceptance rate (FAR) and false rejection rate (FRR) using the Hough transform. It first provides an overview of iris recognition and its typical stages: image acquisition, localization/segmentation, normalization, feature extraction, and pattern matching. It then describes existing methods used in each stage, including the Hough transform and rubber sheet model for localization and normalization. The proposed methodology applies Canny edge detection, Hough transform for boundary detection, normalization with the rubber sheet model, and calculates metrics like mean squared error, root mean squared error, signal-to-noise ratio, and root signal-to-noise ratio to evaluate the accuracy of iris recognition using FAR
This document provides an overview of digital image processing (DIP) and discusses various topics related to it. It begins with welcoming remarks and introductions. It then discusses key areas of application for image processing like optical character recognition, security, compression, and medical imaging. Some main techniques covered include image acquisition, pre-processing, enhancement, segmentation, feature extraction, classification, and understanding. Application areas like remote sensing, astronomy, security, and OCR are also summarized. The document provides examples and illustrations of different image processing concepts.
This document describes a color guided algorithm for thermal image super resolution. It introduces the system setup using an infrared and visible light camera. It also describes preprocessing steps like camera calibration and image registration. The proposed approach is outlined and experimental results show it increases resolution while avoiding over-texture issues compared to other methods. Applications include surveillance, medical diagnosis and remote sensing. Future work may extend this approach to other domains like fluorescence microscopy.
This document proposes ResNeSt, a split-attention network that divides feature maps into groups and applies attention mechanisms across groups. It outperforms ResNet variants on image classification, object detection, semantic segmentation, and instance segmentation while maintaining the same computational efficiency. The paper introduces ResNeSt's split attention block, training strategies including large batches, data augmentation, and regularization methods. Evaluation shows ResNeSt achieves state-of-the-art accuracy on ImageNet and downstream tasks using less computation than NAS models.
We performed the project on Lane detection by using canny edge and Hough transform at the University of Windsor. In this presentation, all the code used in Python are perfectly presented for reference.
Carved visual hulls for image based modelingaftab alam
The document describes a method for 3D reconstruction from images called carved visual hulls. It involves three main steps: (1) identifying rims on the visual hull surface that touch the object, (2) globally optimizing the surface using graph cuts with photoconsistency and rim constraints, and (3) locally refining the surface while enforcing photoconsistency and geometric constraints. The method produces high-quality 3D models but cannot handle overly concave regions. Results on 7 datasets show promising geometric accuracy while balancing computational costs.
OpenCV is a Python library used for computer vision tasks like image classification, object detection, and face recognition. It processes images to understand their content. When analyzing images, OpenCV performs tasks like object classification to categorize objects, object identification to recognize specific instances, and edge detection using techniques like Canny edge detection. Key computer vision algorithms in OpenCV include SIFT for keypoint detection, template matching for finding areas of an image that match a template, and Viola-Jones for real-time object detection. OpenCV is useful for applications like driverless cars that require visual understanding of the environment.
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DeepStrip: High Resolution Boundary Refinement
1. DeepStrip: High Resolution Boundary Refinement
Hwang seung hyun
Yonsei University Severance Hospital CCIDS
University of Maryland & Adobe Research
CVPR 2020
2020.05.17
2. Introduction Related Work Methods and
Experiments
01 02 03
Conclusion
04
Yonsei Unversity Severance Hospital CCIDS
Contents
3. DeepStrip
Introduction – Proposal
• Boundary detection is a well-studied problem and fundamental
for human recognition
• Current methods are usually computed on low resolution(LR)
images, but most photos taken these days are much larger and
high resolution(HR) images
• Most studies simply upsample LR prediction to reach HR
prediction.
• Deep Strip targets on refining the boundaries in high resolution
images given low resolution masks
Introduction / Related Work / Methods and Experiments / Conclusion
4. DeepStrip
Introduction – Contributions
• Propose an approach to predict the boundary in a strip image, which is
computationally and memory wise efficient.
• To improve performance, propose novel losses including boundary distance, matching
and C0 continuity loss.
• Create a high resolution dataset “PixaHR” for evaluation.
Introduction / Related Work / Methods and Experiments / Conclusion
5. Related Work
1. Boundary Refinement
Introduction / Related Work / Methods and Experiments / Conclusion
• Explore rich convolutional features or fuse both low and high level features to
detect edges
• “Conditional Random Fields(CRF)”, “Graph Cuts”
• These methods mainly explore edge detection in LR images, while DeepStrip
target HR boundary refinement.
2. Active Contours
• “Snakes” (Active contour model)
• “Deep active contour” predict boundary pixels in a patch. But, cannot
guarantee a continuous boundary prediction
• These methods process the entire image or perform patch-based training,
which requires heavy computation and memory overhead
3. High Resolution Up-sampling
• Conventional methods reach HR segmentation masks by applying upsampling
to LR mask.
6. Methods and Experiments
DeepStrip – Architecture
Introduction / Related Work / Methods and Experiments / Conclusion
• Predict on strip image that captures the potential boundary region rather than the
entire HR image.
• Refines the edges on the strip image using a network
• Reconstruct prediction in the original image from the strip boundary prediction.
7. Methods and Experiments
DeepStrip – Strip Image Creation
Introduction / Related Work / Methods and Experiments / Conclusion
• Extract pixels near the upsampled boundary to create a strip image
• Use B-spline method to represent contour in the LR mask
• HR region along the normal direction at each point on the curve of the contour is extracted
• For GT label, add labels at the border of strip if no boundary pixel is included in strip image.
• If the strip height is large and multiple boundary pixels are included in each column, filter out
the extraneous boundaries that are not connected to the current one.
8. Methods and Experiments
DeepStrip – Strip Boundary Prediction
Introduction / Related Work / Methods and Experiments / Conclusion
• Train U-Net to predict the corresponding boundaries within the strip domain.
• Use instance normalization to apply for different resolution of images
• Extract the last upsampling layer and apply sigmoid function to predict all potential boundaries.
• Selection layer pick up the target boundary from potential boundaries
s = final output, x = initial prediction, m = softmax output of the selection layer
9. Methods and Experiments
DeepStrip – Loss Function
Introduction / Related Work / Methods and Experiments / Conclusion
1. Basic Loss Function (l1, Dice)
2. Boundary Distance Loss
3. Matching Loss (l1)
4. C0 Continuity Regularization (calculate
marginal difference between columns and penalize
the discontinuous position)
5. Total Loss
10. Methods and Experiments
DeepStrip – Strip Reconstruction at Inference stage
Introduction / Related Work / Methods and Experiments / Conclusion
• Mapping between the predicted strip boundaries and the full HR mask is required at
inference
• For every strip image, coordinates in the HR image are recorded for reconstruction
• Use dynamic programming similar to “seam carving” to find the path.
• Enables different strip sizes (width of strip) for different images
• Fix the height of strip, assuming all target boundaries are involved
w
h
11. Methods and Experiments
Dataset
Introduction / Related Work / Methods and Experiments / Conclusion
- DAVIS 2016 (benchmark for video segmentation, consists of 50 classes with
precise annotations in both 480P and 1080P)
- Pixa HR (100 manually annotated images with average resolution 7K x 7K)
• Downsample HR mask to LR by 8x, 16x, 32x for evaluation and training.
• Boundary-based F score for evaluation metrics
12. Methods and Experiments
Main Results
Introduction / Related Work / Methods and Experiments / Conclusion
* Baseline Model: only trained with l1 loss, without selection layer
18. Methods and Experiments
Ablation Studies
Introduction / Related Work / Methods and Experiments / Conclusion
• Performance increased when dividing the whole contour into 2 segments
which allows variable height for different regions
• Showed effectiveness of having flexible height
19. Conclusion
Introduction / Related Work / Methods and Experiments / Conclusion
• This paper presented a novel strategy to handle HR boundary refinement
computationally and memory efficiently given LR precise masks.
• Proposed extracting boundary regions along the upsampled boundary
spline to form strip images and make prediction within them.
• Boundary distance, matching loss, and C0 continuity regularization have
been proposed
• Current approach still has difficulty predicting complicated topology and
soft boundary regions
• Smarter adaptive strip height adjustment for every pixel might be a
potential solution