This paper proposes a new technique for interactive segmentation of multi-dimensional (N-D) images. The technique allows a user to mark pixels as "object" or "background" to provide hard constraints. A cost function is defined that balances boundary and region properties to find a globally optimal segmentation satisfying the constraints. Graph cuts are used to efficiently compute the optimal segmentation. The technique handles isolated object and background regions and can segment N-D volumes.
Comparative analysis and implementation of structured edge active contour IJECEIAES
This paper proposes modified chanvese model which can be implemented on image for segmentation. The structure of paper is based on Linear structure tensor (LST) as input to the variant model. Structure tensor is a matrix illustration of partial derivative information. In the proposed model, the original image is considered as information channel for computing structure tensor. Difference of Gaussian (DOG) is featuring improvement in which we can get less blurred image than original image. In this paper LST is modified by adding intensity information to enhance orientation information. Finally Active Contour Model (ACM) is used to segment the images. The proposed algorithm is tested on various images and also on some images which have intensity inhomogeneity and results are shown. Also, the results with other algorithms like chanvese, Bhattacharya, Gabor based chanvese and Novel structure tensor based model are compared. It is verified that accuracy of proposed model is the best. The biggest advantage of proposed model is clear edge enhancement.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document presents a method for interactive image segmentation using constrained active contours. It begins with an overview of existing interactive segmentation techniques, including boundary-based methods like active contours/snakes and region-based methods like random walks and graph cuts. The proposed method initializes a contour using region-based segmentation then refines it using a convex active contour model that incorporates both regional information from seed pixels and boundary smoothness. This allows the contour to globally evolve to object boundaries while handling topology changes.
Qcce quality constrained co saliency estimation for common object detectionKoteswar Rao Jerripothula
Despite recent advances in joint processing of images,
sometimes it may not be as effective as single image
processing for object discovery problems. In this paper while
aiming for common object detection, we attempt to address
this problem by proposing a novel QCCE: Quality Constrained
Co-saliency Estimation method. The approach here is to iteratively
update the saliency maps through co-saliency estimation
depending upon quality scores, which indicate the degree of
separation of foreground and background likelihoods (the easier
the separation, the higher the quality of saliency map). In this
way, joint processing is automatically constrained by the quality
of saliency maps. Moreover, the proposed method can be applied
to both unsupervised and supervised scenarios, unlike other
methods which are particularly designed for one scenario only.
Experimental results demonstrate superior performance of the
proposed method compared to the state-of-the-art methods.
1) The document discusses image segmentation in satellite images using optimal texture measures. It evaluates four texture measures from the gray-level co-occurrence matrix (GLCM) with six different window sizes.
2) Principal Component Analysis (PCA) is applied to reduce the texture measures to a manageable size while retaining discrimination information.
3) The methodology consists of selecting an optimal window size and optimal texture measure. A 7x7 window size provided superior performance for classification. PCA is used to analyze correlations between texture measures and window sizes.
A Novel Feature Extraction Scheme for Medical X-Ray ImagesIJERA Editor
X-ray images are gray scale images with almost the same textural characteristic. Conventional texture or color
features cannot be used for appropriate categorization in medical x-ray image archives. This paper presents a
novel combination of methods like GLCM, LBP and HOG for extracting distinctive invariant features from Xray
images belonging to IRMA (Image Retrieval in Medical applications) database that can be used to perform
reliable matching between different views of an object or scene. GLCM represents the distributions of the
intensities and the information about relative positions of neighboring pixels of an image. The LBP features are
invariant to image scale and rotation, change in 3D viewpoint, addition of noise, and change in illumination A
HOG feature vector represents local shape of an object, having edge information at plural cells. These features
have been exploited in different algorithms for automatic classification of medical X-ray images. Excellent
experimental results obtained in true problems of rotation invariance, particular rotation angle, demonstrate that
good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary
patterns.
This document proposes a new model for segmenting overlapping, near-circular objects in images. It combines a multi-layer nonlocal phase field prior model that favors configurations of possibly overlapping near-circular shapes, with a new additive image likelihood model that accounts for intensities summing in overlapping regions. The full posterior energy is minimized using gradient descent to obtain a maximum a posteriori estimate of the object instances. The model is tested on synthetic and fluorescence microscopy cell nucleus image data.
This document describes a narrow band region approach for 2D and 3D image segmentation using deformable curves and surfaces. Specifically, it develops a region energy term involving a fixed-width band around the evolving curve or surface. This energy achieves a balance between local gradient features and global region statistics. The region energy is formulated to allow efficient computation on explicit parametric models and implicit level set models. Two different region terms are introduced, each suited to different configurations of the target object and its surroundings. The document derives the mathematical framework for computing the region energies and their gradients to allow minimization via gradient descent. It then discusses numerical implementations and provides experiments segmenting medical and natural images.
Comparative analysis and implementation of structured edge active contour IJECEIAES
This paper proposes modified chanvese model which can be implemented on image for segmentation. The structure of paper is based on Linear structure tensor (LST) as input to the variant model. Structure tensor is a matrix illustration of partial derivative information. In the proposed model, the original image is considered as information channel for computing structure tensor. Difference of Gaussian (DOG) is featuring improvement in which we can get less blurred image than original image. In this paper LST is modified by adding intensity information to enhance orientation information. Finally Active Contour Model (ACM) is used to segment the images. The proposed algorithm is tested on various images and also on some images which have intensity inhomogeneity and results are shown. Also, the results with other algorithms like chanvese, Bhattacharya, Gabor based chanvese and Novel structure tensor based model are compared. It is verified that accuracy of proposed model is the best. The biggest advantage of proposed model is clear edge enhancement.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document presents a method for interactive image segmentation using constrained active contours. It begins with an overview of existing interactive segmentation techniques, including boundary-based methods like active contours/snakes and region-based methods like random walks and graph cuts. The proposed method initializes a contour using region-based segmentation then refines it using a convex active contour model that incorporates both regional information from seed pixels and boundary smoothness. This allows the contour to globally evolve to object boundaries while handling topology changes.
Qcce quality constrained co saliency estimation for common object detectionKoteswar Rao Jerripothula
Despite recent advances in joint processing of images,
sometimes it may not be as effective as single image
processing for object discovery problems. In this paper while
aiming for common object detection, we attempt to address
this problem by proposing a novel QCCE: Quality Constrained
Co-saliency Estimation method. The approach here is to iteratively
update the saliency maps through co-saliency estimation
depending upon quality scores, which indicate the degree of
separation of foreground and background likelihoods (the easier
the separation, the higher the quality of saliency map). In this
way, joint processing is automatically constrained by the quality
of saliency maps. Moreover, the proposed method can be applied
to both unsupervised and supervised scenarios, unlike other
methods which are particularly designed for one scenario only.
Experimental results demonstrate superior performance of the
proposed method compared to the state-of-the-art methods.
1) The document discusses image segmentation in satellite images using optimal texture measures. It evaluates four texture measures from the gray-level co-occurrence matrix (GLCM) with six different window sizes.
2) Principal Component Analysis (PCA) is applied to reduce the texture measures to a manageable size while retaining discrimination information.
3) The methodology consists of selecting an optimal window size and optimal texture measure. A 7x7 window size provided superior performance for classification. PCA is used to analyze correlations between texture measures and window sizes.
A Novel Feature Extraction Scheme for Medical X-Ray ImagesIJERA Editor
X-ray images are gray scale images with almost the same textural characteristic. Conventional texture or color
features cannot be used for appropriate categorization in medical x-ray image archives. This paper presents a
novel combination of methods like GLCM, LBP and HOG for extracting distinctive invariant features from Xray
images belonging to IRMA (Image Retrieval in Medical applications) database that can be used to perform
reliable matching between different views of an object or scene. GLCM represents the distributions of the
intensities and the information about relative positions of neighboring pixels of an image. The LBP features are
invariant to image scale and rotation, change in 3D viewpoint, addition of noise, and change in illumination A
HOG feature vector represents local shape of an object, having edge information at plural cells. These features
have been exploited in different algorithms for automatic classification of medical X-ray images. Excellent
experimental results obtained in true problems of rotation invariance, particular rotation angle, demonstrate that
good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary
patterns.
This document proposes a new model for segmenting overlapping, near-circular objects in images. It combines a multi-layer nonlocal phase field prior model that favors configurations of possibly overlapping near-circular shapes, with a new additive image likelihood model that accounts for intensities summing in overlapping regions. The full posterior energy is minimized using gradient descent to obtain a maximum a posteriori estimate of the object instances. The model is tested on synthetic and fluorescence microscopy cell nucleus image data.
This document describes a narrow band region approach for 2D and 3D image segmentation using deformable curves and surfaces. Specifically, it develops a region energy term involving a fixed-width band around the evolving curve or surface. This energy achieves a balance between local gradient features and global region statistics. The region energy is formulated to allow efficient computation on explicit parametric models and implicit level set models. Two different region terms are introduced, each suited to different configurations of the target object and its surroundings. The document derives the mathematical framework for computing the region energies and their gradients to allow minimization via gradient descent. It then discusses numerical implementations and provides experiments segmenting medical and natural images.
Documents Image Binarization is performed in the preprocessing stage for document analysis and it aims to
segment the foreground text from the document background. A fast and accurate document image binarization
technique is important for the ensuing document image processing tasks such as optical character recognition
(OCR). Though document image binarization has been studied for many years, the thresholding of degraded
document images is still an unsolved problem due to the high inter/intra variation between the text stroke and
the document background across different document images. The handwritten text within the degraded
documents often shows a certain amount of variation in terms of the stroke width, stroke brightness, stroke
connection, and document background. In addition, historical documents are often degraded by the bleed.
Documents are often degraded by different types of imaging artifact. These different types of document
degradations tend to induce the document thresholding error and make degraded document image binarization a
big challenge to most state-of-the-art techniques. The proposed method is simple, robust and capable of
handling different types of degraded document images with minimum parameter tuning. It makes use of the
adaptive image contrast that combines the local image contrast and the local image gradient adaptively and
therefore is tolerant to the text and background variation caused by different types of document degradations. In
particular, the proposed technique addresses the over-normalization problem of the local maximum minimum
algorithm. At the same time, the parameters used in the algorithm can be adaptively estimated.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document proposes a new algorithm to reduce blocking artifacts in compressed images using a combination of the SAWS technique, Fuzzy Impulse Artifact Detection and Reduction Method (FIDRM), and Noise Adaptive Fuzzy Switching Median Filter (NAFSM). FIDRM uses fuzzy rules to detect noisy pixels, while NAFSM uses a median filter to correct pixels based on local information. Experimental results on test images show the proposed approach achieves better PSNR than other deblocking methods.
This document compares three image restoration techniques - Iterated Geometric Harmonics, Markov Random Fields, and Wavelet Decomposition - for removing noise from images. It describes each technique and the process used to test them. Noise was artificially added to images using different noise generation functions. Wavelet Decomposition and Markov Random Fields were then used to detect the noise locations. These noise locations were then used to create versions of the noisy images suitable for reconstruction via Iterated Geometric Harmonics. The reconstructed images were then compared to the original to evaluate the performance of each technique.
Performance of Efficient Closed-Form Solution to Comprehensive Frontier Exposureiosrjce
This document discusses boundary detection techniques for images. It proposes a generalized boundary detection method (Gb) that combines low-level and mid-level image representations in a single eigenvalue problem to detect boundaries. Gb achieves state-of-the-art results at low computational cost. Soft segmentation and contour grouping methods are also introduced to further improve boundary detection accuracy with minimal extra computation. The document presents outputs of Gb on sample images and concludes that Gb effectively detects boundaries in a principled manner by jointly resolving constraints from multiple image interpretation layers in closed form.
Image segmentation by modified map ml estimationsijesajournal
Though numerous algorithms exist to perform image segmentation there are several issues
related to execution time of these algorithm. Image Segmentation is nothing but label relabeling
problem under probability framework. To estimate the label configuration, an iterative
optimization scheme is implemented to alternately carry out the maximum a posteriori (MAP)
estimation and the maximum likelihood (ML) estimations. In this paper this technique is
modified in such a way so that it performs segmentation within stipulated time period. The
extensive experiments shows that the results obtained are comparable with existing algorithms.
This algorithm performs faster execution than the existing algorithm to give automatic
segmentation without any human intervention. Its result match image edges very closer to
human perception.
Despeckling of Ultrasound Imaging using Median Regularized Coupled PdeIDES Editor
This paper presents an approach for reducing speckle
in ultrasound images using Coupled Partial Differential
Equation (CPDE) which has been obtained by uniting secondorder
and the fourth-order partial differential equations. Using
PDE to reduce the speckle is the noise-smoothing methods
which is getting attention widely, because PDE can keep the
edge well when it reduces the noise. We also introduced a
median regulator to guide energy source to boost the features
in the image and regularize the diffusion. The proposed
method is tested in both simulated and real medical
ultrasound images. The proposed method is compared with
SRAD, Perona Malik diffusion and Non linear coherent
diffusion methods, our method gives better result in terms of
CNR, SSIM and FOM.
An evaluation of two popular segmentation algorithms, the mean shift-based segmentation algorithm and a graph-based segmentation scheme. We also consider a hybrid method which combines the other two methods.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
This paper proposes a new method for visual segmentation based on fixation points. The method segments the region of interest in two steps: (1) generating a probabilistic boundary edge map combining multiple visual cues, and (2) finding the optimal closed contour around the fixation point in the transformed polar edge map. The paper shows this fixation-based segmentation approach improves accuracy over previous methods, especially when incorporating motion and stereo cues. It also introduces a region merging algorithm to further refine segmentation results. Evaluation on video and stereo image datasets demonstrates mean F-measures of 0.95 and 0.96 respectively when combining cues, compared to 0.62 and 0.65 without.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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
This document proposes methods for enhancing and extracting minutiae from fingerprint images using symmetry features. It summarizes previous work on fingerprint enhancement and introduces a new approach using an image pyramid and directional filtering based on the frequency-adapted structure tensor. For minutiae extraction, it adds parabolic symmetry to the local fingerprint model to simultaneously detect minutia position and direction. Experiments on the FVC2004 database show the methods lower the matching error compared to other techniques.
p-Laplace Variational Image Inpainting Model Using Riesz Fractional Different...IJECEIAES
In this paper, p-Laplace variational image inpainting model with symmetric Riesz fractional differential filter is proposed. Variational inpainting models are very useful to restore many smaller damaged regions of an image. Integer order variational image inpainting models (especially second and fourth order) work well to complete the unknown regions. However, in the process of inpainting with these models, any of the unindented visual effects such as staircasing, speckle noise, edge blurring, or loss in contrast are introduced. Recently, fractional derivative operators were applied by researchers to restore the damaged regions of the image. Experimentation with these operators for variational image inpainting led to the conclusion that second order symmetric Riesz fractional differential operator not only completes the damaged regions effectively, but also reducing unintended effects. In this article, The filling process of damaged regions is based on the fractional central curvature term. The proposed model is compared with integer order variational models and also GrunwaldLetnikov fractional derivative based variational inpainting in terms of peak signal to noise ratio, structural similarity and mutual information.
There exists a plethora of algorithms to perform image segmentation and there are several issues related to
execution time of these algorithms. Image Segmentation is nothing but label relabeling problem under
probability framework. To estimate the label configuration, an iterative optimization scheme is
implemented to alternately carry out the maximum a posteriori (MAP) estimation and the maximum
likelihood (ML) estimations. In this paper this technique is modified in such a way so that it performs
segmentation within stipulated time period. The extensive experiments shows that the results obtained are
comparable with existing algorithms. This algorithm performs faster execution than the existing algorithm
to give automatic segmentation without any human intervention. Its result match image edges very closer to
human perception.
Object Shape Representation by Kernel Density Feature Points Estimator cscpconf
This paper introduces an object shape representation using Kernel Density Feature Points
Estimator (KDFPE). In this method we obtain the density of feature points within defined rings
around the centroid of the image. The Kernel Density Feature Points Estimator is then applied to
the vector of the image. KDFPE is invariant to translation, scale and rotation. This method of
image representation shows improved retrieval rate when compared to Density Histogram
Feature Points (DHFP) method. Analytic analysis is done to justify our method and we compared our results with object shape representation by the Density Histogram of Feature Points (DHFP) to prove its robustness.
Land Cover Feature Extraction using Hybrid Swarm Intelligence Techniques - A ...IDES Editor
This document presents a hybrid algorithm using biogeography-based optimization (BBO) and ant colony optimization (ACO) for land cover feature extraction from remote sensing images. The algorithm first analyzes a training image to identify features that BBO and ACO classify efficiently. It then applies BBO to clusters containing these features and ACO to remaining clusters. An evaluation shows the hybrid algorithm achieves a higher kappa coefficient of 0.97 compared to 0.67 for BBO alone, indicating better classification accuracy. The authors conclude the algorithm effectively handles uncertainties in remote sensing images and future work could improve efficiency further.
Gaussian Fuzzy Blocking Artifacts Removal of High DCT Compressed Imagesijtsrd
A new artifact removal method as cascade of Gaussian fuzzy edge decider and fuzzy image correction is proposed. In this design, a highly compressed i.e. low bit rate image is considered. Here, each overlapped block of image is fed to a Gaussian fuzzy based decider to check whether the central pixel of image block needs correction. Hence, the central pixel of overlapped block is corrected by fuzzy gradient of its neighbors. Experimental results shows remarkable improvement with presented gFAR algorithm compared to the past methods subjectively visual quality and objectively PSNR . Deepak Gambhir "Gaussian Fuzzy Blocking Artifacts Removal of High DCT Compressed Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33361.pdf Paper Url: https://www.ijtsrd.com/computer-science/multimedia/33361/gaussian-fuzzy-blocking-artifacts-removal-of-high-dct-compressed-images/deepak-gambhir
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The document summarizes a proposed system for currency recognition on mobile phones. The system has the following modules: 1) segmentation to isolate the currency from background noise, 2) feature extraction and building a visual vocabulary, 3) instance retrieval using inverted indexing and spatial reranking, 4) classification by vote counting spatially consistent features. The system was adapted for mobile by reducing complexity, such as using an inverted index, while maintaining accuracy. Performance is evaluated using metrics like accuracy and precision.
This document provides an introduction to graph neural networks (GNNs). It discusses that GNNs are designed to perform inference on graph data using neural networks. GNNs can analyze graph data by performing node classification, graph classification, graph visualization, link prediction, and graph clustering. The document explains that GNNs apply the concepts of node embedding and message passing to learn node representations that encode structural information from a graph. It describes how GNNs use neural network layers and aggregation functions to learn representations by propagating and transforming node feature information across a graph.
A Combined Method with automatic parameter optimization for Multi-class Image...AM Publications
Multi-class image semantic segmentation deals with many applications in consumer electronics
fields such as image editing and image retrieval. Segmentation is done by combining the top down and bottomup
segmentation. Top-Down Process can be done by Semantic Texton Forest and bottom up- process using
JSEG. These two segmentation process can be executed in a combined manner. But this cannot choose the
optimal value of JSEG parameter for each interested semantic category. Hence an automatic parameter selection
algorithm has been proposed. An automatic parameter selection technique called an automatic multilevel
thresholding algorithm using stratified sampling and PSO is used to remedy the limitations.
1) Biased Normalized Cuts presents a modification of Normalized Cuts that incorporates priors to allow for constrained image segmentation.
2) It seeks solutions that are sufficiently "correlated" with noisy top-down priors, like an object detector, and can be computed quickly given the unconstrained solution.
3) The algorithm constructs a "biased normalized cut vector" that linearly combines eigenvectors such that those correlated with a user-specified seed vector are upweighted while inversely correlated ones have their sign flipped.
Documents Image Binarization is performed in the preprocessing stage for document analysis and it aims to
segment the foreground text from the document background. A fast and accurate document image binarization
technique is important for the ensuing document image processing tasks such as optical character recognition
(OCR). Though document image binarization has been studied for many years, the thresholding of degraded
document images is still an unsolved problem due to the high inter/intra variation between the text stroke and
the document background across different document images. The handwritten text within the degraded
documents often shows a certain amount of variation in terms of the stroke width, stroke brightness, stroke
connection, and document background. In addition, historical documents are often degraded by the bleed.
Documents are often degraded by different types of imaging artifact. These different types of document
degradations tend to induce the document thresholding error and make degraded document image binarization a
big challenge to most state-of-the-art techniques. The proposed method is simple, robust and capable of
handling different types of degraded document images with minimum parameter tuning. It makes use of the
adaptive image contrast that combines the local image contrast and the local image gradient adaptively and
therefore is tolerant to the text and background variation caused by different types of document degradations. In
particular, the proposed technique addresses the over-normalization problem of the local maximum minimum
algorithm. At the same time, the parameters used in the algorithm can be adaptively estimated.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document proposes a new algorithm to reduce blocking artifacts in compressed images using a combination of the SAWS technique, Fuzzy Impulse Artifact Detection and Reduction Method (FIDRM), and Noise Adaptive Fuzzy Switching Median Filter (NAFSM). FIDRM uses fuzzy rules to detect noisy pixels, while NAFSM uses a median filter to correct pixels based on local information. Experimental results on test images show the proposed approach achieves better PSNR than other deblocking methods.
This document compares three image restoration techniques - Iterated Geometric Harmonics, Markov Random Fields, and Wavelet Decomposition - for removing noise from images. It describes each technique and the process used to test them. Noise was artificially added to images using different noise generation functions. Wavelet Decomposition and Markov Random Fields were then used to detect the noise locations. These noise locations were then used to create versions of the noisy images suitable for reconstruction via Iterated Geometric Harmonics. The reconstructed images were then compared to the original to evaluate the performance of each technique.
Performance of Efficient Closed-Form Solution to Comprehensive Frontier Exposureiosrjce
This document discusses boundary detection techniques for images. It proposes a generalized boundary detection method (Gb) that combines low-level and mid-level image representations in a single eigenvalue problem to detect boundaries. Gb achieves state-of-the-art results at low computational cost. Soft segmentation and contour grouping methods are also introduced to further improve boundary detection accuracy with minimal extra computation. The document presents outputs of Gb on sample images and concludes that Gb effectively detects boundaries in a principled manner by jointly resolving constraints from multiple image interpretation layers in closed form.
Image segmentation by modified map ml estimationsijesajournal
Though numerous algorithms exist to perform image segmentation there are several issues
related to execution time of these algorithm. Image Segmentation is nothing but label relabeling
problem under probability framework. To estimate the label configuration, an iterative
optimization scheme is implemented to alternately carry out the maximum a posteriori (MAP)
estimation and the maximum likelihood (ML) estimations. In this paper this technique is
modified in such a way so that it performs segmentation within stipulated time period. The
extensive experiments shows that the results obtained are comparable with existing algorithms.
This algorithm performs faster execution than the existing algorithm to give automatic
segmentation without any human intervention. Its result match image edges very closer to
human perception.
Despeckling of Ultrasound Imaging using Median Regularized Coupled PdeIDES Editor
This paper presents an approach for reducing speckle
in ultrasound images using Coupled Partial Differential
Equation (CPDE) which has been obtained by uniting secondorder
and the fourth-order partial differential equations. Using
PDE to reduce the speckle is the noise-smoothing methods
which is getting attention widely, because PDE can keep the
edge well when it reduces the noise. We also introduced a
median regulator to guide energy source to boost the features
in the image and regularize the diffusion. The proposed
method is tested in both simulated and real medical
ultrasound images. The proposed method is compared with
SRAD, Perona Malik diffusion and Non linear coherent
diffusion methods, our method gives better result in terms of
CNR, SSIM and FOM.
An evaluation of two popular segmentation algorithms, the mean shift-based segmentation algorithm and a graph-based segmentation scheme. We also consider a hybrid method which combines the other two methods.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
This paper proposes a new method for visual segmentation based on fixation points. The method segments the region of interest in two steps: (1) generating a probabilistic boundary edge map combining multiple visual cues, and (2) finding the optimal closed contour around the fixation point in the transformed polar edge map. The paper shows this fixation-based segmentation approach improves accuracy over previous methods, especially when incorporating motion and stereo cues. It also introduces a region merging algorithm to further refine segmentation results. Evaluation on video and stereo image datasets demonstrates mean F-measures of 0.95 and 0.96 respectively when combining cues, compared to 0.62 and 0.65 without.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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
This document proposes methods for enhancing and extracting minutiae from fingerprint images using symmetry features. It summarizes previous work on fingerprint enhancement and introduces a new approach using an image pyramid and directional filtering based on the frequency-adapted structure tensor. For minutiae extraction, it adds parabolic symmetry to the local fingerprint model to simultaneously detect minutia position and direction. Experiments on the FVC2004 database show the methods lower the matching error compared to other techniques.
p-Laplace Variational Image Inpainting Model Using Riesz Fractional Different...IJECEIAES
In this paper, p-Laplace variational image inpainting model with symmetric Riesz fractional differential filter is proposed. Variational inpainting models are very useful to restore many smaller damaged regions of an image. Integer order variational image inpainting models (especially second and fourth order) work well to complete the unknown regions. However, in the process of inpainting with these models, any of the unindented visual effects such as staircasing, speckle noise, edge blurring, or loss in contrast are introduced. Recently, fractional derivative operators were applied by researchers to restore the damaged regions of the image. Experimentation with these operators for variational image inpainting led to the conclusion that second order symmetric Riesz fractional differential operator not only completes the damaged regions effectively, but also reducing unintended effects. In this article, The filling process of damaged regions is based on the fractional central curvature term. The proposed model is compared with integer order variational models and also GrunwaldLetnikov fractional derivative based variational inpainting in terms of peak signal to noise ratio, structural similarity and mutual information.
There exists a plethora of algorithms to perform image segmentation and there are several issues related to
execution time of these algorithms. Image Segmentation is nothing but label relabeling problem under
probability framework. To estimate the label configuration, an iterative optimization scheme is
implemented to alternately carry out the maximum a posteriori (MAP) estimation and the maximum
likelihood (ML) estimations. In this paper this technique is modified in such a way so that it performs
segmentation within stipulated time period. The extensive experiments shows that the results obtained are
comparable with existing algorithms. This algorithm performs faster execution than the existing algorithm
to give automatic segmentation without any human intervention. Its result match image edges very closer to
human perception.
Object Shape Representation by Kernel Density Feature Points Estimator cscpconf
This paper introduces an object shape representation using Kernel Density Feature Points
Estimator (KDFPE). In this method we obtain the density of feature points within defined rings
around the centroid of the image. The Kernel Density Feature Points Estimator is then applied to
the vector of the image. KDFPE is invariant to translation, scale and rotation. This method of
image representation shows improved retrieval rate when compared to Density Histogram
Feature Points (DHFP) method. Analytic analysis is done to justify our method and we compared our results with object shape representation by the Density Histogram of Feature Points (DHFP) to prove its robustness.
Land Cover Feature Extraction using Hybrid Swarm Intelligence Techniques - A ...IDES Editor
This document presents a hybrid algorithm using biogeography-based optimization (BBO) and ant colony optimization (ACO) for land cover feature extraction from remote sensing images. The algorithm first analyzes a training image to identify features that BBO and ACO classify efficiently. It then applies BBO to clusters containing these features and ACO to remaining clusters. An evaluation shows the hybrid algorithm achieves a higher kappa coefficient of 0.97 compared to 0.67 for BBO alone, indicating better classification accuracy. The authors conclude the algorithm effectively handles uncertainties in remote sensing images and future work could improve efficiency further.
Gaussian Fuzzy Blocking Artifacts Removal of High DCT Compressed Imagesijtsrd
A new artifact removal method as cascade of Gaussian fuzzy edge decider and fuzzy image correction is proposed. In this design, a highly compressed i.e. low bit rate image is considered. Here, each overlapped block of image is fed to a Gaussian fuzzy based decider to check whether the central pixel of image block needs correction. Hence, the central pixel of overlapped block is corrected by fuzzy gradient of its neighbors. Experimental results shows remarkable improvement with presented gFAR algorithm compared to the past methods subjectively visual quality and objectively PSNR . Deepak Gambhir "Gaussian Fuzzy Blocking Artifacts Removal of High DCT Compressed Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33361.pdf Paper Url: https://www.ijtsrd.com/computer-science/multimedia/33361/gaussian-fuzzy-blocking-artifacts-removal-of-high-dct-compressed-images/deepak-gambhir
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The document summarizes a proposed system for currency recognition on mobile phones. The system has the following modules: 1) segmentation to isolate the currency from background noise, 2) feature extraction and building a visual vocabulary, 3) instance retrieval using inverted indexing and spatial reranking, 4) classification by vote counting spatially consistent features. The system was adapted for mobile by reducing complexity, such as using an inverted index, while maintaining accuracy. Performance is evaluated using metrics like accuracy and precision.
This document provides an introduction to graph neural networks (GNNs). It discusses that GNNs are designed to perform inference on graph data using neural networks. GNNs can analyze graph data by performing node classification, graph classification, graph visualization, link prediction, and graph clustering. The document explains that GNNs apply the concepts of node embedding and message passing to learn node representations that encode structural information from a graph. It describes how GNNs use neural network layers and aggregation functions to learn representations by propagating and transforming node feature information across a graph.
A Combined Method with automatic parameter optimization for Multi-class Image...AM Publications
Multi-class image semantic segmentation deals with many applications in consumer electronics
fields such as image editing and image retrieval. Segmentation is done by combining the top down and bottomup
segmentation. Top-Down Process can be done by Semantic Texton Forest and bottom up- process using
JSEG. These two segmentation process can be executed in a combined manner. But this cannot choose the
optimal value of JSEG parameter for each interested semantic category. Hence an automatic parameter selection
algorithm has been proposed. An automatic parameter selection technique called an automatic multilevel
thresholding algorithm using stratified sampling and PSO is used to remedy the limitations.
1) Biased Normalized Cuts presents a modification of Normalized Cuts that incorporates priors to allow for constrained image segmentation.
2) It seeks solutions that are sufficiently "correlated" with noisy top-down priors, like an object detector, and can be computed quickly given the unconstrained solution.
3) The algorithm constructs a "biased normalized cut vector" that linearly combines eigenvectors such that those correlated with a user-specified seed vector are upweighted while inversely correlated ones have their sign flipped.
論文紹介:Learning With Neighbor Consistency for Noisy LabelsToru Tamaki
Ahmet Iscen, Jack Valmadre, Anurag Arnab, Cordelia Schmid, "Learning With Neighbor Consistency for Noisy Labels" CVPR2022
https://openaccess.thecvf.com/content/CVPR2022/html/Iscen_Learning_With_Neighbor_Consistency_for_Noisy_Labels_CVPR_2022_paper.html
This document describes a technique called Deep Shading that uses convolutional neural networks to perform screen-space shading at interactive rates. The network takes in attributes from deferred shading buffers like position, normals, reflectance as input and outputs RGB colors. It is trained on example images rendered with path tracing to learn complex shading effects like ambient occlusion, indirect lighting, subsurface scattering, depth of field, motion blur, and anti-aliasing. Deep Shading can achieve quality comparable to hand-written shaders but avoids the programming effort, and generalizes better by learning from example data rather than relying on human assumptions.
ROLE OF HYBRID LEVEL SET IN FETAL CONTOUR EXTRACTIONsipij
Image processing technologies may be employed for quicker and accurate diagnosis in analysis and
feature extraction of medical images. Here, existing level set algorithm is modified and it is employed for
extracting contour of fetus in an image. In traditional approach, fetal parameters are extracted manually
from ultrasound images. An automatic technique is highly desirable to obtain fetal biometric measurements
due to some problems in traditional approach such as lack of consistency and accuracy. The proposed
approach utilizes global & local region information for fetal contour extraction from ultrasonic images.
The main goal of this research is to develop a new methodology to aid the analysis and feature extraction.
Role of Hybrid Level Set in Fetal Contour Extractionsipij
Image processing technologies may be employed for quicker and accurate diagnosis in analysis and
feature extraction of medical images. Here, existing level set algorithm is modified and it is employed for
extracting contour of fetus in an image. In traditional approach, fetal parameters are extracted manually
from ultrasound images. An automatic technique is highly desirable to obtain fetal biometric measurements
due to some problems in traditional approach such as lack of consistency and accuracy. The proposed
approach utilizes global & local region information for fetal contour extraction from ultrasonic images.
The main goal of this research is to develop a new methodology to aid the analysis and feature extraction.
The paper proposes novel kernel descriptors for visual recognition based on gradient, color, and local binary pattern (shape) features. Kernel descriptors reduce granularity of pixel features and better capture image variations compared to existing methods like SIFT. Gradient kernel descriptor performed best on four datasets for image classification, outperforming SIFT and other methods. The descriptors provide a computationally feasible way to learn high-level visual features using kernel methods.
IMAGE SEGMENTATION BY MODIFIED MAP-ML ESTIMATIONScscpconf
This document summarizes an image segmentation algorithm called Modified MAP-ML Estimations. It begins with an abstract describing the algorithm and its benefits of faster execution time compared to existing algorithms. It then reviews related work in image segmentation techniques and their limitations. The document describes the probabilistic model used in the algorithm, which formulates segmentation as a labeling problem. It explains the MAP estimation approach used to estimate label configurations, and the ML estimation used to estimate region properties. The algorithm iterates between these two estimations to perform segmentation.
This document summarizes an image segmentation algorithm called Modified MAP-ML Estimations. It begins with an abstract describing the algorithm and its benefits of faster execution time compared to existing algorithms. It then reviews related work in image segmentation techniques and their limitations. The document describes the probabilistic model used in the algorithm, which formulates segmentation as a labeling problem. It explains the MAP estimation approach used to estimate label configurations, defining energy functions minimized through graph cuts. ML estimation is then used to update the region feature estimates in an iterative process. In summary, this algorithm modifies an existing MAP-ML approach to achieve comparable segmentation results to other algorithms, but in a faster execution time without human intervention.
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Intelligent Auto Horn System Using Artificial IntelligenceIRJET Journal
The document proposes an intelligent auto horn system using artificial intelligence to reduce unnecessary noise pollution from vehicle horns. The system uses sensors and cameras to collect environmental data and an on-board computer uses AI techniques like SIFT, SURF and other computer vision algorithms to process the data. Based on factors like the distance between objects, road width, and object size, the AI will control the horn sound horizontally and vertically. The system aims to only sound the horn as loud as needed based on the situation to reduce noise pollution while maintaining safety. The system is described as not compromising safety and automatically adjusting the horn sound using fixed horn mechanisms based on AI analysis of the environment.
This document provides an overview of mathematical morphology and its applications in image processing. Some key points:
- Mathematical morphology uses concepts from set theory and uses structuring elements to probe and modify binary and grayscale images.
- Basic morphological operations include erosion, dilation, opening, closing, hit-or-miss transformation, thinning, thickening, and skeletonization.
- Erosion shrinks objects and removes small details while dilation expands objects and fills small holes. Opening and closing combine these to smooth contours or fuse breaks.
- Morphological operations have many applications including boundary extraction, region filling, component labeling, convex hulls, pruning, and more. Grayscale images extend these concepts using minimum/maximum
This document provides an overview of mathematical morphology and its applications to image processing. Some key points:
- Mathematical morphology uses concepts from set theory and uses structuring elements to probe and extract image properties. It provides tools for tasks like noise removal, thinning, and shape analysis.
- Basic operations include erosion, dilation, opening, and closing. Erosion shrinks objects while dilation expands them. Opening and closing combine these to smooth contours or fill gaps.
- Hit-or-miss transforms allow detecting specific shapes. Skeletonization reduces objects to 1-pixel wide representations.
- Morphological operations can be applied to binary or grayscale images. Structuring elements are used to specify the neighborhood of pixels
This document discusses image segmentation techniques. It begins by introducing the goal of image segmentation as clustering pixels into salient image regions. Segmentation can be used for tasks like object recognition, image compression, and image editing. The document then discusses several bottom-up image segmentation approaches, including clustering pixels in feature space using mixtures of Gaussians models or K-means, mean-shift segmentation which models feature density non-parametrically, and graph-based segmentation methods which construct similarity graphs between pixels. It provides examples and discusses assumptions and limitations of each approach. The key approaches discussed are clustering in feature space, mean-shift segmentation, and graph-based similarity methods like the local variation algorithm.
This document discusses techniques for image segmentation and edge detection. It proposes a generalized boundary detection method called Gb that combines low-level and mid-level image representations in a single eigenvalue problem to detect boundaries. Gb achieves state-of-the-art results at low computational cost. Soft segmentation is also introduced to improve boundary detection accuracy with minimal extra computation. Common methods for edge detection are described, including gradient-based, texture-based, and projection profile-based approaches. Improved Harris and corner detection algorithms are presented to more accurately detect edges and corners. The output of Gb using soft segmentations as input is shown to correlate well with occlusions and whole object boundaries while capturing general boundaries.
Similar to Interactive graph cuts for optimal boundary and region segmentation of objects (20)
Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdfleebarnesutopia
So… you want to become a Test Automation Engineer (or hire and develop one)? While there’s quite a bit of information available about important technical and tool skills to master, there’s not enough discussion around the path to becoming an effective Test Automation Engineer that knows how to add VALUE. In my experience this had led to a proliferation of engineers who are proficient with tools and building frameworks but have skill and knowledge gaps, especially in software testing, that reduce the value they deliver with test automation.
In this talk, Lee will share his lessons learned from over 30 years of working with, and mentoring, hundreds of Test Automation Engineers. Whether you’re looking to get started in test automation or just want to improve your trade, this talk will give you a solid foundation and roadmap for ensuring your test automation efforts continuously add value. This talk is equally valuable for both aspiring Test Automation Engineers and those managing them! All attendees will take away a set of key foundational knowledge and a high-level learning path for leveling up test automation skills and ensuring they add value to their organizations.
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
AI in the Workplace Reskilling, Upskilling, and Future Work.pptxSunil Jagani
Discover how AI is transforming the workplace and learn strategies for reskilling and upskilling employees to stay ahead. This comprehensive guide covers the impact of AI on jobs, essential skills for the future, and successful case studies from industry leaders. Embrace AI-driven changes, foster continuous learning, and build a future-ready workforce.
Read More - https://bit.ly/3VKly70
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Keywords: AI, Containeres, Kubernetes, Cloud Native
Event Link: https://meine.doag.org/events/cloudland/2024/agenda/#agendaId.4211
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
Introducing BoxLang : A new JVM language for productivity and modularity!Ortus Solutions, Corp
Just like life, our code must adapt to the ever changing world we live in. From one day coding for the web, to the next for our tablets or APIs or for running serverless applications. Multi-runtime development is the future of coding, the future is to be dynamic. Let us introduce you to BoxLang.
Dynamic. Modular. Productive.
BoxLang redefines development with its dynamic nature, empowering developers to craft expressive and functional code effortlessly. Its modular architecture prioritizes flexibility, allowing for seamless integration into existing ecosystems.
Interoperability at its Core
With 100% interoperability with Java, BoxLang seamlessly bridges the gap between traditional and modern development paradigms, unlocking new possibilities for innovation and collaboration.
Multi-Runtime
From the tiny 2m operating system binary to running on our pure Java web server, CommandBox, Jakarta EE, AWS Lambda, Microsoft Functions, Web Assembly, Android and more. BoxLang has been designed to enhance and adapt according to it's runnable runtime.
The Fusion of Modernity and Tradition
Experience the fusion of modern features inspired by CFML, Node, Ruby, Kotlin, Java, and Clojure, combined with the familiarity of Java bytecode compilation, making BoxLang a language of choice for forward-thinking developers.
Empowering Transition with Transpiler Support
Transitioning from CFML to BoxLang is seamless with our JIT transpiler, facilitating smooth migration and preserving existing code investments.
Unlocking Creativity with IDE Tools
Unleash your creativity with powerful IDE tools tailored for BoxLang, providing an intuitive development experience and streamlining your workflow. Join us as we embark on a journey to redefine JVM development. Welcome to the era of BoxLang.
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation F...AlexanderRichford
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation Functions to Prevent Interaction with Malicious QR Codes.
Aim of the Study: The goal of this research was to develop a robust hybrid approach for identifying malicious and insecure URLs derived from QR codes, ensuring safe interactions.
This is achieved through:
Machine Learning Model: Predicts the likelihood of a URL being malicious.
Security Validation Functions: Ensures the derived URL has a valid certificate and proper URL format.
This innovative blend of technology aims to enhance cybersecurity measures and protect users from potential threats hidden within QR codes 🖥 🔒
This study was my first introduction to using ML which has shown me the immense potential of ML in creating more secure digital environments!
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
ScyllaDB is making a major architecture shift. We’re moving from vNode replication to tablets – fragments of tables that are distributed independently, enabling dynamic data distribution and extreme elasticity. In this keynote, ScyllaDB co-founder and CTO Avi Kivity explains the reason for this shift, provides a look at the implementation and roadmap, and shares how this shift benefits ScyllaDB users.
QA or the Highway - Component Testing: Bridging the gap between frontend appl...zjhamm304
These are the slides for the presentation, "Component Testing: Bridging the gap between frontend applications" that was presented at QA or the Highway 2024 in Columbus, OH by Zachary Hamm.
In our second session, we shall learn all about the main features and fundamentals of UiPath Studio that enable us to use the building blocks for any automation project.
📕 Detailed agenda:
Variables and Datatypes
Workflow Layouts
Arguments
Control Flows and Loops
Conditional Statements
💻 Extra training through UiPath Academy:
Variables, Constants, and Arguments in Studio
Control Flow in Studio
Session 1 - Intro to Robotic Process Automation.pdfUiPathCommunity
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
📕 Detailed agenda:
What is RPA? Benefits of RPA?
RPA Applications
The UiPath End-to-End Automation Platform
UiPath Studio CE Installation and Setup
💻 Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
Explore automation development with UiPath Studio
👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
GlobalLogic Java Community Webinar #18 “How to Improve Web Application Perfor...GlobalLogic Ukraine
Під час доповіді відповімо на питання, навіщо потрібно підвищувати продуктивність аплікації і які є найефективніші способи для цього. А також поговоримо про те, що таке кеш, які його види бувають та, основне — як знайти performance bottleneck?
Відео та деталі заходу: https://bit.ly/45tILxj
GlobalLogic Java Community Webinar #18 “How to Improve Web Application Perfor...
Interactive graph cuts for optimal boundary and region segmentation of objects
1. Interactive Graph Cuts
for Optimal Boundary & Region Segmentation of Objects in N-D Images
Yuri Y. Boykov Marie-Pierre Jolly
Imaging and Visualization Department
Siemens Corporate Research
755 College Road East, Princeton, NJ 08540, USA
yuri @ scr.siemens.com
Abstract mentation. A globally optimal segmentation can be very
efficiently recomputed when the user adds or removes any
In this paper we describe a new technique for general hard constraints (seeds). This allows the user to get any
purpose interactive segmentation of N-dimensional images. desired segmentation results quickly via very intuitive in-
The user marks certain pixels as “object” or “background” teractions. Our method applies to N-D images (volumes).
to provide hard constraints for segmentation. Additional One of the main advantages of our interactive segmen-
soji constraints incorporate both boundary and region in- tation method is that it provides a globally optimal solution
formation. Graph cuts are used to find the globally optimal for an N-dimensional segmentation when the cost function
segmentation of the N-dimensional image. The obtained so- is clearly defined. Some earlier techniques (snakes [ 14, 41,
lution gives the best balance of boundary and region prop- deformable templates [21], shortest path [ 151, ratio regions
erties among all segmentations satishing the constraints. [5], and other [ 131) can do that only in 2D applications when
The topology o$our segmentation is unrestricted and both a segmentation boundary is a 1D curve. Many techniques
“object” and “background” segments may consist of sev- either don’t have a clear cost function at all (region growing,
eral isolatedparts. Some experimental results are presented split and merge, see Chapter 10 in [ 1 I]) or compute only an
in the context ofphotohideo editing and medical image seg- approximate solution (e.g. a local minimum) that can be
mentation. We also demonstrate an interesting Gestalt ex- arbitrarily far from the global optimum (region competition
ample. A fast implementation of our segmentation method [22], level set methods [ 161, normalized cuts [IS]). Global
is possible via a new mar-$ow algorithm in [2]. properties of such segmentations may be difficult to analyze
or,predict. Imperfections in the result might come from de-
ficiencies at the minimization stage. In contrast, imperfec-
1. Introduction tions of a globally optimal solution are directly related to
the definition of the cost function. Thus, the segmentation
Interactive segmentation is becoming more and more can be controlled more reliably.
popular to alleviate the problems inherent to fully automatic It is also important that the cost function that we use
segmentation which seems to never be perfect. Our goal is as a soft constraint for segmentation is general enough to
a general purpose interactive segmentation technique that include both region and boundary properties of segments.
divides an image into two segments: “object” and “back- In fact, our cost function is similar to one in [9] obtained
ground”. A user imposes certain hard constraints for seg- in a context of MAP-MRF estimation. Consider an arbi-
mentation by indicating certain pixels (seeds) that abso- trary set of data elements P and some neighborhood sys-
lutely have to be part of the object and certain pixels that tem represented by a set N of all unordered’ pairs { p i q }
have to be part of the background. Intuitively, these hard
‘For simplicity, we present the case o f unordered pairs of neighbors
constraints provide clues on what the user intends to seg- { p , q } . If pairs of neighbors are ordered then we have two distinct pairs
ment. The rest of the image is segmented automatically by ( p , q ) and ( q , p ) . This would allow different (directed) discontinuity
computing a global optimum among all segmentations sat- penalties for two cases when p E “object”, q E “background’ and when
p E “background”, q E “object”. To set such directed costs one should
isfying the hard constraints. The cost function is defined in have prior information on properties of the boundary from object to back-
terms of boundary and region properties of the segments. ground. Algorithmically, generalization from unordered to ordered neigh-
These properties can be viewed as soft constraints for seg- bors means switching from undirected (presented here) to directed graphs.
105
0-7695-1143-0/01 $10.00 0 2001 IEEE
2. of neighboring elements in P. For example, P can con- desired boundary. Moreover, this approach can not be easily
tain pixels (or voxels) in a 2D (or 3D) grid and can generalized to 3D data.
contain all unordered pairs of neighboring pixels (voxels)
under a standard 8- (or 26-) neighborhood system. Let We consider hard constraints that indicate segmentation
regions rather than the boundary. We assume that some pix-
A = (AI ,... , A p , . . . , A l p , ) be a binary vector whose
components A, specify assignments to pixels p in P . Each els were marked as internal and some as external for the
A, can be either “obj” or “bkg” (abbreviations of “object” given object of interest. The subsets of marked pixels will
and “background”). Vector A defines a segmentation. Then, be referred to as “object” and “background” seeds. The seg-
the soft constraints that we impose on boundary and region mentation boundary can be anywhere but it has to separate
properties of A are described by the cost function E ( A ) : the object seeds from the background seeds. Note that the
seeds can be loosely positioned inside the object and back-
E(A) = A.R(A)+B(A) (1) ground regions. Our segmentation technique is quite stable
and normally produces the same results regardless of par-
where ticular seed positioning within the same image object.
Obviously, the hard constraints by themself are not
enough to obtain a good segmentation. A segmentation
method decides how to segment unmarked pixels. [ 101 and
[17] use the same type of hard constraints as us but they
do not employ a clear cost function and segment unmarked
and pixels based on variations of “region growing”. Since the
1 ifA, # A q properties of segmentation boundary are not optimized, the
d(A,,A,) = 0 otherwise. results are prone to “leaking” where the boundary between
The coefficient A 2 0 in (1) specifies a relative impor- objects is blurry. In contrast, we combine the hard con-
tance of the region properties term R(A) versus the bound- straints as above with energy (1) that incorporates region
ary properties term B(A). The regional term R ( A ) as- and boundary properties of segments.
sumes that the the individual penalties for assigning pixel p In this paper we show how to generalize the algorithm
to “object” and “background”, correspondingly R, (“obj”) in [9] to combine soft constraints encoded in (1) with user
and R,(“bkg”), are given. For example, R p ( . ) may reflect defined hard constraints. We also show how the optimal
on how the intensity of pixel p fits into a known intensity segmentations can be efficiently recomputed if the hard con-
model (e.g. histogram) of the object and background. straints are added or changed. Note that initial segmentation
The term B ( A ) comprises the “boundary” properties of may not be perfect. After reviewing it, the user can specify
segmentation A. Coefficient B{p,ql2 0 should be inter- additional object and background seeds depending on the
preted as a penalty for a discontinuity between p and q. Nor- observed results. To incorporate these new seeds the algo-
mally, B { p , q is large when pixels p and q are similar (e.g.
} rithm can efficiently adjust the current segmentation with-
in their intensity) and B { p , q is close to zero when the two
} out recomputing the whole solution from scratch.
are very different. The penalty B{,,,} can also decrease as a
function of distance between p and q. Costs B{p,ql may be Our technique is based on powerful graph cut algorithms
based on local intensity gradient, Laplacian zero-crossing, from combinatorial optimization [7, 81. Our implementa-
gradient direction, and other criteria (e.g. [ 151). tion uses a new version of “max-flow” algorithm reported
The algorithm in [9] computes a globally optimal binary in 121. In the next section we explain the terminology for
segmentation minimizing the energy similar to (1) when no graph cuts and provide some background information on
hard constraints are placed. In contrast, our goal is to com- previous computer vision techniques relying on graph cuts.
pute the global minimum of (1) among all segmentations The details of our segmentation method and its correctness
that satisfy additional hard constraints imposed by a user. are shown in Section 3. Section 4 provides a number of
There are different types of hard constraints that were examples where we apply our technique to photo/video-
proposed in the past for interactive segmentation methods. editing and to segmentation of medical images/volumes.
For example, for intelligent scissors [ 151 and live wire [6], We demonstrate that with a few simple and intuitive ma-
the user has to indicate certain pixels where the segmenta- nipulations a user can always segment an object of interest
tion boundary should pass. The segmentation boundary is as precisely as (s)he wants. Note that some additional ex-
then computed as the “shortest path” between the marked perimental results can be found in our preliminary work [ 11
pixels according to some energy function based on image which can be seen as a restricted case where only bound-
gradient. One difficulty with such hard constraints is that ary term is present in energy (1). Information on possible
the user inputs have to be very accurately positioned at the extensions and future work is given in Section 5.
106
3. the nodes in the graph. As illustrated in Figure 1 (c-d), this
partitioning corresponds to a segmentation of an underlying
image or volume. A minimum cost cut generates a segmen-
tation that is optimal in terms of properties that are built into
the edge weights.
(a) Image with seeds. (d) Segmentation results. Our technique is based on a well-known combinatorial
optimization fact that a globally minimum cut of a graph
U -h with two terminals can be computed efficiently in low-order
polynomial time [7, 8, 21. In Section 3 we show how to set
a two terminal graph so that the minimum cut would give
a segmentation that minimizes (1) among all segmentations
satisfying the given hard constraints.
* It should be noted that graph cuts were used for image
segmentation before. In [20] the image is optimally divided
into K parts to minimize the maximum cut between the seg-
ments. In this formulation, however, the segmentation is
strongly biased to very small segments. Shi and Malik [ 181
(b) Graph. (c) cut. try to solve this problem by normalizing the cost of a cut.
The resulting optimization problem is NP-hard and they use
- .
an approximation technique. In [9, 3, 12, 191 graph cuts are
Figure 1. A simple 2D segmentation example applied to minimize certain energy functions used in im-
for a 3 x 3 image. The seeds are 0 = {w} and age restoration, stereo, 3D object reconstruction, and other
B = { p } . The cost of each edge is reflected problems in computer vision.
by the edge’s thickness. The regional term A fast implementation of theoretically polynomial graph
(2) and hard constraints (4’5) define the costs cut algorithms can be an issue. The most straight-forward
of t-links. The boundary term (3) defines the implementations of the standard graph cut algorithms, e.g.
costs of n-links. Inexpensive edges are at- max-flow [7] or push-relabel [SI, can be slow. The experi-
tractive choices for the minimum cost cut. ments in [ 2 ] compare several well-known “tuned’ versions
of these standard algorithms in the context of graph based
methods in vision. The same paper also describes a new ver-
2. Graph Cuts and Computer Vision sion of the max-flow algorithm that (on typical in vision ex-
amples) significantly outperformed the standard techniques.
Our implementation of the interactive segmentation method
First, we describe the basic terminology that pertains to
of this paper uses the new graph cut algorithm from [ 2 ] .
graph cuts in the context of our segmentation method. An
undirected graph G = ( V , E ) is defined as a set of nodes
(vertices V ) and a set of undirected edges ( E ) that connect 3. Segmentation Technique
these nodes. An example of a graph that we use in this
paper is shown in Figure I@). Each edge e E E in the In this section we provide algorithmic details about our
graph is assigned a nonnegative weight (cost) U,.There are segmentation technique. Assume that 0 and B denote
also two special nodes called terminals. A cut is a subset the subsets of pixels marked as “object” and “background”
of edges C c & such that the terminals become separated seeds. Naturally, the subsets (3 C P and B C P are such
on the induced graph G(C) = (V,&C). It is normal in that 0fl G = 0. Remember that our goal is to compute the
combinatorial optimization to define the cost of a cut as the global minimum of ( I ) among all segmentations .A satisfy-
sum of the costs of the edges that i t severs ing hard constraints
V p E 0, -Ap = “obj” (4)
V p E 6, *Ap = “bkg”. (5)
Graph cut formalism is well suited for segmentation The general work flow is shown in Figure I . Given an
of iningcs. In fact. i t is completely appropriate for N- image (Figure I(a)) we create a graph with two terminals
dinicnsionul volumcs. The nodes o f the graph can represent (Figure I(b)). The edge weights reflect the parameters in
pixcls (or voxels) and the cdges can represent any neigh- the regional ( 2 ) and the boundary (3) terms of the cost func-
borhood relationship bctwccn the pixels. A cut partitions tion. as well as the known positions of seeds in the image.
107
4. The next step is to compute the globally optimal minimum (see Section 2) assuming that the edge weights specified in
cut (Figure l(c)) separating two terminals. This cut gives the table above are non-negative2.
a segmentation (Figure l(d)) of the original image. In the Below we state exactly how the minimum cut C defines
simplistic example of Figure 1 the image is divided into ex- a segmentation A and prove this segmentation is optimal.
actly one “object” and one “background” regions. In gen- We need one technical lemma. Assume that 3denotes a set
eral, our segmentation method generates binary segmenta- of allfeasible cuts C on graph G such that
tion with arbitrary topological properties. Examples in Sec-
tion 4 illustrate that object and background segments may 0 C severs exactly one t-link at each p
consist of several isolated connected blobs in the image.
Below we describe the details of the graph and prove 0 { p , q } E C iff p , q are t-linked to different terminals
that the obtained segmentation is optimal. To segment a
given image we create a graph 6 = ( V ,E ) with nodes cor- 0 if p E 0then { p , T } E C
responding to pixels p E P of the image. There are two
additional nodes: an “object” terminal (a source S ) and a 0 if p E B then { p , S } E C .
“background” terminal (a sink T ) .Therefore,
Lemma 1 The minimum cut on G is feasible, i.e. C E 3.
V=PU{S,T}.
PROOF: C severs et least one t-link at each.pixel since it
The set of edges E consists of two types of undirected edges: is a cut that separates the terminals. On the other hand, it
n-links (neighborhood links) and t-links (terminal links). can not sever both t-links. In such a case it would not be
Each pixel p has two t-links { p , S} and { p , T } connecting minimal since one of the t-links could be returned. Simi-
it to each terminal. Each pair of neighboring pixels { p , q } larly, a minimum cut should sever an n-link { p , q } if p and
in N is connected by an n-link. Without introducing any q are connected to the opposite terminals just because any
ambiguity, an n-link connecting a pair of neighbors p and q cut must separate the termi$s. If p and q are connected
will be denoted by { p , q } . Therefore, to the same terminal then C should not sever unnecessary
n-link { p , q}^due to its minimality. The last two properties
=N U { { P , SI, { P , V ) . are true for C because the constant K is larger than the sum
PEP of all n-links costs for any given pixel p . For example, if
The following table gives weights of edges in E p E 0 and d severs { p , S } (costs K ) then we would con-
struct a smaller cost cut by restoring { p , S } and severing all
edge 1 weight (cost) n-links from p (costs less then K ) as well as the opposite
t-link { p , T } (zero cost). I
For any feasible cut C E 3 we can define a unique cor-
responding segmentation A ( C ) such that
if { p , T } E C
I
AdC) = { “obj”,
“bkg”, if { p , S } E C. (6)
X . R,(“obj”) The definition above is coherent since any feasible cut sev-
ers exactly one of the two t-liyks at each pixel p . The lemma
showed that a minimum cut C is feasible. Thus, we can de-
fine a corresponding segmentation A = A ( C ) . The next
theorem completes the description of our algorithm.
Theorem 1 The segmentation A = A(C) defined by the
minimum cut C as in ( 6 ) minimizes ( I ) among all segmen-
tations satisfying constraints (4,5).
The graph G is now completely defined. We draw the *In fact, our technique does require that the penalties B { p , q } non-
be
segmentation boundary between the object and the back- negative. The restriction that R P ( . ) should be non-negative is easy to
avoid. For any pixel p we can always add a large enough positive con-
ground by finding the minimum cost cut on the graph G.
stant to both R,(“obj”) and R,(“bkg”) to make sure that they become
The minimum cost cut C on G can be computed exactly in non-negative. This operation can not change the minimum A of E ( A ) in
polynomial time via algorithms for two terminal graph cuts (1) since it is equivalent to adding a constant to the whole energy function.
108
5. PROOF: Using the table of edge weights, definition of fea- new cost
sible cuts 3,and equation (6) one can show that a cost of
any C E 3 is
IC1 = X.RP(AP(C))+
PBOUB
These new costs are consistent with the edge weight table
B{p,q} . &4P(C)7 AP(C)) for pixels in U since the extra constant cp at both t-links of
{p,q)EN a pixel does not change the optimal cut4. Then, a maximum
-
= E(A(C)) XR,(“obj”) - XRp(“bkg”). flow (minimum cut) on a new graph can be efficiently ob-
PE0 P€B tained starting from the previous flow without recomputing
the whole solution from scratch.
Therefore, IC1 = E ( A ( C ) )- const(C).Note that for any Note that the same trick can be done to adjust the seg-
C E 3 assignment A ( C )satisfies constraints (4,5). In fact, mentation when a new “background” seed is added or when
equation (6) gives a one-to-one correspondence between the a seed is deleted. One has to figure the right amounts that
set of all feasible cuts in 3 and the set 31: of all assignments have to be added to the costs of two t-links at the corre-
A that satisfy hard constraints (43). Then, sponding pixel. The new costs should be consistent with
E(A) = ~Cl+const = minl~l+const = the edge weight table plus or minus the same constant.
C€3
min E ( A ( C ) ) = minE(A)
CE3 AE31 4. Examples
and the theorem is proved.
We demonstrate our general-purpose segmentation
To conclude this section we would like to show that our method in several examples including photohideo editing
algorithm can efficiently adjust the segmentation to incor- and medical data processing. We show original data and
porate any additional seeds that the user might interactively segments generated by our technique for a given set of
add. To be specific, assume that a max-flow algorithm is seeds. Our actual interface allows a user to enter seeds via
used to determine the minimum cut on G. The max-flow mouse operated brush of red (for object) or blue (for back-
algorithm gradually increases the flow sent from the source ground) color. Due to limitations of this B&W publication
S to the sink T along the edges in G given their capacities we show seeds as strokes of white (object) or black (back-
(weights). Upon termination the maximum flow saturates3 ground) brush. In addition, these strokes are marked by the
the graph. The saturated edges correspond to the minimum letters “0” and “B”. For the purpose of clarity, we employ
cost cut on giving us an optimal segmentation. different methods for the presentation of segmentation re-
Assume now that an optimal segmentation is already sults in our examples below.
computed for some initial set of seeds. A user adds a new Our current implementation actually makes a double use
“object” seed to pixel p that was not previously assigned of the seeds entered by a user. First of all, they provide
any seed. We need to change the costs for two t-links at p the hard constraints for the segmentation process as dis-
I t-link I initial cost I new cost 1 cussed in Section 3. In addition, we use intensities of pix-
els (voxels) marked as seeds to get histograms for “ob-
ject” and “background” intensity distributions: Pr(1)Q)
I I I
I and Pr(llB)s. Then, we use these histograms to set the
regional penalties R p ( . ) negative log-likelihoods:
as
and then compute the maximum Bow (minimum cut) on the Rp(“obj”) = - lnPr(1,JQ)
new graph. In fact, we can start from the flow found at R,(“bkg”) = - lnPr(1,IB).
the end of initial computation. The only problem is that re-
assignment of edge weights as above reduces capacities of Such penalties are motivated by the underlying MAP-MRF
some edges. If there is a flow through such an edge then formulation [9, 31.
we may break the flow consistency. Increasing an edge ca- To set the boundary penalties we use an ad-hoc function
pacity, on the other hand, is never a problem. Then, we can
solve the problem as follows.
To accommodate the new “object” seed at pixel p we
increase the t-links weights according to the table 4Note that the minimum cut severs exactly one of two t-links at pixel p .
’See [7] or (21 for details. SAltematively, the histograms can be learned from historic data.
109
6. B
0
(a) Original image (b) Result for X = 7-43 B
B
B
(a) Original B&W photo (b) Segmentation results
(c) Result for X = 0 (d) Result for X = 60
(c) Details of segmentation (d) Details of segmentation
with regional term without regional term
Figure 2. Synthetic Gestalt example. The seg-
mentation results in (b-d) are shown for vari-
ous levels X of relative importance of “region” Figure 3. Segmentation of a photograph.
versus “boundary” in (1). Note that the result
in (b) corresponds to a wide range of A.
4.2. Photo and Video Editing
In Figure 3 we segmented a bell with a group of people
This function penalizes a lot for discontinuities between from a photograph. The user can start with a few “object”
pixels of similar intensities when 1, - IqI < 0.However,
1 and “background” seeds loosely positioned inside and, cor-
if pixels are very different, 1, - IQI > CJ,then the penalty is
1 respondingly, outside the object(s) of interest. By reviewing
small. Intuitively, this function corresponds to the distribu- the results of initial segmentation the user will see what ar-
tion of noise among neighboring pixels of an image. Thus, eas are segmented incorrectly. Then (s)he can put additional
0 can be estimated as “camera noise”.
seeds6 into the troubled places and efficiently recompute the
Note that we use an 8-neighborhood system in 2D ex- optimal segmentation as explained at the end of Section 3.
amples and 26-neighborhood system in 3D examples. All This process of adding seeds gives more clues to the algo-
running times are given for 333MHz Pentium 111. Our im- rithm and may be continued until the user likes the results.
plementation uses a new “max-flow” algorithm from [ 2 ] .
Naturally, the hope is that the method can quickly iden-
4.1. Gestalt Example tify the right object. The user would not like to keep adding
new seeds until the whole image is covered by these seeds.
Figure 2 illustrates some interesting properties of our This is no better than a manual segmentation. The perfor-
cost function (1). Isolated dark blocks that stay relatively mance of an algorithm can be judged by the efforts required
close to each other in (a) are segmented as one object in (b). from the user. Thus, the results of our segmentation are
This may correspond to the psychological perception of the shown with seeds that we entered to get this segmentation.
original image (a). Only an optimal solution that combines Our segmentation algorithm runs in less than a second
both boundary and region properties can generate such re- for most 2D images (up to 512 x 512) with a fixed set of
sult. Our segmentation does not leak through the numer- seeds. When additional seeds are entered the solution is
ous “holes” as a simple region growing would. As shown recomputed in the blink of an eye. Thus, the speed eval-
in (c), the segmentation based on boundary properties only uation of our method in 2D is mainly concerned with the
“shrinks” the object. The opposite extreme when the region user efforts. The detailed segmentation in Figure 3(b) is
properties strongly dominate the boundary forces is shown obtained in approximately a minute. Note that in this exam-
in (d). In this case the method creates isolated segments for
‘Note that adding correcting “object” seeds to pixels that are already
individual blocks. The bright space between the blocks has segmented as “object” would not change the optimal segmentation. These
a better fit with the “background” histogram and the super- additional hard constraint are already satisfied by the current solution. The
strong regional term forces the “object” segment out. same argument applies to correcting “background” seeds.
110
7. Figure 5. Bone removal in a CT image. Bone
segments are marked by horizontal lines.
tained by placing seeds in 3 out of 21 frames. Each car was
segmented in an independent experiment. We do not show
Figure 4. Segmentation o a video sequence.
f seeds to avoid confusion.
The computation of the minimum cut is slower in 3D
cases. The initial segmentation might take from 2-3 sec-
onds on smaller volumes (200 x 200 x 10)to a few minutes
on bigger ones (512 x 512 x 50). Thus, efficient recom-
ple the algorithm created some isolated “background” seg- puting of an optimal solution when new seeds are added is
ments. In fact, the algorithm automatically decided which crucial. Most of the time the new seeds are incorporated
“background” seeds were grouped together and which were in a few seconds even for bigger volumes. Therefore, we
placed into isolated segments. The same is equally true for can still consider our method as “interactive”. The results
the “object” seeds. The segments can have any topology. in Figure 4 can be obtained in approximately 30 seconds
In many cases the regional term of energy (1) helps to including user interactions.
get the right results faster. In Figures 3(c,d) we show some
details of segmentation with and without the regional term 4.3. Medical Images and Volumes
(A = 0) given the same sets of seeds. In (d) the user would
spend more time by placing additional “background” seeds Figure 5 shows a segmentation that we obtained on a ab-
to correct imperfections. dominal CT image. Our goal was to remove bones from
The globally minimum two-terminal cut can be com- the image for better volume rendering. The right segmenta-
puted on any graph. Thus, our technique is valid for seg- tion was obtained after one “click” on one of the bones and
mentation of N-D data. In Figure 4 we segmented moving a couple of loose “strokes” in the background. The other
cars in a video sequence. The sequence of 2 1 video frames bones were correctly segmented out based on regional prop-
(255 x 189) was treated as a single 3D volume. The nec- erties. The boundary term helped to avoid incorrect “bone”
essary seeds were entered in a simple 3D interface where segments in the bright parts of liver and other tissues. The
we could browse through individual 2D slices (frames) of results are obtained in a few seconds. Without the regional
the volume. Initial seeds can be entered in just a few repre- term in the energy function (1) when X = 0 the user would
sentative frames. Note that the boundary, region, and seed have to add “object” seeds in all isolated bones.
information is automatically propagated between the slices In Figure 6 we demonstrate our segmentation method on
since we compute a globally optimum solution directly on 3D medical data. We segmented out cortex, medulla, and
the 3D data set. Thus, the whole sequence can be segmented collecting system of a kidney from a background. This was
based on seeds placed in just a few frames. For example, en- done in three steps. First, the kidney was separated from the
tering correcting seeds in one frame can fix imperfections background. Then we separated the cortex from the rest of
in many adjacent frames. The results in Figure 4 are ob- the kidney. In the last step the medulla was separated from
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