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.
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.
Automatic Determination Number of Cluster for NMKFC-Means Algorithms on Image...IOSR Journals
This document discusses image segmentation techniques using clustering algorithms. It introduces Fuzzy C-Means (FCM) clustering, which allows data points to belong to multiple clusters with varying degrees of membership. However, FCM does not work well on noisy or non-linearly separable data. The document proposes the Kernel Fuzzy C-Means (KFCM) algorithm, which uses a kernel function to map data to a higher dimensional space, making separation easier. While improving results for noisy images, KFCM does not consider neighboring pixels. Finally, the document introduces the Novel Modified Kernel Fuzzy C-Means (NMKFCM) algorithm, which incorporates neighborhood information into the objective function to further improve segmentation accuracy, especially for noisy images
A PSO-Based Subtractive Data Clustering AlgorithmIJORCS
There is a tremendous proliferation in the amount of information available on the largest shared information source, the World Wide Web. Fast and high-quality clustering algorithms play an important role in helping users to effectively navigate, summarize, and organize the information. Recent studies have shown that partitional clustering algorithms such as the k-means algorithm are the most popular algorithms for clustering large datasets. The major problem with partitional clustering algorithms is that they are sensitive to the selection of the initial partitions and are prone to premature converge to local optima. Subtractive clustering is a fast, one-pass algorithm for estimating the number of clusters and cluster centers for any given set of data. The cluster estimates can be used to initialize iterative optimization-based clustering methods and model identification methods. In this paper, we present a hybrid Particle Swarm Optimization, Subtractive + (PSO) clustering algorithm that performs fast clustering. For comparison purpose, we applied the Subtractive + (PSO) clustering algorithm, PSO, and the Subtractive clustering algorithms on three different datasets. The results illustrate that the Subtractive + (PSO) clustering algorithm can generate the most compact clustering results as compared to other algorithms.
Graph Theory Based Approach For Image Segmentation Using Wavelet TransformCSCJournals
This paper presents the image segmentation approach based on graph theory and threshold. Amongst the various segmentation approaches, the graph theoretic approaches in image segmentation make the formulation of the problem more flexible and the computation more resourceful. The problem is modeled in terms of partitioning a graph into several sub-graphs; such that each of them represents a meaningful region in the image. The segmentation problem is then solved in a spatially discrete space by the well-organized tools from graph theory. After the literature review, the problem is formulated regarding graph representation of image and threshold function. The boundaries between the regions are determined as per the segmentation criteria and the segmented regions are labeled with random colors. In presented approach, the image is preprocessed by discrete wavelet transform and coherence filter before graph segmentation. The experiments are carried out on a number of natural images taken from Berkeley Image Database as well as synthetic images from online resources. The experiments are performed by using the wavelets of Haar, DB2, DB4, DB6 and DB8. The results are evaluated and compared by using the performance evaluation parameters like execution time, Performance Ratio, Peak Signal to Noise Ratio, Precision and Recall and obtained results are encouraging.
Segmentation of Images by using Fuzzy k-means clustering with ACOIJTET Journal
Abstract— Super pixels are becoming increasingly popular for use in computer vision applications. Image segmentation is the process of partitioning a digital image into multiple segments (known as super pixels). In this paper, we developed fuzzy k-means clustering with Ant Colony Optimization (ACO). In this propose algorithm the initial assumptions are made in the calculation of the mean value, which are depends on the colors of neighbored pixel in the image. Fuzzy mean is calculated for the whole image, this process having set of rules that rules are applied iteratively which is used to cluster the whole image. Once choosing a neighbor around that the fitness function is calculated in the optimization process. Based on the optimized clusters the image is segmented. By using fuzzy k-means clustering with ACO technique the image segmentation obtain high accuracy and the segmentation time is reduced compared to previous technique that is Lazy random walk (LRW) methodology. This LRW is optimized from Random walk technique.
MULTI-OBJECTIVE ENERGY EFFICIENT OPTIMIZATION ALGORITHM FOR COVERAGE CONTROL ...ijcseit
Many studies have been done in the area of Wireless Sensor Networks (WSNs) in recent years. In this kind of networks, some of the key objectives that need to be satisfied are area coverage, number of active sensors and energy consumed by nodes. In this paper, we propose a NSGA-II based multi-objective algorithm for optimizing all of these objectives simultaneously. The efficiency of our algorithm is demonstrated in the simulation results. This efficiency can be shown as finding the optimal balance point among the maximum coverage rate, the least energy consumption, and the minimum number of active nodes while maintaining the connectivity of the network
This document proposes a new method called multi-surface fitting for enhancing the resolution of digital images. The method fits multiple surfaces, with one surface fitted for each low-resolution pixel, and then fuses the multi-sampling values from these surfaces using maximum a posteriori estimation. This allows more low-resolution pixel information to be utilized to reconstruct the high-resolution image compared to other interpolation-based methods. The method is shown to effectively preserve image details without requiring assumptions about the image prior, as iterative techniques do. It provides error-free high resolution for test images.
Super-resolution (SR) is the process of obtaining a high resolution (HR) image or
a sequence of HR images from a set of low resolution (LR) observations. The block
matching algorithms used for motion estimation to obtain motion vectors between the
frames in Super-resolution. The implementation and comparison of two different types of
block matching algorithms viz. Exhaustive Search (ES) and Spiral Search (SS) are
discussed. Advantages of each algorithm are given in terms of motion estimation
computational complexity and Peak Signal to Noise Ratio (PSNR). The Spiral Search
algorithm achieves PSNR close to that of Exhaustive Search at less computation time than
that of Exhaustive Search. The algorithms that are evaluated in this paper are widely used
in video super-resolution and also have been used in implementing various video standards
like H.263, MPEG4, H.264.
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.
Automatic Determination Number of Cluster for NMKFC-Means Algorithms on Image...IOSR Journals
This document discusses image segmentation techniques using clustering algorithms. It introduces Fuzzy C-Means (FCM) clustering, which allows data points to belong to multiple clusters with varying degrees of membership. However, FCM does not work well on noisy or non-linearly separable data. The document proposes the Kernel Fuzzy C-Means (KFCM) algorithm, which uses a kernel function to map data to a higher dimensional space, making separation easier. While improving results for noisy images, KFCM does not consider neighboring pixels. Finally, the document introduces the Novel Modified Kernel Fuzzy C-Means (NMKFCM) algorithm, which incorporates neighborhood information into the objective function to further improve segmentation accuracy, especially for noisy images
A PSO-Based Subtractive Data Clustering AlgorithmIJORCS
There is a tremendous proliferation in the amount of information available on the largest shared information source, the World Wide Web. Fast and high-quality clustering algorithms play an important role in helping users to effectively navigate, summarize, and organize the information. Recent studies have shown that partitional clustering algorithms such as the k-means algorithm are the most popular algorithms for clustering large datasets. The major problem with partitional clustering algorithms is that they are sensitive to the selection of the initial partitions and are prone to premature converge to local optima. Subtractive clustering is a fast, one-pass algorithm for estimating the number of clusters and cluster centers for any given set of data. The cluster estimates can be used to initialize iterative optimization-based clustering methods and model identification methods. In this paper, we present a hybrid Particle Swarm Optimization, Subtractive + (PSO) clustering algorithm that performs fast clustering. For comparison purpose, we applied the Subtractive + (PSO) clustering algorithm, PSO, and the Subtractive clustering algorithms on three different datasets. The results illustrate that the Subtractive + (PSO) clustering algorithm can generate the most compact clustering results as compared to other algorithms.
Graph Theory Based Approach For Image Segmentation Using Wavelet TransformCSCJournals
This paper presents the image segmentation approach based on graph theory and threshold. Amongst the various segmentation approaches, the graph theoretic approaches in image segmentation make the formulation of the problem more flexible and the computation more resourceful. The problem is modeled in terms of partitioning a graph into several sub-graphs; such that each of them represents a meaningful region in the image. The segmentation problem is then solved in a spatially discrete space by the well-organized tools from graph theory. After the literature review, the problem is formulated regarding graph representation of image and threshold function. The boundaries between the regions are determined as per the segmentation criteria and the segmented regions are labeled with random colors. In presented approach, the image is preprocessed by discrete wavelet transform and coherence filter before graph segmentation. The experiments are carried out on a number of natural images taken from Berkeley Image Database as well as synthetic images from online resources. The experiments are performed by using the wavelets of Haar, DB2, DB4, DB6 and DB8. The results are evaluated and compared by using the performance evaluation parameters like execution time, Performance Ratio, Peak Signal to Noise Ratio, Precision and Recall and obtained results are encouraging.
Segmentation of Images by using Fuzzy k-means clustering with ACOIJTET Journal
Abstract— Super pixels are becoming increasingly popular for use in computer vision applications. Image segmentation is the process of partitioning a digital image into multiple segments (known as super pixels). In this paper, we developed fuzzy k-means clustering with Ant Colony Optimization (ACO). In this propose algorithm the initial assumptions are made in the calculation of the mean value, which are depends on the colors of neighbored pixel in the image. Fuzzy mean is calculated for the whole image, this process having set of rules that rules are applied iteratively which is used to cluster the whole image. Once choosing a neighbor around that the fitness function is calculated in the optimization process. Based on the optimized clusters the image is segmented. By using fuzzy k-means clustering with ACO technique the image segmentation obtain high accuracy and the segmentation time is reduced compared to previous technique that is Lazy random walk (LRW) methodology. This LRW is optimized from Random walk technique.
MULTI-OBJECTIVE ENERGY EFFICIENT OPTIMIZATION ALGORITHM FOR COVERAGE CONTROL ...ijcseit
Many studies have been done in the area of Wireless Sensor Networks (WSNs) in recent years. In this kind of networks, some of the key objectives that need to be satisfied are area coverage, number of active sensors and energy consumed by nodes. In this paper, we propose a NSGA-II based multi-objective algorithm for optimizing all of these objectives simultaneously. The efficiency of our algorithm is demonstrated in the simulation results. This efficiency can be shown as finding the optimal balance point among the maximum coverage rate, the least energy consumption, and the minimum number of active nodes while maintaining the connectivity of the network
This document proposes a new method called multi-surface fitting for enhancing the resolution of digital images. The method fits multiple surfaces, with one surface fitted for each low-resolution pixel, and then fuses the multi-sampling values from these surfaces using maximum a posteriori estimation. This allows more low-resolution pixel information to be utilized to reconstruct the high-resolution image compared to other interpolation-based methods. The method is shown to effectively preserve image details without requiring assumptions about the image prior, as iterative techniques do. It provides error-free high resolution for test images.
Super-resolution (SR) is the process of obtaining a high resolution (HR) image or
a sequence of HR images from a set of low resolution (LR) observations. The block
matching algorithms used for motion estimation to obtain motion vectors between the
frames in Super-resolution. The implementation and comparison of two different types of
block matching algorithms viz. Exhaustive Search (ES) and Spiral Search (SS) are
discussed. Advantages of each algorithm are given in terms of motion estimation
computational complexity and Peak Signal to Noise Ratio (PSNR). The Spiral Search
algorithm achieves PSNR close to that of Exhaustive Search at less computation time than
that of Exhaustive Search. The algorithms that are evaluated in this paper are widely used
in video super-resolution and also have been used in implementing various video standards
like H.263, MPEG4, H.264.
This document proposes an algorithm for efficiently computing 2D spatial convolution through image partitioning and short convolution. The algorithm partitions an input image into overlapping 6x6 blocks, which are then further partitioned into non-overlapping 3x3 sub-images. Convolution is computed for each sub-image independently using a variable-length filter, reducing computational complexity compared to FFT-based techniques. The outputs from each sub-image convolution are combined to reconstruct the original block. Simulation results demonstrate the effectiveness of the algorithm for tasks like edge detection and noise reduction through local image filtering.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
The document describes an image fusion approach that uses adaptive fuzzy logic modeling for global processing followed by Markov random field modeling for local processing.
It begins by introducing image fusion and its applications. It then discusses existing fusion approaches and their limitations. The proposed approach first uses an adaptive fuzzy logic model to minimize redundant information globally. It then applies Markov random field modeling locally for fusion. Experimental results showed the proposed approach improved the universal image quality index by 30-35% compared to fusion with Markov random field modeling alone.
A study and comparison of different image segmentation algorithmsManje Gowda
This document discusses and compares different image segmentation algorithms. It begins with an introduction to the topic and an agenda that outlines image segmentation techniques, results and discussion, conclusions, and references. Section 2 describes various image segmentation techniques like thresholding, region-based (region growing and data clustering), and edge-based segmentation. Section 3 shows results of applying algorithms like Otsu's method, K-means clustering, quad tree, delta E, and FTH to sample images and compares their performance on simple versus complex images. The conclusion is that delta E performs best for simple images with one object, while for complex images with multiple objects, performance degrades and further work is needed.
Two-dimensional Block of Spatial Convolution Algorithm and SimulationCSCJournals
This paper proposes an algorithm based on sub image-segmentation strategy. The proposed scheme divides a grayscale image into overlapped 6×6 blocks each of which is segmented into four small 3x3 non-overlapped sub-images. A new spatial approach for efficiently computing 2-dimensional linear convolution or cross-correlation between suitable flipped and fixed filter coefficients (sub image for cross-correlation) and corresponding input sub image is presented. Computation of convolution is iterated vertically and horizontally for each of the four input sub-images. The convolution outputs of these four sub-images are processed to be converted from 6×6 arrays to 4×4 arrays so that the core of the original image is reproduced. The present algorithm proposes a simplified processing technique based on a particular arrangement of the input samples, spatial filtering and small sub-images. This results in reducing the computational complexity as compared with other well known FFT-based techniques. This algorithm lends itself for partitioned small sub-images, local image spatial filtering and noise reduction. The effectiveness of the algorithm is demonstrated through some simulation examples.
Mr image compression based on selection of mother wavelet and lifting based w...ijma
Magnetic Resonance (MR) image is a medical image technique required enormous data to be stored and
transmitted for high quality diagnostic application. Various algorithms have been proposed to improve the
performance of the compression scheme. In this paper we extended the commonly used algorithms to image
compression and compared its performance. For an image compression technique, we have linked different
wavelet techniques using traditional mother wavelets and lifting based Cohen-Daubechies-Feauveau
wavelets with the low-pass filters of the length 9 and 7 (CDF 9/7) wavelet transform with Set Partition in
Hierarchical Trees (SPIHT) algorithm. A novel image quality index with highlighting shape of histogram
of the image targeted is introduced to assess image compression quality. The index will be used in place of
existing traditional Universal Image Quality Index (UIQI) “in one go”. It offers extra information about
the distortion between an original image and a compressed image in comparisons with UIQI. The proposed
index is designed based on modelling image compression as combinations of four major factors: loss of
correlation, luminance distortion, contrast distortion and shape distortion. This index is easy to calculate
and applicable in various image processing applications. One of our contributions is to demonstrate the
choice of mother wavelet is very important for achieving superior wavelet compression performances based
on proposed image quality indexes. Experimental results show that the proposed image quality index plays
a significantly role in the quality evaluation of image compression on the open sources “BrainWeb:
Simulated Brain Database (SBD) ”.
An Experiment with Sparse Field and Localized Region Based Active Contour Int...CSCJournals
This paper discusses various experiments conducted on different types of Level Sets interactive segmentation techniques using Matlab software, on select images. The objective is to assess the effectiveness on specific natural images, which have complex image composition in terms of intensity, colour mix, indistinct object boundary, low contrast, etc. Besides visual assessment, measures such as Jaccard Index, Dice Coefficient and Hausdorrf Distance have been computed to assess the accuracy of these techniques, between segmented and ground truth images. This paper particularly discusses Sparse Field Matrix and Localized Region Based Active Contours, both based on Level Sets. These techniques were not found to be effective where object boundary is not very distinct and/or has low contrast with background. Also, the techniques were ineffective on such images where foreground object stretches up to the image boundary.
IRJET-A Review on Implementation of High Dimension Colour Transform in Domain...IRJET Journal
This document reviews algorithms for detecting salient regions in images using high dimensional color transforms. It summarizes several existing methods that use color contrast, frequency analysis, and superpixel segmentation. A key method discussed creates a saliency map by finding the optimal linear combination of color coefficients in a high dimensional color space. This allows more accurate detection of salient objects versus methods using only RGB color. The performance of this high dimensional color transform method is improved by also utilizing relative location and color contrast between superpixels as learned features.
Image in Painting Techniques: A survey IOSR Journals
This document provides a survey of different image inpainting techniques. It discusses approaches such as texture synthesis based inpainting, PDE (partial differential equation) based inpainting, exemplar based inpainting, hybrid inpainting, and semi-automatic inpainting. Texture synthesis approaches recreate textures within missing regions by sampling from surrounding textures. PDE based methods diffuse image information into missing areas. Exemplar based techniques iteratively copy patches from surrounding regions. Hybrid methods combine approaches. The document analyzes strengths and limitations of each technique.
Abstract: Many applications such as robot navigation, defense, medical and remote sensing performvarious processing tasks, which can be performed more easily when all objects in different images of the same scene are combined into a single fused image. In this paper, we propose a fast and effective method for image fusion. The proposed method derives the intensity based variations that is large and small scale, from the source images. In this approach, guided filtering is employed for this extraction. Gaussian and Laplacian pyramidal approach is then used to fuse the different layers obtained. Experimental results demonstrate that the proposed method can obtain better performance for fusion of
all sets of images. The results clearly indicate the feasibility of the proposed approach.
Rough Set based Natural Image Segmentation under Game Theory Frameworkijsrd.com
The Since past few decades, image segmentation has been successfully applied to number of applications. When different image segmentation techniques are applied to an image, they produce different results especially if images are obtained under different conditions and have different attributes. Each technique works on a specific concept, such that it is important to decide as to which image segmentation technique should for a given application domain. On combining the strengths of individual segmentation techniques, the resulting integrated method yields better results thus enhancing the synergy of the individual methods alone. This work improves the segmentation technique of combining results of different methods using the concept of game theory. This is achieved through Nash equilibrium along with various similarity distance measures. Using game theory the problem is divided into modules which are considered as players. The number of modules depends on number of techniques to be integrated. The modules work in parallel and interactive manner. The effectiveness of the technique will be demonstrated by simulation results on different sets of test images.
Kandemir Inferring Object Relevance From Gaze In Dynamic ScenesKalle
As prototypes of data glasses having both data augmentation and gaze tracking capabilities are becoming available, it is now possible to develop proactive gaze-controlled user interfaces to display information about objects, people, and other entities in real-world setups. In order to decide which objects the augmented information should be about, and how saliently to augment, the system needs an estimate of the importance or relevance of the objects of the scene for the user at a given time. The estimates will be used to minimize distraction of the user, and for providing efficient spatial management of the augmented items. This work is a feasibility study on inferring the relevance of objects in dynamic scenes from gaze. We collected gaze data from subjects watching a video for a pre-defined task. The results show that a simple ordinal logistic regression model gives relevance rankings of scene objects with a promising accuracy.
Survey on Single image Super Resolution TechniquesIOSR Journals
Super-resolution is the process of recovering a high-resolution image from multiple lowresolutionimages
of the same scene. The key objective of super-resolution (SR) imaging is to reconstruct a
higher-resolution image based on a set of images, acquired from the same scene and denoted as ‘lowresolution’
images, to overcome the limitation and/or ill-posed conditions of the image acquisition process for
facilitating better content visualization and scene recognition. In this paper, we provide a comprehensive review
of existing super-resolution techniques and highlight the future research challenges. This includes the
formulation of an observation model and coverage of the dominant algorithm – Iterative back projection.We
critique these methods and identify areas which promise performance improvements. In this paper, future
directions for super-resolution algorithms are discussed. Finally results of available methods are given.
A HYBRID MORPHOLOGICAL ACTIVE CONTOUR FOR NATURAL IMAGESIJCSEA Journal
Morphological active contours for image segmentation have become popular due to their low computational complexity coupled with their accurate approximation of the partial differential equations
involved in the energy minimization of the segmentation process. In this paper, a morphological active contour which mimics the energy minimization of the popular Chan-Vese Active Contour without Edges is coupled with a morphological edge-driven segmentation term to accurately segment natural images. By using morphological approximations of the energy minimization steps, the algorithm has a low computational complexity. Additionally, the coupling of the edge-based and region-based segmentation techniques allows the proposed method to be robust and accurate. We will demonstrate the accuracy and robustness of the algorithm using images from the Weizmann Segmentation Evaluation Database and report on the segmentation results using the Sorensen-Dice similarity coefficient.
A HYBRID MORPHOLOGICAL ACTIVE CONTOUR FOR NATURAL IMAGESIJCSEA Journal
Morphological active contours for image segmentation have become popular due to their low computational complexity coupled with their accurate approximation of the partial differential equations involved in the energy minimization of the segmentation process. In this paper, a morphological active contour which mimics the energy minimization of the popular Chan-Vese Active Contour without Edges is coupled with a morphological edge-driven segmentation term to accurately segment natural images. By using morphological approximations of the energy minimization steps, the algorithm has a low computational complexity. Additionally, the coupling of the edge-based and region-based segmentation techniques allows the proposed method to be robust and accurate. We will demonstrate the accuracy and robustness of the algorithm using images from the Weizmann Segmentation Evaluation Database and report on the segmentation results using the Sorensen-Dice similarity coefficient.
EXTENDED WAVELET TRANSFORM BASED IMAGE INPAINTING ALGORITHM FOR NATURAL SCENE...cscpconf
This paper proposes an exemplar based image inpainting using extended wavelet transform. The
Image inpainting modifies an image with the available information outside the region to be
inpainted in an undetectable way. The extended wavelet transform is in two dimensions. The
Laplacian pyramid is first used to capture the point discontinuities, and then followed by a
directional filter bank to link point discontinuities into linear structures. The proposed model
effectively captures the edges and contours of natural scene images
A Survey on Exemplar-Based Image Inpainting Techniquesijsrd.com
Preceding paper include exemplar-based image inpainting technique give idea how to inpaint destroyed region such as Criminisi algorithm, patch shifting scheme, search region prior method. Criminsi’s and Sarawut’s patch shifting scheme needed more time to inpaint an damaged region but proposed method decrease time complexity by searching only in related region of missing portion of image.
The impact of innovation on travel and tourism industries (World Travel Marke...Brian Solis
From the impact of Pokemon Go on Silicon Valley to artificial intelligence, futurist Brian Solis talks to Mathew Parsons of World Travel Market about the future of travel, tourism and hospitality.
Reuters: Pictures of the Year 2016 (Part 2)maditabalnco
This document contains 20 photos from news events around the world between January and November 2016. The photos show international events like the US presidential election, the conflict in Ukraine, the migrant crisis in Europe, the Rio Olympics, and more. They also depict human interest stories and natural phenomena from various countries.
This document summarizes a study of CEO succession events among the largest 100 U.S. corporations between 2005-2015. The study analyzed executives who were passed over for the CEO role ("succession losers") and their subsequent careers. It found that 74% of passed over executives left their companies, with 30% eventually becoming CEOs elsewhere. However, companies led by succession losers saw average stock price declines of 13% over 3 years, compared to gains for companies whose CEO selections remained unchanged. The findings suggest that boards generally identify the most qualified CEO candidates, though differences between internal and external hires complicate comparisons.
1) The document discusses the opportunity for technology to improve organizational efficiency and transition economies into a "smart and clean world."
2) It argues that aggregate efficiency has stalled at around 22% for 30 years due to limitations of the Second Industrial Revolution, but that digitizing transport, energy, and communication through technologies like blockchain can help manage resources and increase efficiency.
3) Technologies like precision agriculture, cloud computing, robotics, and autonomous vehicles may allow for "dematerialization" and do more with fewer physical resources through effects like reduced waste and need for transportation/logistics infrastructure.
The Six Highest Performing B2B Blog Post FormatsBarry Feldman
If your B2B blogging goals include earning social media shares and backlinks to boost your search rankings, this infographic lists the size best approaches.
This document proposes an algorithm for efficiently computing 2D spatial convolution through image partitioning and short convolution. The algorithm partitions an input image into overlapping 6x6 blocks, which are then further partitioned into non-overlapping 3x3 sub-images. Convolution is computed for each sub-image independently using a variable-length filter, reducing computational complexity compared to FFT-based techniques. The outputs from each sub-image convolution are combined to reconstruct the original block. Simulation results demonstrate the effectiveness of the algorithm for tasks like edge detection and noise reduction through local image filtering.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
The document describes an image fusion approach that uses adaptive fuzzy logic modeling for global processing followed by Markov random field modeling for local processing.
It begins by introducing image fusion and its applications. It then discusses existing fusion approaches and their limitations. The proposed approach first uses an adaptive fuzzy logic model to minimize redundant information globally. It then applies Markov random field modeling locally for fusion. Experimental results showed the proposed approach improved the universal image quality index by 30-35% compared to fusion with Markov random field modeling alone.
A study and comparison of different image segmentation algorithmsManje Gowda
This document discusses and compares different image segmentation algorithms. It begins with an introduction to the topic and an agenda that outlines image segmentation techniques, results and discussion, conclusions, and references. Section 2 describes various image segmentation techniques like thresholding, region-based (region growing and data clustering), and edge-based segmentation. Section 3 shows results of applying algorithms like Otsu's method, K-means clustering, quad tree, delta E, and FTH to sample images and compares their performance on simple versus complex images. The conclusion is that delta E performs best for simple images with one object, while for complex images with multiple objects, performance degrades and further work is needed.
Two-dimensional Block of Spatial Convolution Algorithm and SimulationCSCJournals
This paper proposes an algorithm based on sub image-segmentation strategy. The proposed scheme divides a grayscale image into overlapped 6×6 blocks each of which is segmented into four small 3x3 non-overlapped sub-images. A new spatial approach for efficiently computing 2-dimensional linear convolution or cross-correlation between suitable flipped and fixed filter coefficients (sub image for cross-correlation) and corresponding input sub image is presented. Computation of convolution is iterated vertically and horizontally for each of the four input sub-images. The convolution outputs of these four sub-images are processed to be converted from 6×6 arrays to 4×4 arrays so that the core of the original image is reproduced. The present algorithm proposes a simplified processing technique based on a particular arrangement of the input samples, spatial filtering and small sub-images. This results in reducing the computational complexity as compared with other well known FFT-based techniques. This algorithm lends itself for partitioned small sub-images, local image spatial filtering and noise reduction. The effectiveness of the algorithm is demonstrated through some simulation examples.
Mr image compression based on selection of mother wavelet and lifting based w...ijma
Magnetic Resonance (MR) image is a medical image technique required enormous data to be stored and
transmitted for high quality diagnostic application. Various algorithms have been proposed to improve the
performance of the compression scheme. In this paper we extended the commonly used algorithms to image
compression and compared its performance. For an image compression technique, we have linked different
wavelet techniques using traditional mother wavelets and lifting based Cohen-Daubechies-Feauveau
wavelets with the low-pass filters of the length 9 and 7 (CDF 9/7) wavelet transform with Set Partition in
Hierarchical Trees (SPIHT) algorithm. A novel image quality index with highlighting shape of histogram
of the image targeted is introduced to assess image compression quality. The index will be used in place of
existing traditional Universal Image Quality Index (UIQI) “in one go”. It offers extra information about
the distortion between an original image and a compressed image in comparisons with UIQI. The proposed
index is designed based on modelling image compression as combinations of four major factors: loss of
correlation, luminance distortion, contrast distortion and shape distortion. This index is easy to calculate
and applicable in various image processing applications. One of our contributions is to demonstrate the
choice of mother wavelet is very important for achieving superior wavelet compression performances based
on proposed image quality indexes. Experimental results show that the proposed image quality index plays
a significantly role in the quality evaluation of image compression on the open sources “BrainWeb:
Simulated Brain Database (SBD) ”.
An Experiment with Sparse Field and Localized Region Based Active Contour Int...CSCJournals
This paper discusses various experiments conducted on different types of Level Sets interactive segmentation techniques using Matlab software, on select images. The objective is to assess the effectiveness on specific natural images, which have complex image composition in terms of intensity, colour mix, indistinct object boundary, low contrast, etc. Besides visual assessment, measures such as Jaccard Index, Dice Coefficient and Hausdorrf Distance have been computed to assess the accuracy of these techniques, between segmented and ground truth images. This paper particularly discusses Sparse Field Matrix and Localized Region Based Active Contours, both based on Level Sets. These techniques were not found to be effective where object boundary is not very distinct and/or has low contrast with background. Also, the techniques were ineffective on such images where foreground object stretches up to the image boundary.
IRJET-A Review on Implementation of High Dimension Colour Transform in Domain...IRJET Journal
This document reviews algorithms for detecting salient regions in images using high dimensional color transforms. It summarizes several existing methods that use color contrast, frequency analysis, and superpixel segmentation. A key method discussed creates a saliency map by finding the optimal linear combination of color coefficients in a high dimensional color space. This allows more accurate detection of salient objects versus methods using only RGB color. The performance of this high dimensional color transform method is improved by also utilizing relative location and color contrast between superpixels as learned features.
Image in Painting Techniques: A survey IOSR Journals
This document provides a survey of different image inpainting techniques. It discusses approaches such as texture synthesis based inpainting, PDE (partial differential equation) based inpainting, exemplar based inpainting, hybrid inpainting, and semi-automatic inpainting. Texture synthesis approaches recreate textures within missing regions by sampling from surrounding textures. PDE based methods diffuse image information into missing areas. Exemplar based techniques iteratively copy patches from surrounding regions. Hybrid methods combine approaches. The document analyzes strengths and limitations of each technique.
Abstract: Many applications such as robot navigation, defense, medical and remote sensing performvarious processing tasks, which can be performed more easily when all objects in different images of the same scene are combined into a single fused image. In this paper, we propose a fast and effective method for image fusion. The proposed method derives the intensity based variations that is large and small scale, from the source images. In this approach, guided filtering is employed for this extraction. Gaussian and Laplacian pyramidal approach is then used to fuse the different layers obtained. Experimental results demonstrate that the proposed method can obtain better performance for fusion of
all sets of images. The results clearly indicate the feasibility of the proposed approach.
Rough Set based Natural Image Segmentation under Game Theory Frameworkijsrd.com
The Since past few decades, image segmentation has been successfully applied to number of applications. When different image segmentation techniques are applied to an image, they produce different results especially if images are obtained under different conditions and have different attributes. Each technique works on a specific concept, such that it is important to decide as to which image segmentation technique should for a given application domain. On combining the strengths of individual segmentation techniques, the resulting integrated method yields better results thus enhancing the synergy of the individual methods alone. This work improves the segmentation technique of combining results of different methods using the concept of game theory. This is achieved through Nash equilibrium along with various similarity distance measures. Using game theory the problem is divided into modules which are considered as players. The number of modules depends on number of techniques to be integrated. The modules work in parallel and interactive manner. The effectiveness of the technique will be demonstrated by simulation results on different sets of test images.
Kandemir Inferring Object Relevance From Gaze In Dynamic ScenesKalle
As prototypes of data glasses having both data augmentation and gaze tracking capabilities are becoming available, it is now possible to develop proactive gaze-controlled user interfaces to display information about objects, people, and other entities in real-world setups. In order to decide which objects the augmented information should be about, and how saliently to augment, the system needs an estimate of the importance or relevance of the objects of the scene for the user at a given time. The estimates will be used to minimize distraction of the user, and for providing efficient spatial management of the augmented items. This work is a feasibility study on inferring the relevance of objects in dynamic scenes from gaze. We collected gaze data from subjects watching a video for a pre-defined task. The results show that a simple ordinal logistic regression model gives relevance rankings of scene objects with a promising accuracy.
Survey on Single image Super Resolution TechniquesIOSR Journals
Super-resolution is the process of recovering a high-resolution image from multiple lowresolutionimages
of the same scene. The key objective of super-resolution (SR) imaging is to reconstruct a
higher-resolution image based on a set of images, acquired from the same scene and denoted as ‘lowresolution’
images, to overcome the limitation and/or ill-posed conditions of the image acquisition process for
facilitating better content visualization and scene recognition. In this paper, we provide a comprehensive review
of existing super-resolution techniques and highlight the future research challenges. This includes the
formulation of an observation model and coverage of the dominant algorithm – Iterative back projection.We
critique these methods and identify areas which promise performance improvements. In this paper, future
directions for super-resolution algorithms are discussed. Finally results of available methods are given.
A HYBRID MORPHOLOGICAL ACTIVE CONTOUR FOR NATURAL IMAGESIJCSEA Journal
Morphological active contours for image segmentation have become popular due to their low computational complexity coupled with their accurate approximation of the partial differential equations
involved in the energy minimization of the segmentation process. In this paper, a morphological active contour which mimics the energy minimization of the popular Chan-Vese Active Contour without Edges is coupled with a morphological edge-driven segmentation term to accurately segment natural images. By using morphological approximations of the energy minimization steps, the algorithm has a low computational complexity. Additionally, the coupling of the edge-based and region-based segmentation techniques allows the proposed method to be robust and accurate. We will demonstrate the accuracy and robustness of the algorithm using images from the Weizmann Segmentation Evaluation Database and report on the segmentation results using the Sorensen-Dice similarity coefficient.
A HYBRID MORPHOLOGICAL ACTIVE CONTOUR FOR NATURAL IMAGESIJCSEA Journal
Morphological active contours for image segmentation have become popular due to their low computational complexity coupled with their accurate approximation of the partial differential equations involved in the energy minimization of the segmentation process. In this paper, a morphological active contour which mimics the energy minimization of the popular Chan-Vese Active Contour without Edges is coupled with a morphological edge-driven segmentation term to accurately segment natural images. By using morphological approximations of the energy minimization steps, the algorithm has a low computational complexity. Additionally, the coupling of the edge-based and region-based segmentation techniques allows the proposed method to be robust and accurate. We will demonstrate the accuracy and robustness of the algorithm using images from the Weizmann Segmentation Evaluation Database and report on the segmentation results using the Sorensen-Dice similarity coefficient.
EXTENDED WAVELET TRANSFORM BASED IMAGE INPAINTING ALGORITHM FOR NATURAL SCENE...cscpconf
This paper proposes an exemplar based image inpainting using extended wavelet transform. The
Image inpainting modifies an image with the available information outside the region to be
inpainted in an undetectable way. The extended wavelet transform is in two dimensions. The
Laplacian pyramid is first used to capture the point discontinuities, and then followed by a
directional filter bank to link point discontinuities into linear structures. The proposed model
effectively captures the edges and contours of natural scene images
A Survey on Exemplar-Based Image Inpainting Techniquesijsrd.com
Preceding paper include exemplar-based image inpainting technique give idea how to inpaint destroyed region such as Criminisi algorithm, patch shifting scheme, search region prior method. Criminsi’s and Sarawut’s patch shifting scheme needed more time to inpaint an damaged region but proposed method decrease time complexity by searching only in related region of missing portion of image.
The impact of innovation on travel and tourism industries (World Travel Marke...Brian Solis
From the impact of Pokemon Go on Silicon Valley to artificial intelligence, futurist Brian Solis talks to Mathew Parsons of World Travel Market about the future of travel, tourism and hospitality.
Reuters: Pictures of the Year 2016 (Part 2)maditabalnco
This document contains 20 photos from news events around the world between January and November 2016. The photos show international events like the US presidential election, the conflict in Ukraine, the migrant crisis in Europe, the Rio Olympics, and more. They also depict human interest stories and natural phenomena from various countries.
This document summarizes a study of CEO succession events among the largest 100 U.S. corporations between 2005-2015. The study analyzed executives who were passed over for the CEO role ("succession losers") and their subsequent careers. It found that 74% of passed over executives left their companies, with 30% eventually becoming CEOs elsewhere. However, companies led by succession losers saw average stock price declines of 13% over 3 years, compared to gains for companies whose CEO selections remained unchanged. The findings suggest that boards generally identify the most qualified CEO candidates, though differences between internal and external hires complicate comparisons.
1) The document discusses the opportunity for technology to improve organizational efficiency and transition economies into a "smart and clean world."
2) It argues that aggregate efficiency has stalled at around 22% for 30 years due to limitations of the Second Industrial Revolution, but that digitizing transport, energy, and communication through technologies like blockchain can help manage resources and increase efficiency.
3) Technologies like precision agriculture, cloud computing, robotics, and autonomous vehicles may allow for "dematerialization" and do more with fewer physical resources through effects like reduced waste and need for transportation/logistics infrastructure.
The Six Highest Performing B2B Blog Post FormatsBarry Feldman
If your B2B blogging goals include earning social media shares and backlinks to boost your search rankings, this infographic lists the size best approaches.
Study: The Future of VR, AR and Self-Driving CarsLinkedIn
We asked LinkedIn members worldwide about their levels of interest in the latest wave of technology: whether they’re using wearables, and whether they intend to buy self-driving cars and VR headsets as they become available. We asked them too about their attitudes to technology and to the growing role of Artificial Intelligence (AI) in the devices that they use. The answers were fascinating – and in many cases, surprising.
This SlideShare explores the full results of this study, including detailed market-by-market breakdowns of intention levels for each technology – and how attitudes change with age, location and seniority level. If you’re marketing a tech brand – or planning to use VR and wearables to reach a professional audience – then these are insights you won’t want to miss.
Implementation of Fuzzy Logic for the High-Resolution Remote Sensing Images w...IOSR Journals
This document describes an implementation of fuzzy logic for high-resolution remote sensing image classification with improved accuracy. It discusses using an object-based approach with fuzzy rules to classify urban land covers in a satellite image. The approach involves image segmentation using k-means clustering or ISODATA clustering. Features are then extracted from the image objects and fuzzy logic is applied to classify the objects based on membership functions. The method was tested on different sensor and resolution images in MATLAB and showed improved classification accuracy over other techniques, achieving lower entropy in results. Future work planned includes designing an unsupervised classification model combining k-means clustering and fuzzy-based object orientation.
A CONCERT EVALUATION OF EXEMPLAR BASED IMAGE INPAINTING ALGORITHMS FOR NATURA...cscpconf
Image inpainting derives from restoration of art works, and has been applied to repair ancient
art works. Inpainting is a technique of restoring a partially damaged or occluded image in an
undetectable way. It fills the damaged part of an image by employing information of the
undamaged part according to some rules to make it look “reasonable” to human eyes. Digital
image inpainting is relatively new area of research, but numerous and different approaches to
tackle the inpainting problem have been proposed since the concept was first introduced. This
paper analyzes and compares the recent exemplar based inpainting algorithms by Minqin Wang
and Hao Guo et al. A number of examples on real images are demonstrated to evaluate the
results of algorithms using Peak Signal to Noise Ratio (PSNR)
This document summarizes a method for tracking deformable objects in images. It proposes casting the problem as finding optimal cyclic paths in a product space of the template shape and input image. A cost functional is introduced that considers data fidelity, shape consistency, and elastic deformation. The functional is optimized using a minimum ratio cycle algorithm on graphics cards, allowing real-time segmentation and tracking of deformable objects while guaranteeing a globally optimal solution. The method can be extended to track multiple deformable anatomical structures in medical images.
This document summarizes a method for tracking deformable objects in images. It proposes casting the problem as finding optimal cyclic paths in a product space of the template shape and input image. A cost functional is introduced that consists of three terms: data fidelity favoring strong edges, shape consistency favoring similar tangent angles, and an elastic penalty for stretching. Optimization is performed using simulated annealing for segmentation and iterated conditional modes for tracking. The algorithm provides optimal segmentation and point correspondences between template and image curve in linear time.
This document summarizes a research paper that proposes a new approach for tracking multiple deformable anatomical structures in medical images using geometrically deformable templates (GDTs). The GDTs can deform to match similar shapes based on image forces while minimizing a penalty function that measures deformation from the template's equilibrium shape. This allows simultaneous segmentation of multiple objects using intra- and inter-shape information. Simulated annealing is used for segmentation while iterated conditional modes is used for tracking. The paper also reviews previous work on image segmentation, tracking deformable objects, and shape-based image segmentation.
Image Segmentation Using Pairwise Correlation ClusteringIJERA Editor
A pairwise hypergraph based image segmentation framework is formulated in a supervised manner for various images. The image segmentation is to infer the edge label over the pairwise hypergraph by maximizing the normalized cuts. Correlation clustering which is a graph partitioning algorithm, was shown to be effective in a number of applications such as identification, clustering of documents and image segmentation.The partitioning result is derived from a algorithm to partition a pairwise graph into disjoint groups of coherent nodes. In the pairwise correlation clustering, the pairwise graph which is used in the correlation clustering is generalized to a superpixel graph where a node corresponds to a superpixel and a link between adjacent superpixels corresponds to an edge. This pairwise correlation clustering also considers the feature vector which extracts several visual cues from a superpixel, including brightness, color, texture, and shape. Significant progress in clustering has been achieved by algorithms that are based on pairwise affinities between the datasets. The experimental results are shown by calculating the typical cut and inference in an undirected graphical model and datasets.
Validation Study of Dimensionality Reduction Impact on Breast Cancer Classifi...ijcsit
A fundamental problem in machine learning is identifying the most representative subset of features from
which we can construct a predictive model for a classification task. This paper aims to present a validation
study of dimensionality reduction effect on the classification accuracy of mammographic images. The
studied dimensionality reduction methods were: locality-preserving projection (LPP), locally linear
embedding (LLE), Isometric Mapping (ISOMAP) and spectral regression (SR). We have achieved high
rates of classifications. In some combinations the classification rate was 100%. But in most of the cases the
classification rate is about 95%. It was also found that the classification rate increases with the size of the
reduced space and the optimal value of space dimension is 60. We proceeded to validate the obtained
results by measuring some validation indices such as: Xie-Beni index, Dun index and Alternative Dun
index. The measurement of these indices confirms that the optimal value of reduced space dimension is
d=60.
Geometric Correction for Braille Document Images csandit
Image processing is an important research area in computer vision. clustering is an unsupervised
study. clustering can also be used for image segmentation. there exist so many methods for image
segmentation. image segmentation plays an important role in image analysis.it is one of the first
and the most important tasks in image analysis and computer vision. this proposed system
presents a variation of fuzzy c-means algorithm that provides image clustering. the kernel fuzzy
c-means clustering algorithm (kfcm) is derived from the fuzzy c-means clustering
algorithm(fcm).the kfcm algorithm that provides image clustering and improves accuracy
significantly compared with classical fuzzy c-means algorithm. the new algorithm is called
gaussian kernel based fuzzy c-means clustering algorithm (gkfcm)the major characteristic of
gkfcm is the use of a fuzzy clustering approach ,aiming to guarantee noise insensitiveness and
image detail preservation.. the objective of the work is to cluster the low intensity in homogeneity
area from the noisy images, using the clustering method, segmenting that portion separately using
content level set approach. the purpose of designing this system is to produce better segmentation
results for images corrupted by noise, so that it can be useful in various fields like medical image
analysis, such as tumor detection, study of anatomical structure, and treatment planning.
GAUSSIAN KERNEL BASED FUZZY C-MEANS CLUSTERING ALGORITHM FOR IMAGE SEGMENTATIONcscpconf
Image processing is an important research area in computer vision. clustering is an unsupervised study. clustering can also be used for image segmentation. there exist so many methods for image segmentation. image segmentation plays an important role in image analysis.it is one of the first and the most important tasks in image analysis and computer vision. this proposed system presents a variation of fuzzy c-means algorithm that provides image clustering. the kernel fuzzy
c-means clustering algorithm (kfcm) is derived from the fuzzy c-means clustering algorithm(fcm).the kfcm algorithm that provides image clustering and improves accuracy significantly compared with classical fuzzy c-means algorithm. the new algorithm is called gaussian kernel based fuzzy c-means clustering algorithm (gkfcm)the major characteristic of gkfcm is the use of a fuzzy clustering approach ,aiming to guarantee noise insensitiveness and image detail preservation.. the objective of the work is to cluster the low intensity in homogeneity area from the noisy images, using the clustering method, segmenting that portion separately using content level set approach. the purpose of designing this system is to produce better segmentation results for images corrupted by noise, so that it can be useful in various fields like medical image analysis, such as tumor detection, study of anatomical structure, and treatment planning.
GAUSSIAN KERNEL BASED FUZZY C-MEANS CLUSTERING ALGORITHM FOR IMAGE SEGMENTATIONcsandit
Image processing is an important research area in computer vision. clustering is an unsupervised
study. clustering can also be used for image segmentation. there exist so many methods for image
segmentation. image segmentation plays an important role in image analysis.it is one of the first
and the most important tasks in image analysis and computer vision. this proposed system
presents a variation of fuzzy c-means algorithm that provides image clustering. the kernel fuzzy
c-means clustering algorithm (kfcm) is derived from the fuzzy c-means clustering
algorithm(fcm).the kfcm algorithm that provides image clustering and improves accuracy
significantly compared with classical fuzzy c-means algorithm. the new algorithm is called
gaussian kernel based fuzzy c-means clustering algorithm (gkfcm)the major characteristic of
gkfcm is the use of a fuzzy clustering approach ,aiming to guarantee noise insensitiveness and
image detail preservation.. the objective of the work is to cluster the low intensity in homogeneity
area from the noisy images, using the clustering method, segmenting that portion separately using
content level set approach. the purpose of designing this system is to produce better segmentation
results for images corrupted by noise, so that it can be useful in various fields like medical image
analysis, such as tumor detection, study of anatomical structure, and treatment planning.
The document describes a method for tracking objects of deformable shapes in images. It proposes representing the matching of a deformable template to an image as a minimum cost cyclic path in a product space of the template and image. An energy functional is introduced that consists of a data term favoring strong image gradients, a shape consistency term favoring similar tangent angles, and an elastic penalty. Optimization is performed using a minimum ratio cycle algorithm parallelized on GPUs. This provides efficient, pixel-accurate segmentation and correspondence between template and image curve. The method can be extended to 4D to segment and track multiple deformable anatomical structures in medical images.
Medial Axis Transformation based Skeletonzation of Image Patterns using Image...IOSR Journals
1) The document discusses extracting the medial axis transform (MAT) of an image pattern using the Euclidean distance transform. The image is first converted to binary, then the Euclidean distance transform is used to compute the distance of each non-zero pixel to the closest zero pixel.
2) The medial axis transform represents the core or skeleton of an image pattern. There are different algorithms for extracting the skeleton or medial axis, including sequential and parallel algorithms. The skeleton provides a simple representation that preserves topological and size characteristics of the original shape.
3) The document provides background on medial axis transforms and different skeletonization algorithms. It then describes preparing the binary image and applying the Euclidean distance transform to extract the MAT and skeleton
This document provides a survey of various image segmentation techniques used in image processing. It begins with an introduction to image segmentation and its importance in fields like pattern recognition and medical imaging. It then categorizes and describes different segmentation approaches like edge-based, threshold-based, region-based, etc. The literature survey section summarizes several papers on specific segmentation algorithms or applications. It concludes with a table comparing the advantages and disadvantages of different segmentation techniques. The overall document aims to provide an overview of segmentation methods and their uses in computer vision.
A Survey on Image Segmentation and its Applications in Image Processing IJEEE
As technology grows day by day computer vision becomes a vital field of understanding the behavior of an image. Image segmentation is a sub field of computer vision that deals with the partition of objects into number of segments. Image segmentation found a huge application in pattern reorganization, texture analysis as well as in medial image processing. This paper focus on distinct sort of image segmentation techniques that are utilized in computer vision. Thus a survey has been created for various image segmentation techniques that describe the importance of the same. Comparison and conclusion has been created within the finish of this paper.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity
A NOVEL PROBABILISTIC BASED IMAGE SEGMENTATION MODEL FOR REALTIME HUMAN ACTIV...sipij
Automatic human activity detection is one of the difficult tasks in image segmentation application due to
variations in size, type, shape and location of objects. In the traditional probabilistic graphical
segmentation models, intra and inter region segments may affect the overall segmentation accuracy. Also,
both directed and undirected graphical models such as Markov model, conditional random field have
limitations towards the human activity prediction and heterogeneous relationships. In this paper, we have
studied and proposed a natural solution for automatic human activity segmentation using the enhanced
probabilistic chain graphical model. This system has three main phases, namely activity pre-processing,
iterative threshold based image enhancement and chain graph segmentation algorithm. Experimental
results show that proposed system efficiently detects the human activities at different levels of the action
datasets.
Multimodal Biometrics Recognition by Dimensionality Diminution MethodIJERA Editor
Multimodal biometric system utilizes two or more character modalities, e.g., face, ear, and fingerprint,
Signature, plamprint to improve the recognition accuracy of conventional unimodal methods. We propose a new
dimensionality reduction method called Dimension Diminish Projection (DDP) in this paper. DDP can not only
preserve local information by capturing the intra-modal geometry, but also extract between-class relevant
structures for classification effectively. Experimental results show that our proposed method performs better
than other algorithms including PCA, LDA and MFA.
Web image annotation by diffusion maps manifold learning algorithmijfcstjournal
Automatic image annotation is one of the most challenging problems in machine vision areas. The goal of this task is to predict number of keywords automatically for images captured in real data. Many methods are based on visual features in order to calculate similarities between image samples. But the computation cost of these approaches is very high. These methods require many training samples to be stored in memory. To lessen thisburden, a number of techniques have been developed to reduce the number
of features in a dataset. Manifold learning is a popular approach to nonlinear dimensionality reduction. In
this paper, we investigate Diffusion maps manifold learning method for webimage auto-annotation task.Diffusion maps
manifold learning method isused to reduce the dimension of some visual features. Extensive experiments and analysis onNUS-WIDE-LITE web image dataset with
different visual featuresshow how this manifold learning dimensionality reduction method can be applied effectively to image annotation.
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.
Review paper on segmentation methods for multiobject feature extractioneSAT Journals
Abstract Feature extraction and representation plays a vital role in multimedia processing. It is still a challenge in computer vision system to extract ideal features that represents intrinsic characteristics of an image. Multiobject feature extraction system means a system that can extract features and locations of multiple objects in an image. In this paper we have discuss various methods to extract location and features of multiple objects and describe a system that can extract locations and features of multiple objects in an image by implementing an algorithm as hardware logic on a field-programmable gate array-based platform. There are many multiobject extraction methods which can be use for image segmentation based on motion, color intensity and texture. By calculating zeroth and first order moments of objects it is possible to obtain locations and sizes of multiple objects in an image. Keywords: multiobject extraction, image segmentation
Similar to Image segmentation by modified map ml (20)
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
"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.
Northern Engraving | Modern Metal Trim, Nameplates and Appliance PanelsNorthern Engraving
What began over 115 years ago as a supplier of precision gauges to the automotive industry has evolved into being an industry leader in the manufacture of product branding, automotive cockpit trim and decorative appliance trim. Value-added services include in-house Design, Engineering, Program Management, Test Lab and Tool Shops.
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
"Scaling RAG Applications to serve millions of users", Kevin GoedeckeFwdays
How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
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AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
High performance Serverless Java on AWS- GoTo Amsterdam 2024Vadym Kazulkin
Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless community. Java is known for its high cold start times and high memory footprint, comparing to other programming languages like Node.js and Python. In this talk I'll look at the general best practices and techniques we can use to decrease memory consumption, cold start times for Java Serverless development on AWS including GraalVM (Native Image) and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. I'll also provide a lot of benchmarking on Lambda functions trying out various deployment package sizes, Lambda memory settings, Java compilation options and HTTP (a)synchronous clients and measure their impact on cold and warm start times.
2. 2 Computer Science & Information Technology (CS & IT)
shrink and expansion operations minimizing energy function. One problem existing in these
algorithms is that they are easy to get trapped in local minima. In addition, they need manually
specified initial curves close to the objects of interest. Region-based approaches try to classify an
image into multiple consistent regions or classes. Thresholding is the simplest segmentation
method but its performance is usually far from satisfactory.
Watershed segmentation [2] is one of the traditional region-based approaches. It is used for
images containing touching objects. It finds high intensity regions and low intensity regions. It
suffers from over-segmentation. The various morphological operations are used to handle this
problem. Usually, watershed is used for the segmentation of foreground and background (two-
class) of an image. For a general color image with many different regions, it often gives a bad
result. Hence it is not used widely.
The K-means algorithm [3] is the basic one. However, the K-means is not good enough because it
does not take account of the spatial proximity of pixels. It is, thus, often used in the initialization
step for other approaches.
Expectation-maximization (EM) [4] performs segmentation by finding a Gaussian mixture model
in an image feature space. EM is not suitable for images containing different number of regions.
The disadvantage of EM is that it does not change the number of regions during the segmentation,
which leads to wrong segmentation. Theoretically, the minimum description length (MDL)
principle [4] can be used to alleviate this problem, but the segmentation has to be carried out
many times with different region numbers to find the best result. This takes a large amount of
computation, and the theoretically best result may not accord with this perception.
In [5], a mean shift algorithm is proposed for image segmentation. Mean shift is a nonparametric
clustering technique which neither requires to know the number of clusters in advance nor
constrains the shapes of the clusters However, it often obtains over-segmented results for many
natural images.
Recently, a number of graph-based approaches are developed for image segmentation. Shi and
Malik's [6] normalized cuts are able to capture intuitively salient parts in an image. Normalized
cuts are one of the popular spectral clustering algorithms. Normalized cuts are not suitable for
image segmentation because adhoc approximations are to be considered to relax the NP-hard
computational problem. These vague approximations are ambiguous leading to unsatisfactory
results. Also, due to this, spectral clustering algorithms suffer from the expensive computational
cost.
Another popular segmentation approach based upon MRFs is graphcut algorithm [7]. This
algorithm relies on human interaction, and solves the two-class segmentation problem only, i.e.,
separating an image into only background and object regions, with some manually given seed
points.
In [9], authors have used Fuzzy Rule based graphcut to achieve perfect segmentation. This
method definitely gives better results but is time consuming for segmenting large number of
images.
All of the above techniques have their advantages and disadvantages. Some techniques suffer
from over-segmentation while some of the techniques suffer from under-segmentation. The
MAP-ML [1] algorithm overcomes the disadvantages in above algorithms and gives result more
closely to human perception.
3. Computer Science & Information Technology (CS & IT) 3
We are going to implement the MAP-ML algorithm on the Berkeley database containing 500
natural images of size 321 x 481 (or 481x321), with ground truth segmentation results obtained
from human subjects for evaluating segmentation algorithm and we will compare the results with
those obtained by state-of-the-art image segmentation algorithms such as Mean Shift and
Normalized Cuts. Section 2 introduces the probability framework used in the algorithm. Section 3
discusses the proposed modified MAP-ML Algorithm. Section 4 discusses the results obtained.
Section 5 concludes our work.
2. PROBABILISTIC MODEL
For a given image P, the features of every pixel p are expressed by a 4-D vector
I(p)=(ܫ(p), ܫ(p), ܫ(p), ܫ௧(p))T
(1)
where ܫ(p), ܫ(p), ܫ(p) are the components of p in the L*a*b*color space, and ܫ௧(p) denotes the
texture feature of p. In this seminar, the texture contrast defined in [2] (scaled from [0, 1] to
[0,255]) is chosen as the texture descriptor. Fig. 3.4 shows an example of the features.
The task of image segmentation is to group the pixels of an image into relevant regions. If the
problem is formulated as a labeling problem, the objective is then to find a label configuration
݂ = ൛݂หൟ where ݂ is the label of pixel p denoting which region this pixel is grouped into.
Generally speaking, a “good” segmentation means that the pixels within a region i should share
homogeneous features represented by a vector ߮(݅) that does not change rapidly except on the
region boundaries. The introduction of ߮(݅) allows the description of a region, with which high
level knowledge or learned information can be incorporated into the segmentation. Suppose that
there are k possible region labels.
A 4-D vector
߮(݅) = (ܫ̅(݅), ܫ̅(݅), ܫ̅(݅), ܫ̅௧(݅))்
(2)
is used to describe the properties of label (region), where the four components of ߮(݅) have the
similar meanings to those of the corresponding four components of I(p).
Let ߮ = {߮(݅)} be the union of the region features. If P and ߮ are known, the segmentation is to
find an optimal label configuration݂መ, which maximizes the posterior possibility of the label
configuration.
݂ = ܽ݃ݎ ݉ܽݔ ܲݎ (݂│߮, ܲ) (3)
݂
where ߮ can be obtained by either a learning process or an initialized estimation. However, due to
the existence of noise and diverse objects in different images, it is difficult to obtain ߮ that is
precise enough. Thus, an iterative method is used to solve the segmentation problem.
Suppose that ߮
and ݂
are the estimation results in the nth iteration. Then the iterative formulas
for optimization are defined as
݂ାଵ
= ܽ݃ݎ ݉ܽݔ ܲݎ (݂|߮
, ܲ) (4)
݂
߮ାଵ
= ܽ݃ݎ ݉ܽݔ ܲݎ (݂ାଵ
|߮, ܲ) (5)
߮
4. 4 Computer Science & Information Technology (CS & IT)
This iterative optimization is preferred because (4) can be solved by the MAP estimation, and (5)
by the ML estimation.
2.1. MAP Estimation
Given an image P and the potential region features ߮, f is inferred by the Bayesian law, i.e.,
Pr(݂|߮, ܲ) can be obtained by
Pr(݂|߮, ܲ) =
୰(ఝ,|)୰ ()
୰ (ఝ,)
∝ ܲ,߮(ݎ ܲ|݂) ܲ )݂(ݎ (6)
which is a MAP estimation problem and can be modeled using MRFs.
Assuming that the observation of the image follows an independent identical distribution,
Pr(߮, ܲ|݂) is defined as
Pr(φ, P|f) α ∏ exp (−D(p, f୮୮∈ , φ)) (7)
where ܦ൫, ݂, ߮൯ is the data penalty function which imposes the penalty of a pixel p with a label
݂ for given ߮. The data penalty function is defined as
ܦ൫, ݂, ߮൯ = ||)(ܫ − ߮൫݂൯||ଶ
= (ܫ() − ܫ̅(݂))ଶ
+ (ܫ() − ܫ̅(݂))ଶ
+ (ܫ() − ܫ̅(݂))ଶ
+ (ܫ௧() − ܫ̅௧(݂))ଶ
(8)
MRF’s whose clique potentials involve pairs of neighboring pixels only is considered. Thus
Pr (݂) ∝ exp (− ∑ ∑ ܸ,∈ே()∈ (݂, ݂)) (9)
where N(p) is the neighborhood of pixel p. ܸ,(݂, ݂), called the smoothness penalty function, is
a clique potential function, which describes the prior probability of a particular label
configuration with the elements of the clique(p, q). The smoothness penalty function is defined as
follows using a generalized Potts model [7]:
ܸ,൫݂, ݂൯ = ܿ. exp ቀ
ି∆(,)
ఙ
ቁ . ܶ(݂ ≠ ݂) = ܿ. exp ቀ
ି|ூಽ()ିூಽ()|
ఙ
ቁ. ܶ(݂ ≠ ݂) (10)
where ∆(, )ݍ = −|ܫ() − ܫ (,|)ݍ called brightness contrast, denotes how different the
brightnesses of p and q, c>0 is a smoothness factor, σ > 0 is used to control the contribution of
∆(, )ݍ to the penalty, and T(.) is 1 if its argument is true and 0 otherwise. ܸ,(݂, ݂), depicts
two kinds of constraints. The first enforces the spatial smoothness; if two neighboring pixels are
labeled differently, a penalty is imposed. The second considers a possible edge between p and q;
if two neighboring pixels cause a larger ∆, then they have greater likelihood to be partitioned into
two regions.
In this algorithm, the boundaries of the segmentation result are pulled to match the darker pixels
which are more likely to be edge pixels.
From (6), (7), and (9) the equation can be written as,
Pr(݂|߮, ܲ) ∝ ൫∏ exp ൫−,(ܦ ݂, ߮)൯∈ ൯. exp ൫− ∑ ∑ ܸ,∈ே()∈ (݂, ݂)൯ (11)
Taking the logarithm of (11), the following energy function is as:
5. Computer Science & Information Technology (CS & IT) 5
,݂(ܧ ߮) = ∑ ,(ܦ ݂, ߮)∈ + ∑ ∑ ܸ,∈ே()∈ (݂, ݂) (12)
where ,݂(ܧ ߮) ∝ − log Pr (݂|߮, ܲ). It includes two parts: the data term
ܧௗ௧ = ∑ ,(ܦ ݂, ߮)∈ (13)
and the smoothness term
ܧ௦௧ = ∑ ∑ ܸ,∈ே()∈ (݂, ݂) (14)
From (12), it is clear that maximizing Pr(݂|߮, ܲ) is equivalent to minimizing the Markov energy
,݂(ܧ ߮) for a given ߮ .In this paper, graphcut algorithm is used to solve this minimization
problem.
2.2. ML Estimation
A 4-D vector ߮(i) given by equation 2 is used to describe the properties of label (region). The ML
estimation ߮ = ߮(݅) is obtained, where
߮(݅) =
ଵ
௨
∑ )(ܫୀ (15)
with ݊݉ݑ being the number of pixels within region i. Here (15) is exactly the equation to obtain
ܫ̅(i), ܫ̅(i), ܫ̅(i) and ܫ̅௧(i) and in (2).
3. PROPOSED MODIFIED MAP-ML ALGORITHM
The MAP-ML [1] is used to segment the image by each object in the same image. The algorithm
starts with finding the texture and contrast feature of every pixel present in the image. The texture
and contrast feature is used to segment the outline of the each object in the image and labelling is
used to delete the unwanted portion of the image and segment each object by each color. The K-
means Algorithm is used for initializations of the regions. The MAP estimation is used to detect
the edges of the image and the color space is used to segment the images by colors. The graph cut
algorithm is an unsupervised algorithm used for over segmentation and computation problem. We
had modified the existing MAP-ML [1] algorithm and the modified algorithm is given below:
6. 6 Computer Science & Information Technology (CS & IT)
After step 4.1, it is possible that two non adjacent regions are given the same label. The MAP
estimation is an NP-hard problem. Boykov et al. [8] proposed to obtain an approximate solution
via finding the minimum cuts in a graph model. Minimum cuts can be obtained by computing the
maximum flow between the terminals of the graph. In [8], an efficient Maxflow algorithm is
given for solving the binary labelling problem. In addition, an algorithm, called α expansion with
the Maxflow algorithm embedded, is presented to carry out multiple labelling iteratively. In this
algorithm, the α expansion algorithm is used to perform step 4.1. To increase the speed of the
algorithm we had used Maxflow 3.01 algorithm.
In the original MAP-ML Algorithm [1], the authors had initiated the MAP-ML algorithm with
default 10 labels and then in the iteration each region is labelled uniquely. Since the number of
labels is unique and increases with each iteration, the time to execute the MAP Estimation goes
up. So instead of that we had kept the initial number of labels=10 by default but we had not
uniquely labelled the regions so thereby the image will have utmost 10 or less than 10 labels
hence the time to take the MAP Estimation is less comparative to original MAP-ML Algorithm.
To achieve the equivalent result as the original we had calculated the standard deviation (camera
noise) for each image automatically since it will be different for each image. It is calculated by
taking expectation of all the pairs of neighbors in an image. So we had obtained results as near as
possible to the original algorithm in less amount of time.
Briefly we can say that the modified algorithm has three enhancements over Original MAP-ML:
1) Use of Maxflow 3.01 Algorithm with the reuse trees option
2) Unlike original algorithm the regions are not labelled uniquely
3) For every image sigma (standard deviation) is calculated. Sigma is an important factor
used in deciding the smoothness penalty for an image. Here it is calculated based on
average value of all pairs of neighbors in an image.
4. EXPERIMENTAL RESULTS
Our algorithm is tested on the Berkeley benchmark for evaluating segmentation algorithms and
compares the results with those obtained by state-of-the-art image segmentation algorithms. The
Berkeley database contains 500 natural images of size 321 x 481 (or 481 x 321), with ground
truth segmentation results obtained from human subjects.
The compared algorithms in these experiments include: Mean Shift (MS) [5] and Normalized cuts
(NC) [6]. In this algorithm, the initial cluster number in the K-means algorithm is set to 10 and
the smoothness factor c is 100. The region number in NC is set to 20, which is the average
number of segments marked by the human subjects in each image.
In the MS algorithm the default parameters used are hs=15, hr=13, and the minimal region=20
pixels are chosen. Since NC cannot handle an image of size 321 x 481(or 481 x 321) due to the
overflow of the memory, all the input images for them are shrunk into a size 214 x 320 (or 320 x
214), and the segmentation results are enlarged to their original sizes.
All the above experiments had been conducted on Intel Core 2 Duo 2.2 GHz 4GB RAM
Windows 7 platform. The code has been developed in JAVA which makes it portable.
7. Computer Science & Information Technology (CS & IT) 7
4.1. Qualitative Comparison Results
The part of the images in the Berkeley benchmark is classified into 7 sets ("Landscape",
"Grassplot and Sky", "Craft", "Human", "Bird", "Felid" and "Buildings"), and show the
segmentation results obtained by the three algorithms in Figure 1-7.
Figure 1. Results obtained on "Bird" images
Figure 2. Results obtained on "Buildings" images
8. 8 Computer Science & Information Technology (CS & IT)
Figure 3. Results obtained on "Feline" images
Figure 4. Results obtained on "Craft" images
Figure 5. Results obtained on "GrassPlot and Sky" images
9. Computer Science & Information Technology (CS & IT) 9
Figure 6. Results obtained on "Landscape" images
Figure 7. Results obtained on "Humans" images
From these examples, the following observations are seen:
10. 10 Computer Science & Information Technology (CS & IT)
NC tends to partition an image into regions of similar sizes, resulting in the region boundaries
different from the real edges. MS give strongly over-segmented results. Compared with these
other algorithms, it is easy to see that this algorithm obtains the best results, in which the
generated boundaries match the real edges well and the segmented regions are in accordance with
human perception.
4.2. Quantitative Comparisons Results
Quantitative comparisons are also important for objectively evaluating the performance of the
algorithms. There have been several measures proposed for this purpose. Region differencing and
boundary matching are two of them. Region differencing measures the extent to which one
segmentation can be viewed as a refinement of the other. Boundary matching measures the
average displacement error of boundary pixels between the results obtained by an algorithm and
the results obtained from human subjects. However, these two measures are not good enough for
segmentation evaluation. For example, a segmentation result with each pixel being one region
obtains the best score using these two measures. A strongly over-segmented result, which does
not make sense to human visual perception, may be ranked good.
In these experiments, two more stable and significant measures, variation of information (VoI)
and probabilistic rand index (PRI) are used to compare the performances of the three algorithms,
to objectively evaluate image segmentation algorithms. Consider a set of ground truths, labelled
by K persons, {S1, S2… SK}, of an image consisting of N pixels. Let Stest be the segmentation
result to be compared with the ground truths. Then the PRI value is defined as
ܴܲܵ(ܫ௧௦௧, ሼܵሽ) =
ଵ
ቀ
ே
ଶ
ቁ
∑ [
ି
ழ (1 − ̅)ଵି] (16)
where (p, q) is a pixel pair in the image, ܿ = ܶ(݈
ௌೞ
= ݈
ௌೞ
) denotes the event of a pair of
pixels p and q having the same label in the test result Stest ,and ̅ =
ଵ
∑ ܶ(݈
ௌೖ
= ݈
ௌೖ
)
ୀଵ
is regarded as the probability of p and q having the same label. The VoI value is defined as
ܸܵ(ܫ௧௦௧, ሼܵሽ) =
ଵ
∑ [ܵ(ܪ௧௦௧ ) + ܵ(ܪ) − 2ܵ(ܫ௧௦௧, ܵ)] (17)
where H and I denote the entropy and the mutual information, respectively.
VoI is an information-based measure which computes a measure of information content in each
of the segmentations and how much information one segmentation gives about the other. It is
related to the conditional entropies between the region label distributions of the segmentations.
PRI compares an obtained segmentation result with multiple ground truth images through soft
non uniform weighting of pixel pairs as a function of the variability in the ground truth set. The
value of VoI falls in [0,∞], and the smaller, the better. The value of PRI is in [0,1], and the larger,
the better.
The average values of PRI and VoI for the three algorithms are given in Table 1. In this table, the
second column shows the average PRI and VoI values between different human subjects, which
are the best scores. From these results, one can see that this algorithm outperforms the other
algorithms because it obtains the smallest VoI value and the largest PRI value. Among other
algorithms, MS gives sometimes better PRI values to this algorithm. However, their VoI values
are much larger than algorithm.
11. Computer Science & Information Technology (CS & IT) 11
Table 1. Average Values of PRI and VOI on the images.
To demonstrate the performances of these algorithms on each image, the PRI and VOI curves are
shown in Figure 8 (default 10 labels) and Figure 9 (default 20 labels). It is clearly observed
that modified algorithm performs the best. There is slight trade off between speed and accuracy in
the modified MAP-ML Algorithm. The elapsed time calculated between original MAP-ML and
modified MAP-ML Algorithm is shown in Figure 10.
Figure 8. PRI and VOI values achieved on individual images by the three algorithms when default labels
are 10. The values are plotted in increasing order.
Figure 9. PRI and VOI values achieved on individual images by the three algorithms when default labels
are 20. The values are plotted in increasing order.
Figure 10. Elapsed time Comparison between Original MAP-ML and Modified MAP-ML Algorithm when
default labels are a) 10 and b) 20. The values are plotted in increasing order.
12. 12 Computer Science & Information Technology (CS & IT)
4.3. Application
So far, general segmentation has two main applications. The first one is the group of algorithms
for specific objects, like for medical image. The second one is as a part of the algorithms for the
other algorithms, like recognition, classification; et al. Good segmentation results may improve
the final results. This image segmentation can be used as a part of video surveillance system such
that our final goal is to cutout the moving objects video sequences and track the objects.
5. CONCLUSION
We had implemented our modified MAP-ML algorithm which gives comparable results with the
original MAP-ML algorithm performing the image segmentation. Thus from the experimental
results we had successfully shown that the modified MAP-ML algorithm takes less time to
execute as compared to the original MAP-ML algorithm giving nearly same results as the original
algorithm.
REFERENCES
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AUTHORS
Mrudula Karande received the B.E. (Comp) degree from the Nagpur University of India, in
2001, the M.E.(Comp. Engg) degree from the Pune University, in 2013 in first class. She is
working as the Head of the Department of Information Technology in K. K. Wagh
Polytechnic, Nashik India. Her research interests include image processing and data mining.
Prof. D. B. Kshirsagar received the B.E. (CSE), Computer Engineering, from the Walchand
College of Engineering, Sangli, M.E. (CSE), Engineering degree from Shivaji University in
first class with distinction and is currently pursuing Phd. He is working as the Prof. and the
Head of the Department of Computer Engg in S. R. E. S. COE Kopargaon, India. His
research interests include image processing.