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.
Object recognition is the challenging problem in the real world application. Object recognition can be achieved through the shape matching. Shape matching is preceded by i) detecting the edges of the objects from the images. ii) Finding the correspondence between the shapes. iii) Measuring the dissimilarity between the shapes using the correspondence. iv) Classifying the object into classes by using this dissimilarity measures. In order to solve the correspondence problem, we attach a descriptor, the shape context, to each point. The shape context at a reference point captures the distribution of the remaining points relative to it, thus offering a globally discriminative characterization. Corresponding points on two similar shapes will have similar shape contexts. The one to one correspondence is achieved through the cost based bipartite graph matching. The cost matrix is reduced through Hungarian algorithm. The dissimilarity between the two shapes is computed using Canberra distance. The nearest neighbor classifier is used to classify the objects with the matching error. The results are obtained using the MATLAB for MINIST hand written digits.
GRAPH PARTITIONING FOR IMAGE SEGMENTATION USING ISOPERIMETRIC APPROACH: A REVIEWDrm Kapoor
Graph cut is fast method performing a binary segmentation. Graph cuts proved to be a useful multidimensional optimization tool which can enforce piecewise smoothness while preserving relevant sharp discontinuities. This paper is mainly intended as an application of isoperimetric algorithm of graph theory for image segmentation and analysis of different parameters used in the algorithm like generating weights, regulates the execution, Connectivity Parameter, cutoff, number of recursions. We present some basic background information on graph cuts and discuss major theoretical results, which helped to reveal both strengths and limitations of this surprisingly versatile combinatorial algorithm.
Image segmentation by modified map ml estimationsijesajournal
Though numerous algorithms exist to perform image segmentation there are several issues
related to execution time of these algorithm. Image Segmentation is nothing but label relabeling
problem under probability framework. To estimate the label configuration, an iterative
optimization scheme is implemented to alternately carry out the maximum a posteriori (MAP)
estimation and the maximum likelihood (ML) estimations. In this paper this technique is
modified in such a way so that it performs segmentation within stipulated time period. The
extensive experiments shows that the results obtained are comparable with existing algorithms.
This algorithm performs faster execution than the existing algorithm to give automatic
segmentation without any human intervention. Its result match image edges very closer to
human perception.
REMOVING OCCLUSION IN IMAGES USING SPARSE PROCESSING AND TEXTURE SYNTHESISIJCSEA Journal
We provide a solution to problem of occlusion in images by removing the occluding region and filling in the gap left behind. Inpainting algorithms fail in filling occlusions when the occluding region is large since there is loss of both structure and texture. We decompose the image into structure and texture images using a decomposition method based on sparseness of the image. The sparse reconstruction of the decomposed images result in an inpainted image with all the structures made intact. A texture synthesis is performed on the texture only image. Finally the structure and texture images are combined to get an image where the occlusion is filled. The performance of our algorithm in terms of visual effectiveness is compared with other algorithms used for inpainting.
Object recognition is the challenging problem in the real world application. Object recognition can be achieved through the shape matching. Shape matching is preceded by i) detecting the edges of the objects from the images. ii) Finding the correspondence between the shapes. iii) Measuring the dissimilarity between the shapes using the correspondence. iv) Classifying the object into classes by using this dissimilarity measures. In order to solve the correspondence problem, we attach a descriptor, the shape context, to each point. The shape context at a reference point captures the distribution of the remaining points relative to it, thus offering a globally discriminative characterization. Corresponding points on two similar shapes will have similar shape contexts. The one to one correspondence is achieved through the cost based bipartite graph matching. The cost matrix is reduced through Hungarian algorithm. The dissimilarity between the two shapes is computed using Canberra distance. The nearest neighbor classifier is used to classify the objects with the matching error. The results are obtained using the MATLAB for MINIST hand written digits.
GRAPH PARTITIONING FOR IMAGE SEGMENTATION USING ISOPERIMETRIC APPROACH: A REVIEWDrm Kapoor
Graph cut is fast method performing a binary segmentation. Graph cuts proved to be a useful multidimensional optimization tool which can enforce piecewise smoothness while preserving relevant sharp discontinuities. This paper is mainly intended as an application of isoperimetric algorithm of graph theory for image segmentation and analysis of different parameters used in the algorithm like generating weights, regulates the execution, Connectivity Parameter, cutoff, number of recursions. We present some basic background information on graph cuts and discuss major theoretical results, which helped to reveal both strengths and limitations of this surprisingly versatile combinatorial algorithm.
Image segmentation by modified map ml estimationsijesajournal
Though numerous algorithms exist to perform image segmentation there are several issues
related to execution time of these algorithm. Image Segmentation is nothing but label relabeling
problem under probability framework. To estimate the label configuration, an iterative
optimization scheme is implemented to alternately carry out the maximum a posteriori (MAP)
estimation and the maximum likelihood (ML) estimations. In this paper this technique is
modified in such a way so that it performs segmentation within stipulated time period. The
extensive experiments shows that the results obtained are comparable with existing algorithms.
This algorithm performs faster execution than the existing algorithm to give automatic
segmentation without any human intervention. Its result match image edges very closer to
human perception.
REMOVING OCCLUSION IN IMAGES USING SPARSE PROCESSING AND TEXTURE SYNTHESISIJCSEA Journal
We provide a solution to problem of occlusion in images by removing the occluding region and filling in the gap left behind. Inpainting algorithms fail in filling occlusions when the occluding region is large since there is loss of both structure and texture. We decompose the image into structure and texture images using a decomposition method based on sparseness of the image. The sparse reconstruction of the decomposed images result in an inpainted image with all the structures made intact. A texture synthesis is performed on the texture only image. Finally the structure and texture images are combined to get an image where the occlusion is filled. The performance of our algorithm in terms of visual effectiveness is compared with other algorithms used for inpainting.
ROLE OF HYBRID LEVEL SET IN FETAL CONTOUR EXTRACTIONsipij
Image processing technologies may be employed for quicker and accurate diagnosis in analysis and
feature extraction of medical images. Here, existing level set algorithm is modified and it is employed for
extracting contour of fetus in an image. In traditional approach, fetal parameters are extracted manually
from ultrasound images. An automatic technique is highly desirable to obtain fetal biometric measurements
due to some problems in traditional approach such as lack of consistency and accuracy. The proposed
approach utilizes global & local region information for fetal contour extraction from ultrasonic images.
The main goal of this research is to develop a new methodology to aid the analysis and feature extraction.
A MORPHOLOGICAL MULTIPHASE ACTIVE CONTOUR FOR VASCULAR SEGMENTATIONijbbjournal
This paper presents a morphological active contour ideal for vascular segmentation in biomedical images.
The unenhanced images of vessels and background are successfully segmented using a two-step
morphological active contour based upon Chan and Vese’s Active Contour without Edges. Using dilation
and erosion as an approximation of curve evolution, the contour provides an efficient, simple, and robust
alternative to solving partial differential equations used by traditional level-set Active Contour models. The
proposed method is demonstrated with segmented data set images and compared to results garnered from
multiphase Active Contour without Edges, morphological watershed, and Fuzzy C-means segmentations.
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
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.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Template matching is a basic method in image analysis to extract useful information from images. In this
paper, we suggest a new method for pattern matching. Our method transform the template image from two
dimensional image into one dimensional vector. Also all sub-windows (same size of template) in the
reference image will transform into one dimensional vectors. The three similarity measures SAD, SSD, and
Euclidean are used to compute the likeness between template and all sub-windows in the reference image
to find the best match. The experimental results show the superior performance of the proposed method
over the conventional methods on various template of different sizes.
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.
Multiple Ant Colony Optimizations for Stereo MatchingCSCJournals
The stereo matching problem, which obtains the correspondence between right and left images, can be cast as a search problem. The matching of all candidates in the same line forms a 2D optimization task and the two dimensional (2D) optimization is a NP-hard problem. There are two characteristics in stereo matching. Firstly, the local optimization process along each scan-line can be done concurrently; secondly, there are some relationship among adjacent scan-lines can be explored to promote the matching correctness. Although there are many methods, such as GCPs, GGCPs are proposed, but these so called GCPs maybe not be ground. The relationship among adjacent scan-lines is posteriori, that is to say the relationship can only be discovered after every optimization is finished. The Multiple Ant Colony Optimization(MACO) is efficient to solve large scale problem. It is a proper way to settle down the stereo matching task with constructed MACO, in which the master layer values the sub-solutions and propagate the reliability after every local optimization is finished. Besides, whether the ordering and uniqueness constraints should be considered during the optimization is discussed, and the proposed algorithm is proved to guarantee its convergence to find the optimal matched pairs.
Efficient 3D stereo vision stabilization for multi-camera viewpointsjournalBEEI
In this paper, an algorithm is developed in 3D Stereo vision to improve image stabilization process for multi-camera viewpoints. Finding accurate unique matching key-points using Harris Laplace corner detection method for different photometric changes and geometric transformation in images. Then improved the connectivity of correct matching pairs by minimizing
the global error using spanning tree algorithm. Tree algorithm helps to stabilize randomly positioned camera viewpoints in linear order. The unique matching key-points will be calculated only once with our method.
Then calculated planar transformation will be applied for real time video rendering. The proposed algorithm can process more than 200 camera viewpoints within two seconds.
Image Enhancement and Restoration by Image InpaintingIJERA Editor
Inpainting is the process of reconstructing lost or deteriorated part of images based on the background information. i. e .it fills the missing or damaged region in an image utilizing spatial information of its neighboring region. Inpainting algorithm have numerous applications. It is helpfully used for restoration of old films and object removal in digital photographs. The main goal of the algorithm is to modify the damaged region in an image in such a way that the inpainted region is undetectable to the ordinary observers who are not familiar with the original image. This proposed work presents image inpainting process for image enhancement and restoration by using structural, texture and exemplar techniques. This paper presents efficient algorithm that combines the advantages of these two approaches. We first note that exemplar-based texture synthesis contains the essential process required to replicate both texture and structure; the success of structure propagation, however, is highly dependent on the order in which the filling proceeds. We propose a best-first algorithm in which the confidence in the synthesized pixel values is propagated in a manner similar to the propagation of information in inpainting. The actual color values are computed using exemplar-based synthesis. Computational efficiency is achieved by a blockbased sampling process.
Development of Human Tracking System For Video Surveillancecscpconf
Visual surveillance in dynamic scenes, especially for human and some objects is one of the
most active research areas. An attempt has been made to this issue in this work. It has wide
spectrum of promising application including human identification to detect the suspicious
behavior, crowd flux statistics, and congestion analysis using multiple cameras.
In this paper deals with the problem of detecting and tracking multiple moving people in a static
background. Detection of foreground object is done by background subtraction. Detected
objects are identified and analyzed through different blobs. Then tracking is performed by
matching corresponding features of blob. An algorithm has been developed in this perspective
using Angular Deviation of Center of Gravity (ADCG), which gives a satisfying result for segmentation of human object.
Aree Virus. Non si uccide anche così l'architettura. Una riflessione di fine secolo, ma, purtroppo, ancora con spunti attualissimi. Aree ed edifici simbolo di degrado ed abbandono urbano. Può mai esistere un futuro per queste tessere consolidate del mosaico urbano. Il caso di Avellino.
Médico Especialista Álvaro Miguel Carranza Montalvo, soy Médico General Alto, Rubio, de Piel Blanca, ojos claros , soy Atlético Simpático, me esmero a seguir Adelante solucionando los Problemas de las demás Personas para salvar su Vida en Salud y en Enfermedades. Internet, Networds….
Médico Especialista Álvaro Miguel Carranza Montalvo, la VIDA es una VIRTUD que cada Humano, Persona tiene es Valeroso y Digno lograr SALVAR la VIDA de una Persona que está en Peligro, cada Persona es una sóla Unidad único no hay nadie como esa persona somos distintos. Internet, Networds….
Médico Especialista Álvaro Miguel Carranza Montalvo, la NATURALEZA es Bella y Linda Vivirla al Aire Libre, con Agua, la Vegetación, los Bellos Animales en el Ecosistema la Biodiversidad hay que Valorar y Gozar lo que hay en el Mundo Vivirla y Disfrutarla. Internet, Networds….
Médico Especialista Álvaro Miguel Carranza Montalvo, ME GUSTA LO QUE SOY MI FORMA DE SER ME ENCANTA LO QUE SOY YÓ MI FÍSICO, MENTE, PENSAMIENTOS, ALMA Y CUERPO, FÍSICO. Y VIVIR LA VIDA, NATURALEZA LA BELLEZA. Web, Redes Sociales….
Médico Especialista Álvaro Miguel Carranza Montalvo, Me gusta la Naturaleza y la Vida. VIVIR LA VIDA RESPETANDO A LOS DEMÁS CHICAS Y CHICOS A TODAS LAS PERSONAS LES RESPETO Y ADMIRO PORQUE TIENEN SUS VALORES Y DONES. HACER EL BIEN NUNCA EL MAL A LA PERSONA TRATAR COMO A UNO LE GUSTARÍA QUE LE TRATEN. Web, Redes Sociales….
Médico Especialista Álvaro Miguel Carranza Montalvo, "creo que las artes marciales mixtas sirven principalmente para desarrollar la energía. A veces es necesario darse cuenta de un peligro y conocer el medio para salvar la vida. Web, Redes Sociales….
Médico Especialista Álvaro Miguel Carranza Montalvo, La Energía es Vital para lograr una Meta con Fuerza y Salud es lo más Importante en la Vida. ", Web, Internet….
Médico Especialista Álvaro Miguel Carranza Montalvo, "es necesario realizar ejercicios determinados en la columna, para proporcionar oxígeno al cerebro y ayudarle a descansar totalmente", Web, Internet….
Médico Especialista Álvaro Miguel Carranza Montalvo, "hay tres palabras que aprendemos a gritar que llevan consigo descanso y energía; fuerza, valor y convicción", Web, Internet….
ROLE OF HYBRID LEVEL SET IN FETAL CONTOUR EXTRACTIONsipij
Image processing technologies may be employed for quicker and accurate diagnosis in analysis and
feature extraction of medical images. Here, existing level set algorithm is modified and it is employed for
extracting contour of fetus in an image. In traditional approach, fetal parameters are extracted manually
from ultrasound images. An automatic technique is highly desirable to obtain fetal biometric measurements
due to some problems in traditional approach such as lack of consistency and accuracy. The proposed
approach utilizes global & local region information for fetal contour extraction from ultrasonic images.
The main goal of this research is to develop a new methodology to aid the analysis and feature extraction.
A MORPHOLOGICAL MULTIPHASE ACTIVE CONTOUR FOR VASCULAR SEGMENTATIONijbbjournal
This paper presents a morphological active contour ideal for vascular segmentation in biomedical images.
The unenhanced images of vessels and background are successfully segmented using a two-step
morphological active contour based upon Chan and Vese’s Active Contour without Edges. Using dilation
and erosion as an approximation of curve evolution, the contour provides an efficient, simple, and robust
alternative to solving partial differential equations used by traditional level-set Active Contour models. The
proposed method is demonstrated with segmented data set images and compared to results garnered from
multiphase Active Contour without Edges, morphological watershed, and Fuzzy C-means segmentations.
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
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.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Template matching is a basic method in image analysis to extract useful information from images. In this
paper, we suggest a new method for pattern matching. Our method transform the template image from two
dimensional image into one dimensional vector. Also all sub-windows (same size of template) in the
reference image will transform into one dimensional vectors. The three similarity measures SAD, SSD, and
Euclidean are used to compute the likeness between template and all sub-windows in the reference image
to find the best match. The experimental results show the superior performance of the proposed method
over the conventional methods on various template of different sizes.
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.
Multiple Ant Colony Optimizations for Stereo MatchingCSCJournals
The stereo matching problem, which obtains the correspondence between right and left images, can be cast as a search problem. The matching of all candidates in the same line forms a 2D optimization task and the two dimensional (2D) optimization is a NP-hard problem. There are two characteristics in stereo matching. Firstly, the local optimization process along each scan-line can be done concurrently; secondly, there are some relationship among adjacent scan-lines can be explored to promote the matching correctness. Although there are many methods, such as GCPs, GGCPs are proposed, but these so called GCPs maybe not be ground. The relationship among adjacent scan-lines is posteriori, that is to say the relationship can only be discovered after every optimization is finished. The Multiple Ant Colony Optimization(MACO) is efficient to solve large scale problem. It is a proper way to settle down the stereo matching task with constructed MACO, in which the master layer values the sub-solutions and propagate the reliability after every local optimization is finished. Besides, whether the ordering and uniqueness constraints should be considered during the optimization is discussed, and the proposed algorithm is proved to guarantee its convergence to find the optimal matched pairs.
Efficient 3D stereo vision stabilization for multi-camera viewpointsjournalBEEI
In this paper, an algorithm is developed in 3D Stereo vision to improve image stabilization process for multi-camera viewpoints. Finding accurate unique matching key-points using Harris Laplace corner detection method for different photometric changes and geometric transformation in images. Then improved the connectivity of correct matching pairs by minimizing
the global error using spanning tree algorithm. Tree algorithm helps to stabilize randomly positioned camera viewpoints in linear order. The unique matching key-points will be calculated only once with our method.
Then calculated planar transformation will be applied for real time video rendering. The proposed algorithm can process more than 200 camera viewpoints within two seconds.
Image Enhancement and Restoration by Image InpaintingIJERA Editor
Inpainting is the process of reconstructing lost or deteriorated part of images based on the background information. i. e .it fills the missing or damaged region in an image utilizing spatial information of its neighboring region. Inpainting algorithm have numerous applications. It is helpfully used for restoration of old films and object removal in digital photographs. The main goal of the algorithm is to modify the damaged region in an image in such a way that the inpainted region is undetectable to the ordinary observers who are not familiar with the original image. This proposed work presents image inpainting process for image enhancement and restoration by using structural, texture and exemplar techniques. This paper presents efficient algorithm that combines the advantages of these two approaches. We first note that exemplar-based texture synthesis contains the essential process required to replicate both texture and structure; the success of structure propagation, however, is highly dependent on the order in which the filling proceeds. We propose a best-first algorithm in which the confidence in the synthesized pixel values is propagated in a manner similar to the propagation of information in inpainting. The actual color values are computed using exemplar-based synthesis. Computational efficiency is achieved by a blockbased sampling process.
Development of Human Tracking System For Video Surveillancecscpconf
Visual surveillance in dynamic scenes, especially for human and some objects is one of the
most active research areas. An attempt has been made to this issue in this work. It has wide
spectrum of promising application including human identification to detect the suspicious
behavior, crowd flux statistics, and congestion analysis using multiple cameras.
In this paper deals with the problem of detecting and tracking multiple moving people in a static
background. Detection of foreground object is done by background subtraction. Detected
objects are identified and analyzed through different blobs. Then tracking is performed by
matching corresponding features of blob. An algorithm has been developed in this perspective
using Angular Deviation of Center of Gravity (ADCG), which gives a satisfying result for segmentation of human object.
Aree Virus. Non si uccide anche così l'architettura. Una riflessione di fine secolo, ma, purtroppo, ancora con spunti attualissimi. Aree ed edifici simbolo di degrado ed abbandono urbano. Può mai esistere un futuro per queste tessere consolidate del mosaico urbano. Il caso di Avellino.
Médico Especialista Álvaro Miguel Carranza Montalvo, soy Médico General Alto, Rubio, de Piel Blanca, ojos claros , soy Atlético Simpático, me esmero a seguir Adelante solucionando los Problemas de las demás Personas para salvar su Vida en Salud y en Enfermedades. Internet, Networds….
Médico Especialista Álvaro Miguel Carranza Montalvo, la VIDA es una VIRTUD que cada Humano, Persona tiene es Valeroso y Digno lograr SALVAR la VIDA de una Persona que está en Peligro, cada Persona es una sóla Unidad único no hay nadie como esa persona somos distintos. Internet, Networds….
Médico Especialista Álvaro Miguel Carranza Montalvo, la NATURALEZA es Bella y Linda Vivirla al Aire Libre, con Agua, la Vegetación, los Bellos Animales en el Ecosistema la Biodiversidad hay que Valorar y Gozar lo que hay en el Mundo Vivirla y Disfrutarla. Internet, Networds….
Médico Especialista Álvaro Miguel Carranza Montalvo, ME GUSTA LO QUE SOY MI FORMA DE SER ME ENCANTA LO QUE SOY YÓ MI FÍSICO, MENTE, PENSAMIENTOS, ALMA Y CUERPO, FÍSICO. Y VIVIR LA VIDA, NATURALEZA LA BELLEZA. Web, Redes Sociales….
Médico Especialista Álvaro Miguel Carranza Montalvo, Me gusta la Naturaleza y la Vida. VIVIR LA VIDA RESPETANDO A LOS DEMÁS CHICAS Y CHICOS A TODAS LAS PERSONAS LES RESPETO Y ADMIRO PORQUE TIENEN SUS VALORES Y DONES. HACER EL BIEN NUNCA EL MAL A LA PERSONA TRATAR COMO A UNO LE GUSTARÍA QUE LE TRATEN. Web, Redes Sociales….
Médico Especialista Álvaro Miguel Carranza Montalvo, "creo que las artes marciales mixtas sirven principalmente para desarrollar la energía. A veces es necesario darse cuenta de un peligro y conocer el medio para salvar la vida. Web, Redes Sociales….
Médico Especialista Álvaro Miguel Carranza Montalvo, La Energía es Vital para lograr una Meta con Fuerza y Salud es lo más Importante en la Vida. ", Web, Internet….
Médico Especialista Álvaro Miguel Carranza Montalvo, "es necesario realizar ejercicios determinados en la columna, para proporcionar oxígeno al cerebro y ayudarle a descansar totalmente", Web, Internet….
Médico Especialista Álvaro Miguel Carranza Montalvo, "hay tres palabras que aprendemos a gritar que llevan consigo descanso y energía; fuerza, valor y convicción", Web, Internet….
FAST ALGORITHMS FOR UNSUPERVISED LEARNING IN LARGE DATA SETScsandit
The ability to mine and extract useful information automatically, from large datasets, is a
common concern for organizations (having large datasets), over the last few decades. Over the
internet, data is vastly increasing gradually and consequently the capacity to collect and store
very large data is significantly increasing.
Existing clustering algorithms are not always efficient and accurate in solving clustering
problems for large datasets.
However, the development of accurate and fast data classification algorithms for very large
scale datasets is still a challenge. In this paper, various algorithms and techniques especially,
approach using non-smooth optimization formulation of the clustering problem, are proposed
for solving the minimum sum-of-squares clustering problems in very large datasets. This
research also develops accurate and real time L2-DC algorithm based with the incremental
approach to solve the minimum
Médico Especialista Álvaro Miguel Carranza Montalvo, soy Médico General Alto, Rubio, de Piel Blanca, ojos claros , soy Atlético Simpático, me esmero a seguir Adelante solucionando los Problemas de las demás Personas para salvar su Vida en Salud y en Enfermedades. Internet, Networds….
Médico Especialista Álvaro Miguel Carranza Montalvo, la VIDA es una VIRTUD que cada Humano, Persona tiene es Valeroso y Digno lograr SALVAR la VIDA de una Persona que está en Peligro, cada Persona es una sóla Unidad único no hay nadie como esa persona somos distintos. Internet, Networds….
Médico Especialista Álvaro Miguel Carranza Montalvo, la NATURALEZA es Bella y Linda Vivirla al Aire Libre, con Agua, la Vegetación, los Bellos Animales en el Ecosistema la Biodiversidad hay que Valorar y Gozar lo que hay en el Mundo Vivirla y Disfrutarla. Internet, Networds….
Médico Especialista Álvaro Miguel Carranza Montalvo, ME GUSTA LO QUE SOY MI FORMA DE SER ME ENCANTA LO QUE SOY YÓ MI FÍSICO, MENTE, PENSAMIENTOS, ALMA Y CUERPO, FÍSICO. Y VIVIR LA VIDA, NATURALEZA LA BELLEZA. Web, Redes Sociales….
Médico Especialista Álvaro Miguel Carranza Montalvo, Me gusta la Naturaleza y la Vida. VIVIR LA VIDA RESPETANDO A LOS DEMÁS CHICAS Y CHICOS A TODAS LAS PERSONAS LES RESPETO Y ADMIRO PORQUE TIENEN SUS VALORES Y DONES. HACER EL BIEN NUNCA EL MAL A LA PERSONA TRATAR COMO A UNO LE GUSTARÍA QUE LE TRATEN. Web, Redes Sociales….
Médico Especialista Álvaro Miguel Carranza Montalvo, "creo que las artes marciales mixtas sirven principalmente para desarrollar la energía. A veces es necesario darse cuenta de un peligro y conocer el medio para salvar la vida. Web, Redes Sociales….
Médico Especialista Álvaro Miguel Carranza Montalvo, La Energía es Vital para lograr una Meta con Fuerza y Salud es lo más Importante en la Vida. ", Web, Internet….
Médico Especialista Álvaro Miguel Carranza Montalvo, "es necesario realizar ejercicios determinados en la columna, para proporcionar oxígeno al cerebro y ayudarle a descansar totalmente", Web, Internet….
Médico Especialista Álvaro Miguel Carranza Montalvo, "hay tres palabras que aprendemos a gritar que llevan consigo descanso y energía; fuerza, valor y convicción", Web, Internet….
IMAGE SEGMENTATION BY MODIFIED MAP-ML ESTIMATIONScscpconf
Though numerous algorithms exist to perform image segmentation there are several issues
related to execution time of these algorithm. Image Segmentation is nothing but label relabeling
problem under probability framework. To estimate the label configuration, an iterative
optimization scheme is implemented to alternately carry out the maximum a posteriori (MAP)
estimation and the maximum likelihood (ML) estimations. In this paper this technique is
modified in such a way so that it performs segmentation within stipulated time period. The
extensive experiments shows that the results obtained are comparable with existing algorithms.
This algorithm performs faster execution than the existing algorithm to give automatic
segmentation without any human intervention. Its result match image edges very closer to
human perception.
Though numerous algorithms exist to perform image segmentation there are several issues
related to execution time of these algorithm. Image Segmentation is nothing but label relabeling
problem under probability framework. To estimate the label configuration, an iterative
optimization scheme is implemented to alternately carry out the maximum a posteriori (MAP)
estimation and the maximum likelihood (ML) estimations. In this paper this technique is
modified in such a way so that it performs segmentation within stipulated time period. The
extensive experiments shows that the results obtained are comparable with existing algorithms.
This algorithm performs faster execution than the existing algorithm to give automatic
segmentation without any human intervention. Its result match image edges very closer to
human perception.
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.
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.
This article aims at a new algorithm for tracking moving objects in the long term. We have tried to overcome some potential difficulties, first by a comparative study of the measuring methods of the difference and the similarity between the template and the source image. In the second part, an improvement of the best method allows us to follow the target in a robust way. This method also allows us to effectively overcome the problems of geometric deformation, partial occlusion and recovery after the target leaves the field of vision. The originality of our algorithm is based on a new model, which does not depend on a probabilistic process and does not require a data based detection in advance. Experimental results on several difficult video sequences have proven performance advantages over many recent trackers. The developed algorithm can be employed in several applications such as video surveillance, active vision or industrial visual servoing.
An iterative morphological decomposition algorithm for reduction of skeleton ...ijcsit
Shape representation is an important aspect in image processing and computer vision. There are several skeleton transforms that lead to morphological shape representation algorithm. One of the main problems with these algorithms is in selecting the skeleton points that represent the shape component. If the numbers of skeleton subsets are reduced then the reconstruction process will be easy and time consuming. The present paper proposes a skeleton scheme that selects skeleton points based on the largest shape element. By this, overall skeleton subsets will be reduced. The present method is applied on various images and is compared with generalized skeleton transform and octagon-generating decomposition algorithm.
M ESH S IMPLIFICATION V IA A V OLUME C OST M EASUREijcga
We develop a polygonal mesh simplification algorithm based on a novel analysis of the mesh
geometry.
Particularly, we propose first a characterization of vertices as hyperbolic or non
-
hyperbolic depend
-
ing
upon their discrete local geometry. Subsequently, the simplification process computes a volume cost for
each non
-
hyperbolic vertex, in anal
-
ogy with spherical volume, to capture the loss of fidelity if that vertex
is decimated. Vertices of least volume cost are then successively deleted and the resulting holes re
-
triangulated using a method based on a novel heuristic. Preliminary experiments i
ndicate a performance
comparable to that of the best known mesh simplification algorithms
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.
COMPOSITE TEXTURE SHAPE CLASSIFICATION BASED ON MORPHOLOGICAL SKELETON AND RE...sipij
After several decades of research, the development of an effective feature extraction method for texture
classification is still an ongoing effort. Therefore , several techniques have been proposed to resolve such
problems. In this paper a novel composite texture classification method based on innovative pre-processing
techniques, skeletonization and Regional moments (RM) is proposed. This proposed texture classification
approach, takes into account the ambiguity brought in by noise and the different caption and digitization
processes. To offer better classification rate, innovative pre-processing methods are applied on various
texture images first. Pre-processing mechanisms describe various methods of converting a grey level image
into binary image with minimal consideration of the noise model. Then shape features are evaluated using
RM on the proposed Morphological Skeleton (MS) method by suitable numerical characterization
measures for a precise classification. This texture classification study using MS and RM has given a good
performance. Good classification result is achieved from a single region moment RM10 while others failed
in classification.
EDGE DETECTION IN SEGMENTED IMAGES THROUGH MEAN SHIFT ITERATIVE GRADIENT USIN...ijscmcj
In this paper, we propose a new method for edge detection in obtained images from the Mean Shift iterative algorithm. The comparable, proportional and symmetrical images are de?ned and the importance of Ring Theory is explained. A relation of equivalence among proportional images are de?ned for image groups in equivalent classes. The length of the mean shift vector is used in order to quantify the homogeneity of the neighborhoods. This gives a measure of how much uniform are the regions that compose the image. Edge detection is carried out by using the mean shift gradient based on symmetrical images. The difference among the values of gray levels are accentuated or these are decreased to enhance the interest region contours. The chosen images for the experiments were standard images and real images (cerebral hemorrhage images). The obtained results were compared with the Canny detector, and our results showed a good performance as for the edge continuity.
TERRIAN IDENTIFICATION USING CO-CLUSTERED MODEL OF THE SWARM INTELLEGENCE & S...cscpconf
A digital image is nothing more than data -- numbers indicating variations of red, green, and
blue at a particular location on a grid of pixels. Clustering is the process of assigning data
objects into a set of disjoint groups called clusters so that objects in each cluster are more
similar to each other than objects from different clusters. Clustering techniques are applied in
many application areas such as pattern recognition, data mining, machine learning, etc.
Clustering algorithms can be broadly classified as Hard, Fuzzy, Possibility, and Probabilistic .Kmeans
is one of the most popular hard clustering algorithms which partitions data objects into k
clusters where the number of clusters, k, is decided in advance according to application
purposes. This model is inappropriate for real data sets in which there are no definite boundaries
between the clusters. After the fuzzy theory introduced by Lotfi Zadeh, the researchers put the
fuzzy theory into clustering. Fuzzy algorithms can assign data object partially to multiple
clusters. The degree of membership in the fuzzy clusters depends on the closeness of the data
object to the cluster centers. The most popular fuzzy clustering algorithm is fuzzy c-means (FCM)
which introduced by Bezdek in 1974 and now it is widely used. Fuzzy c-means clustering is an
effective algorithm, but the random selection in center points makes iterative process falling into
the local optimal solution easily. For solving this problem, recently evolutionary algorithms such
as genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO) , and particle swarm optimization (PSO) have been successfully applied.
Activity Recognition From IR Images Using Fuzzy Clustering TechniquesIJTET Journal
Infrared sensors ensures that activity recognition is possible in the day and night times. It is used especially for activity monitoring of older adults as falls are more prevalent at night than the day. This paper focus on an application of fuzzy set techniques and it is capable of accurately detecting several different activity states related to fall detection and fall risk assessment and it also includes sitting, standing and being on the floor to ensure that elderly residents gets the help they need quickly in case of emergencies. Fall detection and fall risk assessment is used for an aging in place facility for the elderly people. It describes the silhouette extraction process, the image features , and the fuzzy clustering technique.
A Novel Multiple-kernel based Fuzzy c-means Algorithm with Spatial Informatio...CSCJournals
Fuzzy c-means (FCM) algorithm has proved its effectiveness for image segmentation. However, still it lacks in getting robustness to noise and outliers, especially in the absence of prior knowledge of the noise. To overcome this problem, a generalized a novel multiple-kernel fuzzy cmeans (FCM) (NMKFCM) methodology with spatial information is introduced as a framework for image-segmentation problem. The algorithm utilizes the spatial neighborhood membership values in the standard kernels are used in the kernel FCM (KFCM) algorithm and modifies the membership weighting of each cluster. The proposed NMKFCM algorithm provides a new flexibility to utilize different pixel information in image-segmentation problem. The proposed algorithm is applied to brain MRI which degraded by Gaussian noise and Salt-Pepper noise. The proposed algorithm performs more robust to noise than other existing image segmentation algorithms from FCM family.
Similar to 4 tracking objects of deformable shapes (20)
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
Every time when lecturers/professors need to conduct examinations they have to sit down think about the questions and then create a whole new set of questions for each and every exam. In some cases the professor may want to give an open book online exam that is the student can take the exam any time anywhere, but the student might have to answer the questions in a limited time period. The professor may want to change the sequence of questions for every student. The problem that a student has is whenever a date for the exam is declared the student has to take it and there is no way he can take it at some other time. This project will create an interface for the examiner to create and store questions in a repository. It will also create an interface for the student to take examinations at his convenience and the questions and/or exams may be timed. Thereby creating an application which can be used by examiners and examinee’s simultaneously.
Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
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1. INTRODUCTION
IMAGE segmentation and the tracking of objects are two of the most prominent topics in
computer vision. Numerous authors have tried to solve these problems based on low level
information such as edges or region statistics [25], [34],[3], [42], [21]. However, their success has
been limited: In real-world images, the low-level information is often corrupted, e.g., by changing
lighting conditions and low contrast between object and background. As an example, consider
Figure. 1, where a car is tracked in rainy weather. To cope with such challenges, researchers have
endeavored to integrate prior knowledge into the respective segmentation processes. In numerous
studies [19], [4], [13], [7], this was shown to significantly improve the resulting segmentations.
However, most of these methods find local minima, and hence, require an initialization in the
vicinity of the solution. The one that does find globally optimal segmentations [13] has a
quadratic memory complexity. It is hence well suited for tracking tasks, but for pixel-accurate
image segmentation, only rather coarse resolutions can be handled. In this work, we present the
first globally optimal shape based segmentation method able to yield pixel-accurate
segmentations in effectively linear time.
The segmentation problem in complex images cannot be addressed adequately without the
anatomical a-priori knowledge which usually aids in making decisions about the image
segmentation. In this case two major sources of a-priori knowledge can be identified:
A-priori information about the mean shape and the variability of anatomical objects.
A-priori information about the mean location, orientation and size of the objects with respect to
each other and their variability.
Here we address the segmentation and tracking problem in these images using Geometrically
Deformable Templates (GDT).This novel approach differs in the following points from
previously described models: Its deformation is controlled via a penalty function rather than via
its parameterization .This penalty function is associated with a thin-plate spline(TPS)mapping
function which maps the templates in its equilibrium configuration into a deformed configuration.
One can visualize thin plate spline mapping function as an imaginary rectangular grid associated
with the model in equilibrium. Any deformation of the model would also deform the rectangular
grid. The penalty function requires energy for any non-affine deformation of the grid but does not
penalize affine deformations. Moreover , the model can incorporate not only information about
the mean location, orientation and size of the anatomical objects with respect to each other and
their variability. Thus , the model can be used to segment multiple objects simultaneously.
1.1 Related Work
Image segmentation and tracking are closely related problems, yet each with its own history. We,
therefore, review them separately.
1.1.1 Tracking Deformable Objects
The tracking of objects has traditionally been based on feature points [17], [22]. Starting from the
KLT-tracker [42],subsequent feature-based methods appeared in [21], [30].More recently,
methods have become popular that treat the object as an entity [11], [6], [20] rather than an
independent number of parts. Denzler and Niemann [11] consider a set of patches that is linked
by a ray model. Cremers [6] models the temporal evolution of shapes by a dynamical,
autoregressive model in a level set framework. This is extended by Gui et al. [20] to the case of
competing priors. While many of these methods are based on minimizing a suitable energy, none
guarantees to find the global optimum. However, such methods give neither a guarantee to find
good (i.e., low energy) solutions nor a means to verify if a solution is optimal. To determine
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global optima in the presence of significantly deforming curves has remained an open challenge.
Furthermore, real-world applications typically require fast algorithms that can run in real time.
1.2 Shape-Based Image Segmentation
The task to partition an image into meaningful regions has received considerable attention in the
past. When the limits of low-level methods [25], [34], [3] became apparent, researchers
endeavored to integrate prior knowledge into segmentation processes. The amount of prior
knowledge varies from a part-based (deformable) object structure [15], [14], [35] over a
collection of shapes [19], [4], [29], [43], [9], [37], [7], [8], [28] to a single shape [13].
Figure 1. Tracking a car in bad weather: Despite bad visibility, reflections, and camera shake, the
proposed method allows reliable tracking over a hundreds of frames
Such methods are bound to find local optima of the energy they are optimizing and heavily
depend on the initial contour. In addition, they are based on rather simple shape similarity
measures, which do not attempt to establish correspondences of parts or points.
Recently, Cremers et al. [8] dealt with the first point: Starting from an implicit representation of
shapes and segmentations, they are able to find globally optimal
segmentations while taking into account shape similarity to a number of training shapes.
However, the lack of point correspondences remains. In contrast, a number of discrete approaches
do allow shape priors based on point correspondences while guaranteeing global optimality:
Coughlan et al. [5] are able to match open contours to images, taking into account an elastic
shape similarity measure [31], [32]. Being based on dynamic programming, the method is, in
principle, parallelizable. However, it is limited to open contours, and hence, does not provide a
segmentation. Although the method could be extended to closed contours by performing a
complete search over the start point; in practice, this would be far too time-consuming.
The first globally optimal shape-based segmentation algorithm was proposed by Felzenszwalb
[13]. It is based on dynamic programming in chordal graphs. The algorithm is easy to parallelize
and invariant with respect to translation, rotation, and scale changes. In practice, however, due to
its quadratic memory complexity, pixel accurate segmentations can only be computed on rather
coarse resolution.
1.2 Contribution
In this paper, we present an effectively linear-time algorithm to match contours to images closed
contours reduce the bias toward short curves by reverting to ratio functional and minimum ratio
cycle computation. The proposed method supports different amounts of invariances, including
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translational and rotational ones. By exploiting its high parallelizability, real-time tracking
becomes feasible.
Optimization of GDT’S
Estimating the maximum a posteriori (MAP) solution directly is usually impossible due to the
size of the configuration space, even for template models with very few vertices. Instead we have
implemented two different optimization techniques for the segmentation and tracking process:
Simulated Annealing(SA) minimization technique is used during the segmentation process and
Iterated Conditional Modes(ICM) as an efficient local minimization technique is used during the
tracking process. Simulated Annealing is a stochastic relaxation technique which generates
randomly new configurations by sampling. Iterated Conditional Mode is a deterministic
relaxation algorithm. It is very well suited for tracking objects if the temporal resolution is high
enough.
2 MATCHING’S AS CYCLES IN A PRODUCT SPACE
We are given a prior contour S :S11
-> IR2
(where S1
is the unit circle) with a uniform
parameterization. The task is to match this contour to a given image I : -> IR, where ,<- IR2
is
the (typically rectangular) image domain. The placed contour C:S1->
should be similar to the
input contour and fulfill some data-driven criteria. In this work, we want it to be located at image
edges. Figure 2 gives an illustration of our approach: When a contour and an image are input, the
algorithm locates a deformed version of the contour in the image and computes an alignment to
the prior contour.
Figure 2 Starting from (a) a prior contour and (b) an input image, the proposed method
simultaneously locates (c) the (possibly deformed) contour in the image and computes (d) a
correspondence function between the two curves
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Figure 3 Cyclic paths in a 3D graph (no edges are shown): For any point on the prior contour,
there are K copies of the image in the graph. Any assignment of pixels in the image to
corresponding points on the template contour corresponds to a cyclic path in this graph.
correspondence. This allows to use correspondence-based shape similarity measures, which were
shown to be important for reproducing human notions of shape similarity [18], [26].
While this was known for open curves [5], the computationally much more challenging case of
closed curves has so far not been solved. The product space arises by combining the functions C
and m into a single function Ґ: S1
-> ×S1
which is called a cycle. The space in which these
cycles live is visualized in Figure. 3. It has the form of a torus and arises by placing a copy of the
image for each point on the (onedimensional) prior contour. When splitting a closed contour at
some point, it can be viewed as an open one. The space would then be a solid block. When
additionally imposing that start and end points are identical, the respective end faces of the block
have to meet and the torus is formed.A curve (with winding number 1) in this space now allows
to read off the desired information: The curve C is obtained by projecting _ to the first two
dimensions. The correspondences of the points on C can be read off in the third dimension.
3 ASSIGNING A COST TO EACH CYCLE
We now present an exemplary energy functional for matching shapes to images. The presented
method applies to a much larger class of functionals. For example, in [40], we used a more
sophisticated data term based on patch comparison. Before we state the cost function, we briefly
discuss how curves are represented.
3.1 Representing Curves
There are infinitely many ways to parameterize a specific curve. Naturally, an optimization
problem should not depend on the chosen parameterization.
Figure 4 The three ingredients of the proposed method: (a) An edge detector function assigning
low values to high image gradients.(b) Computation of tangent angles of the contours C and S
(shown for C, the tangent is drawn in black). (c) Computation of length distortion.
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The functional we consider in this paper is indeed invariant with respect to reparameterizations.
For most of this section, we will therefore not assume any specific parameterization of the
contour C to be optimized. Yet, in a few places, it will be convenient to have a uniform
parameterization, i.e., with constant derivative ║Cs(s)║=║C║ kCk everywhere. In the given
setting, the correspondence function m is dependent on the contour C: m(s) will always denote
the correspondence of the point C(s). Hence, if the parameterization of C is changed, the function
m changes as well.In subsequent sections, we prefer the combined function Ґ: S1
-> ×S1
. Since
the objective function is not invariant against reparameterizations of Ґ (data term and shape
measure are not coupled), we state it in terms of C and m.
We allow self-intersecting contours C since we have no means to exclude them. We found them
to arise only seldom as long as the desired object is truly contained in the image.
4 DISCRETIZING COST AND PRODUCT SPACE
To optimize over the cycles , , both the cost function and the product space are
discretized. This section deals with the discretization, the optimization algorithm is detailed in the
next one. The key idea is to represent C as a polygonal curve with (an a priori unknown number
of) vertices on the pixel grid. In addition, the correspondence m is assumed to be linear along
each polygonal line segment. It is therefore uniquely defined by assigning point correspondences
to the two end points of such a segment. Specifically, we consider line segments connecting
neighboring pixels on the pixel grid,where we choose an 8-neighborhood. A cycle can now be
composed out of a finite set of basic parts 4.1 Discretizing Prior Contour and
Correspondence In addition to the cycle , the prior contour S is also discretized. we represent it
in the same form as the contour C, i.e., as an ordered set of points on a suitable pixel
grid, where—for ease of notation— is represented twice. To get a dense representation
of the contour, we require that si be among the eight closest neighbors of The discrete
correspondence function assigns each image pixel on C one of these prior points. To
ensure a monotone matching, we enforce that the start pixel of a segment is assigned a shape
point with index lower than or equal to that of the endpoint. Closure of the matching is obtained
by the fact that .The length distortion penalizer gives two hard constraints, which limit
the minimal and maximal distortion ratio. In the discrete setting, these are realized in terms of the
indices of the two shape points assigned to a line segment. The upper limit corresponds to an
index difference of at most K. Ensuring the lower limit is more intricate since here several line
segments may correspond to the same part of S. We therefore allow the two indices
to be equal.
However, for any shape point si, there may be at most K parts, where both endpoints of
correspond to si.In practice, this is realized by modifying the correspondence function m: In the
discrete setting, m maps to pairs (i,k) where i gives the shape point and k < K gives the number of
image pixels already corresponding to si. If m maps to the same i at the beginning and end of the
contour segment, then the index k must be one higher for the end node. This is formalized in the
following section.
7. International Journal of Computer Science and Engineering Research and Development (IJCSERD), ISSN
2248-9363 (Print), ISSN 2248-9371 (Online), Volume 1, Number 1, April-June (2011)
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Figure 5 Segmentation with a single template: Despite significant deformation, scale change, and
translation, the initial template curve (red) is accurately matched to each template.
5 SHAPE - BASED IMAGE SEGMENTATION
We treat images with significant distortion. As a consequence, we allow K=5 image pixels to be
matched to a single shape point and set a low length distortion weight with λ=0.1. The tangent
angles are given more weight with ν=0.5—this term really drives the process.
5.1 Translation-Invariant Matching
In Figure 5, the contour of a rabbit (viewed from the side) is matched to images from two
different sequences. In the first sequence, the rabbit is shown from different viewpoints but at the
same scale. Despite low contrast between object and background, the algorithm relocates the
object reliably.
5.2 On the Effect of Length Normalization
We introduced the length normalization to reduce the bias toward shorter curves. This effect is
demonstrated in Figure. 6: The figure shows the global optima for the ratio functional and for the
numerator integral alone. The latter corresponds to the geodesic energy we proposed in [41]:
Figure 6 The length normalization removes the bias toward shorter curves.
8. International Journal of Computer Science and Engineering Research and Development (IJCSERD), ISSN
2248-9363 (Print), ISSN 2248-9371 (Online), Volume 1, Number 1, April-June (2011)
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and is minimized globally by a combination of branch and bound and the shortest path
algorithms. Clearly, the ratio functional produces longer curves. We observe this whenever there
is low contrast in some regions along the desired curve.
5.3 Including Rotational Invariance
Aside from translational invariance, sometimes one also wants rotational invariance. The
proposed framework can be easily extended to include this: One simply samples the rotation
angle in sufficiently dense intervals. The prior contour is rotated by the specified amount and the
obtained contour is matched to the image.
6 SHAPE-BASED TRACKING
In this section, we present the problem of tracking deformable objects (or contours). In the first
frame, the contour S is given. Then subsequently, we map the contour determined for the
previous frame to the current frame. This performs better than keeping a fixed template since
large-scale deformations are decomposed into a sequence of smaller ones.
7 CONCLUSION
In this paper, we introduced a polynomial-time solution for matching a given contour to an image
despite translation, rotation, scale change, and deformation. The central idea is to cast the
assignment of an image pixel to each template point as a problem of finding optimal ratio cycles
in a 3D graph that represents the product space of image and template. The energy that is
optimized globally consists of an edge-based data term and a shape similarity measure favoring
similarity of local edge angles and minimal distortion (stretching/shrinking) of the template
curve.
We propose to use an energy-minimizing geometrically deformable template(GDT) which can
deform into similar shapes under the influence of image forces. This allows the simultaneous
segmentation of multiple deformable objects using intra-as well as inter-shape information.
Simulated Annealing(SA).
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