The paper demonstrates accurate, anatomically correct 3D visualization of left and right ventricles of the human heart from tagged magnetic resonance imaging data. Tagged MRI reveals 3D structural and motion informati...
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We propose a graph theoretic technique for recognizing actions at a distance by modeling the visual senses associated with human poses. Identifying the intended meaning of poses is a challenging task because of their ...
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imageprocessing has been a popular topic of research for many years. It refers to any form of processing for which the input is an image and the output can be either an image or a set of characteristics or parameters...
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ISBN:
(纸本)9781601321541
imageprocessing has been a popular topic of research for many years. It refers to any form of processing for which the input is an image and the output can be either an image or a set of characteristics or parameters related to that image. We have studied and implemented the various feature detection algorithms for edge detection, such as Sobel edge detector, Robert edge detector, Prewitt edge detector, Laplacian of Gaussian(LoG) edge detector and Canny edge detector. The various characteristics of these algorithms are analyzed on the basis of image output, the number of operations used and the run-time required. On the basis of this, we have proposed a novel algorithm for edge detection in real-time and implemented the same which gives comparable results for the image output with reduced number of operations at run-time. We compare these results with the standard algorithms and discuss the relative performances.
Parametric coding is a technique in which data is processed to extract meaningful information and then representing it compactly using appropriate parameters. Parametric Coding exploits redundancy in information to pr...
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This paper deals with the problem of estimating structure of 3D scenes and image transformations from observations that are blurred due to unconstrained camera motion. Initially, we consider a fronto-parallel planar s...
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This paper presents a novel traffic simulation scheme capable of modeling most forms of urban, chaotic traffic. Different from other lane-based or following-based approaches, ours models traffic as a large navigationa...
Topic models such as probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA) have been successfully used to discover individual activities in a scene. However these methods do not discover ...
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For several categories of scenes, the representation of an image by a set of local feature vectors is suitable. Such sets of local feature vectors for images of a scene category are well modeled by generative approach...
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ISBN:
(纸本)9781601321541
For several categories of scenes, the representation of an image by a set of local feature vectors is suitable. Such sets of local feature vectors for images of a scene category are well modeled by generative approaches such as Gaussian mixture models (GMMs). For confusable categories, the discriminative training of generative models has been shown to give an improved classification performance. In this paper, we propose to use large margin GMMs (LMGMMs) for scene categorization task. The LMGMM uses large margin principles similar to support vector machine (SVM), for discriminative learning of GMM. The posterior probabilities estimated using LMGMMs are used as soft labels in building the posterior probability support vector machine (PPSVM) based classifier. The performance of LMGMM based classifier is compared with that of conventional GMM, variational Bayes GMM, SVM, PPSVM based classifiers.
Unrestricted camera motion and the ability to operate over a range of lens parameters are often desirable when using an off-the-shelf camera. Variations in intrinsic and extrinsic parameters induce defocus and pixel m...
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Segmentation of brain MR image has become more significant in research and medical applications. It is a process of extraction of various cortical tissues which is a key issue in neuroscience to detect early neural di...
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ISBN:
(纸本)9781601321541
Segmentation of brain MR image has become more significant in research and medical applications. It is a process of extraction of various cortical tissues which is a key issue in neuroscience to detect early neural disorders. The aim of this study is to segment the brain MR image into gray matter, white matter and cerebrospinal fluid. Two popular approaches for the segmentation of human brain images of MR modality using Fuzzy C-means and Markov random field model are discussed in this paper and comparative analysis of the two methods have been presented with results. Further comparison is done between deterministic and stochastic approaches of MRF model which have been proposed to solve the difficult optimization problem.
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