Motivated by a variational formulation of the motion segmentation problem, we propose a fully implicit variant of the (linearized) alternating direction method of multipliers for the minimization of convex functionals...
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ISBN:
(纸本)9781479949847
Motivated by a variational formulation of the motion segmentation problem, we propose a fully implicit variant of the (linearized) alternating direction method of multipliers for the minimization of convex functionals over a convex set. The new scheme does not require a step size restriction for stability and thus approaches the minimum using considerably fewer iterates. In numerical experiments on standard image sequences, the scheme often significantly outperforms other state of the art methods.
Recognizing human-object interactions in videos is a very challenging problem in computervision research. There are two major difficulties lying in this task: (1) The detection of human body parts and objects is usua...
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ISBN:
(纸本)9781479925414
Recognizing human-object interactions in videos is a very challenging problem in computervision research. There are two major difficulties lying in this task: (1) The detection of human body parts and objects is usually affected by the quality of the videos, for instance, low resolutions of the videos, camera motions, and blurring frames caused by fast motions, as well as the self-occlusions during human-object interactions. (2) The spatial and temporal dynamics of human-object interaction are hard to model. In order to overcome those natural obstacles, we propose a new method using social network analysis (SNA) based features to describe the distributions and relationships of low level objects for human-object interaction recognition. In this approach, the detected human body parts and objects are treated as nodes in social network graphs, and a set of SNA features including closeness, centrality and centrality with relative velocity are extracted for action recognition. A major advantage of SNA based feature set is its robustness to varying node numbers and erroneous node detections, which are very common in human-object interactions. An SNA feature vector will be extracted for each frame and different human-object interactions are classified based on these features. Two classification methods, including Support Vector Machine (SVM) and Hidden Markov Model (HMM), have been used to evaluate the proposed feature set on four different human-object interactions from HMDB dataset [1]. The experimental results demonstrated that the proposed framework can effectively capture the dynamical characteristics of human-object interaction and outperforms the state of art methods in human-object interaction recognition.
A fundamental open problem in SLAM is the effective representation of the map in unknown, ambiguous, complex, dynamic environments. Representing such environments in a suitable manner is a complex task. Existing appro...
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Given a set of images, or time-lapsed imagery, that is captured in an unconstrained domain, there are numerous methods to map that data into a domain that is readily displayable on basic rectilinear digital displays. ...
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In this paper we address the feature selection problem for X-SAR images and further the segmentation of specific chosen classes. After defining a suitable feature space for X-SAR images we select the most significant ...
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In this paper we address the feature selection problem for X-SAR images and further the segmentation of specific chosen classes. After defining a suitable feature space for X-SAR images we select the most significant ones via a supervised machine learning approach: the 1-norm SVM. The selected features will be used for segmentation purposes, in order to segment water areas from the background. We shall see that the most relevant features are based on texture elements. So the segmentation is texture based and achieved with variational calculus and levelsetmethods. The work is mainly focused on urban park X-SAR SpotLight images, where lakes and rivers are often present. The images are collected with the COSMO-SkyMed satellites constellation, equipped with a SAR sensor.
We propose a novel nonlinear, probabilistic and variational method for adding shape information to levelset-based segmentation and tracking. Unlike previous work, we represent shapes with elliptic Fourier descriptors...
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Image segmentation is an important branch of computervision. Its aim is to extract meaningful lying in objects images, either by dividing images into contiguous semantic regions, or by extracting one or several objec...
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Image segmentation is an important branch of computervision. Its aim is to extract meaningful lying in objects images, either by dividing images into contiguous semantic regions, or by extracting one or several objects more specific in images, such as medical structures. In general, image segmentation task is very difficult to achieve it since natural images are diverse, complex and the way we perceive them, vary according to individuals. More than a decade ago, a promising mathematical framework, based on variational models and partial differential equations, have been investigated to solve the image segmentation problem. This new approach benefits from well-established mathematical theories that allow people to analyze, understand and extend segmentation methods. Moreover, this framework is defined in a continuous setting which makes the proposed models independent with respect to the grid of digital images.
levelsetmethods have been widely used in image processing and computervision. In conventional levelset formulations, the levelset function typically develops irregularities during its evolution, which may cause n...
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levelsetmethods have been widely used in image processing and computervision. In conventional levelset formulations, the levelset function typically develops irregularities during its evolution, which may cause numerical errors and eventually destroy the stability of the evolution. Therefore, a numerical remedy, called reinitialization, is typically applied to periodically replace the degraded levelset function with a signed distance function. However, the practice of reinitialization not only raises serious problems as when and how it should be performed, but also affects numerical accuracy in an undesirable way. This paper proposes a new variationallevelset formulation in which the regularity of the levelset function is intrinsically maintained during the levelset evolution. The levelset evolution is derived as the gradient flow that minimizes an energy functional with a distance regularization term and an external energy that drives the motion of the zero levelset toward desired locations. The distance regularization term is defined with a potential function such that the derived levelset evolution has a unique forward-and-backward (FAB) diffusion effect, which is able to maintain a desired shape of the levelset function, particularly a signed distance profile near the zero levelset. This yields a new type of levelset evolution called distance regularized levelset evolution (DRLSE). The distance regularization effect eliminates the need for reinitialization and thereby avoids its induced numerical errors. In contrast to complicated implementations of conventional levelset formulations, a simpler and more efficient finite difference scheme can be used to implement the DRLSE formulation. DRLSE also allows the use of more general and efficient initialization of the levelset function. In its numerical implementation, relatively large time steps can be used in the finite difference scheme to reduce the number of iterations, while ensuring sufficient
Image segmentation deals with partitioning an input image into disjoint/non-overlapping regions. Among different segmentation algorithms, levelsetmethods have been very popular. Less sensitivity to initialization, a...
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ISBN:
(纸本)9781424455614
Image segmentation deals with partitioning an input image into disjoint/non-overlapping regions. Among different segmentation algorithms, levelsetmethods have been very popular. Less sensitivity to initialization, ability to split and merge the contour, and also, involving statistical inference have made levelset even more accepted than similar methods like snakes. However, it is very time-consuming. To solve this problem, in this paper a fast variational approach is presented for texture segmentation. For this purpose, first a feature space based on non-linear diffusion is set up from CIE L*a*b* colour components. Then, this feature space is clustered by fusion of clustering algorithms. Finally, the produced cluster map is used in levelset for contour evolution. As it is shown in the simulation results, our algorithm is robust in segmenting noisy texture. Also, it is faster than previous levelset approaches for texture segmentation.
Image segmentation deals with partitioning an input image into disjoint/non-overlapping regions. Among different segmentation algorithms, levelsetmethods have been very popular. Less sensitivity to initialization, a...
详细信息
Image segmentation deals with partitioning an input image into disjoint/non-overlapping regions. Among different segmentation algorithms, levelsetmethods have been very popular. Less sensitivity to initialization, ability to split and merge the contour, and also, involving statistical inference have made levelset even more accepted than similar methods like snakes. However, it is very time-consuming. To solve this problem, in this paper a fast variational approach is presented for texture segmentation. For this purpose, first a feature space based on non-linear diffusion is set up from CIE L*a*b* colour components. Then, this feature space is clustered by fusion of clustering algorithms. Finally, the produced cluster map is used in levelset for contour evolution. As it is shown in the simulation results, our algorithm is robust in segmenting noisy texture. Also, it is faster than previous levelset approaches for texture segmentation.
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