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Applied Imagery Pattern Recognition workshop (AIPR)

作者： Guang Yang Yafeng Yin Hong Man Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA

ISBN: (纸本)9781479925414

Recognizing human-object interactions in videos is a very challenging problem in computer vision 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.

关键词： Social network services Videos Hidden Markov models Feature extraction Vectors Support vector machines Trajectory

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A variational approach to mapping and localization

A variational approach to mapping and localization

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2012 9th Conference on computer and Robot vision, CRV 2012

作者： Hogue, Andrew Khattak, Saad Faculty of Business and IT University of Ontario Institute of Technology Oshawa ON United States

ISBN: (纸本)9780769546834

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 approaches to SLAM use map representations that store individual features (range measurements, image patches, or higher level semantic features) and their locations in the environment. The choice of how we represent the map produces limitations which in many ways are unfavourable for application in real-world scenarios. In this paper, we explore a new approach to SLAM that redefines sensing and robot motion as acts of deformation of a differentiable surface. Distance fields and level set methods are utilized to define a parallel to the components of the SLAM estimation process and an algorithm is developed and demonstrated. The variational framework developed is capable of representing complex dynamic scenes and spatially varying uncertainty for sensor and robot models. © 2012 ieee.

关键词： Mapping

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Sphere2: Jerry's rig, an OpenGL application for non-linear panorama viewing and interaction

Sphere2: Jerry's rig, an OpenGL application for non-linear p...

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2012 Western New York Image Processing workshop, WNYIPW 2012

作者： Keane, Tommy P. Cahill, Nathan D. Rhody, Harvey Hu, Bo Tarduno, John Jacobs, Robert Pelz, Jeff Chester F. Carlson Center for Imaging Science Rochester Institute of Technology Rochester NY 14623 United States School of Mathematical Sciences Rochester Institute of Technology Rochester NY 14623 United States Department of Earth and Environmental Sciences University of Rochester Rochester NY 14611 United States Department of Brain and Cognitive Sciences University of Rochester Rochester NY 14611 United States

ISBN: (纸本)9781467355988

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. However, while these mappings can be mathematically sound, they are methods that modify the spatio-temporal 'scale' of the scene, and thus can become quite semantically confusing when viewed in this manner. In working with data to be mathematically and scientifically analyzed, we have found that we also require a level of semantic consistency, such as in providing accessible imagery for expert annotation or in generating 'virtual field trips'. This is why we have applied the same mathematical rigor to instead develop an interactive, OpenGL-based viewing application for spatio-temporally non-linear imagery. This application is extensible and entirely custom, such that we can not only interact with single sets of related imagery, but also view the relationships across sets of imagery in a semantically viable, and mathematically sound, domain. © 2012 ieee.

关键词： computer vision

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X-SAR SpotLigh images feature selection and water segmentation

X-SAR SpotLigh images feature selection and water segmentati...

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ieee International workshop on Imaging Systems and Techniques (IST)

作者： Bruno Cafaro Silvia Canale Fiora Pirri Department of Computer Control and Management Engineering |A. Ruberti| Sapienza Università di Roma Roma Italy

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 level set methods. 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.

关键词： Image segmentation Equations Mathematical model Image resolution Support vector machines Synthetic aperture radar Joints

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Nonlinear shape manifolds as shape priors in level set segmentation and tracking

Nonlinear shape manifolds as shape priors in level set segme...

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作者： Prisacariu, Victor Adrian Reid, Ian University of Oxford United Kingdom

ISBN: (纸本)9781457703942

We propose a novel nonlinear, probabilistic and variational method for adding shape information to level set-based segmentation and tracking. Unlike previous work, we represent shapes with elliptic Fourier descriptors and learn their lower dimensional latent space using Gaussian Process Latent Variable Models. Segmentation is done by a nonlinear minimisation of an image-driven energy function in the learned latent space. We combine it with a 2D pose recovery stage, yielding a single, one shot, optimisation of both shape and pose. We demonstrate the performance of our method, both qualitatively and quantitatively, with multiple images, video sequences and latent spaces, capturing both shape kinematics and object class variance. © 2011 ieee.

关键词： Numerical methods

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variational image segmentation models: Application to medical images MRI

Variational image segmentation models: Application to medica...

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ieee International Conference on Multimedia Computing and Systems (ICMCS)

作者： Samir Bara Mounir Ait Kerroum Ahmed Hammouch Driss Aboutajdine UFR LRIT Faculty of Sciences Mohamed V-Agdal University Rabat Morocco Equipe Imagerie et Multimedia Laboratoire LARIT ENCG Université Ibn Tofail Kenitra Morocco Laboratory LRGE ENSET Mohammed-V University Rabat Morocco

Image segmentation is an important branch of computer vision. 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.

关键词： Image segmentation level set Mathematical model Biomedical imaging Active contours Pixel Noise

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Distance Regularized level set Evolution and Its Application to Image Segmentation

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ieee TRANSACTIONS ON IMAGE PROCESSING 2010年第12期19卷 3243-3254页

作者： Li, Chunming Xu, Chenyang Gui, Changfeng Fox, Martin D. Vanderbilt Univ Inst Imaging Sci Nashville TN 37232 USA Siemens Corp Res Technol Business Ctr Berkeley CA 94704 USA Univ Connecticut Dept Math Storrs CT 06269 USA Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

level set methods have been widely used in image processing and computer vision. In conventional level set formulations, the level set 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 level set 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 variational level set formulation in which the regularity of the level set function is intrinsically maintained during the level set evolution. The level set 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 level set toward desired locations. The distance regularization term is defined with a potential function such that the derived level set evolution has a unique forward-and-backward (FAB) diffusion effect, which is able to maintain a desired shape of the level set function, particularly a signed distance profile near the zero level set. This yields a new type of level set evolution called distance regularized level set 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 level set 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 level set 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

关键词： Forward and backward diffusion image segmentation level set method narrowband reinitialization

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Locating texture boundaries using a fast unsupervised approach based on clustering algorithms fusion and level set

Locating texture boundaries using a fast unsupervised approa...

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2009 ieee International Conference on Signal and Image Processing Applications, ICSIPA09

作者： Emambakhsh, Mehryar Sedaaghi, Mohammad Hossein Ebrahimnezhad, Hossein Computer Vision Lab. Department of Electrical Engineering Sahand University of Technology Sahand New City Tabriz Iran

ISBN: (纸本)9781424455614

Image segmentation deals with partitioning an input image into disjoint/non-overlapping regions. Among different segmentation algorithms, level set methods have been very popular. Less sensitivity to initialization, ability to split and merge the contour, and also, involving statistical inference have made level set 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 level set 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 level set approaches for texture segmentation.

关键词： Clustering algorithms

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Locating texture boundaries using a fast unsupervised approach based on clustering algorithms fusion and level set

Locating texture boundaries using a fast unsupervised approa...

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ieee International Conference on Signal and Image Processing Applications (ICSIPA)

作者： Mehryar Emambakhsh Mohammad Hossein Sedaaghi Hossein Ebrahimnezhad Computer Vision Laboratory Department of Electrical Engineering Sahand University of Technology Tabriz Iran Department of Electrical Engineering Sahand University of Technology Tabriz Iran

关键词： Clustering algorithms level set Image segmentation Partitioning algorithms Robustness Cities and towns Biomedical imaging Diffusion tensor imaging Signal processing Image processing

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Shape prior segmentation of multiple objects with graph cuts

Shape prior segmentation of multiple objects with graph cuts

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ieee Conference on computer vision and Pattern Recognition

作者： Vu, Nhat Manjunath, B. S. Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA

ISBN: (纸本)9781424422425

We present a new shape prior segmentation method using graph cuts capable of segmenting multiple objects. The shape prior energy is based on a shape distance popular with level set approaches. We also present a multiphase graph cut framework to simultaneously segment multiple, possibly overlapping objects. The multiphase formulation differs from multiway cuts in that the former can account for object overlaps by allowing a pixel to have multiple labels. We then extend the shape prior energy to encompass multiple shape priors. Unlike variational methods, a major advantage of our approach is that the segmentation energy is minimized directly without having to compute its gradient, which can be a cumbersome task and often relies on approximations. Experiments demonstrate that our algorithm can cope with image noise and clutter, as well as partial occlusions and affine transformations of the shape.

关键词： Graphic methods

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