In previous work [14], we modify the hidden Markov model (HMM) framework to incorporate a global parametric variation in the output probabilities of the stares of the HMM. Development of the parametric hidden Markov m...
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ISBN:
(纸本)0818684976
In previous work [14], we modify the hidden Markov model (HMM) framework to incorporate a global parametric variation in the output probabilities of the stares of the HMM. Development of the parametric hidden Markov model (PHMM) was motivated by the task of simultaneously recognizing and interpreting gestures that exhibit meaningful variation. With standard HMMs, such global variation confounds the recognition process. The original PHMM approach assumes a linear dependence of output density means on the global parameter In this paper we extend the PHMM to handle arbitrary smooth (nonlinear) dependencies. We show a generalized expectation-maximization (GEM) algorithm for training the PHMM and a GEM algorithm hm to simultaneously recognize the gesture and estimate the value of the parameter We present results on a pointing gesture, where the nonlinear approach permits the natural! azimuth/elevation parameterization of pointing direction.
We demonstrate that is it possible to automatically find representative example images of a specified object category These canonical examples are perhaps the kind of images that one would show a child to teach them w...
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ISBN:
(纸本)9781424439942
We demonstrate that is it possible to automatically find representative example images of a specified object category These canonical examples are perhaps the kind of images that one would show a child to teach them what, for example a horse is - images with a large object clearly separated from the background. Given a large collection of images returned by a web search for an object category, our approach proceeds without an), user supplied training data for the category. First images are ranked according to a category independent composition model that predicts whether the), contain a large clearly depicted object, and outputs an estimated location of that object. Then local features calculated on the proposed object regions are used to eliminate images not distinctive to the category, and to cluster images by similarity of object appearance. We present results and a user evaluation on a variety of object categories, demonstrating the effectiveness of the approach.
Most of behavior recognition methods proposed so far share the limitations of bottom-up analysis, and single-object assumption;the bottom-lip analysis can be confused by erroneous and missing image features and the si...
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ISBN:
(纸本)0818684976
Most of behavior recognition methods proposed so far share the limitations of bottom-up analysis, and single-object assumption;the bottom-lip analysis can be confused by erroneous and missing image features and the single-object assumption prevents us from analyzing image sequences including multiple moving objects. This paper presents a robust behavior recognition method free from these limitations. Our method is best characterized by I) top-down image feature extraction by selective attention mechanism, 2) object discrimination by colored-token propagation, and 3) integration of multi-viewpoint images. Extensive experiments of human behavior recognition in real world environments demonstrate the soundness and robustness of our method.
The general problem of surface matching is considered in this study. The process described in this work hinges on a geodesic distance equation for a family of surfaces embedded in the graph of a cost function. The cos...
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ISBN:
(纸本)0769506623
The general problem of surface matching is considered in this study. The process described in this work hinges on a geodesic distance equation for a family of surfaces embedded in the graph of a cost function. The cost function represents the geometrical matching criterion between the two 3D surfaces. This graph is a hypersurface in 4-dimensional space, and the theory presented herein is a generalization of the geodesic curve evolution method introduced by R. Kimmel et al [10] it also generalizes the 2D matching process developed in [4]. An Eulerian level-set formulation of the geodesic surface evolution is also used, leading to a numerical scheme for solving partial differential equations originating from hyperbolic conservation laws [14] which has proven to be very robust and stable. The method is applied on examples showing both small and large deformations, and arbitrary topological changes.
In this paper we present the Women in computervision Workshop - WiCV 2019, organized in conjunction with cvpr 2019. This event is meant for increasing the visibility and inclusion of women researchers in computer vis...
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ISBN:
(纸本)9781728125060
In this paper we present the Women in computervision Workshop - WiCV 2019, organized in conjunction with cvpr 2019. This event is meant for increasing the visibility and inclusion of women researchers in computervision field. computervision and machine learning have made incredible progress over the past years, but the number of female researchers is still low both in the academia and in the industry. WiCV is organized especially for this reason: to raise visibility of female researchers, to increase collaborations between them, and to provide mentorship to female junior researchers in the field. In this paper, we present a report of trends over the past years, along with a summary of statistics regarding presenters, attendees, and sponsorship for the current workshop.
A new class of kernels for object recognition based on local image feature representations are introduced in this paper. These kernels satisfy the Mercer condition and incorporate multiple types of local features and ...
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ISBN:
(纸本)0769523722
A new class of kernels for object recognition based on local image feature representations are introduced in this paper. These kernels satisfy the Mercer condition and incorporate multiple types of local features and semilocal constraints between them. Experimental results of SVM classifiers coupled with the proposed kernels are reported on recognition tasks with the COIL-100 database and compared with existing methods. The proposed kernels achieved competitive performance and were robust to changes in object configurations and image degradations.
This work analyzes the problem of homography estimation for robust target matching in the context of real-time mobile vision. We present a device-friendly implementation of the Gaussian Elimination algorithm and show ...
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ISBN:
(纸本)9781479943098
This work analyzes the problem of homography estimation for robust target matching in the context of real-time mobile vision. We present a device-friendly implementation of the Gaussian Elimination algorithm and show that our optimized approach can significantly improve the homography estimation step in a hypothesize-and-verify scheme. Experiments are performed on image sequences in which both speed and accuracy are evaluated and compared with conventional homography estimation schemes.
This paper proposes an effective next view planning strategy for the object recognition and localization task in a model-based robot vision system. A set of rules are designed to automatically predict new features and...
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ISBN:
(纸本)0818658258
This paper proposes an effective next view planning strategy for the object recognition and localization task in a model-based robot vision system. A set of rules are designed to automatically predict new features and calculate the next optimal placement of the sensor so that the most useful information can be gathered from multi-views. A state vector (i,r,t) is defined to describe the current state of the vision system and each possible state corresponds to a subset of rules to deal with it. The recognition and location task can be described as a process of rule calling and state conversions. The most suitable rule is selected at each step to try to acquire more useful information as soon as possible. Experiments are shown in the paper.
This paper analyses the influence of the stereo camera parameters on an existing obstacle detection technique. A theoretical model is derived and evaluated for the concepts of resolution and stability. Resolution is t...
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ISBN:
(纸本)0818658258
This paper analyses the influence of the stereo camera parameters on an existing obstacle detection technique. A theoretical model is derived and evaluated for the concepts of resolution and stability. Resolution is the minimum height that an obstacle must have in order to be detected. Stability is defined as the sensitivity of detection to errors in the camera parameters. It can be seen that detection is very sensitive to some of the parameters and that there is no clear tradeoff between resolution and stability. We derive a stereo head configuration to give stable results and show how the parameters should be adapted in the context of an active vision system. Experiments confirm the theoretical results.
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