This paper presents a novel hierarchical approach to triangular mesh generation from height fields. A wavelet-based multiresolution analysis technique is used to estimate local shape information at different levels of...
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This paper presents a novel hierarchical approach to triangular mesh generation from height fields. A wavelet-based multiresolution analysis technique is used to estimate local shape information at different levels of resolution. Using predefined templates at the coarsest level, the method constructs an initial triangulation in which underlying object shapes are well preserved. Wavelet detail coefficients directly control the selection of appropriate templates, and are then used for subdividing and refining the initial mesh.
In this paper we consider the problem of controlling the motion of a vehicle moving on a ground plane based on aerial imagery. In the course of this work we propose a novel analysis of the relationship between the vel...
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In this paper we consider the problem of controlling the motion of a vehicle moving on a ground plane based on aerial imagery. In the course of this work we propose a novel analysis of the relationship between the velocity of the vehicle on the ground plane and the velocity of its projection in the image. We show that this relationship provides information about a subset of the parameters of the homography relating the ground plane to the aerial image plane and describe how we can recover this relationship from available measurements.
Emotion recognition is one of the latest challenges in intelligent human/computer communication. Most of the previous work on emotion recognition focused on extracting emotions from visual or audio information separat...
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Emotion recognition is one of the latest challenges in intelligent human/computer communication. Most of the previous work on emotion recognition focused on extracting emotions from visual or audio information separately. A novel approach is presented in this paper, including both visual and audio from video clips, to recognize the human emotion. The facial animation parameters (FAPs) compliant facial feature tracking based on active appearance model is performed on the video to generate two vector stream which represent the expression feature and the visual speech one. Combined with the visual vectors, the audio vector is extracted in terms of low level features. Then, a tripled hidden Markov model is introduced to perform the recognition which allows the state asynchrony of the audio and visual observation sequences while preserving their natural correlation over time. The experimental results show that this approach outperforms only using visual or audio separately.
All non-trivial stereo problems need model priors to deal with ambiguities and noise perturbations. To meet requirements of increasingly demanding tasks such as modeling for rendering, a proper model prior should impo...
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All non-trivial stereo problems need model priors to deal with ambiguities and noise perturbations. To meet requirements of increasingly demanding tasks such as modeling for rendering, a proper model prior should impose preference on the true scene structure, while avoiding artificial bias such as fronto-parallel. We introduce a geometric model prior based on a novel technique we call kernel correlation. Maximizing kernel correlation is shown to be equal to distance minimization in the M-estimator sense. As a model prior, kernel correlation is demonstrated to have good properties that can result in renderable, very smooth and accurate depth map. The results are evaluated both qualitatively by view synthesis and quantitatively by error analysis.
Object recognition systems aiming to work in real world settings should use multiple cues in order to achieve robustness. We present a new cue integration scheme, which extends the idea of cue accumulation to discrimi...
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Object recognition systems aiming to work in real world settings should use multiple cues in order to achieve robustness. We present a new cue integration scheme, which extends the idea of cue accumulation to discriminative classifiers. We derive and test the scheme for support vector machines (SVMs), but we also show that it is easily extendible to any large margin classifier. In the case of one-class SVMs the scheme can be interpreted as a new class of Mercer kernels for multiple cues. Experimental comparison with a probabilistic accumulation scheme is favorable to our method. Comparison with voting scheme shows that our method may suffer as the number of object classes increases. Based on these results, we propose a recognition algorithm consisting of a decision tree where decisions at each node are taken using our accumulation scheme. Results obtained using this new algorithm compare very favorably to accumulation (both probabilistic and discriminative) and voting scheme.
Underwater imaging is important for scientific research and technology, as well as for popular activities. We present a computervision approach which easily removes degradation effects in underwater vision. We analyz...
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Underwater imaging is important for scientific research and technology, as well as for popular activities. We present a computervision approach which easily removes degradation effects in underwater vision. We analyze the physical effects of visibility degradation. We show that the main degradation effects can be associated with partial polarization of light. We therefore present an algorithm which inverts the image formation process, to recover a good visibility image of the object. The algorithm is based on a couple of images taken through a polarizer at different orientations. As a by product, a distance map of the scene is derived as well. We successfully used our approach when experimenting in the sea using a system we built. We obtained great improvement of scene contrast and color correction, and nearly doubled the underwater visibility range.
We propose a variational algorithm to jointly estimate the shape, albedo, and light configuration of a Lambertian scene from a collection of images taken from different vantage points. Our work can be thought of as ex...
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We propose a variational algorithm to jointly estimate the shape, albedo, and light configuration of a Lambertian scene from a collection of images taken from different vantage points. Our work can be thought of as extending classical multi-view stereo to cases where point correspondence cannot be established, or extending classical shape from shading to the case of multiple views with unknown light sources. We show that a first naive formalization of this problem yields algorithms that are numerically unstable, no matter how close the initialization is to the true geometry. We then propose a computational scheme to overcome this problem, resulting in provably stable algorithms that converge to (local) minima of the cost functional. Although we restrict our attention to Lambertian objects with uniform albedo, extensions of our framework are conceivable.
In this paper, we develop a new video-to-video face recognition algorithm. The major advantage of the video based method is that more information is available in a video sequence than in a single image. In order to ta...
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In this paper, we develop a new video-to-video face recognition algorithm. The major advantage of the video based method is that more information is available in a video sequence than in a single image. In order to take advantage of the large amount of information in the video sequence and at the same time overcome the processing speed and data size problems we develop several new techniques including temporal and spatial frame synchronization and multi-level subspace analysis for video cube processing. The method preserves all the spatial-temporal information contained in a video sequence. Near perfect classification results are obtained on the XM2VTS face video database.
We present results on the latest advances in thermal infrared face recognition, and its use in combination with visible imagery. Previous research by the authors has shown high performance under very controlled condit...
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We present results on the latest advances in thermal infrared face recognition, and its use in combination with visible imagery. Previous research by the authors has shown high performance under very controlled conditions, or questionable performance under a wider range of conditions. This paper shows results on the use of thermal infrared and visible imagery for face recognition in operational scenarios. In particular, we show performance statistics for outdoor face recognition and recognition across multiple sessions. Our results support the conclusion that face recognition performance with thermal infrared imagery is stable over multiple sessions, and that fusion of modalities increases performance. As measured by the number of images and number of subjects, this is the largest ever reported study on thermal face recognition.
The 2004 Workshop on POCV emphasized novel, far-reaching ideas in PO rather than extensive experimental validation. This was intended to stimulate more discussion and debate than is typically feasible at a conference ...
The 2004 Workshop on POCV emphasized novel, far-reaching ideas in PO rather than extensive experimental validation. This was intended to stimulate more discussion and debate than is typically feasible at a conference such as cvpr. We facilitated this goal by emphasizing novelty and relevance in the reviewing process. Circular reviewing, in which authors reviewed papers, also improved review quality. cvpr authors in PO-related areas were encouraged to submit related, but more advanced (and perhaps unproven) research to POCV. Although this was only marginally successful, it was intended to avoid the common situation where the workshop is primarily a forum for papers rejected from cvpr, while cvpr-accepted papers are not represented at the workshop.
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