Many vision applications require precise measurement of scene radiance. The function relating scene radiance to image brightness is called the camera response. We analyze the properties that all camera responses share...
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Many vision applications require precise measurement of scene radiance. The function relating scene radiance to image brightness is called the camera response. We analyze the properties that all camera responses share. This allows us to find the constraints that any response function must satisfy. These constraints determine the theoretical space of all possible camera responses. We have collected a diverse database of real-world camera response functions (DoRF). Using this database we show that real-world responses occupy a small part of the theoretical space of all possible responses. We combine the constraints from our theoretical space with the data from DoRF to create a low-parameter Empirical Model of Response (EMoR). This response model allows us to accurately interpolate the complete response function of a camera from a small number of measurements obtained using a standard chart. We also show that the model can be used to accurately estimate the camera response from images of an arbitrary scene taken using different exposures. The DoRF database and the EMoR model can be downloaded at http://***/CAVE.
A "graphics for vision" approach is proposed to address the problem of reconstruction from a large and imperfect data set: reconstruction on demand by tensor voting, or ROD-TV. ROD-TV simultaneously delivers...
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A "graphics for vision" approach is proposed to address the problem of reconstruction from a large and imperfect data set: reconstruction on demand by tensor voting, or ROD-TV. ROD-TV simultaneously delivers good efficiency and robustness, by adapting to a continuum of primitive connectivity, view dependence, and levels of detail (LOD). Locally inferred surface elements are robust to noise and better capture local shapes. By inferring per-vertex normals at sub-voxel precision on the fly, we can achieve interpolative shading. Since these missing details can be recovered at the current level of detail, our result is not upper bounded by the scanning resolution. By relaxing the mesh connectivity requirement, we extend ROD-TV and propose a simple but effective multiscale feature extraction algorithm. ROD-TV consists of a hierarchical data structure that encodes different levels of detail. The local reconstruction algorithm is tensor voting. It is applied on demand to the visible subset of data at a desired level of detail, by traversing the data hierarchy and collecting tensorial support in a neighborhood. We compare our approach and present encouraging results.
Recent advancement in 3D digitization techniques have prompted to the need for 3D object retrieval. Our method of comparing 3D objects for retrieval is based on 3D morphing. It computes, for each 3D object, two spatia...
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Recent advancement in 3D digitization techniques have prompted to the need for 3D object retrieval. Our method of comparing 3D objects for retrieval is based on 3D morphing. It computes, for each 3D object, two spatial feature maps that describe the geometry and topology of the surface patches on the object, while preserving the spatial information of the patches in the maps. The feature maps capture the amount of effort required to morph a 3D object into a canonical sphere, without performing explicit 3D morphing. Fourier transforms of the feature maps are used for object comparison so as to achieve invariant retrieval under arbitrary rotation, reflection, and non-uniform scaling of the objects. Experimental results show that our method of retrieving 3D models is very accurate, achieving a precision of above 0.86 even at a recall rate of 1.0.
Principal Component Analysis (PCA) is a well-established tech nique in imageprocessing.and patternrecognition. Incremental PCA and robust PCA are two interesting problems with numerous potential applications. Howeve...
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Principal Component Analysis (PCA) is a well-established tech nique in imageprocessing.and patternrecognition. Incremental PCA and robust PCA are two interesting problems with numerous potential applications. However, these two issues have only been separately addressed in the previous studies. In this paper, we present a novel algorithm for incremental and robust PCA by seamlessly integrating the two issues together. The proposed algorithm has the advantages of both incremental PCA and robust PCA. Moreover, unlike most M-estimation based robust algorithms, it is computational efficient. Experimental results on dynamic background modelling are provided to show the performance of the algorithm with a comparison to the conventional batch-mode and non-robust algorithms.
We consider the average outward flux through a Jordan curve of the gradient vector field of the Euclidean distance function to the boundary of a 2D shape. Using an alternate form of the divergence theorem, we show tha...
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We consider the average outward flux through a Jordan curve of the gradient vector field of the Euclidean distance function to the boundary of a 2D shape. Using an alternate form of the divergence theorem, we show that in the limit as the area of the region enclosed by such a curve shrinks to zero, this measure has very difference behaviours at medial points than at non-medial ones, providing a theoretical justification for its use in the Hamilton-Jacobi skeletonization algorithm of [7]. We then specialize to the case of shrinking circular neighborhoods and show that the average outward flux measure also reveals the object angle at skeletal points. Hence, formulate for obtaining the boundary curves, their curvatures, and other geometric quantities of interest, can be written in terms of the average outward flux limit values at skeletal points. Thus this measure can be viewed as a Euclidean invariant for shape description: it can be used to both detect the skeleton from the Euclidean distance function, as well as to explicitly reconstruct the boundary from it. We illustrate our results with several numerical simulations.
This paper proposes a non-contact type method for estimating human body postures. One of the major problems on the posture estimation using the silhouette image analysis is the overlapping of the body parts' silho...
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The paper focuses on a hidden control neural network (HCNN) based ANN/HMM hybrid approach which handles simultaneously both the global pattern class variation and the local signal primitive variation. HMM is used at t...
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By mapping a set of input images to points in a low-dimensional manifold or subspace, it is possible to efficiently account for a small number of degrees of freedom. For example, images of a person walking can be mapp...
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By mapping a set of input images to points in a low-dimensional manifold or subspace, it is possible to efficiently account for a small number of degrees of freedom. For example, images of a person walking can be mapped to a 1-dimensional manifold that measures the phase of the person's gait. However, when the object is moving around the frame and being occluded by other objects, standard manifold modeling techniques (e.g., principal components analysis, factor analysis, locally linear embedding) try to account for global motion and occlusion. We show how factor analysis can be incorporated into a generative model of layered, 2.5-dimensional vision, to jointly locate objects, resolve occlusion ambiguities, and learn models of the appearance manifolds of objects. We demonstrate the algorithm on a video consisting of four occluding objects, two of which are people who are walking, and occlude each other for most of the duration of the video. Whereas standard manifold modeling techniques fail to extract information about the gaits, the layered model successfully extracts a periodic representation of the gait of each person.
We study the dynamic stereo problem, i.e. to recover the shape of dynamic scene from multiple synchronized image sequences. To incorporate both spatial and temporal information for depth recovery, we propose a statist...
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We study the dynamic stereo problem, i.e. to recover the shape of dynamic scene from multiple synchronized image sequences. To incorporate both spatial and temporal information for depth recovery, we propose a statistical framework that uses pixel process model to encode temporal coherence, and Markov Random Fields (MRFs) for spatial coherence. In this framework, the dynamic depth recovery problem is finally formulated as an optimization problem, and is optimized by using the belief propagation algorithm. Experimental results with the real dynamic scenes illustrate our method's ability of robust shape recovery.
This paper addresses the problem of calibrating camera lens distortion, which can be significant in medium to wide angle lenses. While almost all existing nonmetric distortion calibration methods need user involvement...
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