A pictorial structure is a collection of parts arranged in a deformable configuration. Each part is represented using a simple appearance model and the deformable configuration is represented by spring-like connection...
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A pictorial structure is a collection of parts arranged in a deformable configuration. Each part is represented using a simple appearance model and the deformable configuration is represented by spring-like connections between pairs of parts. While pictorial structures were introduced a number of years ago, they have not been broadly applied to matching and recognition problems. This has been due in part to the computational difficulty of matching pictorial structures to images. In this paper we present an efficient algorithm for finding the best global match of a pictorial structure to an image. With this improved algorithm, pictorial structures provide a practical and powerful framework for qualitative descriptions of objects and scenes, and are suitable for many generic imagerecognition problems. We illustrate the approach using simple models of a person and a car.
Binocular half-occlusion points are those that are visible in one of the two views provided by a binocular imaging system. Due to their importance in binocular matching as well as subsequent interpretation tasks, a nu...
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Binocular half-occlusion points are those that are visible in one of the two views provided by a binocular imaging system. Due to their importance in binocular matching as well as subsequent interpretation tasks, a number of approaches have been developed for dealing with such points. In the current paper, we consider four methods that explicitly detect half-occlusions and report on a more uniform comparison than has previously been performed. Taking a disparity image and its associated match goodness image as input, we generate images that show the half-occluded points in the underlying scene. We quantitatively and qualitatively compare these methods under a variety of conditions.
Multi-resolution techniques have been used in a wide range of vision applications. Unfortunately, the costly operation of building a proper pyramid strongly reduces its value as a tool for reducing computational cost....
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Multi-resolution techniques have been used in a wide range of vision applications. Unfortunately, the costly operation of building a proper pyramid strongly reduces its value as a tool for reducing computational cost. A new approach, physical panoramic pyramid, is introduced in this paper. Physical panoramic pyramid measures multiple resolutions simultaneously resulting in multi-resolution panoramic images. No computation is needed to construct these image pyramids. We also analyze general noise sensitivity in image pyramids, including the interaction of the loss of resolution, random background noise and aliasing noise. The paper also discusses the issue of indexing between the neighboring layer, the viewpoint variation and the applications of the physical panoramic pyramid.
A novel method of incorporating shape information into the image segmentation process is presented. We introduce a representation for deformable shapes and define a probability distribution over the variances of a set...
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A novel method of incorporating shape information into the image segmentation process is presented. We introduce a representation for deformable shapes and define a probability distribution over the variances of a set of training shapes. The segmentation process embeds an initial curve as the zero level set of a higher dimensional surface, and evolves the surface such that the zero level set converges on the boundary of the object to be segmented. At each step of the surface evolution, we estimate the maximum a posteriori (MAP) position and shape of the object in the image, based on the prior shape information and the image information. We then evolve the surface globally, towards the MAP estimate, and locally, based on image gradients and curvature. Results are demonstrated on synthetic data and medical imagery, in 2D and 3D.
Surveillance imaging applications on small autonomous imaging platforms present challenges of highly constrained power supply and form factor, with potentially demanding specifications for target detection and recogni...
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ISBN:
(纸本)0819437670
Surveillance imaging applications on small autonomous imaging platforms present challenges of highly constrained power supply and form factor, with potentially demanding specifications for target detection and recognition. Absent of significant advances in imageprocessing.hardware, such power and space restrictions can imply severely limited computational capabilities. This holds especially for compute-intensive algorithms with high-precision fixed- or floating-point operations in deep pipelines that process large data streams. Such algorithms tend not to be amenable to small or simplified architectures involving (for example) reduced precision, reconfigurable logic, low-power gates, or energy recycling schemes. In this series of two papers, a technique of reduced-power computing called compressive processing.(CXP) is presented and applied to several low- and mid-level computer vision operations. CXP computes over compressed data without resorting to intermediate decompression steps. As a result of fewer data due to compression, fewer operations are required by CXP than are required by computing over the corresponding uncompressed image. In several cases, CXP techniques yield speedups on the order of the compression ratio. Where lossy high-compression transforms are employed, it is often possible to use approximations to derive CXP operations to yield increased computational efficiency via a simplified mix of operations. The reduced work requirement, which follows directly from the presence of fewer data, also implies a reduced power requirement, especially if simpler operations are involved in compressive versus noncompressive operations. Several imageprocessing.algorithms (edge detection, morphological operations, and component labelling) are analyzed in the context of three compression transforms: vector quantization (VQ), visual patternimage coding (VPIC), and EBLAST. The latter is a lossy high-compression transformation developed for underwater communication
In this paper, we present a novel approach for frontal face detection in gray-scale images. We represent both faces and clutter by using two-dimensional wavelet decomposition. To characterize the statistical dependenc...
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In this paper, we present a novel approach for frontal face detection in gray-scale images. We represent both faces and clutter by using two-dimensional wavelet decomposition. To characterize the statistical dependency between different levels of wavelet, we introduce a Hidden Markov Model (HMM), in which a number of discrete states at each level capture the diversity of faces as well as clutter. Our experiments indicate that the proposed algorithm outperforms conventional template-based methods such as matched filter and eigenface methods.
In this paper, we present a four-step technique for simultaneously estimating a human's anthropometric measurements (up to a scale parameter) and pose from a single image. The user initially selects a set of image...
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In this paper, we present a four-step technique for simultaneously estimating a human's anthropometric measurements (up to a scale parameter) and pose from a single image. The user initially selects a set of image points that constitute the projection of selected landmarks. Using this information, along with a priori statistical information about the human body, a set of plausible segment length estimates are generated. The third step produces a set of plausible poses based on joint limit constraints using a geometric method. In the fourth step, pose and anthropometric measurements are obtained by minimizing an appropriate cost function subject to the associated constraints. The novelty of our approach is the use of anthropometric statistics to constrain the estimation process that allows the simultaneous estimation of both anthropometry and pose. We demonstrate the accuracy, advantages and limitations of our method for various classes of both synthetic and real input data.
This paper describes a new method for determining correspondence between points on pairs of surfaces based on shape using a combination of geodesic distance and surface curvature. An initial sparse set of correspondin...
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This paper describes a new method for determining correspondence between points on pairs of surfaces based on shape using a combination of geodesic distance and surface curvature. An initial sparse set of corresponding points are generated using a shape-based matching procedure. Geodesic interpolation is employed in order to capture the complex surface. In addition, surface correspondence and triangulation are computed simultaneously in a hierarchical way. Results applied to human cerebral cortical surfaces are shown to evaluate the approach.
Object oriented representation of image sequences requires accurate motion segmentation and depth ordering techniques. Unfortunately, the lack of precise motion estimates at the object boundaries makes these two tasks...
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Object oriented representation of image sequences requires accurate motion segmentation and depth ordering techniques. Unfortunately, the lack of precise motion estimates at the object boundaries makes these two tasks very difficult. In this paper we present a detailed analysis of the behaviour of dense motion estimation techniques at object boundaries which reveals the systematic nature of the motion estimation error: the motion of the occluding surface is observed in a small neighbourhood on the occluded side. We then show how the joint use of still image segmentation and robust regression can eliminate this error. Furthermore we present a novel technique which uses the position of the error as a depth cue. The validity of this technique, which requires only sub-pixel motion and which is capable of distinguishing between different types of intensity discontinuities, such as object boundaries, surface marks and illumination discontinuities, is then demonstrated on several synthetic and real image sequences.
An algorithm is given for computing projective structure from a set of six points seen in a sequence of many images. The method is based on the notion of duality between cameras and points first pointed out by Carlsso...
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An algorithm is given for computing projective structure from a set of six points seen in a sequence of many images. The method is based on the notion of duality between cameras and points first pointed out by Carlsson and Weinshall. The current implementation avoids the weakness inherent in previous implementations of this method in which numerical accuracy is compromised by the distortion of image point error distributions under projective transformation. It is shown in this paper that one may compute the dual fundamental matrix by minimizing a cost function giving a first-order approximation to geometric distance error in the original untransformed image measurements. This is done by a modification of a standard near-optimal method for computing the fundamental matrix. Subsequently, the error measurements are adjusted optimally to conform with exact imaging geometry by application of the triangulation method of Hartley-Sturm.
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