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.
This paper describes the compilation of high-level language programs written in a single-assignment language called SA-C into the binary codes used for programming reconfigurable hardware. The primary application doma...
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This paper describes the compilation of high-level language programs written in a single-assignment language called SA-C into the binary codes used for programming reconfigurable hardware. The primary application domain is imageprocessing. The paper describes the SA-C language, the compiler and the optimizations it performs, the process of converting the intermediate form called dataflow graphs into VHDL, and the generation of hardware configuration codes. Performance data on a typical imageprocessing.program, written in SA-C and executed on a reconfigurable computing system, is presented and compared to a hand-written VHDL version and a C version running on conventional processors.
imageprocessing.in general and optical imageprocessing.in particular require very accurate and very complex processors. Such processors are sometimes difficult to manufacture and expensive to purchase. They also mig...
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ISBN:
(纸本)0819437328
imageprocessing.in general and optical imageprocessing.in particular require very accurate and very complex processors. Such processors are sometimes difficult to manufacture and expensive to purchase. They also might be non flexible in their design. The principle of generating a single processor by use of several simpler processor-modules in cascade (and / or in parallel) is quite familiar. However, in optics this approach is mainly used for filtering in the Fourier or fractional Fourier planes. In this work the authors introduce multi-stage optical processing.in the Fresnel plane. Using a small number of binary masks (either amplitude or phase) along the path of the light, one may process the incoming beam in the same manner as using a single high-resolution complex mask. The authors present an algorithm for establishing the binary processors and introduce the results obtained by this approach. An important application of this technique is the field of imagerecognition. Simulations demonstrate that minor manipulations on the input, affect the output plane significantly. On the other hand, hiding fractions of the input pattern hardly influence the output whereas the obtained effect reveals information regarding the flaw inserted within the input pattern.
Similarity between images in image retrieval is measured by computing distances between feature vectors. This paper presents a probabilistic approach and describes two likelihood-based similarity measures for image re...
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Similarity between images in image retrieval is measured by computing distances between feature vectors. This paper presents a probabilistic approach and describes two likelihood-based similarity measures for image retrieval. Popular distance measures like the Euclidean distance implicitly assign more weighting to features with large ranges than those with small ranges. First, we discuss the effects of five feature normalization methods on retrieval performance. Then, we show that the probabilistic methods perform significantly better than geometric approaches like the nearest neighbor rule with city-block or Euclidean distances. They are also more robust to normalization effects and using better models for the features improves the retrieval results compared to making only general assumptions. Experiments on a database of approximately 10,000 images show that studying the feature distributions are important and this information should be used in designing feature normalization methods and similarity measures.
A method for reliably detecting change in the 3-D shape of objects that are well-modeled as single-value functions z = f(x,y) is presented. It uses an estimate of the accuracy of the 3-D models derived from a set of i...
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A method for reliably detecting change in the 3-D shape of objects that are well-modeled as single-value functions z = f(x,y) is presented. It uses an estimate of the accuracy of the 3-D models derived from a set of images taken simultaneously. This accuracy estimate is used to distinguish between significant and insignificant changes in 3-D models derived from different image sets. The accuracy of the 3-D model is estimated using a general methodology, called self-consistency, for estimating the accuracy of computer vision algorithms, which does not require prior establishment of 'ground truth'. A novel image-matching measure based on Minimum Description Length (MDL) theory allows us to estimate the accuracy of individual elements of the 3-D model. Experiments to demonstrate the utility of the procedure are presented.
This paper exploits physical models of time-varying brightness in image sequences to estimate optical flow and physical parameters of the scene. Previous approaches handled violations of brightness constancy with the ...
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This paper exploits physical models of time-varying brightness in image sequences to estimate optical flow and physical parameters of the scene. Previous approaches handled violations of brightness constancy with the use of robust statistics or with generalized brightness constancy constraints that allow generic types of contrast and illumination changes. Here, we consider models of brightness variation that have time-dependent physical causes, namely, changing surface orientation with respect to a directional illuminant, motion of the illuminant, and physical models of heat transport in infrared images. We simultaneously estimate the optical flow and the relevant physical parameters. The estimation problem is formulated using total least squares (TLS), with confidence bounds on the parameters.
This paper presents an approach for establishing correspondences in time and in space between two different video sequences of the same dynamic scene, recorded by stationary uncalibrated video cameras. The method simu...
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This paper presents an approach for establishing correspondences in time and in space between two different video sequences of the same dynamic scene, recorded by stationary uncalibrated video cameras. The method simultaneously estimates both spatial alignment as well as temporal synchronization (temporal alignment) between the two sequences, using all available spatio-temporal information. Temporal variations between image frames (such as moving objects or changes in scene illumination) are powerful cues for alignment, which cannot be exploited by standard image-to-image alignment techniques. We show that by folding spatial and temporal cues into a single alignment framework, situations which are inherently ambiguous for traditional image-to-image alignment methods, are often uniquely resolved by sequence-to-sequence alignment. We also present a 'direct' method for sequence-to-sequence alignment. The algorithm simultaneously estimates spatial and temporal alignment parameters directly from measurable sequence quantities, without requiring prior estimation of point correspondences, frame correspondences, or moving object detection. Results are shown on real image sequences taken by multiple video cameras.
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