Deformable models are central to non-rigid motion analysis, shape matching and non-rigid medical image registration. Spline-based deformations are a very popular class of parameterizations of deformable models and hav...
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ISBN:
(纸本)0769512720
Deformable models are central to non-rigid motion analysis, shape matching and non-rigid medical image registration. Spline-based deformations are a very popular class of parameterizations of deformable models and have been heavily used in multiple domains. In a somewhat separate sub-field, weighted graphs are a frequently used object parameterization. Graph matching using weighted graph object parameterizations finds application in a spectrum ranging from rigid pose estimation to deformable object recognition. Here, we demonstrate a hitherto unsuspected relation,ship between spline-based deformable models and weighted graphs. It turns out that spline parameterizations in the kernel representation can be used to construct equivalent weighted graphs. With this connection established, we envision a cross-fertilization between these two seemingly disparate sub-fields of computer vision.
The theory of designing the optical-electronic imageprocessing.computer systems has been presented. A model of parallel imageprocessing.system has been considered, that is based on the principle of function decompos...
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ISBN:
(纸本)0819440825
The theory of designing the optical-electronic imageprocessing.computer systems has been presented. A model of parallel imageprocessing.system has been considered, that is based on the principle of function decomposition. The implementation possibilities of different imageprocessing.operations with the help of optical and electronic computer means have been analyzed. A structure model of computer system has been examined, that is a conveyor of parallel computer devices. The evaluation of time outlay in the system, while processing.an image or a series of them has been made. The differences of time outlay from conveyor length change and the correlation of optical and electronic devices and processing.time in them have been exposed. The designing method of imageprocessing.systems in static mode has been elaborated. There are presented the results of investigations of the influence of the median square deviation, the influence of time of the processing.in the modules on the throughput capacity of the system under the different electronic and optical modules quantity. According to the results of investigations the recommendations of increasing the system's throughput the capacity are formulated. On the basis to these recommendations, the system design method of imageprocessing.in the dynamic mode is elaborated.
To utilize CT or MRI images for computer aided diagnosis applications, robust features that represent 3-D image data need to be constructed and subsequently used by a classification method. In this paper, we present a...
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To utilize CT or MRI images for computer aided diagnosis applications, robust features that represent 3-D image data need to be constructed and subsequently used by a classification method. In this paper, we present a computer aided diagnosis system for early diagnosis of colon cancer. The system extracts features by a new 3-D patternprocessing.method and processes them using a support vector machine classifier. Our 3-D patternprocessing.method, called Random Orthogonal Shape Section(ROSS) mimics the radiologist's way of viewing these images and combines information from many random triples of mutually orthogonal sections going through the volume. Another contribution of this paper is a new feedback framework between the classification algorithm and the definition of the features. This framework, called Distinctive Component Analysis combines support vector samples with linear discriminant analysis to map the features of clustered support vectors to a lower dimensional space where the two classes of objects of interest are optimally separated so as to obtain better features. We show that the combination of these better features with support vector machines classification provides a good recognition rate.
A neural network based system to identify images transmitted through a Coherent Fiber-optic Bundle (CFB) is presented. patterns are generated in a computer, displayed on a Spatial Light Modulator (SLM), imaged onto th...
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ISBN:
(纸本)0819439835
A neural network based system to identify images transmitted through a Coherent Fiber-optic Bundle (CFB) is presented. patterns are generated in a computer, displayed on a Spatial Light Modulator (SLM), imaged onto the input face of the CFB, and recovered optically by a CCD sensor array for further processing. Input and output optical subsystems were designed and used to that end. The recognition step of the transmitted patterns is made by a powerful, widely-used, neural network simulator running on the control PC. A complete PC-based interface was developed to control the different tasks involved in the system. An optical analysis of the system capabilities was carried out prior to performing the recognition step. Several neural network topologies were tested, and the corresponding numerical results are also presented and discussed.
We present a geometric method for (i) matching 2D line segments from multiple oriented images, (ii) optimally reconstructing 3D line segments and (iii) grouping 3D line segments to corners. The proposed algorithm uses...
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ISBN:
(纸本)0769512720
We present a geometric method for (i) matching 2D line segments from multiple oriented images, (ii) optimally reconstructing 3D line segments and (iii) grouping 3D line segments to corners. The proposed algorithm uses two developments in combining projective geometry and statistics, which are described in this article: (i) the geometric entities points, lines and planes in 2D and 3D and their uncertainty are represented in homogeneous coordinates and new entities may be constructed including their propagated uncertainty. The construction can be performed directly or as an estimation. (ii) relations such as incidence, equality, parallelity and orthogonality between points, lines and planes can be tested statistically based on a given significance level. Using these tools, the resulting algorithm is straightforward and gives reasonable results. It is only based on geometric information and does not use any image intensities, though it can be extended to use other information. The matching of 3D lines does not need any thresholds other than a significance value for the hypotheses tests.
We present a method to extract relative depth information from an uncalibrated monocular video sequence. Our method detects occlusions caused by an object moving in a static scene to infer relative depth relationships...
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ISBN:
(纸本)0769512720
We present a method to extract relative depth information from an uncalibrated monocular video sequence. Our method detects occlusions caused by an object moving in a static scene to infer relative depth relationships between scene parts. Our approach does not rely on any strong assumptions about the object or the scene to aid in this segmentation into layers. In general, the problem of building relative depth relationships from occlusion events is under-constrained, even in the absence of observation noise. A minimum description length algorithm is used to reliably calculate, layer opacities and their depth relationships in the absence of hard constraints. Our approach extends previously published approaches that are restricted to work with a certain type of moving object or require strong image edges to allow for an a-priori segmentation of the scene. We also discuss ideas on how to extend our algorithm to make use of a richer set of observations.
In this paper, we investigate the problem of the automatic creation of 3D models of man-made environments that we represent as collections of textured planes. A typical approach is to automatically compute a sparse fe...
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ISBN:
(纸本)0769512720
In this paper, we investigate the problem of the automatic creation of 3D models of man-made environments that we represent as collections of textured planes. A typical approach is to automatically compute a sparse feature reconstruction and to manually give their plane-memberships as well as the delineation of the planes. Textures are then extracted from the images while optimizing the model, typically the disparity between marked and predicted edges. We propose a means to automatically estimate the model of the scene, in terms of the number of planes and their parameters from a point feature reconstruction. The method is based on random sampling of reconstructed points to generate plane hypotheses. Each of these is then evaluated using a measure of approximate photoconsistency while recovering the corresponding plane delineation. We then compute the maximum likelihood estimate of all scene parameters, i.e. the set of planes and reconstructed points as well as relative camera pose, with respect to actual images. The approach is validated on simulated data and real images.
We present a framework for articulated body model acquisition and tracking from voxel data. A 3D voxel reconstruction of the person's body is computed from silhouettes extracted from four cameras. The model acquis...
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ISBN:
(纸本)0769512720
We present a framework for articulated body model acquisition and tracking from voxel data. A 3D voxel reconstruction of the person's body is computed from silhouettes extracted from four cameras. The model acquisition process is fully automated. In the first frame, body parts are located sequentially. The head,is located first, since its shape and size are unique and stable. Other parts are found by sequential template growing and fitting. This initial estimate of body part locations, sizes and orientations is then used as a measurement for the extended Kalman filter which ensures a valid articulated body model. The same filter, with a slightly modified state and state transition matrix, is then used for tracking. The performance of the system has been evaluated on several video sequences with promising results.
Integration of multiple range images is important to make use of 3D data acquired from stereo systems, laser range finders, etc. We propose a new range image integration method based on volumetric representation. Unli...
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ISBN:
(纸本)0769512720
Integration of multiple range images is important to make use of 3D data acquired from stereo systems, laser range finders, etc. We propose a new range image integration method based on volumetric representation. Unlike other volume-based integration methods, we adoptively subdivide voxels depending on the curvature of the surface to be reconstructed, providing efficient representation of the underlying geometry and efficient use of computational resources. In our range image merging framework, additional attributes, e.g., color, laser reflectance power, etc., can be taken into account as well as 3D geometric information. This ability allows us to generate 3D models preserving sharp edges around texture boundaries, thereby providing a good basis for efficient rendering and texture mapping. The overall framework is designed to be robust against noise, taking consensus carefully in both geometry and color, which could be suitable for 3D model reconstruction from noisy stereo images. In this paper, we describe the system, and present several results of. applying our framework to real data We also present some other future applications based on our framework.
images of outdoor scenes captured in bad weather suffer from poor contrast. Under bad weather conditions, the light reaching a camera is severely scattered by the atmosphere. The resulting decay in contrast varies acr...
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images of outdoor scenes captured in bad weather suffer from poor contrast. Under bad weather conditions, the light reaching a camera is severely scattered by the atmosphere. The resulting decay in contrast varies across the scene and is exponential in the depths of scene points. Therefore, traditional space invariant imageprocessing.techniques are not sufficient to remove weather effects from images. In this paper, we present a fast physics-based method to compute scene structure and hence restore contrast of the scene from two or more images taken in bad weather. In contrast to previous techniques, our method does not require any a priori weather-specific or scene information, and is effective under a wide range of weather conditions including haze, mist, fog and other aerosols. Further, our method can be applied to gray-scale, RGB color, multi-spectral and even IR images. We also extend the technique to restore contrast of scenes with moving objects, captured using a video camera.
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