The (iterative) relaxation algorithms, used in the framework of Markov Random Field-based image analysis may quickly lead to prohibitive computation times on workstations when sophisticated models and real-time applic...
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ISBN:
(纸本)0818658258
The (iterative) relaxation algorithms, used in the framework of Markov Random Field-based image analysis may quickly lead to prohibitive computation times on workstations when sophisticated models and real-time applications have to be handled. Stochastic relaxation algorithms are drastically time consuming while deterministic schemes often get 'stuck' in local minima of the energy function. Besides, it is known that multigrid methods can improve significantly the convergence rate of iterative relaxation schemes. On the other hand, the computations involved by these different algorithms are regular and local, and lead naturally to massive data parallelism which is well suited for parallel processing.on array processor architectures. In this paper, we present a new algorithmic framework which enables making a full use of the large potential of data parallelism available on 2D processor arrays for the implementation of non-linear multigrid relaxation methods. This framework leads to fast convergence towards quasi-optimal solutions. It is demonstrated on two different low-level vision applications.
The image Understanding Environment (IUE) project is a five year program, sponsored by ARPA, to develop a common object-oriented software environment for the development of algorithms and application systems. This pap...
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ISBN:
(纸本)0818658274
The image Understanding Environment (IUE) project is a five year program, sponsored by ARPA, to develop a common object-oriented software environment for the development of algorithms and application systems. This paper reviews the design of this system and provides an overview of the distributed implementation effort currently underway at Amerinex AI, Advanced Decision Systems, Carnegie Mellon University, and Colorado State University. The ultimate goal of the project is to provide a software infrastructure of class hierarchies, user interface tools, and IU algorithms that are required to carry out state of the art research in image understanding.
Color histogram matching has been shown to be a promising way to quickly indexing into a large image database. Yet, few experiments have been done to test the method on truly large databases, and even if they were per...
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ISBN:
(纸本)0818658274
Color histogram matching has been shown to be a promising way to quickly indexing into a large image database. Yet, few experiments have been done to test the method on truly large databases, and even if they were performed, they would give little guidance to a user wondering if the technique would be useful with his or her database. In this paper we define and analyze a measure relevant to extending color histogram indexing to large databases: capacity (how many distinguishable histograms can be stored).
Alterations in the appearance of a 3-D object as it moves from one frame to another was observed by using adaptive adjacency graph (AAG), a network of active contours. Utilization of an aspect prediction graph (APG) e...
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ISBN:
(纸本)0818658274
Alterations in the appearance of a 3-D object as it moves from one frame to another was observed by using adaptive adjacency graph (AAG), a network of active contours. Utilization of an aspect prediction graph (APG) enabled the symbolic tracker to monitor the object from various aspects. The image tracker was employed to predict visual occurrences in the symbolic tracker. Combination of AAG and APG provided tracking of both external and internal discontinuities of a 3-D object. Moreover, prediction in changes of an object enabled monitoring the qualitative object orientation with regards to diverse aspects.
Vista is a software environment supporting the modular implementation and execution of computer vision algorithms. Because it is extensible, portable, and freely available, Vista is an appropriate medium for the excha...
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ISBN:
(纸本)0818658274
Vista is a software environment supporting the modular implementation and execution of computer vision algorithms. Because it is extensible, portable, and freely available, Vista is an appropriate medium for the exchange of standard implementations of algorithms. This paper, an overview of Vista, describes its file format, its data abstraction, its conventions for UNIX filter programs and library routines, and its user interface toolkit. Unlike systems that are designed principally to support imageprocessing. Vista provides for the easy creation and use of arbitrary data types, such as are needed for many areas of computer vision research.
In this paper, we propose a new statistical framework for modeling and extracting 2D moving deformable objects from image sequences. The object representation relies on a hierarchical description of the deformations a...
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ISBN:
(纸本)0818658274
In this paper, we propose a new statistical framework for modeling and extracting 2D moving deformable objects from image sequences. The object representation relies on a hierarchical description of the deformations applied to a template. Global deformations are modeled using a Karhunen Loeve expansion of the distorsions observed on a representative population. Local deformations are modeled by a (first-order) Markov process. The optimal Bayesian estimate of the global and local deformations is obtained by maximizing a non-linear joint probability distribution using stochastic and deterministic optimization techniques. The use of global optimization techniques yields robust and reliable segmentations in adverse situations such as low signal-to-noise ratio, non-Gaussian noise or occlusions. Moreover, no human interaction is required to initialize the model. The approach is demonstrated on synthetic as well as on real-world image sequences showing moving hands with partial occlusions.
We present a framework for image registration algorithms that finds a lowest-order model of the flow between two images. Low-order models are useful in image registration, because they leave scene structure intact. Bu...
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ISBN:
(纸本)0818658274
We present a framework for image registration algorithms that finds a lowest-order model of the flow between two images. Low-order models are useful in image registration, because they leave scene structure intact. But in real images complexity varies, and cannot be determined ahead of time. Algorithms in our framework adapt model complexity to image data during a coarse-fine parameter estimation process. Complexity increases keep residual flow small enough that motion can be correctly estimated at each subsequent resolution level. We present one algorithm within this framework which increases complexity by replacing global estimates with estimates over successively smaller patches. We show results of applying this algorithm to the task of mosaicing panoramic aerial images with unknown lens distortion and unknown camera position.
This paper presents a new representation called 'hierarchical Gabor filters' and associated novel local measures which are used to detect potential objects of interest in images. The 'first stage' of t...
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ISBN:
(纸本)0818658274
This paper presents a new representation called 'hierarchical Gabor filters' and associated novel local measures which are used to detect potential objects of interest in images. The 'first stage' of the approach uses a wavelet set of wide-bandwidth separable Gabor filters to extract local measures from an image. The 'second stage' makes certain spatial groupings explicit by creating small-bandwidth, non-separable Gabor filters that are tuned to elongated contours or periodic patterns. The non-separable filter responses are obtained from a weighted combination of the separable basis filters, which preserves the computational efficiency of separable filters while providing the distinctiveness required to discriminate objects from clutter. This technique is demonstrated on images obtained from a forward looking infrared (FLIR) sensor.
We consider the problem of image segmentation and describe an algorithm that is based on the Minimum Description Length (MDL) principle, is fast, is applicable to multi-band images, and guarantees closed regions. We c...
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ISBN:
(纸本)0818658274
We consider the problem of image segmentation and describe an algorithm that is based on the Minimum Description Length (MDL) principle, is fast, is applicable to multi-band images, and guarantees closed regions. We construct an objective function that, when minimized, yields a partitioning of the image into regions where the pixel values in each band of each region are described by a polynomial surface plus noise. The polynomial orders and their coefficients are determined by the algorithm. The minimization is difficult because (1) it involves a search over a very large space and (2) there is extensive computation required at each stage of the search. To address the first of these problems we use a region-merging minimization algorithm. To address the second we use an incremental polynomial regression that uses computations from the previous stage to compute results in the current stage, resulting in a significant speed up over the non-incremental technique. The segmentation result obtained is suboptimal in general but of high quality. Results on real images are shown.
We propose a geometric smoothing method based on local curvature in shapes and images which is governed by the geometric heat equation and is a special case of the reaction-diffusion framework proposed by [28]. For sh...
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ISBN:
(纸本)0818658274
We propose a geometric smoothing method based on local curvature in shapes and images which is governed by the geometric heat equation and is a special case of the reaction-diffusion framework proposed by [28]. For shapes, the approach is analogous to the classical heat equation smoothing, but with a renormalization by arc-length at each infinitesimal step. For images, the smoothing is similar to anisotropic diffusion in that, since the component of diffusion in the direction of the brightness gradient is nil, edge location and sharpness are left intact. We present several properties of curvature deformation smoothing of shape: it preserves inclusion order, annihilates extrema and inflection points without creating new ones, decreases total curvature, satisfies the semi-group property allowing for local iterative computations, etc. Curvature deformation smoothing of an image is based on viewing it as a collection of iso-intensity level sets, each of which is smoothed by curvature and then reassembled. This is shown to be mathematically sound and applicable to medical, aerial, and range images.
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