A Bayesian framework for deformable pattern classification has been proposed in [1] with promising results for isolated handwritten character recognition. Its performance, however, degrades significantly when it is ap...
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A Bayesian framework for deformable pattern classification has been proposed in [1] with promising results for isolated handwritten character recognition. Its performance, however, degrades significantly when it is applied to detect deformable patterns in complex scenes, where the amount of outliers due to other neighboring objects or the background is usually large. Also, the fact that the associated evidence measure does not penalize models resting on white space results in a high false alarm rate. In this paper, another Bayesian framework for deformable pattern detection is proposed. the framework possesses the intrinsic property of matching with only part of an image (segmentation) and its associated evidence measure can penalize white space implicitly. However, limited data exploration capability is the major trade-off. By properly combining the two frameworks, a new matching algorithm called bidirectional matching is proposed. this combined approach possesses the advantages of the two frameworks and gives robust results for non-rigid shape extraction. To evaluate the performance of the proposed approach, we have applied it to shape-based handwritten word retrieval. Using a subset of the bb dataset in the CEDAR database, we can achieve a recall rate of 59% and a precision rate of 43%.
the needs for accurate and efficient object localization prevail in many industrial applications, such as automated visual inspection and factory automation. Image reference approach is very popular in automatic visua...
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the needs for accurate and efficient object localization prevail in many industrial applications, such as automated visual inspection and factory automation. Image reference approach is very popular in automatic visual inspection due to its general applicability to a variety of inspection tasks. However, it requires very precise alignment of the inspection pattern in the image. To achieve very precise pattern alignment, traditional template matching is extremely time-consuming when the search space is large. In this paper, we present a new FLASH (Fast Localization with Advanced Search Hierarchy) algorithm for fast and accurate object localization in a large search space. this object localization algorithm is very useful for applications in automated visual inspection and pick-and-place systems for automatic factory assembly. It is based on the assumption that the surrounding regions of the pattern within the search range are always fixed, which is valid for most industrial inspection applications. the FLASH algorithm comprises a hierarchical nearest-neighbor search algorithm and an optical-flow based energy minimization algorithm. the hierarchical nearest-neighbor search algorithm produces a rough estimate of the transformation parameters for the initial guess of the iterative optical-flow based energy minimization algorithm, which provides very accurate estimation results and associated confidence measures. Experimental results demonstrate the accuracy and efficiency of the proposed FLASH algorithm.
Current systems for object detection in video sequences rely on explicit dynamical models like Kalman filters or hidden Markov models. there is significant overhead needed in the development of such systems as well as...
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Current systems for object detection in video sequences rely on explicit dynamical models like Kalman filters or hidden Markov models. there is significant overhead needed in the development of such systems as well as the a priori assumption that the object dynamics can be described with such a dynamical model. this paper describes a new pattern classification technique for object detection in video sequences that uses a rich, overcomplete dictionary of wavelet features to describe an object class. Unlike previous work where a small subset of features was selected from the dictionary, this system does no feature selection and learns the model in the full 1,326 dimensional feature space. Comparisons using different sized sets of several types of features are given. We extend this representation into the time domain without assuming any explicit model of dynamics. this data driven approach produces a model of the physical structure and short-time dynamical characteristics of people from a training set of examples;no assumptions are made about the motion of people, just that short sequences characterize their dynamics sufficiently for the purposes of detection. One of the main benefits of this approach is that transient false positives are reduced. this technique compares favorably withthe static detection approach and could be applied to other object classes. We also present a real-time version of one of our static people detection systems.
We study the recognition of surfaces made from different materials such as concrete, rug, marble or leather on the basis of their textural appearance. Such natural textures arise from spatial variation of two surface ...
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We study the recognition of surfaces made from different materials such as concrete, rug, marble or leather on the basis of their textural appearance. Such natural textures arise from spatial variation of two surface attributes: (1) reflectance and (2) surface normal. In this paper, we provide a unified model to address boththese aspects of natural texture. the main idea is to construct a vocabulary of prototype tiny surface patches with associated local geometric and photometric properties. We call these 3D textons. Examples might be ridges, grooves, spots or stripes or combinations thereof. Associated with each texton is an appearance vector, which characterizes the local irradiance distribution, represented as a set of linear Gaussian derivative filter outputs, under different lighting and viewing conditions. Given a large collection of images of different materials, a clustering approach is used to acquire a small (on the order of 100) 3D texton vocabulary. Given a few (1 to 4) images of any material, it can be characterized using these textons. We demonstrate the application of this representation for recognition of the material viewed under novel lighting and viewing conditions.
While an exact definition of texture is somewhat elusive, texture can be qualitatively described as a distribution of color, albedo or local normal on a surface. In the literature, the word texture is often used to de...
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While an exact definition of texture is somewhat elusive, texture can be qualitatively described as a distribution of color, albedo or local normal on a surface. In the literature, the word texture is often used to describe a color or albedo variation on a smooth surface. We refer to such texture as 2D texture. In real world scenes, texture is often due to surface height variations and can be termed 3D texture. Because of local foreshortening and masking, oblique views of 3D texture are not simple transformations of the frontal view. Consequently, texture representations such as the correlation function or power spectrum are also affected by local foreshortening and masking. this work presents a correlation model for a particular class of 3D textures. the model characterizes the spatial relationship among neighboring pixels in an image of 3D texture and the change of this spatial relationship with viewing direction.
In this paper we address the problem of global registration between multiple d-dimens-ional point patterns with a given correspondence. the actual overlapping is not necessarily between pairs. Instead, it can be betwe...
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In this paper we address the problem of global registration between multiple d-dimens-ional point patterns with a given correspondence. the actual overlapping is not necessarily between pairs. Instead, it can be between any number of patterns. It is assumed that each pattern is a portion of an image of an unobserved object under a distinct rigid transformation. We derive an iterative solution for the problem of global registration of the patterns in order to reconstruct the original object. Our solution is based on the EM algorithm and it generalizes the well known solutions for the two-pattern case. We also suggest a very efficient method to implement the proposed algorithm. Experimental results demonstrate the improved performance of the proposed method.
Recent developments in computervision and patternrecognition have enabled the development of sophisticated vision-based quality control systems for automatic inspection. In this paper we present a new geometrical an...
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Recent developments in computervision and patternrecognition have enabled the development of sophisticated vision-based quality control systems for automatic inspection. In this paper we present a new geometrical analysis of rigid body transformation parameters from properties of reflected correspondence vectors. Based on this analysis, we propose a novel algorithm to calibrate transformation parameters of 3D objects in a fast production line of filter components. the algorithm performs a rigidity analysis from range images acquired from two cameras, making full use of distance and angle information providing a closed form solution to all parameters of interest. the method is used in conjunction with a decision support system where components falling outside specified thresholds are to be rejected. For a comparative study of algorithm performance, we also implemented a well known procedure for rigidity analysis based on quaternions. Experimental results demonstrate that our novel algorithm has a number of advantages over the quaternion method and that its performance is superior or similar to the quaternion method.
through the use of the retino-cortical transform and multiresolution search, a complete working iconic recognition system have been demonstrated. the induction-based system was shown to provide a means for constructin...
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through the use of the retino-cortical transform and multiresolution search, a complete working iconic recognition system have been demonstrated. the induction-based system was shown to provide a means for constructing icon databases without recourse to manual intervention. the icon tree algorithm used gave very reasonable recognition performance when applied to training and test images of real objects. the approach has improved search efficiency over brute force linear search of the training examples through in-class clustering. Applications of the system in areas of way-point finding for robot vision, security/surveillance, document image processing, image databases and aerial image interpretation were also discussed.
this article describes the use of gesture recognition techniques in computervision as a natural interface for video content navigation, and the design of a navigation and browsing system that caters to these natural ...
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this article describes the use of gesture recognition techniques in computervision as a natural interface for video content navigation, and the design of a navigation and browsing system that caters to these natural means of computer-human interaction. For consumer applications, video content navigation presents two challenges: (1) how to parse and summarize multiple video streams in an intuitive and efficient manner, and (2) what type of interface will enhance the ease of use for video browsing and navigation in a living room setting or an interactive environment. In this paper, we address the issues and propose the techniques that combine video content navigation with gestures, seamlessly and intuitively, in an integrated system. the current framework can incorporate speech recognition technology. We present a new type of browser for browsing and navigating video content, as well as a gesture recognition interface for this browser.
A pattern locating tool, called SmARTTM (Smart Alignment and Registration Tool) Search, has been developed for the accurate and precise location of patterns despite normal process variations. SmART Search features Tra...
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A pattern locating tool, called SmARTTM (Smart Alignment and Registration Tool) Search, has been developed for the accurate and precise location of patterns despite normal process variations. SmART Search features Training WizardTM, a time-saving utility that takes the guesswork and uncertainty of the pattern training process. With state-of-the-art GeoSearch-assist, SmART Search enables manufacturers of vision-automated equipment to build more robust machines that will automatically adapt to changes in object appearance due to normal process variations.
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