This paper presents a prediction-and-verification segmentation scheme using attention images from multiple fixations. A major advantage of this scheme is that it can handle a large number of different deformable objec...
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ISBN:
(纸本)0818672587
This paper presents a prediction-and-verification segmentation scheme using attention images from multiple fixations. A major advantage of this scheme is that it can handle a large number of different deformable objects presented in complex backgrounds. The scheme is also relatively efficient since the segmentation is guided by the past knowledge through a prediction-and-verification scheme. The system has been tested to segment hands in the sequences of intensity images, where each sequence represents a hand sign. The experimental result showed a 95% correct segmentation rate with a 3% false rejection rate.
We present a surface radiance model for diffuse lighting that incorporates shadows, interreflections, and surface orientation. We show that, for smooth surfaces, the model is an excellent approximation of the radiosit...
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ISBN:
(纸本)0818672587
We present a surface radiance model for diffuse lighting that incorporates shadows, interreflections, and surface orientation. We show that, for smooth surfaces, the model is an excellent approximation of the radiosity equation. We present a new data structure and algorithm that uses this model to compute shape-from-shading under diffuse lighting. The algorithm was tested on both synthetic and real images, and performs more accurately than the only previous algorithm for this problem. Various causes of error are discussed, including approximation errors in image modelling, poor local constraints at the image boundary, and ill-conditioning of the problem itself.
The purpose of this study is not only to recognize some kind of facial expressions which is associated with human emotion but also to estimate its degree. Our method is based on the idea that facial expression recogni...
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ISBN:
(纸本)0780342364
The purpose of this study is not only to recognize some kind of facial expressions which is associated with human emotion but also to estimate its degree. Our method is based on the idea that facial expression recognition can be achieved by extracting a variation from expressionless face with considering face area as a whole pattern. For the purpose of extracting subtle changes in the face such as the degree of expressions, it is necessary to eliminate the individuality appearing in the facial image. Using a elastic net model, a variation of facial expression is represented as motion vectors of the deformed Net from a facial edge image. Then, applying K-L expansion, the change of facial expression represented as the motion vectors of nodes is mapped into low dimensional eigen space, and estimation is achieved by projecting input images on to the Emotion Space. In this paper we have constructed three kinds of expression models: happiness, anger, surprise, curd experimental results are evaluated.
A new patternrecognition approach to face recognition is presented that can deal with drastic differences in the appearance of a face. Given a pair of sample sets of facial images with potential correspondences, each...
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ISBN:
(纸本)0769506623
A new patternrecognition approach to face recognition is presented that can deal with drastic differences in the appearance of a face. Given a pair of sample sets of facial images with potential correspondences, each being drawn from a distinctive distribution, the algorithm reliably finds correspondences over those different distributions. Unlike the traditional approaches that model the face images as having a consistent distribution and so use the same feature extraction function for both of the image sets, the new method applies to each sample a function specific to the distribution from which ii is drawn. This function is derived by maximizing a newly defined class-separability criterion over the different distributions. Results efface recognition are presented on images including drivers' license pictures. Drastic improvements are shown over algorithms based on the traditional Fisher's discriminant analysis.
In order to reduce false alarms and to improve the target detection performance of an automatic target detection and recognition system operating in a cluttered environment, it is important to develop the models not o...
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ISBN:
(纸本)0818672587
In order to reduce false alarms and to improve the target detection performance of an automatic target detection and recognition system operating in a cluttered environment, it is important to develop the models not only for man-made targets but also of natural background clutters. Because of the high complexity of natural clutters, this clutter model can only be reliably built through learning from real examples. If available, contextual information that characterizes each training example can be used to further improve the learned clutter model. In this paper, we present such a clutter model aided target detection system. Emphases are placed on two topics: (1) learning the background clutter model from sensory data through a self-organizing process, (2) reinforcing the learned clutter model using contextual information.
We describe an approach to the classification of 3-D objects using a multi-scale representation. This approach starts with a smoothing algorithm for representing objects at different scales. Smoothing is applied in cu...
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ISBN:
(纸本)0780342364
We describe an approach to the classification of 3-D objects using a multi-scale representation. This approach starts with a smoothing algorithm for representing objects at different scales. Smoothing is applied in curvature space directly, thus avoiding the usual shrinkage problems and allowing for efficient implementations. A 3-D similarity measure that integrates the representations of the objects at multiple scales is introduced Given a library of models, objects that are similar based an this multi-scale measure are grouped together into classes. Thtr objects that are in the same class ave combined into a single prototype object. Finally the prototypes are used for hierarchical recognition by first comparing the scene representation to the prototypes and then matching it only to the objects in the most likely class rather than to the entire library of models. Beyond its application to object recognition, this approach provides an attractive implementation of the intuitive nations of scale and approximate similarity for 3-D shapes.
This paper introduces a novel image representation capturing feature dependencies through the mining of meaningful combinations of visual features. This representation leads to a compact and discriminative encoding of...
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ISBN:
(纸本)9781479951178
This paper introduces a novel image representation capturing feature dependencies through the mining of meaningful combinations of visual features. This representation leads to a compact and discriminative encoding of images that can be used for image classification, object detection or object recognition. The method relies on (i) multiple random projections of the input space followed by local binarization of projected histograms encoded as sets of items, and (ii) the representation of images as Histograms of pattern Sets (HoPS). The approach is validated on four publicly available datasets (Daimler Pedestrian, Oxford Flowers, KTH Texture and PASCAL VOC2007), allowing comparisons with many recent approaches. The proposed image representation reaches state-of-the-art performance on each one of these datasets.
This paper addresses the problem of recognizing objects in large image databases. The method is based on local characteristics which are invariant to similarity transformations in the image. These characteristics are ...
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ISBN:
(纸本)0818672587
This paper addresses the problem of recognizing objects in large image databases. The method is based on local characteristics which are invariant to similarity transformations in the image. These characteristics are computed at automatically detected keypoints using the greyvalue signal. The method therefore works on images such as paintings for which geometry based recognition fails. Due to the locality of the method, images can be recognized being given part of an image and in the presence of occlusions. Applying a voting algorithm and semi-local constraints makes the method robust to noise, scene clutter and small perspective deformations. Experiments show an efficient recognition for different types of images. The approach has been validated on an image database containing 1020 images, some of them being very similar by structure, texture or shape.
During a fixed axis camera rotation every image point is moving on a conic section. If the point is a vanishing point the conic section is invariant to possible translations of the observer. Given the rotation axis an...
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ISBN:
(纸本)0818672587
During a fixed axis camera rotation every image point is moving on a conic section. If the point is a vanishing point the conic section is invariant to possible translations of the observer. Given the rotation axis and the inter-frame correspondence of a set of parallel lines we are able to compute the intrinsic parameters without knowledge of the rotation angles. We propagate the error covariances and we remove the bias in the computation of the conic. We experimentally study the sensitivity of calibration to the amount of rotation and we compare our performance to the performance of a recent active calibration technique.
We present five performance measures to evaluate grouping modules in the context of constrained search and indexing based object recognition. Using these measures, we demonstrate a sound experimental framework based o...
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ISBN:
(纸本)0780342364
We present five performance measures to evaluate grouping modules in the context of constrained search and indexing based object recognition. Using these measures, we demonstrate a sound experimental framework based on statistical ANOVA bests to compare and contrast three edge based organization modules, namely those of Etemadi et al. [1], Jacobs [2], and Sarkar-Boyer [3] in the domain of aerial objects using 50 images. With adapted parameters, the Jacobs module is overall the best choice for constraint based recognition. For fixed parameters, the Sarkar-Boyer module is the best In terms of recognition accuracy and indexing speedup. Etemadi et al.'s module performs equally well with fixed and adapted parameters while the Jacobs module is most sensitive to fled and adapted parameter choices. The overall performance ranking of the modules is Jacobs, Sarkar-Boyer, and Etemadi et al..
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