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.
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.
We present an extremely simple yet robust multi-view stereo algorithm and analyze its properties. The algorithm first computes individual depth maps using a window-based voting approach that returns only good matches....
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This study combines two useful methods in recognition: consensus or voting-based approaches and moment-based representations. Matches between image patches are generated using a Gaussian-weighted moment encoding of th...
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ISBN:
(纸本)0818672587
This study combines two useful methods in recognition: consensus or voting-based approaches and moment-based representations. Matches between image patches are generated using a Gaussian-weighted moment encoding of the patches and a feature indexing process. Each match implies an object 3D position and orientation (pose) and generates a vote for this pose. recognition is accomplished by detecting significant clusters of votes in pose space. This combined method is an improvement over voting and moment methods in isolation. Using image brightness moments, the idea is demonstrated on examples of human faces undergoing full 3D pose change, as well as changes in features such as talking and blinking. The idea is then extended to moments of local texture orientation and successfully demonstrated under large variations in lighting nature and geometry.
We demonstrate real-time face tracking and pose estimation in an unconstrained office environment with an active foveated camera. Using vision routines previously implemented for an interactive environment, we determi...
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ISBN:
(纸本)0818672587
We demonstrate real-time face tracking and pose estimation in an unconstrained office environment with an active foveated camera. Using vision routines previously implemented for an interactive environment, we determine the spatial location of a user's head and guide an active camera to obtain foveated images of the face. Faces are analyzed using a set of eigenspaces indexed over both pose and world location. Closed loop feedback from the estimated facial location is used to guide the camera when a face is present in the foveated view. Our system can detect the head pose of an unconstrained user in real-time as he or she moves about an open room.
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.
This paper addresses two important issues related to texture pattern retrieval: feature extraction and similarity search. A Gabor feature representation for textured images is proposed, and its performance in pattern ...
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ISBN:
(纸本)0818672587
This paper addresses two important issues related to texture pattern retrieval: feature extraction and similarity search. A Gabor feature representation for textured images is proposed, and its performance in pattern retrieval is evaluated on a large texture image database. These features compare favorably with other existing texture representations. A simple hybrid neural network algorithm is used to learn the similarity by simple clustering in the texture feature space. With learning similarity, the performance of similar pattern retrieval improves significantly. An important aspect of this work is its application to real image data. Texture feature extraction with similarity learning is used to search through large aerial photographs. Feature clustering enables efficient search of the database as our experimental results indicate.
There are at least two situations in practical computervision where displacement of a point in an image is accompanied by a defocus blur. The first is when a camera of limited autofocal capability moves in depth, and...
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ISBN:
(纸本)0818672587
There are at least two situations in practical computervision where displacement of a point in an image is accompanied by a defocus blur. The first is when a camera of limited autofocal capability moves in depth, and the second is when a limited autofocal camera zooms. Motion and zooming are two popular strategies for acquiring more detail or for acquiring depth. The defocus blur has been considered noise or at best been ignored. However, the defocus blur is in itself a cue to depth, and hence we proceed to show how it can be calculated simultaneously with affine motion. We first introduce the theory, then develop a solution method and finally demonstrate the validity of the theory and the solution by conducting experiments with real scenery.
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