The proceedings contain 76 papers. The topics discussed include: a new vehicle detection approach in traffic jam conditions;non-pixel robot stereo;a novel method to recognize complex dynamic gesture by combining HMM a...
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ISBN:
(纸本)1424407079
The proceedings contain 76 papers. The topics discussed include: a new vehicle detection approach in traffic jam conditions;non-pixel robot stereo;a novel method to recognize complex dynamic gesture by combining HMM and FNN models;boundary refined texture segmentation based on K-views and datagram methods;single-row superposition-type spherical compound-like eye for pan-tilt motion recovery;bare bones strategy for human detection and tracking;Daubechies complex wavelet transform based moving object tracking;identification of dynamic nonlinear systems using computationalintelligence techniques;a new invariant descriptor for shape representation and recognition;a wavelet-fuzzy logic based system to detect and identify electric disturbs;evolution strategies based particle filters for fault detection;and a multi-window stereo vision algorithm with improved performance at object borders.
This paper outlines a solution to the multi-channel synthetic aperture radar (SAR) moving target indication and detection (MTI/MTD) by means of inverse systems approach. A novel model of the problem is presented and a...
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ISBN:
(纸本)9781424407071
This paper outlines a solution to the multi-channel synthetic aperture radar (SAR) moving target indication and detection (MTI/MTD) by means of inverse systems approach. A novel model of the problem is presented and an approximate analytic solution to it will be given. It will be demonstrated how a moving target indicator can benefit from a multichannel SAR system as opposed to a traditional approach that separates MTI and SAR systems. It will be shown that the problem of separation of moving targets from stationary ones can be solved completely by using multi-channel approach and in such a way that a spatial distribution of the stationary targets does not play a role.
This paper presents a neural network based approach for vehicle classification. The proposed vehicle classification approach extracts various features from a vehicle image, normalises and classifies them into one of t...
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ISBN:
(纸本)9781424407071
This paper presents a neural network based approach for vehicle classification. The proposed vehicle classification approach extracts various features from a vehicle image, normalises and classifies them into one of the known classes. It is based on structural features and a direct solution training method. The preliminary experiments on training and testing of 4 types of vehicles patterns were conducted. The experimental results are very promising and demonstrate the effectiveness and usefulness of the proposed approach.
In this paper an algorithm to cluster face images found in video sequences is proposed. A novel method for creating a dissimilarity matrix using SIFT image features is introduced. This dissimilarity matrix is used as ...
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ISBN:
(纸本)9781424407071
In this paper an algorithm to cluster face images found in video sequences is proposed. A novel method for creating a dissimilarity matrix using SIFT image features is introduced. This dissimilarity matrix is used as an input in a hierarchical average linkage clustering algorithm, which yields the clustering result. Three well known clustering validity measures are provided to asses the quality of the resulting clustering, namely the F measure, the overall entropy (OE) and the Gamma statistic. The final result is found to be quite robust to significant scale, pose and illumination variations, encountered in facial images.
Discrete signalprocessing using fuzzy fractal dimension and grade of fractality is proposed based on the novel concept of merging fuzzy theory and fractal theory. The fuzzy concept of fractality, or self-similarity, ...
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ISBN:
(纸本)9781424407071
Discrete signalprocessing using fuzzy fractal dimension and grade of fractality is proposed based on the novel concept of merging fuzzy theory and fractal theory. The fuzzy concept of fractality, or self-similarity, in discrete time series can be reconstructed as a fuzzy-attribution, i.e., a kind of fuzzy set. The objective short time series can be interpreted as an objective vector, which can be used by a newly proposed membership function. Sliding measurement using the local fuzzy fractal dimension (LFFD) and the local grade of fractality (LGF) is proposed and applied to fluctuations in seawater temperature around the Izu peninsula of Japan. Several remarkable characteristics are revealed through "fuzzy signalprocessing" using LFFD and LGF.
In this paper, a novel color image quantization algorithm is presented. This new algorithm addresses the question of how to incorporate the principle of human visual perception to color variation sensitivity into colo...
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ISBN:
(纸本)9781424407071
In this paper, a novel color image quantization algorithm is presented. This new algorithm addresses the question of how to incorporate the principle of human visual perception to color variation sensitivity into color image quantization process. Color variation measure (CVM) is calculated first in CIE Lab color space. CVM is used to evaluate color variation and to coarsely segment the image. Considering both color variation and homogeneity of the image, the number of colors that should be used for each segmented region can be determined. Finally, CF-tree algorithm is applied to classify pixels into their corresponding palette colors. The quantized error of our proposed algorithm is small due to the combination of human visual perception and color variation. Experimental results reveal the superiority of the proposed approach in solving the color image quantization problem.
This paper examines the feasibility of an approach to image retrieval from a heterogeneous collection based on texture. For each texture of interest (T), a T-vs-other classifier is evolved for small n x n windows usin...
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ISBN:
(纸本)9781424407071
This paper examines the feasibility of an approach to image retrieval from a heterogeneous collection based on texture. For each texture of interest (T), a T-vs-other classifier is evolved for small n x n windows using genetic programming. The classifier is then used to segment the images in the collection. If there is a significant contiguous area of T in an image, it is considered to contain that texture for retrieval purposes. We have experimented with sky and grass textures in the Corel Volume 12 image set Experiments with a single image indicate that classifiers for the two textures can be learned to a high accuracy. Experiments with a test set of 714 Corel images gave a retrieval accuracy of 84% for both sky and grass textures. These results suggest that the use of texture could enhance retrieval accuracy in content based image retrieval systems.
In this paper a method for image segmentation using an opposition-based reinforcement learning scheme is introduced. We use this agent-based approach to optimally find the appropriate local values and segment the obje...
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ISBN:
(纸本)9781424407071
In this paper a method for image segmentation using an opposition-based reinforcement learning scheme is introduced. We use this agent-based approach to optimally find the appropriate local values and segment the object. The agent uses an image and its manually segmented version and takes some actions to change the environment (the quality of segmented image). The agent is provided with a scalar reinforcement signal as reward/punishment. The agent uses this information to explore/exploit the solution space. The values obtained can be used as valuable knowledge to fill the Q-matrix. The results demonstrate potential for applying this new method in the field of medical image segmentation.
In classical graph-based image segmentation, a data-driven matrix is constructed representing similarities between every pair of pixels. The eigenvectors of such matrices contain relevant information about the cluster...
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ISBN:
(纸本)9781424407071
In classical graph-based image segmentation, a data-driven matrix is constructed representing similarities between every pair of pixels. The eigenvectors of such matrices contain relevant information about the clusters present on the image. An approach to image segmentation using spectral clustering with out-of-sample extensions is presented. This approach is based on the weighted kernel PCA framework. An advantage of the proposed method is the possibility to train and validate the clustering model on subsampled parts of the image to be segmented. The cluster indicators for the remaining pixels can then be inferred using the out-of-sample extension. This subsampling scheme can be used to reduce the computation time of the segmentation. Simulation results with grayscale and color images show improvements in terms of computation times together with visually appealing clusters.
The segmentation of MR images has been playing an important role to improve the detection and diagnosis of breast cancer. Main problem in breast images is the identification of the boundary between chest wall and brea...
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ISBN:
(纸本)9781424407071
The segmentation of MR images has been playing an important role to improve the detection and diagnosis of breast cancer. Main problem in breast images is the identification of the boundary between chest wall and breast tissue. Minimizing the effects of patient motion is also important step in segmentation process. In imageprocessing, there are many different segmentation algorithms. The most common used method among them is thresholding. However, classic thresholding methods are not effective for axial MR breast images completely because of the fact that the sequence artifacts in axial MR breast images are very high. For this reason, we have proposed a regional thresholding algorithm to segment MR images successfully. The outstanding problem is how to obtain an automatic procedure for detecting boundary between breast tissue and chest wall.
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