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共 276 条记录，以下是121-130 订阅

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Color image Quantization Using Color Variation Measure

Color Image Quantization Using Color Variation Measure

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Yu-Chou Chang Dah-Jye Lee Department of ECEn Brigham Young University Provo UT USA

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

关键词： Quantization signal processing algorithms Clustering algorithms image segmentation Humans Visual perception image color analysis Iterative algorithms computational intelligence USA Councils

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Towards image Retrieval by Texture Segmentation with Genetic Programming

Towards Image Retrieval by Texture Segmentation with Genetic...

引用

ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Vic Ciesielski Djaka Kurniawan Andy Song School of Computer Science and Information Technology RMIT University Melbourne Australia

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 times 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

关键词： image retrieval image segmentation Genetic programming Information retrieval Content based retrieval Clouds Testing computational intelligence signal processing Computer science

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CIREC : Cluster Correlogram image Retrieval and Categorization using MPEG-7 Descriptors

CIREC : Cluster Correlogram Image Retrieval and Categorizati...

引用

ieee symposium on computational intelligence in image and signal processing, ciisp

作者： A. Abdullah Marco A. Wiering Intelligent Systems Group Department of Information and Computing Sciences University of Utrecht Utrecht Netherlands

Content-based image retrieval is generally about understanding of information in the images concerned. The more the system is able to understand the content of images the more effective it will be in retrieving desired images. In this paper, we have developed a method that combines a clustering technique with the color correlation histogram. According to this method, an image is divided into several blocks using a fixed partitioning scheme. Then, selected lower-level MPEG-7 features are used to represent the partitions and a clustering technique is applied to group similar patterns into a cluster. The cluster indices will then be used to construct a corellogram which is not based on the lower-level primitives, but on regions consisting of a distribution of primitives. The result shows that the proposed method significantly outperforms other methods on image retrieval and categorization

关键词： image retrieval MPEG 7 Standard Histograms Indexing Content based retrieval Information retrieval image databases computational intelligence signal processing Intelligent systems

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Video Summarization based on Subclass Support Vector Data Description

Video Summarization based on Subclass Support Vector Data De...

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ieee symposium Series on computational intelligence

作者： Mygdalis, Vasileios Iosifidis, Alexandros Tefas, Anastasios Pitas, Ioannis Aristotle Univ Thessaloniki Dept Informat Thessaloniki Greece

ISBN: (纸本)9781479945092

In this paper, we describe a method for video summarization that operates on a video segment level. We formulate this problem as the one of automatic video segment selection based on a learning process that employs salient video segment paradigms. We design an hierarchical learning scheme that consists of two steps. At the first step, an unsupervised process is performed in order to determine salient video segment types. The second step is a supervised learning process that is performed for each of the salient video segment type independently. For the latter case, since only salient training examples are available, the problem is stated as an one-class classification problem. In order to take into account subclass information that may appear in the video segment types, we introduce a novel formulation of the Support Vector Data Description method that exploits subclass information in its optimization process. We evaluate the proposed approach in three Hollywood movies, where the performance of the proposed Subclass SVDD (SSVDD) algorithm is compared with that of related methods. Experimental results show that the adoption of both hierarchical learning and the proposed SSVDD method contribute to the final classification performance.

关键词： image classification image segmentation support vector machines unsupervised learning video signal processing Hollywood movies SSVDD algorithm automatic video segment selection hierarchical learning scheme one-class classification problem optimization process salient video segment types subclass support vector data description unsupervised process video summarization Feature extraction Motion pictures Optimization Streaming media Support vector machines Training Vectors Support Vector Network Cinema Streaming Video Optimization Procedure Video signal processing Unsupervised learning Videotapes image classification support vector data description Feature extraction Films image segmentation Segment Type Functional training Learning processes

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image registration for sequence of visual images captured by UAV

Image registration for sequence of visual images captured by...

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2009 ieee symposium computational intelligence for Multimedia signal and Vision processing, CIMSVP 2009

作者： McCartney, Matthew I. Sabato, Saleh Zein Malkani, Mohan College of Engineering Technology and Computer Science Tennessee State University United States

ISBN: (纸本)9781424427710

Unmanned Aerial Vehicles (UAVs) areregularly outfitted with payloads that include high resolution surveillance cameras. These surveillance systems have provided the military with the opportunity to monitor battlefields and remote terrain, carryout reconnaissance missions and track targets all from distant ground stations without endangering UAV operators. As with any remote sensing technology there are technical challenges that exist. The problem often arises where large sequences of images containing significant redundancies are being sent to the ground station processing systems and operators. Consequently, the ground station systems can become "weighed down" and operators can become overwhelmed. In addition, the decision making process of detection and recognition algorithms could be rendered ineffective because of the limited visual field of view of individual image frames. In this research image registration software is designed and implemented to integrate a sequence of visual images based on the modified Scale-Invariant Feature Transform(SIFT) algorithm. Since computational efficiency is a critical issue in any real-time interactive system, a modified version of the SIFT algorithm is devised and utilized in this work. Implementation and testing results of the developed software are obtained from real data collected from aerial footage and a collaborative camera network. © 2009 ieee.

关键词： image registration

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Progressive image Reconstruction based on Multi-scale Edge Model

Progressive Image Reconstruction based on Multi-scale Edge M...

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Paul Bao Xianjun Zhang University of South Florida USA East China Jiao Tong University Shanghai China

In this paper, we present a progressive image reconstruction scheme based on the semantically scalable multi-scale edge representation of images, with the resolution and visual quality scalable to various bitrate requirements. In the multi-scale edge representation an image is decomposed into its multi-scale primal sketch and the background where the multi-scale primal sketch preserves the structural semantics of images, and the background represents the smooth locale. Edge compensation is performed to smoothly remove edges at each scale. The multi-scale edges are then embedded encoded using the GFA modeling. The image reconstruction is progressively achieved by synthesizing multi-scale edges on the reconstructed image obtained from previous scale. As edge synthesis is performed at consecutive scales, the visual quality of the reconstructed image is progressively enhanced. Experiment shows that the proposed scheme performs well at low bit-rate multiresolution representation and progressive reconstruction

关键词： image reconstruction image edge detection image resolution Wavelet transforms image representation Layout Streaming media computational intelligence signal processing USA Councils

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computational intelligence for Automated Keg Identification and Deformnation Detection

Computational Intelligence for Automated Keg Identification ...

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Duncan Campbell Andrew Keir Michael Lees School of Engineering Systems Queensland University of Technology Brisbane Australia Yatala Operations Fosters Yatala Australia

A machine vision based keg inspection system can allow cost effective keg tracking and preventative maintenance programs to be implemented, leading to substantial savings for breweries with large keg fleets. A robust keg serial number recognition and keg condition assessment process is required to cater for different keg brands and a range of keg ages in the fleet. It has been demonstrated that the proposed image processing methodology, and neural network based number recognition system, successfully located and identified keg serial numbers with a 92% digit accuracy. Furthermore, the vision system allowed the concurrent assessment of the keg condition by assessing deformity of the keg rim, and that of the filler valve. A correlation coefficient, generated using a template matching process, proved to be a suitable metric which adequately indicated rims within and outside acceptable deformity bounds

关键词： computational intelligence Machine vision Inspection Costs Preventive maintenance Robustness image processing Neural networks image recognition Valves

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OWA filters: A Robust Filtering Method and its Application to Color images

OWA filters: A Robust Filtering Method and its Application t...

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ieee symposium on computational intelligence in image and signal processing

作者： Basu, A. Nachtegael, M. Formerly with the Indian Statistical Institute Machine Intelligence Unit Kolkata India Ghent University Dept. of Applied Mathematics and Computer Science Fuzziness and Uncertainty Modelling Research Unit 9000 Gent Krijgslaan 281 - S9 Belgium

ISBN: (纸本)9781424427604

Bilateral filtering provides a scheme for non-iterative edge-preserving smoothing, but the results could be strongly affected by the presence of outliers. In this paper we develop a robust bilateral filter for color images, and in order to achieve this we propose to improve the bilateral filtering technique [13] by using Ordered Weighted Averaging operators. We adopt a fuzzy logic based approach: if the filtering is considered as a weighted averaging, then each filter is associated with a fuzzy set and the membership values of these fuzzy sets represent the weights. In this context, the bilateral filter is a conjunction of two fuzzy sets in the case of grayscale images: one in the spatial domain and one in a photometric domain. Applied to color images, we propose to extend the conjunction to three fuzzy sets: one in the spatial domain, one in the brightness domain and one in the chromatic domain. Taking into account the robustness of rank filters, we propose to define an OWA filter in order to obtain robust adaptive filters in brightness and chromaticity. The robustness and performance of the filter is illustrated with several experiments, revealing its ability to remove different types of noise in the presence of outliers, while preserving edges. The noise types considered are impulse noise and a combination of Gaussian noise with "salt and pepper" noise types.

关键词： Impulse noise

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Human-Controlled Vs. Semi-automatic Content-Based image Retrieval

Human-Controlled Vs. Semi-automatic Content-Based Image Retr...

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Kambiz Jarrah Ling Guan Department of Electrical and Computer Engineering Ryerson University Toronto Canada

The overall objective of this paper is to present n methodology for reducing the human workload through adapting an automatic scheme for content-based image retrieval (CBIR) engines. The proposed system utilizes an unsupervised hierarchical clustering algorithm, known as the directed self-organizing tree map (DSOTM) that aims to closely mimic the process of information classification thought to be at work in the human brain. In further refine the search process and increase retrieval accuracy, a semi-automatic relevance feedback approach is presented in this work. The semi-automatic scheme refers to a relevance feedback CBIR engine, structured around the DSOTM algorithm. This system aims to learn from and adapt to different users' subjectivity under the guidance of an additional objective verdict provided by the DSOTM. Comprehensive comparisons with the rank-based, relevance feedback, and automatic CBIR engines, demonstrate feasibility of adapting the semi-automatic approach

关键词： image retrieval Content based retrieval Engines computational intelligence signal processing Sun Electrical capacitance tomography Tellurium Bismuth Legged locomotion

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Analysis of Gray Scale Watermark in RGB Host using SVD and PSO

Analysis of Gray Scale Watermark in RGB Host using SVD and P...

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ieee symposium on computational intelligence for Multimedia, signal and Vision processing (CIMSIVP)

作者： Ansari, Irshad Ahmad Pant, Millie Neri, Ferrante Indian Inst Technol Roorkee Dept Appl Sci & Engn Roorkee Uttar Pradesh India De Montfort Univ Ctr Computat Intelligence Leicester Leics England

ISBN: (纸本)9781479945030

the present study is conducted in two phases. In the first phase we analyze the different aspects of gray image watermarking in a colored host. Robustness and imperceptibility are used as analysis parameters. The approaches explored and compared in this study are - watermark embedding with any one of the three RGB (Red-Green-Blue) components (single channel embedding), multichannel watermark embedding (same watermark with all channels) and multichannel embedding with equally segmented watermark. SVD (Singular Value Decomposition) is used to calculate the singular values of host image and then appropriate scaling factor isused to embed the watermark and the watermarked image is subjected to different attacks. To secure the watermark from an unauthorized access Arnold transform is implemented. From the simulation results it is observed that segmented watermark approach is better than the other two approaches in terms of both robustness and imperceptibility. In the second phase, change of robustness and imperceptibility is studied with the change of scaling factor for which PSO (Particle swarm optimization) is employed to determine the optimal values of scaling factor. The results here indicate that the use of different scaling factors (optimal) for each RGB component provides better result in comparison to a single (optimal) scaling factor in segmented multichannel approach. Overall, the experimental analysis shows that the equal distribution of gray watermark over RGB components with PSO optimized scaling factors provides significant improvement in the quality of watermarked image and the quality of retrieved watermark even from the distorted watermarked image.

关键词： Singular value decomposition Watermarking in color host Cross-Correlation Multi channel embedding Gray watermark Arnold Transform

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