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检索条件"任意字段=2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007"

共 247 条记录，以下是11-20 订阅

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A novel approach to collect training images from WWW for image thesaurus building

A novel approach to collect training images from WWW for ima...

引用

ieee symposium on computational intelligence in image and signal processing

作者： Park, Joohyoun Nang, Jongho Sogang Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9781424407071

This paper introduces a novel approach to change gathered images from WWW into training images to build an image thesaurus. The requirements for being training images are a large number of images and with highly relevant to a given concept. To fulfill these requirements, a system should be able to collect a large number of relevant images to a given concept from WWW by the proposed criterion of relevance to the concept for each image. Then, the irrelevant images would be filtered out by the modified hierarchical clustering method based on the weighted combination of 5 MPEG-7 visual descriptors[9] and the proposed criterion of relevance to the concept for each cluster. Upon experimental results, the precision of the set of images generated by the proposed method is about 18% higher than that of the set of images generated by other methods[1][2].

关键词： auto image annotation content based image retrieval

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Real-time image-based stylistic rendering using graphics hardware acceleration

Real-time image-based stylistic rendering using graphics har...

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ieee symposium on computational intelligence in image and signal processing

作者： Chiang, Chun-Chung Hsueh, Yu-Hung Liu, Damon Shing-Min Natl Chung Cheng Univ Dept Comp Sci & Informat Engn Chiayi Taiwan

ISBN: (纸本)9781424407071

Silhouette is an important research issue in the field of Non-Photorealistic Rendering (NPR) and it is also a popular drawing feature in illustrations and line-drawing artworks. In this paper, we present a real-time image-based stylized rendering system. First, we project a 3D model to image-space. Then we extract edges in the image-space data. We perform edge-detection algorithms on GPU (Graphics processing Units) for speedup. GPU is good at floating-points calculating and processing with parallelism. Both features match the property of most image processing tasks. Our system can run at an interactive frame rate when combining our edge-detection algorithms with graphic hardware architecture. We demonstrate that this system performance can reach real-time and render images in good NPR style.

关键词： Computer graphics

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Application of opposition-based reinforcement learning in image segmentation

Application of opposition-based reinforcement learning in im...

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ieee symposium on computational intelligence in image and signal processing

作者： Sahba, Farhang Tizhoosh, Hamid R. Salama, Magdy M. M. A. Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada Univ Waterloo Dept Elect & Comp Engn Waterloo ON Canada Univ Waterloo PAMI Waterloo ON Canada

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.

关键词： Reinforcement learning

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An adaptive approach to the segmentation of DCE-MR images of the breast: Comparison with classical thresholding algorithms

An adaptive approach to the segmentation of DCE-MR images of...

引用

ieee symposium on computational intelligence in image and signal processing

作者： Kaleli, Fatih Aydin, Nizamettin Ertas, Gokhan Gulcur, H. Ozcan Bahcesehir University Engineering Faculty Turkey Bogazici University Biomedical Engineering Institute Turkey

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 image processing, 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.

关键词： image segmentation

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Neural networks applied for cork tiles image classification

Neural networks applied for cork tiles image classification

引用

ieee symposium on computational intelligence in image and signal processing

作者： Georgieva, A. Jordanov, I. Rafik, T. Univ Portsmouth Sch Comp Portsmouth PO1 3HE Hants England Univ Wales Coll Cardiff Sch Product & Engn Design Cardiff CF5 2YB S Glam Wales

ISBN: (纸本)9781424407071

In this paper a hybrid global optimization method GLPS is further investigated and applied for feed-forward neural networks supervised learning. The method is initially tested on several benchmark problems and subsequently employed for pattern recognition problem. The proposed technique is used for training Neural Networks (NN) that have to inspect and classify three types of cork tiles images. During the feature extraction phase, statistical textural characteristics are obtained from the tiles' images and then used for training several different NN architectures. Results from the testing phase are discussed and analysed, showing good generalization abilities of the trained NN. Finally, directions of future work are briefly stated.

关键词： supervised neural networks learning global optimization image processing feature extraction cork tiles classification

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Life-long semi-supervised learning: Continuation of both learning and recognition

Life-long semi-supervised learning: Continuation of both lea...

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ieee symposium on computational intelligence in image and signal processing

作者： Kamiya, Youki Ishii, Toshiaki Hasegawa, Osamu Tokyo Inst Technol Interdisciplinary Grad Sch Sci & Engn Dept Comp Intelligence & Syst Sci Kanagawa 2268503 Japan Tokyo Inst Technol Imaging Sci & Engn Lab Tokyo Japan

ISBN: (纸本)9781424407071

This paper presents a new method for continuous and incremental learning and recognition based on self-organized incremental neural networks. It is available in the fluctuating environment where the number of recognition classes cannot be defined. In this method, the learning process and recognition process are not separated. This method can acquire concept when multiple feature vectors of new input object come, and then can recognize it using previously acquired concept. We experiment an examples of life-long semi-supervised learning tasks in real world. In the result, the proposed method was able to learn and recognize 104 objects incrementally, non-stop, and in real time.

关键词： Supervised learning

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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

作者： Bao, Paul Zhang, Xianjun Univ S Florida Tampa FL 33620 USA E China Normal Univ Shanghai Peoples R China

ISBN: (纸本)9781424407071

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 multiresoultion representation and progressive reconstruction.

关键词： image reconstruction

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Performance analysis of the feedforward and SOM neural networks in the face recognition problem

Performance analysis of the feedforward and SOM neural netwo...

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ieee symposium on computational intelligence in image and signal processing

作者： Chacon M., M. I. Rivas-Perea, P. Chihuahua Inst Technol Digital Signal Proc & Vis Lab Chih Mexico

ISBN: (纸本)9781424407071

This paper presents a comparative study between a feedforward neural network and a SOM network. The paper also proposes the incorporation of a new spatial feature, face feature lines, FFL, to represent the faces. FFL are considered as new features based on previous studies related to face recognition tasks on newborns. Besides the face feature lines, the feature vector incorporates eigenvectors of the face image obtained with the Karhunen-Loeve transformation. A face recognition system is based on a feedforward neural network, FFBP, method. The second classification scheme uses a Self Organized Map, SOM, architecture combined with the k-means clustering algorithm. Experiments comparing both architectures show no significant differences for the ORL database, 92% for the FFBP and 90% for the SOM. However results obtained for the Yale database, 60% for the FFBP network and 70% for the SOM, indicate a better performance with the SOM architecture.

关键词： Face recognition

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Intelligent signal processing in an automated measurement data analysis system

Intelligent signal processing in an automated measurement da...

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ieee symposium on computational intelligence in image and signal processing

作者： Isernhagen, Henrik Neemann, Helmut Kuehn, Steffen Guehmann, Clemens Tech Univ Berlin Dept Elect Measurement & Diagnost Technol Sekr EN 13Einsteinufer 17 D-10587 Berlin Germany IAV GmbH D-38518 Gifhorn Germany

ISBN: (纸本)9781424407071

In the automotive sector a huge amount of measurement data is recorded for validation and safeguarding of vehicle components. These data has to be automatically evaluated for an effective data analysis. Therefore, we need a sophisticated approach, which offers a flexible and powerful parametrisation and different signal processing algorithms for multiple applications. In this paper software and signal evaluation modules for an automated analysis of vehicle measurement data are presented. The data can be evaluated signal or message based with a parametrisation with reusable XML templates. Exemplary, we describe three evaluation modules integrating different signal processing approaches: signal analysis using an analytical signal description in combination with fuzzy logic, an efficient sliding frequency detection and the detection of predefined patterns using a modified dynamic time warping algorithm. Furthermore, an approach for a connected evaluation in consideration of time correlation is presented. Concluding, we discuss a practical application.

关键词： Data reduction

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Improved H.264/AVC requantization transcoding using low-complexity interpolation filters for 1/4-pixel motion compensation

Improved H.264/AVC requantization transcoding using low-comp...

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ieee symposium on computational intelligence in image and signal processing

作者： Notebaert, Stijn De Cock, Jan Van de Walle, Rik Univ Ghent IBBT Dept Elect & Informat Syst Multimedia Lab B-9050 Ledeberg Ghent Belgium

ISBN: (纸本)9781424407071

Transcoding is a fast technique for video adaptation, which offers great flexibility for changing the characteristics of already coded video sequences in order to match the properties of the terminal display devices and the bandwidth availability of the transmission networks. For reduction of the bitrate, requantization transcoding is often used. In our previous work, we introduced a hybrid architecture for requantization transcoding, which is able to reduce the bitrate for H.264/AVC bitstreams with quality results approaching the rate-distortion optimal decoder-encoder cascade. In this paper, we focus on the filters used for interpolation of the accumulated requantization errors, which are used for compensation of the current (sub-)macroblock partition. We show that by using reduced complexity filters, computational complexity can be significantly reduced without quality loss.

关键词： image coding

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