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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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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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computational intelligence for automated keg identification and deformation detection

Computational intelligence for automated keg identification ...

引用

ieee symposium on computational intelligence in image and signal processing

作者： Campbell, Duncan Keir, Andrew Lees, Michael Queensland Univ Technol Sch Engn Syst Brisbane Qld Australia Yatala Operat Fosters Yatala Australia

ISBN: (纸本)9781424407071

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： neural networks OCR keg deformation detection keg tracking

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Intelligent signal processing in an automated measurement data analysis system

Intelligent signal processing in an automated measurement da...

引用

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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Identification of dynamic nonlinear systems using computational intelligence techniques

Identification of dynamic nonlinear systems using computatio...

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ieee symposium on computational intelligence in image and signal processing

作者： Turchetti, Claudio Gianfelici, Francesco Univ Politecn Marche Dipartimento Elettron Intelligenza Artificiale & I-60131 Ancona Italy

ISBN: (纸本)9781424407071

A novel approach based on computational intelligence techniques for the identification of nonlinear dynamic systems is presented in this paper. The technique encompasses both the properties of the Karhunen-Loeve Transform in representing stochastic processes and the approximation capabilities of multi-layer neural networks. Experimental results on nonlinear systems governed by difference equations demonstrate the effectiveness of the proposed approach that is based on a real-time learning algorithm. Exhaustive experimentation on specific case studies was performed and some experimental results were compared with other existing techniques such as the Lee-Schetzen method, Least Mean Square (LMS), Recursive Least Square (RLS) and Normalized Least Mean Square (NLMS) algorithms. A better identification-accuracy was also achieved, and a reduction of some orders of magnitude in training-times compared with the well-known Lee-Schetzen method was obtained, thus making the proposed methodology one of the current best practices in this field.

关键词： system identification multi-layer neural networks Karhunen-Loeve Transform nonlinear dynamic systems

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

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

引用

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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Experience based surface discernment by a quadruped robot

Experience based surface discernment by a quadruped robot

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ieee symposium on computational intelligence in image and signal processing

作者： Holmstrom, Lars Toland, Andrew Lendaris, George Portland State Univ NW Computat Intelligence Lab Portland OR 97201 USA

ISBN: (纸本)9781424407071

The task of autonomous surface discernment by an AIBO robotic dog is addressed. Different surface textures (plywood board, thin foam, short carpet, shag carpet) as well as different inclines (0 and 10 degrees) are considered. Using a genetic algorithm, gaits are designed which allow the robot to traverse each of these surfaces in an (approximately) optimal fashion. Frequency domain analysis of actuator readings from individual leg joints is performed for data collected using each gait on each surface type. It is found that the spectral content of these signals is significantly dependent on the characteristics of both the gait in use and the surface being walked upon. Using tap-delay Adaline neural networks to integrate actuator readings from 15 independent joints into a set of models of different gait/surface experiences, an algorithm is designed which uses these experiences to yield high classification rates across surface transitions and with low latency.

关键词： Gait analysis

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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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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

作者： Jarrah, Kambiz Guan, Ling Ryerson Univ Dept Elect & Comp Engn Toronto ON Canada

ISBN: (纸本)9781424407071

The overall objective of this paper is to present a 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 [1, 2]. To 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： Content based retrieval

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