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

共 276 条记录，以下是31-40 订阅

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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...

引用

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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image indexing and retrieval in compressed domain using color clusters

Image indexing and retrieval in compressed domain using colo...

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ieee symposium on computational intelligence in image and signal processing

作者： Bajaj, Meekal Lay, Jose A. Netaji Subhas Inst Technol Elect & Commun Engn Dept New Delhi 110045 India Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia

ISBN: (纸本)9781424407071

A compressed domain color-based image indexing method that avoids overheads associated with full decompression and color space transformation by operating in the YCbCr space has been presented. The proposed method performs a hard segmentation of the color space into predefined clusters based on perceptual similarity. A three dimensional color descriptor is generated by extracting the average intensity value of the Y, Cb and Cr components for each 8X8 DCT block. Each blocks descriptor is subsequently allocated to its corresponding cluster, whose centroid is updated using a weighted mean. The similarity measure is evaluated using a compact four dimensional feature vector from each cluster, which consists of the quantized centroid co-ordinates and the percentage contribution to the image composition by the cluster. Retrieval is performed by comparing the feature attributes of the clusters having significant membership in the query image, to those with the database images;the obtained matches are then integrated to evaluate the final ranking. The experimental results of the proposed model on a database of 7380 images are reported.

关键词： color indexing compressed domain content based retrieval DCT

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High performance medical image registration using a distributed blackboard architecture

High performance medical image registration using a distribu...

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ieee symposium on computational intelligence in image and signal processing

作者： Tait, Roger J. Schaefer, Gerald Hopgood, Adrian A. Nakashima, Tomoharu Nottingham Trent Univ Sch Comp & Informat Clifton Campus Nottingham NG11 8NS England Aston Univ Sch Engn & Appl Sci Birmingham B4 7ET W Midlands England Osaka Univ Osaka Japan

ISBN: (纸本)9781424407071

A major drawback of medical image registration techniques is the performance bottleneck associated with similarity computation. Such bottlenecks limit registration applications in situations where fast execution times are required. In this paper a novel framework for high performance intensity-based medical image registration is presented. Geometric alignment of both reference and sensed images is achieved through a combination of scaling, translation, and rotation. Crucially, similarity computation is performed intelligently by knowledge sources (KSs) organised in a worker/manager model. The KSs work in parallel and communicate with each other by means of a distributed blackboard architecture. Partitioning of the blackboard is used to balance communication and processing workloads. The registration framework presented demonstrates the flexibility of the coarse-grained parallelism employed and shows how high performance medical image registration can be achieved with non-specialised architectures. Experimental results obtained during testing show that substantial speedups can be achieved.

关键词： image registration

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CIREC: Cluster correlogram image retrieval and categorization using MPEG-7 descriptors

CIREC: Cluster correlogram image retrieval and categorizatio...

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ieee symposium on computational intelligence in image and signal processing

作者： Abdullah, A. Wiering, Marco A. Univ Utrecht Dept Informat & Comp Sci Intelligent Syst Grp NL-3508 TB Utrecht Netherlands

ISBN: (纸本)9781424407071

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： content-based retrieval image classification image coding image colour analysis image retrieval pattern clustering CIREC MPEG-7 descriptors cluster correlogram image retrieval color correlation histogram content-based image retrieval fixed partitioning image retrieval content-based image retrieval image colour analysis Content retrieval image coding image classification Cluster Analysis

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Evolved multiresolution analysis transforms for improved image compression and reconstruction under quantization

Evolved multiresolution analysis transforms for improved ima...

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ieee symposium on computational intelligence in image and signal processing

作者： Babb, Brendan J. Moore, Frank W. Marshall, Pat Univ Alaska Anchorage Dept Math Sci 3211 Providence Dr Anchorage AK 99516 USA Univ So Calif Dept Comp Sci Los Angeles CA USA Univ Hawaii Dept Comp Sci Hilo HI USA

ISBN: (纸本)9781424407071

The research described in this paper uses a genetic algorithm (GA) to evolve wavelet and scaling coefficients for transforms that outperform discrete wavelet transforms (DWTs) under conditions subject to quantization. Compression and reconstruction transform pairs evolved against a representative training image reduce mean squared error (MSE) by more than 22% (1.126 dB) when subsequently applied to test images at a single level of decomposition, while evolved three-level multiresolution analysis (MRA) transforms average more than 11% (0.50 dB) MSE reduction when applied to test images in comparison to the Daubechies-4 (D4) wavelet, without increasing the size of the compressed file.

关键词： Vector quantization

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A pseudo-labeling framework for content-based image retrieval

A pseudo-labeling framework for content-based image retrieva...

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ieee symposium on computational intelligence in image and signal processing

作者： Yap, Kim-Hui Wu, Kui Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9781424407071

This paper presents a new pseudo-label fuzzy support vector machine (PLFSVM)-based active learning framework in interactive content-based image retrieval (CBIR) systems. One of the main issues associated with relevance feedback in CBIR systems is the small sample problem where only a limited number of labeled samples are available for learning. This is because image labeling is time consuming and users are often reluctant to label too many images for feedback. Learning from insufficient training samples often constrains the retrieval performance. To address this problem, we propose a new algorithm based on the concept of pseudo-labeling. It incorporates carefully selected unlabeled images to enlarge the training data set and assigns proper pseudo-labels to them. Further, some fuzzy rules are utilized to automatically estimate class membership of the pseudo-labeled images. Fuzzy support vector machine (FSVM) is designed to take into account the fuzzy nature of some training samples during its training. In order to exploit the advantages of pseudo-labeling, active learning and the structure of FSVM, we develop a unified framework to perform content-based image retrieval. Experimental results based on a database of 10,000 images demonstrate the effectiveness of the proposed method.

关键词： Content based retrieval

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Bare bones strategy for human detection and tracking

Bare bones strategy for human detection and tracking

引用

ieee symposium on computational intelligence in image and signal processing

作者： Siddiqui, M. N. Yousaf, B. Natl Univ Comp & Emerging Sci Islamabad Pakistan

ISBN: (纸本)9781424407071

we present a scaled down version of a Human Detection and Tracking System designed to run on relatively low-end machines of developing countries. The system uses sequence of monocular images of a single, fixed surveillance camera to extract data of moving objects. A Linear Motion Model coupled with Pre-computed Average Color Intensities is used to track the detected subjects across a series of frames. Head Detection algorithm is applied to form multiple hyotheses so as to accurately detect individuals in case of occlusion caused by people overlapping with each other. A final Shape Fitting algorithm is applied on the detected form to verify each hypothesis. Experiments conducted on real world data show the robustness of the algorithm, the speed of the process and its potential in lightweight, economical real- time applications.

关键词： computer vision feature extraction image colour analysis image motion analysis image sequences object detection surveillance target tracking color intensity head detection human detection human tracking linear motion model monocular image sequence moving image sequences image motion analysis COLOR STRENGTH image colour analysis Object detection Target tracking Moving computer vision Feature extraction Detective cameras

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An automatic wavelet-based approach for lung segmentation and density analysis in dynamic CT

An automatic wavelet-based approach for lung segmentation an...

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ieee symposium on computational intelligence in image and signal processing

作者： Talakoub, Omid Helm, Emma Alirezaie, Javad Babyn, Paul Kavanagh, Brian Grasso, Francesco Engelberts, Doreen Ryerson Univ Dept Elect & Comp Engn Toronto ON Canada Hosp Sick Children Dept Med Imaging Toronto ON Canada Univ Toronto Dept Med Imaging Toronto ON Canada Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada Hosp Sick Children Dept Crit Care Med Toronto ON Canada

ISBN: (纸本)9781424407071

Acute respiratory distress syndrome (ARDS) can occur in people with or without previous lung disease. Analysis of aeration in artificial ventilation for ARDS is one of the major applications of Computed Tomography (CT) lung density examination. A movie of an affected rabbit lung over the respiratory cycle was produced by dynamic CT with a cine loop technique. This technique can produce thousands of CT images for analysis with a single experiment. A fully automated algorithm based on the capability of wavelet transformation to detect edges in the image is proposed. This method accurately and consistently segments the lung in pulmonary CT images. The speed and accuracy of this technique allows it to outperform other methods when dealing with the large number of images created by dynamic Computed Tomography.

关键词： image segmentation

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Evolutionary algorithms in the classification of mammograms

Evolutionary algorithms in the classification of mammograms

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ieee symposium on computational intelligence in image and signal processing

作者： Hope, D. C. Munday, E. Smith, S. L. Univ York Dept Elect York YO10 5DD N Yorkshire England

ISBN: (纸本)9781424407071

The application of pattern recognition techniques to radiology has the potential to detect cancer earlier and save lives, and consequently much research has been devoted to this problem. This worked tackled a subset of the problem, investigating a novel method of classifying mammograms using an evolutionary approach known as Cartesian Genetic Programming (CGP). Microcalcifications, one of two major indicators of cancer on mammograms, were used for the classification. A large software framework was written in order to investigate this, which allows the viewing of images, manual segmentation of lesions and then automatic classification. Two classification approaches were pursued, the first classifying on texture features and the second, a new approach, classifying by using the lesion's raw pixel array. Early results using the system showed some potential. It was found that during training, networks could obtain correct classification rates of between 80 and 100%. The best results were approaching those in the contemporary literature and suggest the technique warrants further investigation.

关键词： cancer diagnostic radiography feature extraction genetic algorithms image classification image texture mammography medical image processing Cartesian genetic programming cancer detection cancer indicators evolutionary algorithms lesi image classification evolutionary algorithm Genetic Algorithm (GA) diagnostic radiography image texture Medical image processing cancer test Feature extraction pictorial pattern recognition algorithm

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Influence of signal pre-processing in the efficiency of algorithms based on neural networks for disturbance classification

Influence of signal pre-processing in the efficiency of algo...

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ieee symposium on computational intelligence in image and signal processing

作者： de Medeiros, Manoel F., Jr. Santos, Crisluci K. S. de Oliveira, Jose T. Pires, Paulo S. da M. de Melo, Jorge D. Doria Neto, Adriao D. Leitao, Jose J. A. L. Univ Fed Rio Grande do Norte DCA Natal RN Brazil CHESF Recife PE Brazil

ISBN: (纸本)9781424407071

Post dispatch analysis of signals obtained from digital disturbances registers provide important information to identify and classify disturbances in power systems, looking for a more efficient management of the supply. In order to enhance the task of identifying and classifying the disturbances - providing an automatic assessment - techniques of digital signal processing can be helpful. The Wavelet Transform has become a very efficient tool for the analysis of voltage or current signals, obtained immediately after disturbances occurrences in the network. This paper presents a methodology based on the Discrete Wavelet Transform to implement this process. It uses a comparison between distribution curves of signals energy, with and without disturbance. This is done for different resolution levels of its decomposition in order to obtain descriptors that permit its classification, using artificial neural networks.

关键词： Digital signal processing

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