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共 276 条记录，以下是71-80 订阅

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Proceedings of the 2013 ieee symposium on computational intelligence for Multimedia, signal and Vision processing, CIMSIVP 2013 - 2013 ieee symposium Series on computational intelligence, SSCI 2013

Proceedings of the 2013 IEEE Symposium on Computational Inte...

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2013 ieee symposium on computational intelligence for Multimedia, signal and Vision processing, CIMSIVP 2013 - 2013 ieee symposium Series on computational intelligence, SSCI 2013

ISBN: (纸本)9781467359177

The proceedings contain 8 papers. The topics discussed include: efficient determination of spatial relations using composition tables and decision trees;smooth transition neighborhood graphs for 3D spatial relations;biomimetic target detection: modeling 2nd order correlation of OFF and ON channels;a novel algorithm for detecting dim point target in infrared images based on MRF;robust gesture detection and recognition using dynamic time warping and multi-class probability estimates;regularized high temporal resolution fish motion synthesis;using fuzzy rough feature selection for image retrieval system;and forward-backward minima controlled recursive averaging to speech enhancement.

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Incremental Semi-Supervised Fuzzy Clustering for Shape Annotation

Incremental Semi-Supervised Fuzzy Clustering for Shape Annot...

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

作者： Castellano, Giovanna Fanelli, Anna Maria Torsello, Maria Alessandra Univ Bari A Moro Dept Informat Bari Italy

ISBN: (纸本)9781479945030

In this paper, we present an incremental clustering approach for shape annotation, which is useful when new sets of images are available over time. A semi-supervised fuzzy clustering algorithm is used to group shapes into a number of clusters. Each cluster is represented by a prototype that is manually labeled and used to annotate shapes belonging to that cluster. To capture the evolution of the image set over time, the previously discovered prototypes are added as pre-labeled objects to the current shape set and semi-supervised clustering is applied again. The proposed incremental approach is evaluated on two benchmark image datasets, which are divided into chunks of data to simulate the progressive availability of images during time.

关键词： fuzzy set theory image classification learning (artificial intelligence) pattern clustering incremental approach incremental semisupervised fuzzy clustering progressive availability simulation shape annotation Clustering algorithms Context History Marine animals Prototypes Shape Transform coding

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2D Ultrasound image Segmentation Using Graph Cuts and Local image Features

2D Ultrasound Image Segmentation Using Graph Cuts and Local ...

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ieee symposium on computational intelligence in image and signal processing

作者： Zouqi, Mehrnaz Samarabandu, Jagath Univ Western Ontario Dept Elect & Comp Engn London ON Canada

ISBN: (纸本)9781424427604

Ultrasound imaging is a popular imaging modality due to a number of favorable properties of this modality. However, the poor quality of ultrasound images makes them a bad choice for segmentation algorithms. In this paper, we present a semi-automatic algorithm for organ segmentation in ultrasound images, by posing it as an energy minimization problem via appropriate definition of energy terms. We use graph-cuts as our optimization algorithm and employ a fuzzy inference system (FIS) to further refine the optimization process. This refinement is achieved by using the FIS to incorporate domain knowledge in order to provide additional constraints. We show that by integrating domain knowledge via FIS, the accuracy is improved significantly so that further manual refinement of object boundary is often unnecessary. Our algorithm was applied to detect prostate and carotid artery boundaries in clinical ultrasound images and shows the success of the proposed approach.

关键词： Graphic methods

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A Design of Versatile image processing Platform Based on the Dual Multi-core DSP and FPGA

A Design of Versatile Image Processing Platform Based on the...

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5th International symposium on computational intelligence and Design (ISCID)

作者： Zhan, ZhenHuan Hao, Wei Tian, Yan Yao, DaWei Wang, XianHong Chinese Acad Sci Grad Univ Beijing Peoples R China

ISBN: (纸本)9780769548111

As an application of TI's latest multi-core DSP chip TMS320C6678 and Xilinx's FPGA chip XC5VLX110T, This paper designed and implemented a dual-DSP and FPGA real-time image processing system based on the Serial Rapid IO (SRIO), Hyperlink and reconfigurable technology. It used TMS320C6678 on-chip SRIO and Hyperlink interface module, XC5VLX110T on-chip Rocket IO modules, reconfigurable technology to implement a DSP and FPGA loosely coupled parallel interconnection reconfigurable system. On Embedded operating system's DSP / BIOS architecture, this paper implemented the program of hardware driver of bottom layer and the corresponding data transfer procedures, and also completed the transmission of digital images.

关键词： multi-core digital signal processor (DSP) field programmable gate array (FPGA) serial rapid input output (SRIO) Hyperlink Reconfigurable technology

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Stereoscopic Video Description for Human Action Recognition

Stereoscopic Video Description for Human Action Recognition

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

作者： Mademlis, Ioannis Iosifidis, Alexandros Tefas, Anastasios Nikolaidis, Nikos Pitas, Ioannis Aristotle Univ Thessaloniki Dept Informat Thessaloniki Greece

ISBN: (纸本)9781479945030

In this paper, a stereoscopic video description method is proposed that indirectly incorporates scene geometry information derived from stereo disparity, through the manipulation of video interest points. This approach is flexible and able to cooperate with any monocular low-level feature descriptor. The method is evaluated on the problem of recognizing complex human actions in natural settings, using a publicly available action recognition database of unconstrained stereoscopic 3D videos, coming from Hollywood movies. It is compared both against competing depth-aware approaches and a state-of-the-art monocular algorithm. Experimental results denote that the proposed approach outperforms them and achieves state-of-theart performance.

关键词： feature extraction geometry object recognition stereo image processing video signal processing visual databases Hollywood movies action recognition database depth-aware approach human action recognition monocular algorithm monocular low-level feature descriptor scene geometry information stereo disparity stereoscopic video description unconstrained stereoscopic 3D videos video interest point manipulation Cameras Erbium image color analysis Pattern recognition Stereo image processing Three-dimensional displays Vectors Stereo image processing three-dimensional displays image colour analysis visual databases Video signal processing Erbium Cameras object recognition Pattern recognition Feature extraction Stereoscopic Videotapes geometry

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signal Detection in Three-Dimensional Confocal Microscopy images through Deep Learning 18

Signal Detection in Three-Dimensional Confocal Microscopy Im...

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18th ieee International symposium on Applied computational intelligence and Informatics (SACI)

作者： Paulik, Robert Kozlovszky, Miklos Molnar, Bela Obuda Univ 3DHISTECH Ltd Budapest Hungary Obuda Univ BioTech Res Ctr Budapest Hungary 3DHISTECH Ltd Image Anal Dept Budapest Hungary

ISBN: (纸本)9798350329537;9798350329520

As routine pathology moves into the digital age;the spread of high-efficiency and high-resolution tissue scanners opens up the possibility of routine analysis of three-dimensional samples containing fluorescent genetic signals. One of these cornerstones is confocal microscopy, with the help of which cell nuclei and their signals can be imaged in three dimensions. This article presents a novel deep learning-based algorithm for detecting signals within three-dimensional confocal microscopy images. By leveraging the power of convolutional neural networks, our approach significantly improves the accuracy and efficiency of signal detection compared to traditional image processing methods, especially in the case of thick sections. We demonstrate the algorithmic effectiveness through validation on various samples, highlighting its potential to advance research in biology and medicine.

关键词： confocal microscope 3D nuclei signal detection convolutional neural network deep learning U-Net

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Contextual Classification of High-Resolution Satellite images

Contextual Classification of High-Resolution Satellite Image...

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ieee symposium on computational intelligence in image and signal processing

作者： Besbes, Olfa Boujemaa, Nozha Belhadj, Ziad IMEDIA INRIA Rocquencourt F-78135 Le Chesnay France URISA SUPCOM Tunis Tunisia

ISBN: (纸本)9781424427604

We propose a non-homogeneous Conditional Random Field built over an adjacency graph of superpixels for contextual classification of high-resolution satellite images. By introducing the contextual histogram descriptor, our model includes spatially dependent unary and pairwise potentials that capture contextual interactions of the data as well as the labels. This results the non-homogeneity of the fields which improves the accuracy of the classification. Furthermore, our discriminative model performs a multi-cue combination by incorporating efficiently color, texture, edge, curvilinear continuity and familiar configuration cues. As for potentials, both local and global feature functions are learned using joint boosting whereas a likelihood ratio is learned to derive the pairwise edge potential. In this model, the optimal scene interpretation is inferred using a cluster sampling method, the Swendsen-Wang Cut algorithm. Promising results are shown on SPOT-5 satellite images.

关键词： image classification

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Automatic Tumor Lesion Detection and Segmentation Using Histogram-Based Gravitational Optimization Algorithm

Automatic Tumor Lesion Detection and Segmentation Using Hist...

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

作者： Nabizadeh, Nooshin Dorodchi, Mohsen Univ Miami Dept Elect & Comp Engn Coral Gables FL 33146 USA Univ N Carolina Dept Comp Sci Charlotte NC 28223 USA

ISBN: (纸本)9781479945030

In this paper, an automated and customized brain tumor segmentation method is presented and validated against ground truth applying simulated T1-weighted magnetic resonance images in 25 subjects. A new intensity-based segmentation technique called histogram based gravitational optimization algorithm is developed to segment the brain image into discriminative sections (segments) with high accuracy. While the mathematical foundation of this algorithm is presented in details, the application of the proposed algorithm in the segmentation of single T1-weighted images (T1-w) modality of healthy and lesion MR images is also presented. The results show that the tumor lesion is segmented from the detected lesion slice with 89.6% accuracy.

关键词： brain segmentation tumor detection magnetic resonance image histogram-based gravitational optimization algorithm

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Using Fuzzy Rough Feature Selection for image Retrieval System

Using Fuzzy Rough Feature Selection for Image Retrieval Syst...

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

作者： Lotfabadi, Maryam Shahabi Shiratuddin, Mohd Fairuz Wong, Kok Wai Murdoch Univ Sch Informat Technol Murdoch WA 6150 Australia

ISBN: (纸本)9781467359177

Feature selection is an important step in processing the images especially for applications such as content based image retrieval. In large multimedia databases, it may not be practical to search through the entire database in order to retrieve similar images from a query. Good data structures for similarity search and indexing are needed, and the existing data structures do not scale well for the high dimensional multimedia descriptors. Thus feature selection is an important step. Fuzzy rough feature selection method has many advantages in determining the relevant features. In this paper, five feature selection methods are compared with the fuzzy rough method. These five feature selection methods are Relief-F, Information Gain, Gain Ratio, OneR and the statistical measure chi(2). The main purpose of the comparison is to rank the image features and see which method provides better results. An image retrieval dataset (COREL dataset) was used in the comparison. In order to evaluate the performance of the six methods, ranking of the important features is defined. This is then used to compare with the automated ranking produced by the aforesaid feature selection methods. Results show that the retrieval system using fuzzy rough feature selection has better retrieval accuracy and provide good Precision Recall performance. The advantages of the use of fuzzy rough feature selection will also be discussed in the paper.

关键词： fuzzy rough set feature selection image retrieval system

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Variational Inference of Finite Generalized Gaussian Mixture Models

Variational Inference of Finite Generalized Gaussian Mixture...

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ieee symposium Series on computational intelligence (SSCI)

作者： Amudala, Srikanth Ali, Samr Najar, Fatma Bouguila, Nizar Concordia Univ Concordia Inst Informat Syst Engn Montreal PQ Canada Concordia Univ Dept Elect & Comp Engn Montreal PQ Canada

ISBN: (纸本)9781728124858

This paper presents a variational learning framework to analyze finite g eneralized G aussian m ixture models (GGMM). The model incorporates several mixtures that are widely used in signal and image processing applications. The motivation behind this work is the shape flexibility characteristics of the generalized Gaussian distribution (GGD) because of which it can be applied to different types of data. We present a method to evaluate the posterior distribution and Bayes estimators using the variational expectation-maximization algorithm. The effective number of components of the GGMM is determined automatically. The test results show the adequacy of the proposed algorithm by applying it to medical, astrological, and image segmentation applications;while comparing it with various other approaches.

关键词： generalized gaussian mixture model variational inference taylor approximation binary classification image segmentation

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