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

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Low-cost photoplethysmograph solutions using the Raspberry Pi

Low-cost photoplethysmograph solutions using the Raspberry P...

引用

14th ieee International symposium on computational intelligence and Informatics (CINTI)

作者： Nagy, Tamas Gingl, Zoltan Univ Szeged Dept Tech Informat Szeged Hungary

ISBN: (纸本)9781479901944;9781479901975

Photoplethysmography is a prevalent, non-invasive heart monitoring method. In this paper an implementation of photoplethysmography on the Raspberry Pi is presented. Two modulation techniques are discussed, which make possible to measure these signals by the Raspberry Pi, using an external sound card as A/D converter. Furthermore, it is shown, how can digital signal processing improve signal quality. The presented methods can be used in low-cost cardiac function monitoring, in telemedicine applications and in education as well, since cheap and current hardware are used. Full documentation and open-source software for the measurement available: http://***. hu/Instruments/raspberryplet/

关键词： analogue-digital conversion digital signal processing chips medical signal processing patient monitoring photoplethysmography

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cobICA: A Concentration-Based, Immune-Inspired Algorithm for ICA Over Galois Fields

cobICA: A Concentration-Based, Immune-Inspired Algorithm for...

引用

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

作者： Silva, Daniel G. Montalvao, Jugurta Attux, Romis Univ Brasilia Dept Elect Engn Brasilia DF Brazil Univ Estadual Campinas Campinas SP Brazil Univ Fed Sergipe Sao Cristovao SE Brazil Univ Estadual Campinas DSPCom Lab Campinas SP Brazil

ISBN: (纸本)9781479945030

An interesting and recent application of population-based metaheuristics resides in an unsupervised signal processing task: independent component analysis (ICA) over finite fields. Based on a state-of-the-art immune-inspired method, this work proposes a new ICA algorithm for finite fields of arbitrary order that employs mutation and local search operators specifically customized to the problem domain. The results obtained with the new technique indicate that the proposal is effective in performing component separation, and the analysis includes a preliminary study on image separation.

关键词： Galois fields independent component analysis mathematical operators search problems signal processing arbitrary order cobICA component separation concentration-based algorithm finite fields image separation immune-inspired algorithm immune-inspired method local search operator mutation operator population-based metaheuristics unsupervised signal processing task Algorithm design and analysis Cloning Entropy Immune system Search problems Sociology Statistics

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Analysis of Three-Dimensional Vasculature Using Multifractal Theory 2

Analysis of Three-Dimensional Vasculature Using Multifractal...

引用

ieee symposium on computational intelligence in Healthcare and e-health

作者： Ward, Wil O. C. Ding, Yuchun Bai, Li Univ Nottingham Sch Comp Sci Nottingham England

ISBN: (纸本)9781479953752

This paper investigates the use of multifractal formalism for characterising 3D brain vasculature of 2 different mammalian species. Multifractal properties were found across all the 3D vascular models. Variations in the analysis results appear to correspond with vessel density ans morphology. The implication of the research is that multifractal analysis could potentially provide a useful tool for clinical assessment of diseases that are known to alter density and structure of brain microvasculature.

关键词： fractals biomedical image processing multi-dimensional signal processing

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Hybridization of Particle Swarm Optimization with the K-Means Algorithm for image Classification

Hybridization of Particle Swarm Optimization with the K-Mean...

引用

ieee symposium on computational intelligence in image and signal processing

作者： Hung, Chih-Cheng Wan, Li So Polytech State Univ Sch Comp & Software Engn Marietta GA 30060 USA

ISBN: (纸本)9781424427604

The K-means algorithm is one of the widely used clustering algorithms in the image classification systems. However, the K-Means algorithm is easily trapped into the local optimal solutions. Several optimization techniques have been proposed to solve this problem such as genetic algorithms, simulated annealing and swarm intelligence. In this paper, we develop hybrid techniques using different particle swarm optimization (PSO) heuristics to optimize the K-Means algorithm and examine the reliability of parametric values for different variants of PSO and K-means algorithms. These PSO heuristics include linear inertia reduction, constriction factor, and dynamic inertia and maximum velocity reduction. The performance of these hybridization of PSO and the K-means algorithms was tested on the image segmentation. These PSO heuristics can make the K-means algorithm more stable for finding better solutions and less dependent on the initial cluster centers based on the preliminary experimental results.

关键词： Particle swarm optimization (PSO)

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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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Research of Sorting Technology based on Industrial Robot of Machine Vision image processing and machine vision

<i>Research of Sorting Technology based on Industrial Robot ...

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

作者： Liu Zhen-yu Zhao Bin Zhu Hai-bo Shenyang Univ Technol Informat Sci & Engn Sch Shenyang 110870 Peoples R China

ISBN: (纸本)9780769548111;9781467326469

Aimed at the industrial sorting technology problems, this paper researched correlative algorithm of image processing and analysis, and completed the construction of robot vision sense. The operational process was described as follows: The camera acquired image sequences of the metal workpiece in the sorting region. image sequence was analyzed to use algorithms of image pre-processing, Hough circle detection, corner detection and contour recognition. In the mean time, this paper also explained the characteristics of three main function model (image pre-processing, corner detection and contour recognition), and proposed algorithm of multi-objective center and a corner recognition. The simulated results show that the sorting system can effectively solve the sorting problem of regular geometric workpiece, and accurately calculate center and edge of geometric workpiece to achieve the sorting purpose.

关键词： component machine vision industrial robot sorting target recognition image processing

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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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17th ieee International symposium on computational intelligence and Informatics (CINTI)

作者： Gludovatz, Attila Bacsardi, Laszlo Univ West Hungary Inst Econ & Informat Sopron Hungary

ISBN: (纸本)9781509039098

In 2011, the 4th industrial revolution started. Industry 4.0 is a trend in this industrial area which affect the production lines as well. In this paper, we show eight selected case studies which illustrate the different heterogonous systems which are used in Hungarian factories to support its production. A case-study is chosen in which image processing tools are applied in the operation of a furniture company.

关键词： image processing

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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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