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

共 2926 条记录，以下是381-390 订阅

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image Super-Resolution Method based on Non-Local Means and Self Similarity

Image Super-Resolution Method based on Non-Local Means and S...

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International symposium on Intelligent signal processing and Communications Systems (ISPACS)

作者： Yoshida, Taichi Murakami, Tomoya Ikehara, Masaaki Keio Univ EEE Dept Yokohama Kanagawa 2238522 Japan

ISBN: (纸本)9781467363617;978146736360+0

In this paper, we propose an image super-resolution method based on the non-local means and the self similarity. Various super-resolution methods can correctly estimate the missing high frequency components of enlarged images. However, they mostly require high computational costs, which is not suitable for real-time processing. For a super-resolution with low computational costs, the proposed method is simply realized via the block matching technique with a small search area. Since it utilizes the image self similarity and sparsity, it produces visually efficient interpolated images. In the simulation, it is shown that the proposed method greatly outperforms the bicubic in a visual quality of enlarged images, objectively and perceptually.

关键词： image super-resolution self similarity non-local means sparse representation

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Channel selection in EEG-based prediction of shoulder/elbow movement intentions involving stroke patients: A computational approach

Channel selection in EEG-based prediction of shoulder/elbow ...

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ieee symposium on computational intelligence in Bioinformatics and computational Biology

作者： Zhou, Jie Yedida, Sundeep Northern Illinois Univ Dept Comp Sci De Kalb IL 60115 USA

ISBN: (纸本)9781424407101

Brain computer Interface (BCI) has gained a lot of attention recently, as a means to detect individuals' intents using brain signals such as electroencephalographic (EEG) for control of machines. In order to achieve the possible use of BCI in stroke rehabilitation, computational intelligent algorithms are important for reliable separation of shoulder versus elbow movement intentions. Efforts have been made on developing data processing and classification algorithm for such task Differently, this paper investigates the optimal use of electrodes and signal channels, which is formulated as a data-driven feature selection problem. 163 EEG electrodes are used to collect scalp recordings to predict shoulder abduction and elbow flexion intentions in healthy and stroke subjects. We combine the support vector channel selection with a time-frequency synthesized classification algorithm and examine the performances of using different subsets of channel inputs. Preliminary results show that 1) a reduced number of electrodes can be used to achieve the same or better performance than using the full set of signal channels;2) besides the fact that the accuracy on able-bodied subjects is expectedly higher than the stroke subject, the stroke subject tends to need more electrodes to achieve the best performance;3) visualization of spatial distribution of channel rankings shows reasonable connection with functional motor cortex areas.

关键词： Electrodes

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Entropy Dependent Compressive Sensing based image Fusion

Entropy Dependent Compressive Sensing based Image Fusion

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International symposium on Intelligent signal processing and Communications Systems (ISPACS)

作者： Jameel, Amina Ghafoor, Abdul Riaz, Muhammad Mohsin Natl Univ Sci & Technol Dept Elect Engn Coll Signals Islamabad Pakistan

ISBN: (纸本)9781467363617;978146736360+0

An entropy dependent compressive sensing based image fusion is proposed which adaptively adjusts the number of compressive measurements depending on the amount of information (computed using entropy). Since the computational complexity is dependent on the number of compressive measurements, the proposed scheme is more efficient and accurate. Simulation results show the significance of the proposed scheme compared to existing compressive sensing based state of art scheme.

关键词： image fusion compressive sensing image entropy

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An Adaptive Method for Vein Recognition Enhancement Using Deep Learning 19

An Adaptive Method for Vein Recognition Enhancement Using De...

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19th ieee International symposium on signal processing and Information Technology (ISSPIT)

作者： Varastehpour, Soheil Sharifzadeh, Hamid Ardekani, Iman Baghaei, Nilufar Francis, Xavier Unitec Inst Technol Sch Comp Auckland New Zealand Otago Polytech Dept Informat Technol Auckland Campus OPAIC Auckland New Zealand

ISBN: (纸本)9781728153414

Authentication methods based on some human traits including fingerprints, face, and palm prints, have developed significantly, and currently are sophisticated enough to be reliably considered for identification purposes. As state-of-the-art techniques, computational methods have recently been introduced to extract vein patterns for identification purposes. However, these methods suffer in a lack of image enhancement which the vein pattern cannot be uncovered properly. To address image enhancement weaknesses, this paper proposes a new image enhancement method based on deep learning, which can improve the robustness of vein recognition and feature extraction. Making use of deep neural network techniques, a sparse auto-encoder algorithm is applied to develop an adaptive method for image enhancement. We use a pair of synchronised colour and near-infrared images for verifying the outcome of the proposed method and measure the length of veins. The experimental results on 605 colour images and 605 infrared images from 66 forearms, 66 thighs, 33 chests, 33 ankles, and 50 random internet images show that the proposed enhancement method significantly improves the feature extraction procedure.

关键词： image enhancement forensics vein pattern deep learning

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Cone-beam Computed Tomography image Pretreatment and Segmentation 11

Cone-beam Computed Tomography Image Pretreatment and Segment...

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11th International symposium on computational intelligence and Design (ISCID)

作者： Zheng, Jia Zhang, Dinghua Huang, Kuidong Sun, Yuanxi Northwestern Polytech Univ Minist Educ Key Lab Contemporary Design & Integrated Mfg Tech Xian Shaanxi Peoples R China

ISBN: (纸本)9781538685273

Segmentation accuracy is critical in CBCT (cone-beam computed tomography) nondestructive detection. And it is influenced by the segmentation accuracy of CBCT serial slice images. However, the noise and artifacts in CBCT images make it hard to segment CBCT images precisely. To increase CBCT image segmentation accuracy, the 3D information in CBCT images should be fully used. We proposed and compared four connection models for CBCT images pretreatment. They can decrease the noise in CBCT images. Moreover, we propose a 3D CBCT image segmentation method based on the accumulated FCM_S. In the experiment, CBCT slice images of a workpiece are segmented by our proposed method and comparing methods. The segmentation results certified the effectiveness of our method.

关键词： CBCT image processing segmentation

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Finding behaviour patterns along highways by image processing

Finding behaviour patterns along highways by image processin...

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8th International symposium of Hungarian Researchers on computational intelligence and Informatics, CINTI 2007

作者： Max, Gyula Department of Automatization and Applied Informatics Budapest University of Technology and Economics Po. Box. 91 H-1521 Budapest Hungary

We spend more and more time on travelling. Travelling by metro or tram gives us almost certain time to get home. And what about is travelling by car? Many persons travel hours to go working or to get home. In rush time the main roads and highways are full of cars. Can we simulate drivers' behaviour and their decision making process? This paper tries to find patterns in drivers' behaviour and simulate these processes.

关键词： image processing

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Modified Block Matching Algorithm Improving Rate-Distortion Performance for Stereoscopic image Coding 15

Modified Block Matching Algorithm Improving Rate-Distortion ...

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ieee International symposium on signal processing and Information Technology (ISSPIT)

作者： Kadaikar, Aysha Dauphin, Gabriel Mokraoui, Anissa Univ Paris 13 Inst Galilee L2TI 99 Ave Jean Baptiste Clement F-93430 Villetaneuse France

ISBN: (纸本)9781509004805

This paper deals with the block-based disparity map estimation of a stereoscopic image. While most existing algorithms estimate this map by minimizing a dissimilarity metric, the proposed optimization algorithm aims at minimizing the rate distortion compromise using the disparity map yielded by the traditional block matching algorithm as an initial reference map. The developed algorithm analyzes the performance impact of the permutation of each disparity of the reference map with all possible disparities. The retained disparity is one that improves the joint rate-distortion metric. This process is repeated as long as improvements are observed. Moreover, a particular attention is given to the updating process of the joint metric so that the algorithm computational cost is not affected. Simulation results clearly show that our approach achieves better performance than the traditional block matching algorithm in terms of rate-distortion compromise.

关键词： Block matching algorithm Optimization Disparity estimation Entropy Distortion

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Learning Features and their Transformations from Natural Videos

Learning Features and their Transformations from Natural Vid...

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ieee symposium on computational intelligence in Dynamic and Uncertain Environments (CIDUE)

作者： Dutta, Jayanta K. Banerjee, Bonny Univ Memphis Inst Intelligent Syst Memphis TN 38152 USA Univ Memphis Dept Elect & Comp Engn Memphis TN 38152 USA

ISBN: (纸本)9781479945153

Learning features invariant to arbitrary transformations in the data is a requirement for any recognition system, biological or artificial. It is now widely accepted that simple cells in the primary visual cortex respond to features while the complex cells respond to features invariant to different transformations. We present a novel two-layered feedforward neural model that learns features in the first layer by spatial spherical clustering and invariance to transformations in the second layer by temporal spherical clustering. Learning occurs in an online and unsupervised manner following the Hebbian rule. When exposed to natural videos acquired by a camera mounted on a cat's head, the first and second layer neurons in our model develop simple and complex cell-like receptive field properties. The model can predict by learning lateral connections among the first layer neurons. A topographic map to their spatial features emerges by exponentially decaying the flow of activation with distance from one neuron to another in the first layer that fire in close temporal proximity, thereby minimizing the pooling length in an online manner simultaneously with feature learning.

关键词： Hebbian learning feedforward neural nets image sensors object recognition pattern clustering unsupervised learning video signal processing Hebbian rule arbitrary transformations artificial recognition system biological recognition system camera cat head complex cell-like receptive field properties complex cells first layer neurons learning features natural videos pooling length primary visual cortex spatial spherical clustering temporal proximity temporal spherical clustering topographic map two-layered feedforward neural model Biological system modeling computational modeling Feedforward neural networks Neurons Predictive models Radio frequency Videos feedforward neural nets Videotapes Biological system modeling Striate Cortex Hebbian principle Sensors (image) Video signal processing Unsupervised learning Cameras radiofrequency Cluster Analysis recognition system Neurons computational modeling Prediction models

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Composite techniques for quality analysis in automotive laser welding

Composite techniques for quality analysis in automotive lase...

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ieee International symposium on computational intelligence for Measurement Systems and Applications

作者： Alippi, C D'Angelo, G Matteucci, M Pasquettaz, G Piuri, V Scotti, F Politecn Milan Dept Elect & Informat I-20133 Milan Italy

ISBN: (纸本)0780377834

Real-time monitoring of the laser-based applications is becoming a main issue for quality analysis in the steel manufacturing industry. The paper suggests a solution achieving an automated real-time quality inspection in laser welding applications. A composite system composed of soft-computing and traditional techniques has been considered for its positive impact on the reduced computational once compared with more traditional approaches.

关键词： Automotive engineering Chromium Gears Information technology Inspection Laser tuning Power lasers Ring lasers signal processing algorithms Welding

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Interactive Evolutionary Parameter Optimization for Eulerian Video Magnification 8

Interactive Evolutionary Parameter Optimization for Eulerian...

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8th ieee symposium Series on computational intelligence (ieee SSCI)

作者： Rizvi, Syed Raazi Rahnamayan, Shahryar UOIT Dept Elect Comp & Software Engn Nat Inspired Computat Intelligence NICI Lab Oshawa ON Canada

ISBN: (纸本)9781538692769

This paper explores control parameter tuning for the Eulerian Video Magnification (EVM) process using a pair comparison-based interactive differential evolution (PCB-IDE) algorithm. Interactive evolutionary optimizers have a history of being applied to video/image post-processing tasks because assessing the quality of video data is objectively difficult without subjective assessment by users. The EVM technique magnifies motion and color variations within a video in a manner similar to a visual microscope. Applying EVM to video sequences allows the observation of known physical phenomenon by using only video data, which is critical to development of camera based monitoring applications. The proposed PCB IDE assists experts in determining the optimal parameters for EVM process to better identify visual changes and correlate them to physical phenomenons.

关键词： Eulerian Video Magnification Parameter Estimation Video Post-processing Interactive Differential Evolution

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