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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81-90 订阅

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Multivariate PDF Matching via Kernel Density Estimation

Multivariate PDF Matching via Kernel Density Estimation

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

作者： Fantinato, Denis G. Boccato, Levy Attux, Romis Neves, Aline Univ Estadual Campinas Sch Elect & Comp Engn Campinas SP Brazil Fed Univ ABC Engn Modeling & Appl Social Sci Ctr Sao Andre SP Brazil

ISBN: (纸本)9781479945030

In this work, a measure of similarity based on the matching of multivariate probability density functions (PDFs) is proposed. In consonance with the information theoretic learning (ITL) framework, the affinity comparison between the joint PDFs is performed using a quadratic distance, estimated with the aid of the Parzen window method with Gaussian kernels. The motivation underlying this proposal is to introduce a criterion capable of quantifying, to a significant extent, the statistical dependence present on information sources endowed with temporal and/or spatial structure, like audio, images and coded data. The measure is analyzed and compared with the canonical ITL-based approach - correntropy - for a set of blind equalization scenarios. The comparison includes elements like surface analysis, performance comparison in terms of bit error rate and a qualitative discussion concerning image processing. It is also important to remark that the study includes the application of two computational intelligence paradigms: extreme learning machines and differential evolution. The results indicate that the proposal can be, in some scenarios, a more informative formulation than correntropy.

关键词： Gaussian processes evolutionary computation image processing learning (artificial intelligence) probability Gaussian kernel ITL framework Parzen window method blind equalization scenario computational intelligence paradigm correntropy approach differential evolution extreme learning machines information theoretic learning framework kernel density estimation multivariate PDF matching probability density function quadratic distance statistical dependence computational efficiency Estimation Joints Kernel Measurement Probability density function Vectors

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Endoscope image Analysis Method for Evaluating the Extent of Early Gastric Cancer

Endoscope Image Analysis Method for Evaluating the Extent of...

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

作者： Hiroyasu, Tomoyuki Hayashinuma, Katsutoshi Ichikawa, Hiroshi Yagi, Nobuaki Yamamoto, Utako Doshisha Univ Fac Life & Med Sci Kyoto Japan Doshisha Univ Grad Sch Life & Med Sci Kyoto Japan Asahi Univ Murakami Mem Hosp Dept Gastroenterol Gifu Japan

ISBN: (纸本)9781479945030

In this study, a system is proposed to help physicians perform processing on images taken with a magnifying endoscopy with narrow band imaging. In our proposed system, the transition from lesion to normal zone is quantitatively analyzed and presented by texture analysis. Eleven feature values are calculated, i.e., six from a co-occurrence matrix and five from a run length matrix with a scanning window. Integrating these feature values formulates an effective and representative feature value, which is used to draw a color map, so the transition from lesion to normal zone can be visibly illustrated. In this paper, the proposed method is applied to images, and the efficacy is considered. This method is also applied to some rotated images to examine whether it could work effectively on such images.

关键词： cancer endoscopes feature extraction image colour analysis image texture matrix algebra medical image processing color map cooccurrence matrix early gastric cancer endoscopic image analysis image processing representative feature value run length matrix texture analysis Cancer Educational institutions Endoscopes Feature extraction image color analysis Lesions

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Multiresolution superpixels for visual saliency detection

Multiresolution superpixels for visual saliency detection

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

作者： Singh, Anurag Chu, Chee-Hung Henry Pratt, Michael A. Univ Louisiana Lafayette Ctr Adv Comp Studies Lafayette LA 70504 USA Univ Louisiana Lafayette William Hansen Hall Dept Elect & Comp Engn Lafayette LA 70504 USA

ISBN: (纸本)9781479945030

Salient regions are those that stand out from others in an image. We present an algorithm to detect salient regions in an image that is represented as superpixels at a number of resolutions. Superpixels are segments generated by oversegmenting an image and they form a perceptually meaningful representation that preserves the underlying image structure. The novelty of our method is the ranking of a superpixel by its dissimilarities with respect to other superpixels and highlighting the statistically salient region proportional to their rank. This is based on the premise that salient region group together and they stand out. We tested our method using standard data sets containing images of varied complexity and compared the results to ground truth data. Our results show that our saliency detection algorithm is robust to changes in color, object size, object location in image and background type.

关键词： image Perception Saliency Foreground-background separation Superpixel Multi-resolution analysis

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Visual Weighted SPECK image Coding Based on WBCT 8

Visual Weighted SPECK Image Coding Based on WBCT

引用

8th International symposium on computational intelligence and Design (ISCID)

作者： Tang, Guowei Liu, Yantong Zhang, Yan Northeast Petr Univ Sch Comp & Informat Technol Daqing Peoples R China

ISBN: (纸本)9781467395878

Wavelet transform is not the optimal or the most sparse function representation for two-dimension images. Contourlet transform, as a kind of Multi-scale Geometric Analysis (MGA) methods, extends the advantages of wavelet transform to high dimensional space. It can describe the properties of high dimensional information better and is more suitable for processing information with hyper-plane singularity. In this paper, a visual weighted image coding algorithm was proposed, in which Wavelet-Based Contourlet Transform (WBCT) was combined with SPECK algorithm. According to the sensitivity of human vision to different wavelet sub-bands, each sub-band was endued with different weights. The wavelet coefficients were organized in way of set partitioning and SPECK algorithm was applied to carry out image coding procedure. Experiment results show that the proposed method not only improve the Peak signal-to-Noise Ratio (PSNR) of the reconstructed images, but also reduce the local texture distortion obviously.

关键词： image Coding Wavelet Transform Contourlet Transform WBCT SPECK

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Color image cryptosystem using chaotic maps

Color image cryptosystem using chaotic maps

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symposium Series on computational intelligence, ieee SSCI 2011 - 2011 ieee symposium on computational intelligence for Multimedia, signal and Vision processing, CIMSIVP 2011

作者： Mazloom, Sahar Eftekhari-Moghadam, Amir-Masud Faculty of Electrical Computer and IT Engineering Qazvin Islamic Azad University Qazvin Iran

ISBN: (纸本)9781424499151

image encryption is somehow different from text encryption due to some inherent features of image such as bulk data capacity and high correlation among pixels, which are generally difficult to handle by traditional methods. The exceptionally desirable properties of the chaotic maps such as sensitivity to initial conditions and random-like behavior have attracted the attention of cryptographers to develop new encryption algorithms. This paper proposes a new symmetric image cipher based on the widely used confusion-diffusion architecture which utilizes the chaotic 2D Standard map and 1D Logistic map. It is specifically designed for the color images, which are 3D arrays of data streams. We prefer chaotic Standard map to Baker and Cat maps since the key space of the chaotic Standard map is large enough as compared to the Baker and Cat maps, which makes the brute-force attack infeasible. The initial conditions and system parameters of the chaotic maps constitute the secret key of the algorithm. To further enhance the security, the control parameters used in the confusion stage and the keystream employed for diffusion stage are distinct in different rounds and related to the plain-image. These control parameters are generated by a Tent map. For getting higher security and higher complexity, the current scheme employs two kinds of diffusion processes namely the horizontal and vertical diffusions which are completed by mixing the properties of horizontally and vertically adjacent pixels using a Logistic map, respectively. The results of several experiments, including the most important ones like key space analysis, key sensitivity test, statistical analysis, and visual test by histograms of encrypted images, the correlation coefficients of adjacent pixels, and differential analysis, demonstrate the satisfactory security and efficiency of the proposed image encryption scheme for real-time image encryption and transmission. © 2011 ieee.

关键词： Diffusion

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Minimum Noiseless Description Length (MNDL) Thresholding

Minimum Noiseless Description Length (MNDL) Thresholding

引用

ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Azadeh Fakhrzadeh Soosan Beheshti Electrical and Computer Engineering Department Ryerson University Toronto Canada

In this paper, a new thresholding approach for data denoising is presented. The approach is based minimum noiseless description length (MNDL), a new method for optimum sub-space selection in data representation. By using the observed noisy data, this information theoretic approach provides the optimum threshold that minimizes the description length of the noiseless signal. Comparison of the new method with the existing thresholding methods is provided

关键词： Noise reduction Additive noise image reconstruction computational intelligence signal processing Data engineering signal denoising Upper bound Random variables signal restoration

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A Novel ANN based Approach for Angle Invariant Face Verification

A Novel ANN based Approach for Angle Invariant Face Verifica...

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Basabi Chakraborty Faculty of Software and Information Science Iwate Prefectural University Iwate Japan

Face recognition and verification under varying pose and illumination is still a challenging problem. In this work, a novel approach for pose invariant face recognition based on artificial neural network under similar illumination condition is proposed. The neural network is trained to learn the face features with variation of pose and interpolate the face features for any unknown pose, leading to a good matching with the probe image. The simple implementation by simulation experiment with HOIP data base shows promising result

关键词： computational intelligence signal processing Teleprinting Indium tin oxide

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Study of Unsaturated Magnetic image processing in Wire Rope Aplication

Study of Unsaturated Magnetic Image Processing in Wire Rope ...

引用

10th International symposium on computational intelligence and Design (ISCID)

作者： Tan, Xiaojiang Zhang, Juwei Henan Univ Sci & Technol Coll Elect Engn Luoyang Peoples R China

ISBN: (纸本)9781538636749

Traditional magnetic flux leakage signal need be processed through each channels in magnetic array data, that increased processing system complexity. This paper proposed a processing system for unsaturated magnetic image, the processes included image transformation, filtering and resolution enhancement. This system overcame the shortcomings of interpolation method and digital signal processing. The experimental results showed that every defects could effectively be extracted and a good reconstruction image of multi-frame super-resolution was obtained with 3 simulated sampling intervals.

关键词： wire rope magnetic imageing image processing super-resolution

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A computational Algorithm Based on Convolutional Neural Networks Aimed at Estimating the MOS Quality Parameter According to the Norm UIT-T P.862 22

A Computational Algorithm Based on Convolutional Neural Netw...

引用

22nd symposium on image, signal processing and Artificial Vision (STSIVA)

作者： Gutierrez, Rodrigo Asca, Brallan Kemper, Guillermo Univ Peruana Ciencias Aplicadas Sch Elect Engn Fac Engn Lima Peru

ISBN: (纸本)9781728114910

This paper proposes an algorithm based on convolutional neural networks for the estimation of the quality level of voice signals transmitted through cellular communication systems. The objective is to take advantage of artificial intelligence methods to estimate the MOS parameter and obtain a similar accuracy to that obtained by methods and procedures established in the international norms and international licensed standards. The proposed algorithm uses the MOS results obtained by the method detailed in the ITU-T P.862 standard. The values were obtained for different signals acquired at different reception points. With this information we proceeded to design and train a convolutional neuronal network of 4 layers, achieving very satisfactory results. For the validation, the mean square error was used to measure the degree of similarity of the MOS values obtained by ITU-T P.862 and by the proposed algorithm. The results show a mean square error of 0.00007 for the proposed algorithm.

关键词： Convolutional neural network MOS PESQ UIT-T P.862 QoE

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Mitosis Detection Using Convolutional Neural Network Based Features 17

Mitosis Detection Using Convolutional Neural Network Based F...

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

作者： Albayrak, Abdulkadir Bilgin, Gokhan Yildiz Tech Univ Dept Comp Engn Signal & Image Proc Lab SIMPLAB TR-34220 Istanbul Turkey

ISBN: (纸本)9781509039098

Breast cancer is the second leading cause of cancer death in women according to World Health Organization (WHO). Development of computer aided diagnostic (CAD) systems has great importance as a secondary reader systems for a correct diagnosis and treatment process. In this paper, a deep learning based feature extraction method by convolutional neural network (CNN) is proposed for automated mitosis detection for cancer diagnosis and grading by histopathological images. The proposed framework is tested on the MITOS data set provided for a contest on mitosis detection in breast cancer histological images released for research purposes in International Conference on Pattern Recognition (ICPR' 2014). By using provided histopathological images, cellular structures are initially found by combined clustering based segmentation and blob analysis after preprocessing step. Then, obtained cellular image patches are cropped automatically from the histopathological images for feature extraction stage. CNN, which is a prominent deep learning method on image processing tasks, is utilized for extracting discriminative features. Due to the high dimensional output of the CNN, combination of PCA and LDA dimension reduction methods are performed respectively for regularization and dimension reduction process. Afterwards, a robust kernel based classifier, support vector machine (SVM), is used for final classification of mitotic and non-mitotic cells. The test results on MITOS data set prove that the proposed framework achieved promising results for mitosis detection on histopathological images.

关键词： Grading

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