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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361-370 订阅

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image Deblurring with Impulse Noise Using Split Bregman Algorithm

Image Deblurring with Impulse Noise Using Split Bregman Algo...

引用

2nd International symposium on computational intelligence and Design

作者： Yi Liya Lu Xiaolei Wang Jinjun Huang Benxiong Huazhong Univ Sci & Technol Dept Elect & Informat Engn Wuhan 430074 Peoples R China NEC Labs Amer Inc Cupertino CA 95014 USA

ISBN: (纸本)9780769538655

we propose an effective method to resolve blurred images with impulse noise. Our method has two steps. First, an improved adaptive median filter is proposed for image denoising;And second, the problem of deblurring the denoised image is formulated as to minimize the object function which consists of L1 data-fedility term and double regularization term. The minimization problem is solved by split Bregman algorithm. Numerical results using image with different blurs and impulse noise show that the proposed method gives better performance than the variable splitting alternative minimization algorithm in [10] by objective peak signal to noise ratio and subjective vision quality, which demonstrates the efficiency of our proposed algorithms.

关键词： image deblurring impulse noise double regularization split Bregman algorithm

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A survey on the use and the construction of fuzzy similarity measures in image processing

A survey on the use and the construction of fuzzy similarity...

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ieee International Conference on computational intelligence for Measurement Systems and Applications

作者： Van der Weken, D Nachtegael, M De Witte, V Schulte, S Kerre, EE Univ Ghent Fuzziness & Uncertainty Modelling Res Unit B-9000 Ghent Belgium

ISBN: (纸本)0780390253

Fuzzy techniques can be applied in several domains of image processing. In this paper we will give a survey on how fuzzy similarity measures can be used in establishing measures for image comparison. Objective quality measures or measures of comparison are of great importance in the field of image processing. These measures serve as a tool to evaluate and to compare different algorithms designed to solve particular problems, such as noise reduction, deblurring, compression, ... Consequently these measures serve as a basis on which one algorithm is preferred to another. Furthermore, it is well-known that classical quality measures, such as the RMSE (Root Mean Square Error) or the PSNR (Peak signal to Noise Ratio), do not always correspond to visual observations.

关键词： Fuzzy sets

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An Improved Evolution-COnstructed (iECO) Features Framework Distribution Statement A: Approved for public release;distribution is unlimited.

An Improved Evolution-COnstructed (iECO) Features Framework ...

引用

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

作者： Price, Stanton R. Anderson, Derek T. Luke, Robert H. Mississippi State Univ Elect & Comp Engn Dept Mississippi State MS 39762 USA US Army RDECOM CERDEC Night Vis & Elect Sensors Directorate Ft Belvoir VA USA

ISBN: (纸本)9781479945030

In image processing and computer vision, significant progress has been made in feature learning for exploiting important cues in data that elude non-learned features. While the field of deep learning has demonstrated state-of-the-art performance, the Evolution-COnstructed (ECO) work of Lillywhite et. al has the advantage of interpretability, and it does not predispose the solution to one of convolution. This paper presents a novel approach for extending the ECO framework. We achieve this through two overarching ideas. First, we address a potential major shortcoming of ECO features- the "features" themselves. The so-called ECO features are simply a transformed image that has been unrolled into a large one dimensional vector. We propose employing feature descriptors to extract pertinent information from the ECO imagery. Furthermore, it is our hypothesis that there exists a unique set of transforms for each feature descriptor used on a given problem domain that leads to the descriptors extracting maximal discriminative information. Second, we introduce constraints on each individual's chromosome to promote population diversity and prevent infeasible solutions. We show through experiments that our proposed iECO framework results in, and benefits from, a unique series of transforms for each descriptor being learned and maintaining population diversity.

关键词： computer vision evolutionary computation feature extraction learning (artificial intelligence) object recognition vectors 1D vector ECO imagery and computer vision feature descriptors feature learning iECO features framework image processing improved evolution-constructed features framework maximal discriminative information extraction pertinent information extraction population diversity promotion public release Biological cells Data mining Feature extraction Object recognition Sociology Statistics Transforms

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Automatic Mask Detecion using Convolutional Neural Networks and Variational Autoencoder 15

Automatic Mask Detecion using Convolutional Neural Networks ...

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ieee 15th International symposium on Applied computational intelligence and Informatics (SACI)

作者： Silabela, Mxolisi Bogdandy, Bence Toth, Zsolt Eszterhazy Karoly Univ Fac Informat Eger Hungary

ISBN: (纸本)9781728195438

The importance of proper hygienical behaivour is essential in today's word especially during an ongoing pandemic. Wearing mask became mandatory in many countries during the COVID-19 Pandemic. Recognizing whether people are wearing masks is complicated image recognition task which could be facilitated and automated with machine learning techniques. Camera streams are widely available in indoor environments which can be used for object detection and image processing. Convolutional Neural Networks have been successfully applied in image classification and object recognition task in various application areas. There are already trained and openly available general purpose convolutional neural networks which can be used as an initial version for specific applications. A number of different image datasets are also available for research and industrial purposes. The InceptionV3 Neural Network architecture was used to tailored to determine whether a mask is being worn or not using transfer learning techniques, and convolutional neural networks. A variational autoencoder has also been trained to normalize the dataset with respect to skin colour, angle of the head and among other parameters. This paper describes the implementation of a mask recognition software using transfer learning, a convolutional neural network and a variational autoencoder.

关键词： Machine Learning Feedforward neural networks Variational Autoencoder image Recognition Deep learning

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A pilot study of automatic lung tumor segmentation from positron emission tomography images using standard uptake values

A pilot study of automatic lung tumor segmentation from posi...

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ieee symposium on computational intelligence in image and signal processing

作者： Kanakatte, Aparna Gubbi, Jayavardhana Mani, Nallasamy Kron, Tomas Binns, David Monash Univ Dept Elect & Comp Syst Clayton Vic 3800 Australia Univ Melbourne Dept Elect & Elect Engn Melbourne Vic 3010 Australia Peter MacCallum Canc Ctr Melbourne Vic 3002 Australia

ISBN: (纸本)9781424407071

Positron Emission Tomography (PET) is a medical imaging procedure that shows the physiological function of an organ or tissue. The role of PET during the past decade has evolved rapidly in the detection of lung tumors but the research on quantitative evaluation of PET images is still in its infancy. PET commonly involves scanning the patient after administration of a radioactive analogue of glucose called fluoro deoxy-glucose (FDG). Tumor cells metabolise more glucose than most normal cells. In PET lung images the heart is often visible and because of its constant pumping of blood it requires more glucose and hence both the tumor and the heart appear brighter than the rest in the PET image. In this paper we present a novel segmentation scheme for detecting the tumor alone in lung PET images using standard uptake values (SUV) and connected component analysis. We perform the segmentation in two steps. In coarse segmentation, a non linear scaling of SUV values is performed and then a threshold is chosen adaptively to convert the gray image into the binary image. Fine segmentation is performed on the coarse segmented data in order to narrow down the region of interest using connected component labeling. To our knowledge no one has used connected component analysis for segmenting PET images. We compare our proposed scheme with several commonly used medical image segmentation techniques like threshold, sobel edge detector, laplacian of gaussian (LoG) edge detectors region growing and SUV based segmentation (applied only to PET as SUV is specific to PET). One of the problems in lung tumor detection is the presence of the heart in the image which accumulates activity and often gets recognized as a hot spot (a probable tumor). All the other segmentation schemes detected both the heart and the tumor as hot spots while our segmentation scheme detected the tumor alone as the hot spot. The preliminary study of the proposed scheme has yielded very promising results and will be st

关键词： image segmentation

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A construction method of interval-valued fuzzy sets for image processing

A construction method of interval-valued fuzzy sets for imag...

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ieee/ACM symposium on Advances in Type-2 Fuzzy Logic Systems

作者： Jurio, A. Paternain, D. Lopez-Molina, C. Bustince, H. Mesiar, R. Beliakov, G. Dpto. de Automatica Y Computacion Universidad Publica de Navarra 31006 Pamplona Spain Dep. of Math. and Desc. Geometry Slovak University of Technology Bratislava Slovakia Inst. Information Theory and Automation Academy of Sciences Czech Republic CR-18208 Prague Czech Republic Sch. of Information Technology Deakin University Burwood NSW 3125 Australia

In this work we present a new construction method of IVFSs from Fuzzy Sets. We use these IVFSs for image processing. Concretely, in this contribution we introduce a new image magnification algorithm using IVFSs. This ... 详细信息

ISBN: (纸本)9781612840789

关键词： image processing

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An image Enhancement Method Based on Gamma Correction

An Image Enhancement Method Based on Gamma Correction

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2nd International symposium on computational intelligence and Design

作者： Xu Guan Su Jian Pan Hongda Zhang Zhiguo Gong Haibin Jilin Univ Traff & Transportat Coll Changchun 130023 Peoples R China

ISBN: (纸本)9780769538655

The research of the wheel alignment system based on computer vision is a popular field for automobile safety. For realizing the effective and accurate feature recognition for the chessboard calibration target of the system, the enhancement algorithms based on Gamma correction are indicated. According to the research object, identification requires decreasing the pixel values in low grayscale and increasing the pixel values in high grayscale while keeping the pixels values in the middle grayscale. For this purpose, the novel Gamma correction curve is presented to enhance the target image. Experimental result shows that the enhancement method increases the contrast ratio of the chessboard target image which is benefit for feature extraction, points matching and vision measurement. Its effect and reliability meet the requirements of the detecting system for vehicle wheel alignment based on computer vision.

关键词： image processing image enhancement Gammma correction wheel alignment system

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Memory Saving Method for Enhanced Convolution of Deep Neural Network 11

Memory Saving Method for Enhanced Convolution of Deep Neural...

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

作者： Li, Ling Tong, Yuqi Zhang, Hangyu Wan, Dayu Jilin Univ Coll Commun Engn Changchun Jilin Peoples R China Nanjing Univ Coll Comp Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538685273

Convolution neural network (CNN) is a typical feedforward neural network, which gains extraordinary performance in image recognition. Features are extracted by convolution layers automatically for classification, but high in computation complexity. As an enhanced convolution algorithm, image to column (im2col) method accelerates the calculation with redundant memory overhead. In this work, we present Memory Saving Method (MSM) to improve convolution efficiency with lower memory consumption by elements rearrangement of input blocks. Block calculation can be executed both in serial and in parallel for their independence. It is demonstrated by the experimental results that MSM achieves the same acceleration effect yet sparing memory space for approximately two orders of magnitude with no drop of accuracy.

关键词： CNN neural network block calculation memory saving method convolution acceleration image processing

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Application of computation intelligence techniques for energy load and price forecast in some states of USA

Application of computation intelligence techniques for energ...

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ieee International symposium on Intelligent signal processing

作者： Mourao, Joao C. Ruano, Antonio E. Univ Algarve Fac Sci & Technol Ctr Intelligent Syst P-8005139 Faro Portugal

ISBN: (纸本)9781424408290

The purpose of this paper is to forecast the load and the price of electricity, 49 hours ahead. To accomplish these goals. computational intelligence techniques were used, specifically artificial neural networks and genetic algorithms. The neural networks employed are RBFs (Radial Basis Functions), fully connected and with just one hidden layer. The genetic algorithm used was MOGA (Multiple Objective Genetic Algorithm), which, as the name indicates, minimizes not a single objective but several. The neural networks are trained for one step ahead, and Its output is feedback until 49 hours are calculated. MOGA is used for the input selection and for topology determination. The data used was kindly given by the University of Auburn, USA, and refers to real data from some North-American states.

关键词： load and price forecast genetic algorithms neural networks

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Data Extraction from Data-Embedded Printed image Using Embedding Markers for image Correction

Data Extraction from Data-Embedded Printed Image Using Embed...

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

作者： Tamaki, Kimitoshi Muneyasu, Mitsuji Kansai Univ Fac Engn Sci 3-3-35 Yamate Cho Suita Osaka 5648680 Japan

ISBN: (纸本)9781479961207

In this paper, we propose a new method of embedding data and extracting it from a printed image. By embedding the data in a translation-invariant region of a target image, extraction of the target image from the background becomes unnecessary. Therefore, the data can be embedded at an arbitrary position in the target image. Additionally, a marker detection method using invisible markers is proposed to correct the geometrical distortion in the captured image. From our experimental results, the effectiveness of the proposed method is shown.

关键词： image capture image processing data extraction data-embedded printed image extraction embedding marker detection method geometrical distortion Correction image correction invisible marker target image translation-invariant region Cameras Data mining Discrete Fourier transforms Discrete cosine transforms Mobile handsets Orbits PSNR image capture image RECTIFICATION extracting data Mobile phones discrete cosine transforms discrete Fourier transform target image Peak signal-to-noise ratio Cameras Eye Sockets Orbits data mining image processing embedding

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