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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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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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Memory Saving Method for Enhanced Convolution of Deep Neural Network 11

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

引用

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

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

引用

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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Marathon Bib Number Recognition using Deep Learning 11

Marathon Bib Number Recognition using Deep Learning

引用

11th International symposium on image and signal processing and Analysis (ISPA)

作者： Apap, Adrian Seychell, Dylan Univ Malta Dept Artificial Intelligence Msida Malta

ISBN: (纸本)9781728131405

Bib number recognition (BNR) from unstructured marathon images can be a challenging task. This is because the images captured at these events are very inconsistent since, they are often captured by multiple photographers, at various locations and times. This results in images containing different backgrounds, angles and illumination. The images often contain multiple participants in various poses, where the bib numbers can be obstructed by the participants themselves. The bib numbers are often printed on flexible paper and can easily be deformed which distorts the printed numbers. In this work we present a BNR system based on deep learning which is able to locate bib numbers in unstructured, complex marathon images. Using the segmented bib numbers the system then, recognizes the digits and finally outputs the bib numbers that it was able to detect in the image. The first stage consists of a fully Convolutional Neural Network (CNN) to segment the bib numbers while the second stage consists of a Convolutional Recurrent Neural Network (CRNN) used to recognize the detected numbers. The proposed method obtained an F1 score of 0.69 which outperformed existing methods.

关键词： Convolutional Neural Network Segmentation Detection Recognition

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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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A Hardware-Efficient Post-processing Algorithm for Motion Compensated Frame Rate Up-Conversion

A Hardware-Efficient Post-Processing Algorithm for Motion Co...

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ieee International symposium on Circuits and Systems (ieee ISCAS)

作者： Wang, Hang Wang, Tiancheng Mi, Yunqi Sun, Hongbin Zheng, Nanning Xi An Jiao Tong Univ Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9781728103976

Post-processing is an important module in motion compensated frame rate up-conversion design, and has a direct impact on the image quality of interpolated frame. However, how to balance between image quality and computational efficiency is still very challenging for post-processing, especially in hardware design. This paper proposes a hardware-efficient post-processing algorithm which leverages the temporal and spatial constraints to locally refine interpolated pixels. Moreover, we employ the quantization and approximation techniques to further reduce the computational intensity of the proposed post-processing algorithm. The quality of interpolated frame has been greatly improved both in objective and subjective aspects. The proposed post-processing algorithm is extensively evaluated by a set of video test sequences. Evaluation results demonstrate that, compared with the reference designs, the proposed algorithm can improve PSNR by at least 1.69 dB with comparable computational complexity.

关键词： Hardware Optimization Real-time systems image quality Quantization (signal) Interpolation Approximation algorithms

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On Some Performances of the Exponentiated Convex Variable Step-Size (ECVSS) Algorithm

On Some Performances of the Exponentiated Convex Variable St...

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8th International symposium on image and signal processing and Analysis (ISPA)

作者： Rusu, Corneliu Cowan, Colin F. N. Tech Univ Cluj Napoca FETTI Signal Proc Grp Str Baritiu 26-28 RO-400027 Cluj Napoca Romania Queens Univ Belfast Dept Elect & Elect Engn Belfast Antrim North Ireland

ISBN: (纸本)9789531841948;9789531841870

Recently, there were proposed few stochastic gradient algorithms which are based on cost functions that have exponential dependence on the chosen error. However, in certain framework the convergence of the cost function based on exponential of the squared error is not always satisfactorily. Thus this cost function has been modified and the Exponentiated Convex Variable Step-Size (ECVSS) algorithm has been obtained. It has been shown that with an optimal selection of one tuning parameter the ECVSS algorithm provides attractive results. In this brief we present some performances of the ECVSS stochastic gradient algorithm (time constants, misadjustment, computational complexity) in data echo cancellation framework when the tuning parameter has different values.

关键词： computational complexity convergence convex programming gradient methods least mean squares methods signal processing stochastic processes

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1D CNN Architectures for Music Genre Classification

1D CNN Architectures for Music Genre Classification

引用

ieee symposium Series on computational intelligence (ieee SSCI)

作者： Allamy, Safaa Koerich, Alessandro Lameiras Univ Quebec Ecole Technol Super Montreal PQ Canada

ISBN: (纸本)9781728190488

This paper proposes a 1D residual convolutional neural network (CNN) architecture for music genre classification and compares it with other recent 1D CNN architectures. The 1D CNNs learn a representation and a discriminant directly from the raw audio signal. Several convolutional layers capture the time-frequency characteristics of the audio signal and learn various filters relevant to the music genre recognition task. The proposed approach splits the audio signal into overlapped segments using a sliding window to comply with the fixed-length input constraint of the 1D CNNs. As a result, music genre classification can be carried out on a single audio segment or on aggregating the predictions on several audio segments, which improves the final accuracy. The performance of the proposed 1D residual CNN is assessed on a public dataset of 1,000 audio clips. The experimental results have shown that it achieves 80.93% of mean accuracy in classifying music genres and outperforms other 1D CNN architectures.

关键词： Convolutional neural networks deep learning audio processing

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Feature Extraction with computational intelligence for Head Pose Estimation 8

Feature Extraction with Computational Intelligence for Head ...

引用

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

作者： Reid, Shane Coleman, Sonya Kerr, Dermot Vance, Philip O'Neill, Siobhan Ulster Univ Magee Coll Sch Comp Engn & Intelligent Syst Coleraine Londonderry North Ireland Ulster Univ Magee Coll Sch Psychol Coleraine Londonderry North Ireland

ISBN: (纸本)9781538692769

Measuring social signals has often proved challenging as they are often characterized by subtle movements which are difficult to detect. Head pose is one such social signal used to indicate where an individual's attention is focused. This paper will discuss the problem of head pose estimation by defining the problem in terms of two fields of view, pan and tilt. A novel approach for head pose estimation is described that uses histogram of oriented gradients with support vector machines. The approach is compared with a template matching approach, among others, using a well-known dataset. The results show that the histogram of oriented gradients approach is able to determine head pan to within one class approximately 72% of the time, and head tilt to within one class approximately 71% of the time.

关键词： Head Pose estimation Template matching Social signal processing Histogram of oriented gradients Support vector machine

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