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histogram equalization using a reduced feature set of background speakers' utterances for speaker recognition

Frontiers of Information Technology & Electronic Engineering 2017年第5期18卷 738-750页

作者： Myung-jae KIM Il-ho YANG Min-seok KIM Ha-jin YU School of Computer Science University of Seoul

We propose a method for histogram equalization using supplement sets to improve the performance of speaker recognition when the training and test utterances are very short. The supplement sets are derived using outputs of selection or clustering algorithms from the background speakers' utterances. The proposed approach is used as a feature normalization method for building histograms when there are insufficient input utterance *** addition, the proposed method is used as an i-vector normalization method in an i-vector-based probabilistic linear discriminant analysis(PLDA) system, which is the current state-of-the-art for speaker verification. The ranks of sample values for histogram equalization are estimated in ascending order from both the input utterances and the supplement set. New ranks are obtained by computing the sum of different kinds of ranks. Subsequently, the proposed method determines the cumulative distribution function of the test utterance using the newly defined ranks. The proposed method is compared with conventional feature normalization methods, such as cepstral mean normalization(CMN), cepstral mean and variance normalization(MVN), histogram equalization(HEQ), and the European Telecommunications Standards Institute(ETSI) advanced front-end methods. In addition, performance is compared for a case in which the greedy selection algorithm is used with fuzzy C-means and K-means *** YOHO and Electronics and Telecommunications Research Institute(ETRI) databases are used in an evaluation in the feature space. The test sets are simulated by the Opus Vo IP codec. We also use the 2008 National Institute of Standards and Technology(NIST) speaker recognition evaluation(SRE) corpus for the i-vector system. The results of the experimental evaluation demonstrate that the average system performance is improved when the proposed method is used, compared to the conventional feature normalization methods.

关键词： Speaker recognition histogram equalization i-vector

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Contrast Enhancement Using Weighted Coupled histogram equalization with Laplace Transform

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Journal of Harbin Institute of Technology(New Series) 2022年第4期29卷 32-40页

作者： Huimin Hao Wenbin Xin Minglong Bu He Wang Yuan Lan Xiaoyan Xiong Jiahai Huang College of Mechanical and Vehicle Engineering Taiyuan University of TechnologyTaiyuan 030024China Harbin Electric Machinery Company Limited Harbin 150040China

histogram equalization is a traditional algorithm improving the image contrast,but it comes at the cost of mean brightness shift and details *** order to solve these problems,a novel approach to processing foreground pixels and background pixels independently is proposed and *** details are mainly contained in the foreground,the weighted coupling of histogram equalization and Laplace transform were adopted to balance contrast enhancement and details *** weighting factors of image foreground and background were determined by the amount of their respective *** proposed method was conducted to images acquired from CVG⁃UGR and US⁃SIPI image databases and then compared with other methods such as clipping histogram spikes,histogram addition,and non⁃linear transformation to verify its *** show that the proposed algorithm can effectively enhance the contrast without introducing distortions,and preserve the mean brightness and details well at the same time.

关键词： contrast enhancement weighted processing histogram equalization Laplace transform

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A fracture enhancement method based on the histogram equalization of eigenstructure-based coherence

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Applied Geophysics 2014年第2期11卷 179-185,253页

作者：窦喜英韩立国王恩利董雪华杨庆鄢高韩吉林大学地球探测科学与技术学院长春130026 中石油勘探开发研究院西北分院兰州730020

Eigenstructure-based coherence attributes are efficient and mature techniques for large-scale fracture detection. However, in horizontally bedded and continuous strata, buried fractures in high grayscale value zones are difficult to detect. Furthermore, middleand small-scale fractures in fractured zones where migration image energies are usually not concentrated perfectly are also hard to detect because of the fuzzy, clouded shadows owing to low grayscale values. A new fracture enhancement method combined with histogram equalization is proposed to solve these problems. With this method, the contrast between discontinuities and background in coherence images is increased, linear structures are highlighted by stepwise adjustment of the threshold of the coherence image, and fractures are detected at different scales. Application of the method shows that it can also improve fracture cognition and accuracy.

关键词： Fault fracture histogram equalization coherence enhancement

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Improved Algorithm of histogram equalization and Its Actualization

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Journal of China University of Mining and Technology 2003年第1期13卷 52-54页

作者：仲伟波赵福军李鹏宁书年 School of Mechanical Electronic and Information EngineeringCUMT School of Resource and Safety CUMT

A new improved algorithm of histogram equalization was discussed and actualized by analyzing the traditional algorithm. This improved algorithm has better effect than the traditional one, especially it is used to proc... 详细信息

关键词： improve histogram equalization algorithm gray

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Recursive Plateau histogram equalization for the Contrast Enhancement of the Infrared Images

Recursive Plateau Histogram Equalization for the Contrast En...

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2010 3rd International Conference on Computer and Electrical Engineering(ICCEE 2010)

作者： Zongwei Lu Zhide Tang Lin Zhou Hao Yang Lisen Lin State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University

One simple and efficient algorithm based on recursive plateau histogram equalization for the contrast enhancement of the infrared images is proposed in this paper. In this method, the plateau threshold is selected automatically as the average value of the probability density function. Based on the threshold value, the probability density function is divided into two sub-groups. The sub-group whose values are greater than the threshold value is clipped. New threshold value is calculated for another sub-group whose values are less than the old threshold value and the separation process is performed recursively on it. Experimental results show that our method can enhance the contrast of the infrared images better than other histogram equalization based methods. Furthermore, our method is simple enough to make it suitable to be implemented by FPGA for real time image process.

关键词： Contrast enhancement histogram equalization Plateau histogram equalization Image enhancement

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Balanced Quantization： An Effective and Efficient Approach toQuantized Neural Networks

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Journal of Computer Science & Technology 2017年第4期32卷 667-682页

作者： Shu-Chang Zhou Yu-Zhi Wang He Wen Qin-Yao He Yu-Heng Zou University of Chinese Academy of Sciences Beijing 100049 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Megvii Inc. Beijing 100190 China Department of Electronic Engineering Tsinghua University Beijing 100084 China Department of Computer Science and Technology Tsinghua University Beijing 100084 China School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

Quantized neural networks (QNNs), which use low bitwidth numbers for representing parameters and performing computations, have been proposed to reduce the computation complexity, storage size and memory usage. In QNNs, parameters and activations are uniformly quantized, such that the multiplications and additions can be accelerated by bitwise operations. However, distributions of parameters in neural networks are often imbalanced, such that the uniform quantization determined from extremal values may underutilize available bitwidth. In this paper, we propose a novel quantization method that can ensure the balance of distributions of quantized values. Our method first recursively partitions the parameters by percentiles into balanced bins, and then applies uniform quantization. We also introduce computationally cheaper approximations of percentiles to reduce the computation overhead introduced. Overall, our method improves the prediction accuracies of QNNs without introducing extra computation during inference, has negligible impact on training speed, and is applicable to both convolutional neural networks and recurrent neural networks. Experiments on standard datasets including ImageNet and Penn Treebank confirm the effectiveness of our method. On ImageNet, the top-5 error rate of our 4-bit quantized GoogLeNet model is 12.7%, which is superior to the state-of-the-arts of QNNs.

关键词： quantized neural network percentile histogram equalization uniform quantization

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Improved Model for Genetic Algorithm-Based Accurate Lung Cancer Segmentation and Classification

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Computer Systems Science & Engineering 2023年第5期45卷 2017-2032页

作者： K.Jagadeesh A.Rajendran Department of Electronics and Communication Engineering SNS College of EngineeringCoimbatoreTamilnaduIndia Department of Electronics and Communication Engineering Karpagam College of EngineeringCoimbatoreTamilnaduIndia

Lung Cancer is one of the hazardous diseases that have to be detected in earlier stages for providing better treatment and clinical support to *** lung cancer diagnosis,the computed tomography(CT)scan images are to be processed with image processing techniques and effective classification process is required for appropriate cancer *** present scenario of medical data processing,the cancer detection process is very time consuming and *** that,this paper develops an improved model for lung cancer segmentation and classification using genetic *** the model,the input CT images are pre-processed with the filters called adaptive median filter and average *** filtered images are enhanced with histogram equalization and the ROI(Regions of Interest)cancer tissues are segmented using Guaranteed Convergence Particle Swarm Optimization *** classification of images,Probabilistic Neural Networks(PNN)based classification is *** experimentation is carried out by simulating the model in MATLAB,with the input CT lung images LIDC-IDRI(Lung Image Database Consortium-Image Database Resource Initiative)benchmark *** results ensure that the proposed model outperforms existing methods with accurate classification results with minimal processing time.

关键词： Cancer diagnosis segmentation enhancement histogram equalization probabilistic rate neural networks(PNN) classification

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A Bi-histogram Shifting Contrast Enhancement for Color Images

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Journal of Quantum Computing 2021年第2期3卷 65-77页

作者： Lord Amoah Ampofo Twumasi Kwabena Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science&TechnologyNanjing210044China School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China Department of Computer Science University of Ghana-LegonAccra00233Ghana

Recent contrast enhancement(CE)methods,with a few exceptions,predominantly focus on enhancing gray-scale *** paper proposes a bi-histogram shifting contrast enhancement for color images based on the RGB(red,green,and blue)color *** proposed method selects the two highest bins and two lowest bins from the image histogram,performs an equalized number of bidirectional histogram shifting repetitions on each RGB channel while embedding secret data into marked *** proposed method simultaneously performs both right histogram shifting(RHS)and left histogram shifting(LHS)in each histogram shifting repetition to embed and split the highest bins while combining the lowest bins with their neighbors to achieve histogram equalization(HE).The least maximum number of histograms shifting repetitions among the three RGB channels is used as the default number of histograms shifting repetitions performed to enhance original *** to an existing contrast enhancement method for color images and evaluated with PSNR,SSIM,RCE,and RMBE quality assessment metrics,the experimental results show that the proposed method's enhanced images are visually and qualitatively superior with a more evenly distributed *** proposed method achieves higher embedding capacities and embedding rates in all images,with an average increase in embedding capacity of 52.1%.

关键词： Contrast enhancement bi-histogram shifting histogram equalization

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Local Retouching of Images by histogram-based Similarity Evaluation

Local Retouching of Images by Histogram-based Similarity Eva...

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第二届亚洲机械电子学学术会议

作者： Shun’ichi Kaneko Takayuki Tanaka Hokkaido University Kita 14Nishi 9Kita-kuSapporoJapan

Quality of local regions in the scene could be deteriorated by the effects of ill-conditioned lighting effects and *** develop a method to improve the quality of local *** local region of the image is firstly selected by an algorithm that is based on similarity evaluation of the *** the histogram of brightness and saturation of the local region are expanded using the histogram equalization,both the brightness and saturation are *** the next step,based on the distance from the point selected by user,the improved data are combined with the original image. The local part of the image is naturally merged with the surrotmding scenes.

关键词： Local region similarity evaluation histogram equalization brightness saturation merge

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A Fast Algorithm for Improving the Visual Distance in Fog

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Semiconductor Photonics and Technology 2009年第4期15卷 241-246页

作者： YANG Wei XIAO Zhi-tao YU Jian YAN Zhi-jie School of Information and Communication Engineering Tianjin Polytechnic University Tianjin 300160 CHN School of Computer Science and Technology Tianjin University Tianjin 300072 CHN

Images captured outdoor usually degenerate because of the bad weather conditions,among which fog,one of the widespread phenomena,affects the video quality *** physical features of fog make the video blurred and the visible distance shortened,seriously impairing the reliability of the video *** order to satisfy the requirement of image processing in real-time,the normal distribution curve fitting technology is used to fit the histogram of the sky part and the region growing method is used to segment the region of *** for the non-sky part,a method of self-adaptive interpolation to equalize the histogram is adopted to enhance the contrast of the *** results show that the method works well and will not cause block effect.

关键词： fog image interpolation region growing histogram equalization fast algorithm normaldistribution

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