Rib chest radiographs for detection of the cancer using double stage adaptive processing

This paper suggests elegant two enhancement approaches for rib chest images. The first approach is based on adaptive contrast and luminance model (ACLM).The second approach is depended on mixing the Exponential Contrast Limited Adaptive Histogram Equalization model (ECLAHE) with the Local Histogram Equalization (LHE). The idea of this approach is depended on applying on rib chest radiograph and make optimization for clip limit for ECLAHE. This second algorithm has helped rib chest radiograph details are more important for the detection of cancerous cells. The performance qualities of the suggested models are entropy, average gradient, contrast factor, Sobel magnitude, lightness order error and the similarity of edges point of views. The second approach presents enhancement of rib chest images with better resolution visual details and quality metrics point of views with comparing the first approach.

     

An efficient proposed framework for infrared night vision imaging system

This research presents new three proposed approaches to enhancement the visibility of the Infrared (IR) night vision images. The first proposed approach depends on Hybrid Adaptive Gamma Correction (AGC) with Histogram Matching (HGCHM). The second proposed approach stands up Merging Gamma Correction with Contrast Limited Adaptive Histogram Equalization (MGCCLAHE). The HM uses a reference visual image for converting of night vision images into daytime images. The third approach mixes the benefits of the CLAHE with the undecimated Additive Wavelet Transform (AWT) Using Homomorphic processing (CSAWUH). The quality assessments for the suggested approaches are entropy, average gradient, contrast improvement factor, Sobel edge magnitude, spectral entropy, lightness order error and the similarity of edges. Simulation results clear that the third proposed approach gives superior results to the two proposed approaches from entropy, average gradient, contrast improvement factor, Sobel edge magnitude, spectral entropy and the computation time perspectives. On the other hand, the second proposed approach takes long computation time in the implementation with respect to the two proposed approaches. The second proposed approach gives better results to the first proposed approach entropy, average gradient, contrast improvement factor, Sobel edge magnitude, and spectral entropy perspectives. The first proposed approach gives better results to the two proposed approaches from lightness order error and the similarity of edges perspectives.

     

Pedestrian detection and tracking using temporal differencing and HOG features

This article proposes a multiple human detection and tracking approach. A moving person identification technique is provided first. The video objects are detected using a novel temporal differencing based procedure and several mathematical morphology-based operations. Then, our technique determines what moving image objects represent pedestrian people, by testing several conditions related to human bodies and detecting the skin regions from the movie frames. A robust human tracking method using a Histogram of Oriented Gradient (HOG) based template matching process is then introduced in our paper. Some person detection and tracking experiments and method comparisons are also described.     

基于变化检测的投资项目搜索技术研究

一些露于地表的固定资产投资项目可用遥感影像变化检测的方法自动的搜索并检测出来。其间需要完成对两幅不同时向的影像完成几何校正、影像配准、直方图匹配、影像做差、二值化、小斑去除、搜索结果修正等一系列工作。如果算法参数设置合理,其中大部分操作流程可以使用计算机自动实现。本文详细描述了本智能搜索技术的核心算法与技术流程,并通过基于TitanImage二次开发工具实验证明了其可行性。     

基于多特征融合的防爆自动导引车避障方法研究

为解决在工厂环境中防爆自动导引车(Automated Guided Vehicle,AGV)难以在不同光照条件下实现行人检测的问题,本文提出将"结构光+双目视觉相机"的图像采集方案应用于爆炸性危险环境.针对传统的基于RGB(代表红、绿、蓝三个通道的颜色)图像的HOG-LBP(Histogram of Oriented ...     

基于特征融合的行人检测技术

首先研究了三种不同的特征算子在基于图像行人检测中的应用. 他们分别是: 梯度直方图(Histogram of Oriented Gradient, HOG)、局部三值模式特征(Local Ternary Patterns, LTP)以及改进了的局部三值模式特征(Sqrt Local Ternary Patterns, S-LTP). 对以上三种特征算子进行了实验比较, 最后将HOG和S-LTP算子融合得到HOG+S-LTP的基于多特征的行人检测算子, 利用SVM分离器在INRIA人体库上进行了实验, 实验表明, 融合后的特征显著地提高了行人检测率, 同时也满足实时性要求.     

Adaptive height-modified histogram equalization and chroma correction in YCbCr color space for fast backlight image compensation

Automatic exposure controls in commercially available cameras often encounter difficulties in capturing scenes with backlight luminance which dominates the entire image. An Adaptive Height-Modified Histogram Equalization (AHMHE) algorithm is proposed as a compensation technique for backlight images. It simultaneously enhances contrast in both the dark and the bright areas without creating regions of degraded local contrast. Moreover AHMHE is an adaptive algorithm: thus it requires minimal user input, and its reduced computational requirement makes it suitable for real-time application. In addition to AHMHE, a chroma correction technique was applied to chroma components in the YCbCr color space to produce more vivid color images. A series of subjective and index evaluations were conducted to measure the resultant image quality improvements by the AHMHE and the chroma correction algorithms.     

Contrast enhancement and brightness preserving of digital mammograms using fuzzy clipped contrast-limited adaptive histogram equalization algorithm

A novel fuzzy logic and histogram based algorithm called Fuzzy Clipped Contrast-Limited Adaptive Histogram Equalization (FC-CLAHE) algorithm is proposed for enhancing the local contrast of digital mammograms. A digital mammographic image uses a narrow range of gray levels. The contrast of a mammographic image distinguishes its diagnostic features such as masses and micro calcifications from one another with respect to the surrounding breast tissues. Thus, contrast enhancement and brightness preserving of digital mammograms is very important for early detection and further diagnosis of breast cancer. The limitation of existing contrast enhancement and brightness preserving techniques for enhancing digital mammograms is that they limit the amplification of contrast by clipping the histogram at a predefined clip-limit. This clip-limit is crisp and invariant to mammogram data. This causes all the pixels inside the window region of the mammogram to be equally affected. Hence these algorithms are not very suitable for real time diagnosis of breast cancer. In this paper, we propose a fuzzy logic and histogram based clipping algorithm called Fuzzy Clipped Contrast-Limited Adaptive Histogram Equalization (FC-CLAHE) algorithm, which automates the selection of the clip-limit that is relevant to the mammogram and enhances the local contrast of digital mammograms. The fuzzy inference system designed to automate the selection of clip-limit requires a limited number of control parameters. The fuzzy rules are developed to make the clip limit flexible and variant to mammogram data without human intervention. Experiments are conducted using the 322 digital mammograms extracted from MIAS database. The performance of the proposed technique is compared with various histogram equalization methods based on image quality measurement tools such as Contrast Improvement Index (CII), Discrete Entropy (DE), Absolute Mean Brightness Coefficient (AMBC) and Peak Signal-to-Noise Ratio (PSNR). Experimental results show that the proposed FC-CLAHE algorithm produces better results than several state-of-art algorithms.     

基于直方图均衡化与MSRCR的沙尘降质图像增强算法

针对沙尘天气下图像色彩偏移严重及对比度低等问题,提出一种基于直方图均衡化与带色彩恢复的多尺度视网膜（MSRCR）增强的沙尘降质图像增强算法。通过偏色校正和图像增强两个步骤进行图像恢复,将RGB图像各通道预处理后利用限制对比度自适应直方图均衡方法得到校正后的图像,对图像采用双边滤波进行降噪处理,通过MSRCR算法进一步解决色彩失衡问题。由于处理后的图像对比度较低,存在一定色偏,利用伽马校正和基于图像分析的偏色检测及颜色校正方法进行处理得到最终结果。对大量沙尘降质图像进行仿真实验,结果表明,该算法能够有效处理不同偏色程度的沙尘图像,不仅提高了图像的对比度,而且有效避免了图像颜色偏移现象,相比GCANet、MSRCR等算法,平均时间效率提升了46.2%~94.7%。     

深度学习及时空约束的行人跟踪算法研究

由于以往的行人跟踪方法大部分不能有效地解决目标被遮挡后以及目标尺寸变化再跟踪的问题,所以引入了深度学习的方法,但是经实验发现单纯使用深度学习行人跟踪会因行人检测部分的误差而出现整体的跟踪准确率不高的问题。提出了一种基于深度学习和时空约束后处理的行人跟踪方法,深度学习的行人检测部分采用了根据实际应用场景优化过的SSD算法,行人匹配部分采用了一种计算交叉输入领域差异然后进行块总结的方法,最后进行时空约束的后处理。在OTB数据集上做实验,与传统跟踪算法以及单纯深度学习算法进行了对比。     

基于梯度场的工业X射线图像增强算法

在X射线成像检测厚薄不均构件时，经常会出现对比度低或对比度不均以及照度低的问题，这会导致图像显示时构件的一些细节难以被观察与分析。针对这一问题，提出一种基于梯度场的X射线图像增强算法。该算法以梯度场增强为核心，分为两步：首先，提出一种基于对数变换的算法，压缩图像的灰度范围、去除图像冗余灰度信息、提升图像对比度；然后，提出一种基于梯度场的算法，增强图像细节、提升图像局部对比度、提高图像质量，使构件细节清晰显示在检测屏上。选择一组厚薄不均构件的X射线图像进行了实验，并与对比度受限自适应直方图均衡化（CLAHE）、同态滤波等算法进行了比较。实验结果表明所提算法具有更明显的增强效果，能更好地显示构件的细节信息，并且通过计算平均梯度和无参考结构清晰度（NRSS）纹理分析的定量评价标准进一步表明了该算法的有效性。     

基于PCA降维的多特征级联的行人检测

在行人检测中,针对梯度方向直方图(HOG)冗余信息过多、检测速度慢等不足,提出了运用PCA降维的多特征级联的行人检测。首先利用PCA对HOG特征进行降维,其次将HOG特征和Gabor特征、颜色特征级联作为行人检测的特征,最后使用SVM的径向基(RBF)核函数进行分类。在INRIA行人库上的实验表明,该方法不但提高了分类的速度,而且提高了检测的准确率。     

中值直方图均衡的动态场景多曝光图像融合算法

为解决动态场景下多曝光融合图像出现鬼影的问题,提出一种新的动态多曝光图像融合算法。引入中值图像均衡对输入图像和参考图像的直方图进行处理,将获取的图像对做差分,并对差分图进行阈值分割和形态学优化得到运动权重图。中值直方图均衡可以为一对图像分配相同的直方图,同时保持其灰度动态,因此对多曝光图像对调整其亮度差异,有利于运动区域检测的准确性。通过强度映射函数将参考图像分别映射为各个输入图像的亮度,并将输入图像的运动区域替换为参考图像的一部分,得到具有亮度过渡自然的图像序列。在此基础上,对静态图像序列进行融合得到最终的融合图像。实验结果表明,该算法可有效地避免鬼影现象,且能够获得细节丰富、视觉效果良好的高动态范围图像,经该算法融合后的图像在标准差、边缘强度、相关系数和动态场景结构一致4个指标上与DGF、FMSD等算法相比具有明显的优势。     

基于多级特征的红外图像行人检测算法

由于可见光图像和红外图像的成像原理不同,可见光图像的行人检测算法难以直接应用于红外图像中.为此,提出一种基于多级梯度特征的红外图像行人检测算法.使用改进的图像显著性检测算法提取红外图像的关键区域,应用质心重定位的滑窗算法快速定位其中的高亮区,采用Zernike矩判断图像的对称性及与行人特征的相似性,通过基于边缘信息输入的卷积神经网络模型逐级缩小判定范围.在OTCBVS红外图像行人数据集上的实验结果表明,与稀疏表示算法相比,该算法的检测准确率较高.     

Tree-structured image difference for fast histogram and distance between histograms computation

In this paper we present a new method for fast histogram computing and its extension to bin to bin histogram distance computing. The idea consists in using the information of spatial differences between images, or between regions of images (a current one and a reference one), and encoding it into a specific data structure: a tree. The histogram of the current image or of one of its regions is then computed by updating the histogram of the reference one using the temporal data stocked into the tree. With this approach, we never need to store any of the current histograms, except the reference image ones, as a preprocessing step. We compare our approach with the well-known Integral Histogram one, and obtain better results in terms of processing time while reducing the memory footprint. We show theoretically and with experimental results the superiority of our approach in many cases. We also extend our idea to the computation of the Bhattacharyya distance between two histograms, using a similar incremental approach that also avoid current histogram computations: we just need histograms of the reference image, and spatial differences between the reference and the current image to compute this distance using an updating process. Finally, we demonstrate the advantages of our approach on a real visual tracking application using a particle filter framework by improving its correction step computation time.     

基于直方图均衡化的手背静脉图像对比度增强

手背静脉图像对比度往往较低,这将影响整个手背静脉识别系统的识别准确率。首先提取手背静脉图像中的有效区域,然后利用直方图均衡化 (HE) 及其各种改进算法对提取的手背静脉图像进行对比度增强处理。实验结果表明,子块部分重叠局部直方图均衡化算法（POSHE）不但能够增强图像的整体对比度,而且图像中细节与背景之间的对比度也得到了增强,同时该算法效率较高,适合于手背静脉图像的对比度增强处理。     

Artificial Bee Colony-based satellite image contrast and brightness enhancement technique using DWT-SVD

In this article, a new contrast enhancement approach is presented for quality enhancement of low-contrast satellite images. The proposed technique is based on the Artificial Bee Colony (ABC) algorithm using Discrete Wavelet Transform and Singular Value Decomposition (DWT-SVD). The method employs the ABC technique to learn the parameters of the adaptive thresholding function required for optimum enhancement. In this approach, the input image is primarily decomposed into four sub-bands through DWT, and then each sub-band of DWT is optimized through the ABC algorithm. After that, a singular value matrix of the low–low thresholded sub-band image is estimated and, finally, the enhanced image is constructed by applying inverse DWT. The results obtained through this method reveal that the proposed methodology gives better performance in terms of peak signal-to-noise ratio (PSNR), mean square error (MSE), and mean and standard deviation as compared to General Histogram Equalization (GHE), Discrete Cosine Transform and Singular Value Decomposition (DCT-SVD), DWT-SVD, Particle Swarm Optimization (PSO), and modified versions of the PSO-based enhancement approach.     

Variance Based Brightness Preserved Dynamic Histogram Equalization for Image Contrast Enhancement

This paper proposes a novel variant of Brightness Preserving Dynamic Histogram Equalization (BPDHE) having more brightness preserving capability with less computational time. This variant, called Variance based Brightness Preserve Dynamic Histogram Equalization (VBBPDHE) uses the interclass and intraclass variance information to segment out the histogram recursively. This variant does not need the smoothing operation of input histogram and also no need to compute local maxima or minima to segment out the histogram unlike BPDHE. Visual analysis, quality metrics and execution time clearly demonstrate the efficiency of the proposed VBBPDHE over well-known existing methods.     

Bi-histogram equalization using modified histogram bins

The shifting of image mean brightness and the domination of high-frequency bins during histogram equalization (HE) often result in the deteriorating quality of enhanced images and a considerable amount of information loss. This study proposes a novel approach based on bi-histogram equalization to improve its abilities in preserving information entropy and mean brightness. The proposed technique, named Bi-histogram Equalization using Modified Histogram Bins (BHEMHB), segments the input histogram based on the median brightness of an image and alters the histogram bins before HE is applied. Histogram segmentation enables mean brightness preservation, whereas the modification of histogram bins restricts the enhancement rate, thus minimizing the domination effects of high-frequency histogram bins. Simulation results show that BHEMHB significantly outperforms its peers in preserving the details and mean brightness of an image. The output image is visually pleasant with a natural appearance.     

Gabor特征结合快速HOG特征的行人检测

针对行人检测中HOG特征提取速度慢且易忽视细节特征的问题,提出了一种Gabor特征结合快速HOG特征的行人检测算法.首先对输入图像进行小波变换,并引入积分图思想和主成分分析算法快速提取图像HOG特征;其次融合Gabor小波变换得到的Gabor特征,最后采用混合特征训练分类器,实现行人的有效检测.测试集上的实验结果表明,在使用相同分类器的情况下,该混合特征提取方法比单一特征提取方法的检测正确率最多可提高7.37％,因此所提出的算法可以有效地提高行人检测的精度.