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.

     

Dark infrared night vision imaging proposed work for pedestrian detection and tracking

This framework presents three efficient proposed algorithms for pedestrian detection and tracking in Dark Infrared Night Vision (DIRNV) images. The first approach is relied on Gradient Estimation (GE) after mixing structure Equalization Exponential Contrast Limited Adaptive Histogram Equalization (ECLAHE) with Gamma Correction, and finally Cumulative Histogram (GECUGC) for discrimination. The GECUGC relies on enhancement using mixing ECLAHE Using Gamma Correction (ECUG) in addition to pre-processing followed by the GE using Laplacian Filter (LAF), and finally Cumulative Histograms (CH) for the detection or classification task. The second approach is based GE after a hybrid structure Histogram Equalization (HE) with Nonlinear Technique and finally CH (GHNTC) for discrimination. The GHNTC depends on enhancement by merging HE with Nonlinear Technique (NT) (HENT) followed by the GE using LAF and finally CH for pedestrian detection and tracking using DIRNV imaging. After the CH estimation, the difference between cumulative histograms with and without objects is estimated and used for pedestrian detection and tracking using DIRNV imaging. The third algorithm is based scale space analysis with the number of the Speeded Up Robust Features (SURF) points as the key parameters for classification. This technique is presented to detect the features of DIRNV pedestrian images and tracking. The performance metrics are the difference area between the cumulative histograms of DIRNV images with and without pedestrian, computation time, points of features and speed up factor. Simulation results prove that the success of three suggested techniques in pedestrian detection and tracking using DIRNV imaging. By comparing the three presented algorithms, it is clear that the second suggested technique gives superior for pedestrian detection and tracking from point view difference area between the cumulative histograms.On the other hand the first suggested technique is the best algorithms for pedestrian detection and tracking from point view the computation time. The obtained results clear that the third approach has sucesseded in gait pedestrian detection and tracking using DIRNV imaging.

     

Rule-based soft computing for edge detection

In this paper, we present a robust rule-based edge detection method. Although generalized edge detection approaches are effective for most images they often fail in others. Thus the goal of our method is to provide more reliable edge detection results that are effective in most images. We implement the proposed method as follows: (1) transform RGB images to YCbCr format, (2) apply Sobel mask in four edge directions (horizontal, vertical, diagonal, anti-diagonal), (3) apply a bi-directional mask in four edge directions (horizontal–diagonal, vertical–diagonal, horizontal–anti-diagonal, vertical–anti-diagonal), and (4) detect rule-based edges by calculating membership degrees. Simulation results demonstrate that the proposed method is effective in most given images. We used three benchmarks approaches (Canny edge mask, high-pass filter, and Sobel mask) to compare the subjective performance quality.     

新型边缘检测法

灰度图阈值法、跟踪法、小波变换法是边缘检测的常用方法,这些方法所获得的边缘线出现断点,而且掺杂噪声,效果不很理想.给出了一种新型边缘检测方法:连续点过滤和多尺度边缘跟踪相结合.连续点过滤的边缘检测法可以获得较好的边缘,在此基础上利用多尺度的跟踪法,解决了检测图像的边缘断点问题,使得图像的边缘线更加确切和平滑.经实验验证,该方法比普通的方法在边缘精确性上有较大提高.     

基于概率估计的Sobel算子医学图像边缘细化方法

针对存在大量噪声和目标边缘模糊的医学CT图像难以提取精确边缘的难点,提出边缘检测精确定位算法。该算法利用Sobel梯度图以及一阶微分期望阈值,从概率分布的方法进行定位估值,从而获得较高的定位精度。实验结果表明,本文算法比传统Sobel等算子对提取医学CT图像边缘更有效。     

SEDMI: Saliency based edge detection in multispectral images

Detecting edges in multispectral images is difficult because different spectral bands may contain different edges. Existing approaches calculate the edge strength of a pixel locally, based on the variation in intensity between this pixel and its neighbors. Thus, they often fail to detect the edges of objects embedded in background clutter or objects which appear in only some of the bands.We propose SEDMI, a method that aims to overcome this problem by considering the salient properties of edges in an image. Based on the observation that edges are rare events in the image, we recast the problem of edge detection into the problem of detecting events that have a small probability in a newly defined feature space. The feature space is constructed by the spatial gradient magnitude in all spectral channels. As edges are often confined to small, isolated clusters in this feature space, the edge strength of a pixel, or the confidence value that this pixel is an event with a small probability, can be calculated based on the size of the cluster to which it belongs.Experimental results on a number of multispectral data sets and a comparison with other methods demonstrate the robustness of the proposed method in detecting objects embedded in background clutter or appearing only in a few bands.     

Logarithmic dyadic wavelet transform with its applications in edge detection and reconstruction

In this paper, based on the logarithmic image processing model and the dyadic wavelet transform (DWT), we introduce a logarithmic DWT (LDWT) that is a mathematical transform. It can be used in image edge detection, signal and image reconstruction. Comparative study of this proposed LDWT-based method is done with the edge detection Canny and Sobel methods using Pratt's Figure of Merit, and the comparative results show that the LDWT-based method is better and more robust in detecting low contrast edges than the other two methods. The gradient maps of images are detected by using the DWT- and LDWT-based methods, and the experimental results demonstrate that the gradient maps obtained by the LDWT-based method are more adequate and precisely located. Finally, we use the DWT- and LDWT-based methods to reconstruct one-dimensional signals and two-dimensional images, and the reconstruction results show that the LDWT-based reconstruction method is more effective.     

Accurate video text detection through classification of low and high contrast images

Detection of both scene text and graphic text in video images is gaining popularity in the area of information retrieval for efficient indexing and understanding the video. In this paper, we explore a new idea of classifying low contrast and high contrast video images in order to detect accurate boundary of the text lines in video images. In this work, high contrast refers to sharpness while low contrast refers to dim intensity values in the video images. The method introduces heuristic rules based on combination of filters and edge analysis for the classification purpose. The heuristic rules are derived based on the fact that the number of Sobel edge components is more than the number of Canny edge components in the case of high contrast video images, and vice versa for low contrast video images. In order to demonstrate the use of this classification on video text detection, we implement a method based on Sobel edges and texture features for detecting text in video images. Experiments are conducted using video images containing both graphic text and scene text with different fonts, sizes, languages, backgrounds. The results show that the proposed method outperforms existing methods in terms of detection rate, false alarm rate, misdetection rate and inaccurate boundary rate.     

A high performance edge detector based on fuzzy inference rules

Edge detection is an important topic in computer vision and image processing. In this paper, a novel edge detector based on fuzzy If-Then inference rules and edge continuity is proposed. The fuzzy If-Then rule system is designed to model edge continuity criteria. The maximum entropy principle is used in the parameter adjusting process. We also discuss the related issues in designing fuzzy edge detectors. We compare it with the popular edge detectors: Sobel and Canny edge detectors. The proposed fuzzy edge detector does not need parameter setting as Canny edge detector does, and it can preserve an appropriate detection in details. It is very robust to noise and can work well under high level noise situations, while other edge detectors cannot. The detector efficiently extracts edges in images corrupted by noise without requiring the filtering process. The experimental results demonstrate the superiority of the proposed method to existing ones.     

最大差值图决策的低照度图像自适应增强算法

应用于光照分布不均的低照度图像,传统的图像增强算法会出现色彩失真、亮区过度增强等问题,因此提出一种最大差值图决策的低照度图像自适应增强算法。首先,提出最大差值图的概念,通过最大差值图粗略估计出初始光照分量;然后,提出交替引导滤波的算法,利用交替引导滤波对初始光照分量进行校正,实现光照分量的准确估计;最后,设计了图像亮度自适应的伽马变换,能够根据获取的光照分量自适应调整伽马变换参数,从而在增强图像的同时消除光照不均带来的影响。实验结果表明,增强后的图像有效消除了光照分布不均带来的影响,图像亮度、对比度、细节表现能力和色彩保真度都得到了明显提升,平均梯度提升了1倍以上,信息熵提升了14%以上。由于提出的算法对光照分量估计准确,自适应伽马变换针对低照度图像进行了优化,因此,对于夜间等弱光源条件下的彩色图像具有十分有效的增强效果。     

方向邻域全变分图像去噪

为了弥补传统全变分（TV）算法忽略了图像边缘方向的不足, 结合梯度幅度和方向提出了基于方向全变分的去噪算法。该算法运用图像梯度幅度将图像像素划分为边缘区域和非边缘区域, 运用梯度方向对不同区域的像素选取不同的四邻域像素, 针对不同邻域对传统TV算法进行离散分析, 完成了图像的保边去噪。实验结果表明, 结合边缘方向信息改进了传统TV算法的邻域选择方式, 不仅更好地保留了图像边缘信息和重要细节, 且提高了图像的PSNR和视觉效果。     

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。     

基于局部期望阈值分割的图像边缘检测算法

传统图像边缘特征检测通过梯度算子卷积计算获取梯度图,并根据梯度变化情况设定阈值得到边缘信息,但图像的各局部区域梯度变化不均匀,采用统一阈值分割边缘信息往往会造成获取的边缘信息不准确。本文提出一种基于图像局部区域期望的自适应阈值方法,首先采用Sobel算子获取图像梯度矩阵,然后将梯度矩阵分割为多个子区域,并计算每个子区域的局部期望作为该区域阈值,进行边缘特征提取。实验表明,提出的方法提高了图像主要目标物边缘特征的识别度,区域边缘信息划分准确。     

Combining Entropy Optimization and Sobel Operator for Medical Image Fusion

Fusing medical images is a topic of interest in processing medical images. This is achieved to through fusing information from multimodality images for the purpose of increasing the clinical diagnosis accuracy. This fusion aims to improve the image quality and preserve the specific features. The methods of medical image fusion generally use knowledge in many different fields such as clinical medicine, computer vision, digital imaging, machine learning, pattern recognition to fuse different medical images. There are two main approaches in fusing image, including spatial domain approach and transform domain approachs. This paper proposes a new algorithm to fusion multimodal images. This algorithm is based on Entropy optimization and the Sobel operator. Wavelet transform is used to split the input images into components over the low and high frequency domains. Then, two fusion rules are used for obtaining the fusing images. The first rule, based on the Sobel operator, is used for high frequency components. The second rule, based on Entropy optimization by using Particle Swarm Optimization (PSO) algorithm, is used for low frequency components. Proposed algorithm is implemented on the images related to central nervous system diseases. The experimental results of the paper show that the proposed algorithm is better than some recent methods in term of brightness level, the contrast, the entropy, the gradient and visual information fidelity for fusion (VIFF), Feature Mutual Information (FMI) indices.     

Straight road edge detection from high-resolution remote sensing images based on the ridgelet transform with the revised parallel-beam Radon transform

Roads are important basic geographical phenomena and the automatic recognition and extraction of road features from remote sensing images has many applications. However, automated road extraction from high-resolution remote sensing imagery is problematic. In recent years, many approaches have been explored for automatic road extraction, particularly involving road edge detection. Traditional edge detection operators such as the Canny or the Sobel operator are used frequently but there are serious problems of over- or underdetection, and time-consuming and complicated post-processing work is often required. In this paper, a new revised parallel-beam Radon transform (RPRT) approach is proposed. The traditional PRT can have problems with step values, resulting in false edge detection. To overcome these problems we introduced the RPRT, using the harmonic average of the pixel value in every strip of the Radon slice. An algorithm suitable for straight edge detection of roads in high-resolution remote sensing imagery was designed based on the ridgelet transform with the RPRT. The experimental results show that our algorithm can detect straight road edges efficiently and accurately, and avoid cumbersome and complicated post-processing work.     

Comparative evaluation of linear edge detection methods

A comparative evaluation of the most commonly used linear methods for edge detection in grayscale images are presented. Detectors based on the first and second derivatives of image brightness are considered. The method for automatic edge tracking in grayscale images is proposed. The model for assessing errors and artifacts caused by sampling during digitization of real input images is proposed. Investigation of edge detectors isotropy and errors caused by input images sampling is conducted. The advantage of the Isotropic operator for edge tracking is shown. The noise immunity of linear edge detection methods is assessed and the superiority of 3 × 3 gradient operators for noisy images is shown. Isotropic and Sobel operators are identified to be optimal on a basis of sampling errors, output noise level, and computational complexity.     

Robust Visual Tracking Based on Convolutional Features with Illumination and Occlusion Handing

视觉追踪是在计算机视觉的一个重要区域。怎么处理照明和吸藏问题是一个挑战性的问题。这份报纸论述一篇小说和有效追踪算法处理如此的问题。一方面,一起始的外观总是有的目标清除轮廓,它对照明变化光不变、柔韧。在另一方面,特征在追踪起一个重要作用,在哪个之中 convolutional 特征显示出有利性能。因此,我们采用卷的轮廓特征代表目标外观。一般来说,一阶的衍生物边坡度操作员在由卷检测轮廓是有效的他们与图象。特别, Prewitt 操作员对水平、垂直的边更敏感,当 Sobel 操作员对斜边更敏感时。内在地, Prewitt 和 Sobel 与对方一起是补足的。技术上说,这份报纸设计二组 Prewitt 和 Sobel 边察觉者提取一套完全的 convolutional 特征,它包括水平、垂直、斜的边特征。在第一个框架,轮廓特征从目标被提取构造起始的外观模型。在有这些轮廓特征的试验性的图象的分析以后,明亮的部分经常提供更有用的信息描述目标特征,这能被发现。因此,我们建议一个方法比较候选人样品和我们仅仅使用明亮的象素的训练模型的类似,它使我们的追踪者有能力处理部分吸藏问题。在得到新目标以后,变化以便改编外观,我们建议相应联机策略逐渐地更新我们的模型。convolutional 特征由井综合的 Prewitt 和 Sobel 边察觉者提取了的实验表演能是足够有效的学习柔韧的外观模型。九个挑战性的序列上的众多的试验性的结果证明我们的建议途径与最先进的追踪者比较很有效、柔韧。     

An image contrast enhancement algorithm for grayscale images using particle swarm optimization

This paper addresses a contrast enhancement technique that combines classical contrast enhancement with an evolutionary approach. The central goal of this work is to increase the information content and enhance the details of an image using an adaptive gamma correction technique aided by particle swarm optimization. Gamma correction is a well established technique that preserves the mean brightness of an image that produces natural looking images by the choice of an optimal gamma value. Here, Swarm intelligence based particle swarm optimization is employed to estimate an optimal gamma value. In the proposed method, the edge and information content (entropy) are the parameters used to formulate the fitness function. The proposed method is compared with state-of-the-art of techniques in terms of Weighted Average Peak Signal to Noise Ratio (WPSNR), Contrast, Homogeneity, Contrast Noise Ratio (CNR), and Measure of Enhancement (EME). Simulation results demonstrate that the proposed particle swarm optimization based contrast enhancement method improves the overall image contrast and enriches the information present in the image. In comparison to other contrast enhancement techniques, the proposed method brings out the hidden details of an image and is more suitable for applications in satellite imaging and night vision.     

Omnidirectional edge detection

In this paper we propose a new method for extending 1-D step edge detection filters to two dimensions via complex-valued filtering. Complex-valued filtering allows us to obtain edge magnitude and direction simultaneously. Our method can be viewed either as an extension of n-directional complex filtering of Paplinski to infinite directions or as a variant of Canny’s gradient-based approach. In the second view, the real part of our filter computes the gradient in the x direction and the imaginary part computes the gradient in the y direction. Paplinski claimed that n-directional filtering is an improvement over the gradient-based method, which computes gradient only in two directions. We show that our omnidirectional and Canny’s gradient-based extensions of the 1-D DoG coincide. In contrast to Paplinski’s claim, this coincidence shows that both approaches suffer from being confined to the subspace of two 2-D filters, even though n-directional filtering hides these filters in a single complex-valued filter. Aside from these theoretical results, the omnidirectional method has practical advantages over both n-directional and gradient-based approaches. Our experiments on synthetic and real-world images show the superiority of omnidirectional and gradient-based methods over n-directional approach. In comparison with the gradient-based method, the advantage of omnidirectional method lies mostly in freeing the user from specifying the smoothing window and its parameter. Since the omnidirectional and Canny’s gradient-based extensions of the 1-D DoG coincide, we have based our experiments on extending the 1-D Demigny filter. This filter has been proposed by Demigny as the optimal edge detection filter in sampled images.     

基于局部尺度控制的模糊边缘检测算法

基于容错思想定位边缘是为了解决自然图像中难以获取典型边缘特征的前后景相融处的模糊边缘定位问题,它为图像的每一点建立唯一的、局部可计算的最小可靠尺度以对模糊边缘定位并提取。文中对算法中求图像二阶导数时每个像素点都要沿着各自梯度方向确定卷积模板计算各像素点二阶导数的过程进行简化分析。通过将局部尺度判定与LoG算法相结合,避免各梯度方向上所进行的繁琐的二阶导数运算,并提出一个近似确定零交叉点位置的模糊边缘判别和定位流程。详细分析算法的可行性,对比多种算法对3类不同程度模糊的典型图像的边缘定位效果。实验表明,该算法对模糊边缘的定位和提取效果更好,运算速度更快,算法更实用。