Hybrid Human Skin Detection Using Neural Network and K-Means Clustering Technique

Human skin detection is an essential step in most human detection applications, such as face detection. The performance of any skin detection system depends on assessment of two components: feature extraction and detection method. Skin color is a robust cue used for human skin detection. However, the performance of color-based detection methods is constrained by the overlapping color spaces of skin and non-skin pixels. To increase the accuracy of skin detection, texture features can be exploited as additional cues. In this paper, we propose a hybrid skin detection method based on YIQ color space and the statistical features of skin. A Multilayer Perceptron artificial neural network, which is a universal classifier, is combined with the k-means clustering method to accurately detect skin. The experimental results show that the proposed method can achieve high accuracy with an F₁-measure of 87.82% based on images from the ECU database.     

彩色图像人脸高光区域的自动检测与校正方法

人脸检测和识别受不同环境照明的影响很大,彩色图像中人脸高光区域的自动检测和辐射校正有助于对人脸的正确分析和识别.提出了一种在TSL(tint-saturation-luminance)彩色空间基于双色反射模型进行高光检测和辐射校正的新方法.通过考察肤色在各种不同彩色空间中的分布形态,以及对皮肤光谱反射特性的分析,采用关键的2维平面代替3维彩色空间进行高光分析.一方面降低了算法复杂度,另一方面又可以在序贯主分量分析中提出特征值比值,迅速而准确地自动检测是否有皮肤高光区域的存在,同时还可以鲁棒地确定皮肤双色反射矢量,应用双色反射模型进行肤色高光区域的辐射校正.     

Effective detection of exposed target regions based on deep learning from multimedia data

With the development of high-performance visual sensors, it has been very easy to obtain a variety of image data. Of these image data, human face regions contain personal information to distinguish one from the others. Therefore, it is important to accurately detect unhidden face regions from an input image. This paper proposes a method of robustly detecting human face regions from an input color image with the use of a deep learning algorithm, one of the machine learning algorithms. The proposed method first transforms the RGB color model of an input image to the YC_bC_r color model, and then removes other regions than face regions to segment skin regions with the use of the pre-learned elliptical skin color distribution model. Subsequently, a CNN model-based deep learning algorithm was applied to robustly detect human face regions from the detected skin regions in the previous step. As a result, the proposed method segments face regions more efficiently than an existing method. The face region detection method proposed in this paper is expected to be usefully applied to practical areas related to multimedia data processing, such as video surveillance, target blocking, image security, visual data analysis, and object recognition and tracking.

     

Face detection of golden monkeys via regional color quantization and incremental self-paced curriculum learning

Animal detection plays a very vital role in wildlife protection and many other real life applications. In this paper, we focus on face detection of Golden monkeys who live in Qinling Mountains, Shaanxi province, China, and present a relatively complete face detection algorithm to detect these monkeys’ faces, which mainly includes three parts: the location of the monkeys’ bodies, the detection of th+e suspicious facial skin and the accurate detection of the true faces. Firstly, regional color quantization is proposed to quantize the HSV color space for the nature images with different sizes, and we can get the areas of the monkeys’ bodies according to the color distribution of the monkeys’ hair in the histogram of the quantized color space. Then the areas of suspicious facial skin can be extracted from these areas of the monkeys’ bodies. Further, we propose incremental self-paced curriculum learning (ISPCL) to detect the true monkeys’ faces accurately. In our method, regional color quantization can increase the color differences between the background and the monkeys’ hair, so that the segmented results have fewer background pixels. Besides, the basic idea of the incremental learning is introduced into the training process of SPCL, which is to simulate the process in which human learn something from easy samples to hard ones, this idea is able to improve the performances of face detection. The experimental results demonstrate that the proposed algorithm can locate the monkeys’ bodies in the images with different sizes, and can detect their faces effectively. This research lays a foundation for face recognition and behavior analysis of golden monkeys.     

A novel unsupervised 3D skeleton detection in RGB-D images for video surveillance

In this paper we present a novel moment-based skeleton detection for representing human objects in RGB-D videos with animated 3D skeletons. An object often consists of several parts, where each of them can be concisely represented with a skeleton. However, it remains as a challenge to detect the skeletons of individual objects in an image since it requires an effective part detector and a part merging algorithm to group parts into objects. In this paper, we present a novel fully unsupervised learning framework to detect the skeletons of human objects in a RGB-D video. The skeleton modeling algorithm uses a pipeline architecture which consists of a series of cascaded operations, i.e., symmetry patch detection, linear time search of symmetry patch pairs, part and symmetry detection, symmetry graph partitioning, and object segmentation. The properties of geometric moment-based functions for embedding symmetry features into centers of symmetry patches are also investigated in detail. As compared with the state-of-the-art deep learning approaches for skeleton detection, the proposed approach does not require tedious human labeling work on training images to locate the skeleton pixels and their associated scale information. Although our algorithm can detect parts and objects simultaneously, a pre-learned convolution neural network (CNN) can be used to locate the human object from each frame of the input video RGB-D video in order to achieve the goal of constructing real-time applications. This much reduces the complexity to detect the skeleton structure of individual human objects with our proposed method. Using the segmented human object skeleton model, a video surveillance application is constructed to verify the effectiveness of the approach. Experimental results show that the proposed method gives good performance in terms of detection and recognition using publicly available datasets.

     

基于肤色分割和AdaBoost算法的彩色图像的人脸检测

文章提出了肤色分割和AdaBoost算法结合的人脸检测算法。首先,对彩色图像进行肤色分割,通过人脸肤色的统计特征得到候选人脸区域：然后,基于AdaBoost算法,使用由强分类器组成的级联分类器对候选人脸区域进行扫描,最终得到精确定位的人脸。实验证明,该方法具有肤色检测快速和AdaBoost算法误检率低的优点,可以有效的运用于多姿态、多人脸和复杂背景的情况。     

YCbCr空间中一种基于贝叶斯判决的肤色检测方法

皮肤颜色是人脸检测、定位、跟踪时的一种十分有效的特征,而且裸露的皮肤区域也是色情图像的最重要特征之一.为了有效地进行图像的皮肤检测,提出了一种新的肤色检测方法.该方法首先通过统计1809 502个肤色像素点和1763682个非肤色像素点,并使用贝叶斯规则来建立肤色分类器;然后考虑亮度对肤色的影响,采用Y-Cb和Y-Cr两个子空间的查询表来建立肤色模型.为了联合使用两个查询表,先采用高斯归一化和线性化方法来将阈值范围调整至[0,1];同时对查询表进行中值滤波处理,以除去离散孤立点.实验表明,与其他3种方法相比,该方法不仅有着较低的漏检率（9.814%）和误检率（3.5%）,而且对于不同光照条件也有较好的检测效果.     

Image specific discriminative feature extraction for skin segmentation

In recent times, the majority of colour-based skin detection methods used skin modelling in different colour spaces, and they are capable of skin classification at a pixel level. However, the accuracy of these methods is significantly affected by different issues, such as the presence of skin-like colours in scene background, variations in skin pigmentation, scene illumination, etc. Recent developments show that the discriminating power of a colour-based skin classifier can be increased by employing texture and spatial features. However, we observed that discriminability between skin and non-skin regions does not follow any statistics, and the discrimination is extremely image specific. In this paper, a novel adaptive discriminative analysis (ADA) is proposed to extract most discriminant features between skin and non-skin regions from an image itself in an unsupervised manner. Experimental results for standard databases show that the proposed method can efficiently segment out skin pixels in the presence of skin-like background colours.

     

Human fall detection using normalized shape aspect ratio

In video surveillance, automatic human fall detection is important to protect vulnerable groups such as the elderly. When the camera layout varies, the shape aspect ratio (SAR) of a human body may change substantially. In order to rectify these changes, in this paper, we propose an automatic human fall detection method using the normalized shape aspect ratio (NSAR). A calibration process and bicubic interpolation are implemented to generate the NSAR table for each camera. Compared with some representative fall detection methods using the SAR, the proposed method integrates the NSAR with the moving speed and direction information to robustly detect human fall, as well as being able to detect falls toward eight different directions for multiple humans. Moreover, while most of the existing fall detection methods were designed only for indoor environment, experimental results demonstrate that this newly proposed method can effectively detect human fall in both indoor and outdoor environments.

     

复杂背景基于肤色的人脸检测

本文针对复杂背景的彩色静止图像的人脸检测提出了一种基于肤色检测和分块面部特验证方法,。先在类肤色区域内提取出面部特征,然后用分块验证的方法来确定人脸。本算法可以快速检测不同大小,不同平面及一定侧面旋转角度的人脸,而且可以适应一定程度的表情变化。     

基于肤色信息和主分量分析的人脸实时检测系统

提出了一种基于肤色模型、人脸面部结构和模板匹配的人脸检测算法。该方法首先建立肤色模型来对原始图像进行肤色区域分割,再根据人脸的面部结构特征对分割区域进行过滤,最后用基于主元素分析的模板匹配算法对候选区域进行判断。实验结果表明该算法能够适应复杂背景以及多人脸的检测,而且检测速度快,能够达到实时检测的目的。     

自适应邻域相关性的背景建模

目的 背景建模在计算机视觉领域中是检测、跟踪、行为学习和识别的基础,被广泛地应用于视频监控的运动目标检测。混合高斯（MOG）和Codebook是其中具有代表性的方法,但它们假设像素点间信息是独立的,只保留了时域信息而忽略了空域信息,使得模型对背景的描述局限于时间上的连续性。针对上述问题,提出了一种自适应邻域相关性的背景建模方法（ANC）。方法 ANC在保留原始方法时域信息建模特性的同时,增加对邻域模型的复用,同时利用计算结果反馈自适应调整邻域区域,提高对前景值判断的准确性。首先利用原始基于像素点的背景建模方法进行候选前景检测,然后将候选前景检测结果为前景点的像素与邻域像素点模型进行对比,若邻域范围存在匹配则为背景点,若不存在则为前景点;最后引入像素置信度概念,自适应调整邻域范围的大小。结果 与MOG和Codebook相比,在changedetection标准数据库上,ANC在ROC（受试者工作特征曲线）和度量值等方面的平均精度和F-measure都提高了7%以上。结论 自适应邻域相关性的背景建模方法适用于复杂多模态背景,克服了基于像素点背景建模方法假设的局限性。与普通基于像素点的背景建模方法相比,具有更好的鲁棒性和抗噪性,对复杂背景具有更强的适应性。     

Dissimilar pixel counting based impulse detector for two-phase mixed noise removal

Mixed noise is a challenging noise model due to its statistical complexity. A new two-phase denoising method based on an impulse detector using dissimilar pixel counting is proposed in this paper. This method consists of two stages: detection and filtering. For the detection phase, average difference scheme is proposed to distinguish whether two neighboring pixels are similar or not, and then the number of dissimilar pixels is compared with a threshold to locate the outlier point in noisy image. An iterative framework is used for detection accuracy with the least numbers of iteration. For the filtering phase, an extended trilateral filter is used to remove the mixture of Gaussian and impulse noise, which are treated differently depending on the guidance matrix from the detection phase. Extensive experimental results demonstrate that the proposed method exhibits better noise detection capability and outperforms many existing two-phase mixed noise removal methods in both quantitative evaluation and visual quality.

     

基于支持向量机的肤色检测

根据支持向量机理论和肤色信息分布特点,提出用支持向量机方法进行肤色检测.在YCbCr、YIQ、YUV颜色空间中,去除照度分量,分别以各像素点的色度向量为输入,用像素点所属类别--皮肤区域与非皮肤区域为输出,建立各颜色空间的支持向量机肤色检测器.实验表明,该方法具有良好的肤色检测效果.     

一种基于改进渗流模型的混凝土表面裂缝快速检测算法

由于混凝土表面不平整、光照不均、裂缝背景较为复杂等因素的干扰,传统的基于图像处理的裂缝检测方法对裂缝检测的效果不佳,尤其是不清晰和比较细小的裂缝。基于渗流模型的裂缝检测方法充分考虑了裂缝亮度低、形状较为细长的特点,对裂缝的检测效果很好,尤其是图像中的细小裂缝,但是该方法需要大量的处理时间。为了解决上述问题,提出了一种加速渗流处理的算法,该算法通过暗点预提取来减少渗流时需要处理的像素点个数,以此来减少渗流处理时间。实验结果表明,所提算法能明显加快渗流处理的速度,并且精确率基本保持不变。     

基于YCgCr颜色空间的不良图像肤色检测

针对目前互联网图像内容过滤系统识别率低的情况,提出了一种基于YCgCr空间的不良图像肤色检测方法。首先检测图像中可能存在的人脸区域,利用人脸肤色像素来检测获得人体肤色,其次对不含人脸图像,则利用离线构建的肤色模型来实现肤色检测。实验结果证明,在不同光照以及复杂背景下,该算法能够较好地提高不良图像的肤色检测率和背景检测率。     

分层信息融合的物体级显著性检测

目的 显著性检测是基于对人类视觉的研究,用来帮助计算机传感器感知世界的重要研究手段。现有显著性检测方法大多仅能检测出人类感兴趣的显著点或区域,无法突出对象整体的显著性以及无法区分对象不同层次的显著性。针对上述问题,提出一种基于分层信息融合的物体级显著性检测方法。方法 与当前大多数方法不同,本文同时运用了中级别超像素和物体级别区域两种不同层次的结构信息来获取对象的显著图。首先,将图像分割为中级别的超像素,利用自下而上的方法构造初始显著图;然后通过谱聚类方法将中级别的超像素聚类成物体级的区域,并运用自上而下的先验来调整初始先验图;最后,通过热核扩散过程,将超像素级别上的显著性扩散到物体级的区域上,最终获得一致的均匀的物体级显著性图。结果 在MSRA1000标准数据库上与其他16种相关算法在准确率-召回率曲线及F度量等方面进行了定量比较,检测的平均精度和F-检验分数比其他算法高出5%以上。结论 通过多层次信息融合最终生成的显著图,实现了突出对象整体显著性以及区分不同对象显著性的目标。本文方法同样适用于多目标的显著性检测。     

Difference representation learning using stacked restricted Boltzmann machines for change detection in SAR images

In this paper, we establish a deep neural network using stacked Restricted Boltzmann Machines (RBMs) to analyze the difference images and detect changes between multitemporal synthetic aperture radar (SAR) images. Given the two multitemporal images, a difference image which shows difference degrees between corresponding pixels is generated. Then, RBMs are stacked to form a deep hierarchical neural network to learn to analyze the difference image and recognize the changed pixels and unchanged pixels. The learning process includes unsupervised layer-wise feature learning and supervised fine-tuning of network parameters. Unsupervised learning aims to learn the representation of the difference image. Supervised fine-tuning aims to learn to classify the changed and unchanged pixels. The network can learn from datasets that have few labeled data. The labeled data can be selected from the results obtained by other methods because there is no prior information in image change detection. The system learns to detect the changes instead of recognizing the changes by fixed equations as in traditional change detection algorithms. We test the network with real synthetic aperture radar datasets and the labeled samples are extracted from the results obtained, respectively, by several methods, including a thresholding method, a level set method and two clustering methods. The results achieved by the trained network outperform that of other methods.     

Robust skin segmentation using color space switching

Skin detection is very popular and has vast applications among researchers in computer vision and human computer interaction. The skin-color changes beyond comparable limits with considerable change in the nature of the light source. Different properties are taken into account when the colors are represented in different color spaces. However, a unique color space has not been found yet to adjust the needs of all illumination changes that can occur to practically similar objects. Therefore a dynamic skin color model must be constructed for robust skin pixel detection, which can cope with natural changes in illumination. This paper purposes that skin detection in a digital color image can be significantly improved by employing automated color space switching. A system with three robust algorithms has been built based on different color spaces towards automatic skin classification in a 2D image. These algorithms are based on the statistical mean of value of the skin pixels in the image. We also take Bayesian approaches to discriminate between skin-alike and non-skin pixels to avoid noise. This work is tested on a set of images which was captured in varying light conditions from highly illuminated to almost dark.     

Iterative deviation filter for fixed-valued impulse noise removal

An iterative deviation filter for fixed-valued impulse noise removal is proposed, with the aim to overcome the defects of existing filters, and further improve the denoising performance. In the proposed filter, a noise detection method based on the extreme intensity values and the deviation of neighbor pixels is proposed, i.e., the pixels with the extreme intensity and differ greatly from the mean of neighbor pixels, are identified as noises. A noise removal method based on the minimum deviation of neighbor pixels is proposed, i.e., the intensity of one neighbor noise free pixel, which is closest to the mean of neighbor noise free pixels, is used as estimated intensity of noisy pixel under consideration. Furthermore, the noise removal strategy performs iteratively and takes full advantage of the previous denoising results. Simulation results show that the proposed method has better denoising performance than the existing distinguished filters in terms of visual representation, peak signal to noise ratio and structural similarity index.