基于在线估计的视觉SLAM低光照图像增强算法

为了提升基于特征点的双目视觉定位算法在低光照环境下定位的准确性,提出一种基于在线估计的视觉同步定位与地图构建(simultaneous localization and mapping,SLAM)低光照图像增强算法.通过在线估计图像亮度值,实时更新图像增强算法的参数,解决了基于固定参数的图像增强算法在图像较亮、较暗等情况下的不适用性问题.首先,通过ORB-SLAM2系统寻找定位准确度的影响因素,并通过在线估计参数的方法实时更新相关参数.其次,利用低光照图像增强算法(low-light image enhancement,LIME)改善图像效果.最后,根据增强后的图像进行特征点提取,提升了特征匹配准确度,进而提升了定位的准确度.在公开EuRoC数据集上,通过与目前广泛使用的ORB-SLAM2算法进行对比实验,结果表明本文提出的视觉SLAM系统,具有更好的定位准确性及鲁棒性.     

多模型ASM及其在人脸面部特征点定位中的应用

为了提高ASM在非均匀光照条件下的人脸面部特征点定位的精确度,提出了一种融合Log-Gabor小波特征的多模型ASM方法.该方法的主要特点有:在精确定位目标图像虹膜位置的基础上对全局形状模型进行较准确的初始化;特征点局部纹理特征采用灰度和Log-Gabor小波特征共同描述,减少光照和噪音对算法的影响;建立包括全局ASM和基于人脸面部显著特征区域的局部ASM的多模型ASM,交替使用这两种ASM模型在边缘约束策略基础上对特征点的定位结果进行约束.实验表明,多模型ASM算法对人脸面部特征点定位的准确率比传统ASM算法有明显提高.     

基于Faster R-CNN的保偏光纤偏振轴检测方法研究

针对基于端面成像的偏振轴检测方法光照鲁棒性不强、无法同时检测多根保偏光纤偏振轴的问题,提出一种基于Faster R-CNN的检测方法.训练Faster R-CNN神经网络模型,通过参数调优增强了应力区识别的鲁棒性,采用基于Zernike矩算法对应力区边缘点进行亚像素级定位以提高测量精度,分析了测量精度与误差的关系.仿真与实验表明,该方法的测量精度达到±0.1°,在增强光照鲁棒性的同时实现了对多根保偏光纤偏振轴的检测.     

彩色图像人脸特征点定位算法研究

本文提出了一种基于复合局部信息模型的改进Active Shape Model(ASM)算法,并进一步提出了基于人脸特征点Gabor小波特征降维分类的特征点搜索方法,对改进ASM的结果进行精确校正,达到鲁棒精确定位特征点的目的.本文首先对经过Adaboost检测的彩色图像人脸区域进行光照补偿,然后根据眼睛和唇色的色度特性定位双眼和嘴唇中心位置,从而得到较好的人脸特征点的初始位置.在特征点位置搜索中,本文结合肤色概率信息对ASM方法进行了改进,从而提高了仅基于灰度梯度信息的传统ASM方法的鲁棒性和准确性.最后选取改进ASM搜索后的某些特征点一定领域内梯度值较高的点,提取其Gabor小波特征,通过线性判别式分析法(Linear Discriminant Analysis)和最近邻分类法对其进行分类,搜寻最符合训练样本Gabor特征的点作为最佳位置点,修正了ASM的搜索结果,使得搜寻结果更加精确.     

基于特征点扩充及PCA特征提取的ASM定位算法

活动形状模型ASM(Active Shape Model)在目标对象的定位中得到广泛应用,但传统ASM算法定位精度较低,模型容易收敛到错误位置,为此,本文提出了一种基于特征点扩充和主成分分析PCA(Principal Component Analysis)灰度特征提取的ASM改进算法:首先,采用等距插值的方法扩充手工标定的特征点;其次,提出采用主成分分析PCA处理特征点法线灰度信息代替原算法中的灰度值求导,统计特征点局部灰度特征,以提高目标定位的精度.实验结果表明,与传统ASM算法相比,本文的改进算法的目标定位精度和鲁棒性都有了显著的提高,实验数据显示,平均定位误差降低了38％以上.     

基于颜色和边缘特征的均值迁移目标跟踪算法

综合颜色特征对旋转、变形不敏感和边缘特征对光照、颜色不敏感的优点,提出了一种基于颜色和边缘特征的均值迁移目标跟踪算法,提高了跟踪算法的鲁棒性。利用加权求和的方法获得联合特征的Bhattacharyya系数,以此作为衡量目标模型与候选目标相似程度的依据。实验结果表明,在光照变化时,本文算法的跟踪误差大约是基于颜色特征的均...     

疲劳驾驶检测中人眼实时定位与跟踪

针对疲劳驾驶检测中现存的一些难点,提出了一种新颖的驾驶员眼睛定位与跟踪方法.首先采用Gamma校正和图像自商算法对红外光源采集到的驾驶员行车图像进行预处理,进而采用基于Haar-like特征的AdaBoost算法初定位驾驶员眼眉区域,并在此基础上采用眼眉区域15点ASM(Active Shape Models)模型来准确定位驾驶员的眼睛区域,最后采用了Unscented卡尔曼滤波算法完成驾驶员眼睛的实时跟踪.实验结果表明,该算法不仅可以提高对光照、姿态变化以及驾驶员眼睛运动的强非线性问题的鲁棒性,同时对驾驶员配戴眼镜的情况也能得到较为理想的处理结果.     

视觉注意力机制在货运列车车号定位中的应用

为了定位货运列车车厢上的车号信息,提出并实现一种基于视觉注意力机制的定位新方法.该方法利用眼动跟踪技术获取真实眼动数据,结合提炼的车厢图像显著特征,建立车号注意力机制模型,并利用该模型预测车号显著区域,最后采用图像处理技术完成车号区域的分析定位.实验表明,相比传统基于灰度的处理方法,该算法更能适应不同光照条件下各类车厢的车号定位,具有更好的鲁棒性和普适性.     

基于Kinect的人脸检测算法研究

基于Kinect的人脸检测算法是一种快速并高效的人脸检测方法,可以在多视角以及光线复杂的情况下检测出人脸,并准确且实时地定位人脸的特征点。由于传统的人脸检测方法,大多都是人脸处于正脸的情况下,存在多视角情况下检测不准确问题;传统的AAM算法在仅利用灰度信息来建立纹理模型,在光线条件不理想的情况下,AAM特征点定位算法的精度和鲁棒性会显著下降。文章利用微软的Kinect传感器,提出一种Kinect+AAM的人脸检测算法。通过实验对比,本文算法在定位精度和鲁棒性上都有比较大的提高。     

基于快速鲁棒性特征匹配的目标识别

针对基于特征匹配的目标识别算法复杂度高、难以实时处理的问题,提出基于快速鲁棒性特征(SURF)的快速特征匹配算法.通过应用双阈值顺序聚类算法对特征点进行聚类,并对每一个聚类建立k-d搜索树,采用优先搜索算法匹配模板与图像的特征点,提高了算法实时性.采用RANSAC鲁棒估计算法消除错误匹配点对,计算模板与图像平面之间的单应矩阵,进而实现对目标的准确识别定位.仿真实验证明了算法的有效性和实用性.     

基于特征点定位与肤色分割的胡子检测算法

针对具有一定程度偏转和模糊的人脸图像难以实现胡子检测的问题,提出基于人脸特征点定位与肤色分割的胡子检测算法.该算法的设计思路是首先使用主动形状模型(ASM)算法定位人脸特征点进而获取下巴区域,然后利用提出的自动选择聚类中心(ASCC)的肤色分割算法分离出下巴的非肤色区域,最后在下巴非肤色区域中使用胡子颜色判别法检测得到胡子.在LFW人脸库上的实验表明,该算法能够准确地检测出入脸的胡子,特别地,对有一定程度偏转和模糊的人脸图像,算法依然能获取良好的检测效果.     

Automatic scalable face model design for 2D model-based video coding

Scalable low bit-rate video coding is vital for the transmission of video signals over wireless channels. A scalable model-based video coding scheme is proposed in this paper to achieve this. This paper mainly addresses automatic scalable face model design. Firstly, a robust and adaptive face segmentation method is proposed, which is based on piecewise skin-colour distributions. 43 million skin pixels from 900 images are used to train the skin-colour model, which can identify skin-colour pixels reliably under different lighting conditions. Next, reliable algorithms are proposed for detecting the eyes, mouth and chin that are used to verify the face candidatures. Then, based on the detected facial features and human face muscular distributions, a heuristic scalable face model is designed to represent the rigid and non-rigid motion of head and facial features. A novel motion estimation algorithm is proposed to estimate the object model motion hierarchically. Experimental results are provided to illustrate the performance of the proposed algorithms for facial feature detection and the accuracy of the designed scalable face model for representing face motion.     

基于低秩特征脸与协同表示的人脸识别算法

在人脸识别中,人脸图像往往受到表情、光照、遮挡、姿态变化的影响,对此本文提出一种基于低秩特征脸与协同表示的人脸识别算法。该算法先用低秩矩阵恢复算法分解出训练样本图像的误差图像,再分别对训练样本与误差图像提取特征构造特征字典,计算测试样本图像特征字典下的协同表示系数,最后通过重构误差进行分类。通过AR和ORL人脸库进行实验,结果表明,本文提出的人脸识别算法的识别率、识别速率得到有效提高。     

一种复杂背景下快速人脸检测算法

论文针对彩色图片的人脸检测在复杂的背景下检测难度大、检测时间长的问题,提出一种将非线性分段色彩变化的肤色模型、Gabor特征提取和多层感知机MLP分类决策相结合的人脸检测算法。该算法首先对输入图像进行自适应的光照补偿,根据非线性分段色彩变化建立的YCb＇Cr＇肤色模型筛选出潜在的人脸区域;然后对潜在人脸区域进行Gabor小波特征分析,利用MLP网络进行分类判别。通过计算机仿真得出此算法计算复杂度低、检测时间短。     

Multi-task feature learning-based improved supervised descent method for facial landmark detection

Facial landmark detection has played an important role in many face understanding tasks, such as face verification, facial expression recognition, age estimation et al. Model initialization and feature extraction are crucial in supervised landmark detection. Mismatching caused by detector error and discrepant initialization is very common in these existing methods. To solve this problem, we have proposed a new method called multi-task feature learning-based improved supervised descent method (MtFL-iSDM) for the robust facial landmark localization. In this new method, firstly, a fast detection will be processed to locate the eyes and mouth, and the initialization model will adapt to the real location according to fast facial points detection. Secondly, multi-task feature learning is adopted on our improved supervised descent method model to achieve a better performance. Experiments on four benchmark databases show that our method achieves state-of-the-art performance.     

An Image Model and Segmentation Algorithm for Reflectance Confocal Images of In Vivo Cervical Tissue

The automatic segmentation of nuclei in confocal reflectance images of cervical tissue is an important goal toward developing less expensive cervical precancer detection methods. Since in vivo confocal reflectance microscopy is an emerging technology for cancer detection, no prior work has been reported on the automatic segmentation of in vivo confocal reflectance images. However, prior work has shown that nuclear size and nuclear-to-cytoplasmic ratio can determine the presence or extent of cervical precancer. Thus, segmenting nuclei in confocal images will aid in cervical precancer detection. Successful segmentation of images of any type can be significantly enhanced by the introduction of accurate image models. To enable a deeper understanding of confocal reflectance microscopy images of cervical tissue, and to supply a basis for parameter selection in a classification algorithm, we have developed a model that accounts for the properties of the imaging system and of the tissues. Using our model in conjunction with a powerful image enhancement tool (anisotropic median-diffusion), appropriate statistical image modeling of spatial interactions (Gaussian Markov random fields), and a Bayesian framework for classification-segmentation, we have developed an effective algorithm for automatically segmenting nuclei in confocal images of cervical tissue. We have applied our algorithm to an extensive set of cervical images and have found that it detects 90% of hand-segmented nuclei with an average of 6 false positives per frame.     

A subspace model-based approach to face relighting under unknown lighting and poses

We present a new approach to face relighting by jointly estimating the pose, reflectance functions, and lighting from as few as one image of a face. Upon such estimation, we can synthesize the face image under any prescribed new lighting condition. In contrast to commonly used face shape models or shape-dependent models, we neither recover nor assume the 3-D face shape during the estimation process. Instead, we train a pose- and pixel-dependent subspace model of the reflectance function using a face database that contains samples of pose and illumination for a large number of individuals (e.g., the CMU PIE database and the Yale database). Using this subspace model, we can estimate the pose, the reflectance functions, and the lighting condition of any given face image. Our approach lends itself to practical applications thanks to many desirable properties, including the preservation of the non-Lambertian skin reflectance properties and facial hair, as well as reproduction of various shadows on the face. Extensive experiments show that, compared to recent representative face relighting techniques, our method successfully produces better results, in terms of subjective and objective quality, without reconstructing a 3-D shape.     

Locating the eye in human face images using fractal dimensions

Facial feature extraction is an important step in many applications such as human face recognition, video conferencing, surveillance systems, human computer interfacing etc. The eye is the most important facial feature. A reliable and fast method for locating the eye pairs in an image is vital to many practical applications. A new method for locating eye pairs based on valley field detection and measurement of fractal dimensions is proposed. Possible eye candidates in an image with a complex background are identified by valley field detection. The eye candidates are then grouped to form eye pairs if their local properties for eyes are satisfied. Two eyes are matched if they have similar roughness and orientation as represented by fractal dimensions. A modified approach to estimating fractal dimensions that is less sensitive to lighting conditions and provides information about the orientation of an image under consideration is proposed. Possible eye pairs are further verified by comparing the fractal dimensions of the eye-pair window and the corresponding face region with the respective means of the fractal dimensions of the eye-pair windows and the face regions. The means of the fractal dimensions are obtained based on a number of facial images in a database. Experiments have shown that this approach is fast and reliable     

Automatic facial expression recognition: feature extraction and selection

In this paper, we investigate feature extraction and feature selection methods as well as classification methods for automatic facial expression recognition (FER) system. The FER system is fully automatic and consists of the following modules: face detection, facial detection, feature extraction, selection of optimal features, and classification. Face detection is based on AdaBoost algorithm and is followed by the extraction of frame with the maximum intensity of emotion using the inter-frame mutual information criterion. The selected frames are then processed to generate characteristic features using different methods including: Gabor filters, log Gabor filter, local binary pattern (LBP) operator, higher-order local autocorrelation (HLAC) and a recent proposed method called HLAC-like features (HLACLF). The most informative features are selected based on both wrapper and filter feature selection methods. Experiments on several facial expression databases show comparisons of different methods.     

人脸检测跟踪与特征点定位系统

实现的人脸检测跟踪与特征点定位系统,基于VC++6.0开发平台,使用opencv作为开发工具,有效缩短了系统的开发时间。首先,本系统采用adaboost算法进行人脸检测,通过合理的特征模板的选择实现了人脸的实时检测;其次,人脸跟踪模块选用camshift算法,利用人脸检测模块生成的人脸坐标传递给跟踪模块,实现人脸的自动实时跟踪,同时建立多个camshift跟踪器对多人脸进行跟踪,并有效地解决了人脸遮挡的问题;最后,通过ASM(active shapemodel)算法实现了实时人脸特征点定位。实验结果表明该系统实现的人脸实时检测跟踪及特征点定位,效果明显,可以作为表情分析和情感计算、视频人脸识别开发的基础。