Good match exploration for thermal infrared face recognition based on YWF-SIFT with multi-scale fusion

Stable local feature detection is a critical prerequisite in the problem of infrared (IR) face recognition. Recently, Scale Invariant Feature Transform (SIFT) is introduced for feature detection in an infrared face frame, which is achieved by applying a simple and effective averaging window with SIFT termed as Y-styled Window Filter (YWF). However, the thermal IR face frame has an intrinsic characteristic such as lack of feature points (keypoints); therefore, the performance of the YWF-SIFT method will be inevitably influenced when it was used for IR face recognition. In this paper, we propose a novel method combining multi-scale fusion with YWF-SIFT to explore more good feature matches. The multi-scale fusion is performed on a thermal IR frame and a corresponding auxiliary visual frame generated from an off-the-shelf low-cost visual camera. The fused image is more informative, and typically contains much more stable features. Besides, the use of YWF-SIFT method enables us to establish feature correspondences more accurately. Quantitative experimental results demonstrate that our algorithm is able to significantly improve the quantity of feature points by approximately 38%. As a result, the performance of YWF-SIFT with multi-scale fusion is enhanced about 12% in infrared human face recognition.     

金刚石近红外增透滤光保护窗口的制备及应用

报道金刚石近红外增透滤光保护窗口的研制及其在工业中的应用。采用热灯丝化学气相沉积方法成功地制备出金刚石近红外增透滤光保护窗口，其工作波长为１．３￣１．８μｍ，具有良好的光学透过性和优良的物理化学性质。该窗口已在上海宝钢获得成功应用。     

基于CamShift和Kalman组合的改进目标跟踪算法

针对应用CamShift算法进行目标跟踪过程中,当目标被严重遮挡、目标被与目标颜色相近的背景干扰时易丢失跟踪目标的问题,提出了一种基于CamShift和Kalman滤波组合的改进跟踪算法;为克服目标因严重遮挡而丢失的缺陷,利用自适应算法改进了传统的CamShift算法,扩大了搜索窗口,使运动目标位于搜索窗口内;为解决目标因颜色相近背景干扰而丢失的问题,改善跟踪准确率,利用卡尔曼滤波预测目标运动空间位置,作为下一帧搜索窗口的质心坐标;基于上述改进,利用C++语言,研发了改进的CamShift目标跟踪软件模块,给出了该模块的算法流程;实验结果表明,改进后的目标跟踪算法能有效地克服传统CamShift算法的缺陷,大大提高运动目标跟踪的准确性;所提的算法可以应用于运动小车跟踪,人脸识别等领域。     

Double fusion filtering based multi-view face recognition

Two-dimensional (2D) face recognition by correlation is a key challenge of telecommunication and optical information processing. Although this issue has been the focus of intense research, its utilization still has some drawbacks especially when the face is in rotation. In this paper, we propose an alternative method based on a newly designed optical correlation filter which allows recognizing faces with different view angles. This filter called “Multi-View Binary Phase-Only Filter” is based on a double fusion of reference images allowing an optimisation of the use of the spatial-bandwidth product (SBWP) in the filter Fourier plane. The first fusion is performed in the image (space) domain, and the second one is conducted in the spectral domain. Simulations results with the Pointing Head Pose Image Database illustrate the performance of the designed correlation filter for multi-view face recognition.     

一种新的红外成像末制导目标跟踪方法

为了稳定跟踪导弹末制导阶段的红外目标,提出了一种基于尺度不变特征变换的红外目标跟踪算法.尺度不变性特征变换所提取的图像纹理特征具有尺度和旋转不变性,跟踪算法分别提取目标模板和待跟踪图像的尺度不变特征变换特征.根据最小欧氏距离准则提取目标模板与待跟踪图像间相匹配的尺度不变特征变换特征点对,利用该特征点对拟合反映两图像间映射关系的仿射模型,并据此估计目标中心位置及调整目标模板尺寸.仿真结果表明,跟踪算法能够较好地实现在导弹末制导阶段对红外地面杂波背景下目标的稳定跟踪,其跟踪准确度和稳定度优于传统方法.
关键词:末制导跟踪|尺度不变性特征变换|特征匹配|仿射模型     

多波段红外图像的海面舰船目标检测

现有的基于单个红外宽波段的海面舰船目标探测系统在面对复杂海天背景、岛岸背景、恶劣天气、亮带干扰或诱饵弹干扰等情况时,系统的探测率、虚警率、探测距离等性能指标均会受到严重的影响;为此,开展了基于多波段红外图像的海面舰船目标检测方法的研究。通过中波红外多波段数据采集系统实际采集107组五个中波红外波段的图像;波段1-5分别为3.7～4.8,3.7～4.1,4.4～4.8,3.7～3.9和4.65～4.75 μm;对多波段图像进行手动标注构建样本数据集,其中,正样本舰船目标298个,负样本非舰船目标353个。对于多波段红外图像,首先进行PCA降维并采用选择性搜索算法生成初始目标候选区域;针对候选区域中存在大量明显的非舰船目标区域的问题,利用积分图像计算候选区域的局部对比度,依据红外舰船目标的几何和灰度特征从初始目标候选区域中筛选出舰船目标可能性大的区域作为舰船目标候选区域。然后对舰船目标候选区域进行拓展以融入局部上下文信息,对于候选区域对应的5波段红外图像,分别提取每个波段图像的稠密SIFT特征,并将128维SIFT特征向量降为64维,融入SIFT特征的空间和波段位置分布信息得到新的特征向量,基于高斯混合模型对候选区域的特征向量集合进行编码融合得到舰船目标候选区域的费舍尔向量表示,最后利用线性SVM分类器识别出舰船目标。对多波段图像进行舰船目标候选区域生成实验,所提出的基于红外舰船目标的几何和灰度特征的约束方法可以有效地克服选择性搜索算法的不足,从初始目标候选区域中快速定位出舰船目标候选区域,对25组多波段图像进行实验,舰船目标候选区域生成的整体耗时为0.353 s,定位舰船目标区域耗时0.005 s。对100个正负样本进行目标识别测试,所提出的目标识别算法融合了目标的多波段图像特征信息,通过引入费舍尔向量挖掘了多波段图像梯度统计特征的深层次信息,算法的识别率达到了0.97,显著高于单波段红外图像的目标识别率。对25组多波段图像进行舰船目标检测实验,所提出的舰船目标检测方法能够在海天背景、岛岸背景以及亮带干扰等不同场景下完成海面舰船目标的检测工作,舰船目标定位准确,舰船目标召回率达到了0.95,每组多波段图像的平均检测耗时为1.33 s。研究结果表明,充分考虑海面舰船目标在红外图像中与局部海洋背景的辐射差异以及有效地融合舰船目标在多个红外波段图像中的辐射特征,可以增强舰船目标的可分性,提高舰船目标的识别率以及检测率,为基于多波段红外图像的海面舰船目标检测提供了新的技术支持。     

Noisy face recognition using compression-based joint wavelet-transform correlator

A new method for noisy face recognition by incorporating wavelet filter into compression-based joint transform correlator (JTC) is proposed. The simulation results show that the proposed method has advantages over the conventional compression-based JTC in that regardless of the contrast and the noise level of the target, the wavelet filter can optimize the recognition performance to be higher than the classical JTC, provided compressed references have high contrast.     

A Highly Accurate Face Recognition System Using Filtering Correlation

The authors previously constructed a highly accurate fast face recognition optical correlator (FARCO) [E. Watanabe and K. Kodate: Opt. Rev. 12 (2005) 460], and subsequently developed an improved, super high-speed FARCO (S-FARCO), which is able to process several hundred thousand frames per second. The principal advantage of our new system is its wide applicability to any correlation scheme. Three different configurations were proposed, each depending on correlation speed. This paper describes and evaluates a software correlation filter. The face recognition function proved highly accurate, seeing that a low-resolution facial image size (64 × 64 pixels) has been successfully implemented. An operation speed of less than 10 ms was achieved using a personal computer with a central processing unit (CPU) of 3 GHz and 2 GB memory. When we applied the software correlation filter to a high-security cellular phone face recognition system, experiments on 30 female students over a period of three months yielded low error rates: 0% false acceptance rate and 2% false rejection rate. Therefore, the filtering correlation works effectively when applied to low resolution images such as web-based images or faces captured by a monitoring camera.     

Robust and discriminating method for face recognition based on correlation technique and independent component analysis model

We demonstrate a novel technique for face recognition. Our approach relies on the performances of a strongly discriminating optical correlation method along with the robustness of the independent component analysis (ICA) model. Simulations were performed to illustrate how this algorithm can identify a face with images from the Pointing Head Pose Image Database. While maintaining algorithmic simplicity, this approach based on ICA representation significantly increases the true recognition rate compared to that obtained using our previously developed all-numerical ICA identity recognition method and another method based on optical correlation and a standard composite filter.     

多谱段短波红外小鼠静脉荧光观测成像研究

短波红外(short-wave infrared,SWIR)一般指900～1700 nm的光波段,是肉眼不可见的光波段,这种波段目前主流的探测器以InGaAs为主,主要用于军事、生物以及材料光谱分析等领域.短波红外荧光成像以其对生物组织光学损伤小、成像深度大、成像信噪比高、空间和时间成像分辨率高等特点,使得基于InGa...     

A hierarchical structure with improved OMP sparse representation used with face recognition

With the rapid development of the face recognition technology, more and more optical products are applied in people's real life. The recognition accuracy can be improved by increasing the number of training samples, but the colossal training samples will result in the increase of computational complexity. In recent years, sparse representation method becomes a research hot spot on face recognition. In this paper we propose an energy constrain orthogonal matching pursuit (ECOMP) algorithm for sparse representation to select the few training samples and a hierarchical structure for face recognition. We filter the training samples with ECOMP algorithm and then we compute the weights by all selected training samples. At last we find the closest recovery sample to the test sample. Simultaneously the experimental results in AR, ORL and FERET database also show that our proposed method has better recognition performance than the LRC and SRC_OMP method.     

改进SIFT变换与客观评价结合的图像配准算法

针对SIFT(Scale Invariant Feature Transform)算子在大幅复杂图像中提取的过多不稳定特征点及在只有少量重合区域下图像配准过程中出现的过多误匹配,导致图像配准精度下降。提出一种改进的SIFT算法,在对目标图像提取SIFT特征后,利用双向BBF(Best-Bin-First)匹配算法对提取的特征点进行匹配,采用SIFT描述子的尺度以及梯度方向信息建立最小邻域匹配剔除误匹配点,通过随机抽取一致性算法(RANSAC)进一步筛选匹配点,并利用最小二乘法结合多项式近似拟合出变换模型,利用局部均方根有效值(RMS)评价映射矩阵与实际图像的误差,找出并删除引起误差的误匹配点,迭代至配准图像符合评价标准后,计算出精确变换模型。实验结果表明,该算法提高了大幅复杂图像在少量重合区域时的配准精度。     

A new infrared small and dim target detection algorithm based on multi-directional composite window

This work presents a new method based on gray characteristic analysis for infrared dim small target detection under complex backgrounds. Firstly, an improved detection window with eight directions and three layers is introduced to investigate the gray distribution characteristic of different structure in an infrared image. Secondly, we adopt a pretreatment process based on morphology filter and mean filter to reduce the running time and propose a detection rule on characteristic analysis for infrared targets. Meanwhile a new parameter optimization algorithm based on fuzzy control theory is employed so that the detection rule could be independent of the initial parameters. Finally, experimental results indicate that the proposed method can effectively detect the dim small targets and has better tracking performance.     

多模跟踪技术在轮式侦察车图像处理器的应用

为解决目标旋转形变、遮挡、光照变化等目标跟踪的难题,对粒子滤波和尺度不变特征变换(SIFT)算法进行了改进,结合两种算法提出了决策主导模式的多模跟踪技术。该技术采用粒子滤波预测目标位置进行粗定位,SIFT特征匹配进行精定位的方法,在解决上述难题上有很好的鲁棒性。将该技术应用于轮式侦察车图像处理器,并进行了各种实验验证,结果证明了提出算法的有效性。     

基于无人机红外光谱技术的影像数据匹配方法研究

无人机加载红外光谱载荷对区域内影像进行获取现已成为遥感领域一种重要的技术手段,可通过对携带位置信息的影像进行分类提取,得到植被盖度、温度指数等一系列因子指标。利用FREE BIRD(自由鸟)小型低空无人机系统挂载Tetracam红外相机(310万像素)对新疆玛纳斯县一河道进行影像获取。无人机飞行面积约为20.5 km2,为了得到更加精确的植被、温度等因子,需要对无人机红外影像进行配准,通过优化SIFT匹配参数和RANSAC粗差剔除后,获取了可靠的匹配结果,即经过算法匹配后的影像与原影像进行了误差比对,能够满足后期的应用需要, 这也是本文的创新点之一。将影像进行配准后进行二维影像拼接,将多张红外影像按照航向重叠度不低于60%,旁向重叠度不低于50%的概率进行拼接,得到拼接后的红外影像图。另外比较了SIFT和SUFT两种算法,利用优化的SIFT算法及改进的FLIR传感器获取1 600张热红外影像,利用地面同步测量数据对拼接后的红外影像进行算法匹配,并利用ENVI(完整的遥感图像处理平台)软件进行温度及植被盖度的影像反演,得到了研究区域的单一影像及红外影像的温度反演图及植被反演图。通过对两种算法的对比得到更加优化的算法模型,并对该模型进行回归分析和精度检验,得到该模型的相关系数R2为0.767,匹配精度为81.51%,模型精度较高。本模型的建立对日后无人机红外影像的配准及提取反演奠定了理论和实践基础。     

改进Canny算法的红外人脸图像边缘轮廓提取

红外人脸图像的边缘轮廓特征对于红外人脸检测、识别等相关应用具有重要价值。针对红外人脸图像边缘轮廓提取时存在伪边缘的问题,提出了一种改进Canny算法的红外人脸图像边缘轮廓提取方法。首先通过对引导滤波算法引入“动态阈值约束因子”替换原始算法中的高斯滤波,解决了原始算法滤波处理不均匀和造成红外人脸图像弱边缘特征丢失的弊端;接着对原始算法的非极大值抑制进行了改进,在原始计算梯度方向的基础上又增加了4个梯度方向,使得非极大值抑制的插值较原始算法更加精细;最后改进OTSU(大津)算法,构造灰度-梯度映射函数确定最佳阈值,解决了原始算法人为经验确定阈值的局限性。实验结果表明:提出的改进Canny算法的红外人脸轮廓提取方法滤波后的图像,相较于原始Canny算法滤波处理,信噪比性能提升了34.40%,结构相似度性能提升了21.66%;最终的红外人脸边缘轮廓提取实验的优质系数值高于对比实验的其他方法,证明改进后的算法对于红外人脸图像边缘轮廓提取具有优越性。     

尺度不变特征与几何特征融合的人耳识别方法

要提高人耳的识别率,关键是特征的提取与表达.尺度不变特征变换(SIFT)技术是局部点特征提取算法,在尺度空间寻找极值点,提取对图像的尺度和旋转变化具有不变性,对光照变化和图像变形具有较强的适应性的特征向量.尝试用SIFT技术来提取外耳图像的结构特征点以形成稳定的特征描述子,为了克服一幅图像中有多个局部描述子相似的问题,在SIFT特征描述子中融入一个耳廓几何特征.最后采用特征向量的欧氏距离作为两幅图像相似性度量标准进行人耳识别.在耳图像库七进行实验.结果表明,该方法不仅可以有效地提取人耳特征,通过少量特征可获得较高的识别率,而且对耳图像刚体变化具有较强的稳健性.     

基于方向滤波和自商图像的人脸光照补偿

为了实时处理恶劣光照条件下的人脸图像,在自商图像的基础上,提出了一种新的人脸光照补偿的方法.该方法首先在人脸三维光照简化模型的基础上,利用方向滤波削弱附着阴影.然后结合高斯低通滤波器,进一步削弱投射阴影.两者非线性结合,显著改善光照图像的质量.利用Yale B数据库提供的光照图像,在10万人脸数据库系统中进行测试,结果表明在光照条件恶劣的情况下能显著提高识别率.     

复合结构界面粘接强度的声-超声评价研究

基于Ritec-SNAP测量系统建立了声-超声技术实验系统,分析了在声-超声技术评价过程中用声信号的幅频特性及应力波因子表征复合结构界面粘接强度的可行性。用粘接层的固化过程模拟复合粘接板粘接强度的变化过程,以粘接层的固化时间作为粘接强度的间接表征参量,借助于已建立的实验系统对复合结构粘接强度的评价问题进行了实验研究。实验结果表明,应力波因子与反映界面粘接强度的粘接层固化时间呈单调对应关系,且不同固化时期的应力波因子存在明显的不同特性,表明用应力波因子评价复合结构的界面粘接强度具有可行性。