基于光流法的迭代反投影超分辨率重构算法

为了提高重构图像或者视频的分辨率．提出把新型的基于光流法的图像配准算法应用于迭代反投影（IBP）超分辨率算法中。在所提出的方法中．基于光流法的图像配准算法用来提高图像配准的准确性。首先,为了得到像素级别的运动矢量．基于光流法的图像配准算法被用于估计图像间的运动矢量。以得到更加准确的运动矢量矩阵。接着,利用所获得的运动矢量矩阵结合迭代反投影算法重构高分辨率的图像。同时．由于基于光流法的图像配准能够很好地估计视频图像间的运动．所提出的方法同样适用于视频图像的超分辨。实验结果表明．提出的方法对于图像或者视频的超分辨率效果．在主观效果和客观评价上都有一定的提升。     

综合颜色特征的彩色图像检索方法

基于内容的图像检索技术已成为当前的研究热点,文章提出了一种综合利用两种颜色特征进行图像检索的新方法。首先,在变换空间建立色度直方图表示图像的颜色分布特征。为进行图像间的相似性度量,对Swain定义的直方图相似性度量作了改进,为弥补全局直方图不包含颜色空间分布关系的缺点,文章提取了另一种颜色特征,即分块的颜色矩,其距离度量为特征矢量的比值相似度。最后,综合利用上述两个特征对图像进行共同检索。通过对真实图像数据的检索实验表明:综合两种特征检索图像比单一特征检索效果更好。     

基于GPU的实时图像拼接

大视野、高质量的图像信息对地面移动机器人的遥控操作具有非常重要的意义。提出了一种基于先验信息的自适应图像拼接方法。该方法在图像大致重叠区域中均匀选取待匹配点,利用改进的具有旋转不变性的NCC(Normalized Cross Correlation,归一化互相关)匹配方法进行区域相似性度量,通过RANSAC(Random Sample Con-sensus,随机采样一致性)算法估计图像射影变换模型,采用线性淡入淡出法进行图像融合。利用GPU强大的并行处理能力对算法进行了并行化实现,使图像拼接效率比单独采用CPU提高了60倍以上,稳定的拼接速度可达21.3fps。     

基于小波变换的低频分量图像检索研究

图像检索是图像数据库的最基本功能,传统检索技术对图像数据库来说存在很大的局限性,图像的检索应采用基于图像内容本身的检索方式,目前流行的各种图像匹配技术主要针对特征明显的图像,需要图像先验知识,缺乏广泛性.分析了传统的模板匹配技术,采用小波变换技术抽取图像低频分量为逻辑对象,利用改进的模板匹配技术进行检索,实验结果表明可大大减少计算量,提高检索效率.     

多视点视频中基于局部直方图匹配的亮度和色差校正

由于相机性能的差异,多视点视频序列之间总是存在亮度和颜色的差异,为降低这种差异对多视点系统中各种后续处理效果的影响,提出了一种基于部分重叠局部直方图匹配的亮度和色差校正算法。该直方图匹配算法先通过原图像直方图和参考图像直方图的匹配建立映射函数,然后使用该映射函数来校正原图像的亮度和色差值。根据映射函数的计算方法不同,直方图匹配可以分为全局直方图匹配和局部自适应直方图匹配。在全局直方图匹配算法中,由于整幅图像是使用统一的映射函数,因此校正性能较差。而局部自适应直方图匹配算法则是利用局部信息为每个像素建立唯一的映射函数,因此能够准确地校正图像不同区域的差异,但是算法的复杂度非常高。该部分交叠的局部直方图匹配方法中,一小块范围内的像素是使用同样的局部直方图来建立映射函数,然后使用条件去块滤波器去除可能存在的块效应。与自适应局部直方图匹配算法相比,该算法不仅可减少计算直方图和映射函数的次数,而且在降低计算复杂度的同时,还能够自适应地校正图像不同区域的差异。该算法可作为多视点视频系统中的预处理技术,实验结果表明,该算法能够提高后续压缩过程的性能。     

Locally adaptive subspace and similarity metric learning for visual data clustering and retrieval

Subspace and similarity metric learning are important issues for image and video analysis in the scenarios of both computer vision and multimedia fields. Many real-world applications, such as image clustering/labeling and video indexing/retrieval, involve feature space dimensionality reduction as well as feature matching metric learning. However, the loss of information from dimensionality reduction may degrade the accuracy of similarity matching. In practice, such basic conflicting requirements for both feature representation efficiency and similarity matching accuracy need to be appropriately addressed. In the style of “Thinking Globally and Fitting Locally”, we develop Locally Embedded Analysis (LEA) based solutions for visual data clustering and retrieval. LEA reveals the essential low-dimensional manifold structure of the data by preserving the local nearest neighbor affinity, and allowing a linear subspace embedding through solving a graph embedded eigenvalue decomposition problem. A visual data clustering algorithm, called Locally Embedded Clustering (LEC), and a local similarity metric learning algorithm for robust video retrieval, called Locally Adaptive Retrieval (LAR), are both designed upon the LEA approach, with variations in local affinity graph modeling. For large size database applications, instead of learning a global metric, we localize the metric learning space with kd-tree partition to localities identified by the indexing process. Simulation results demonstrate the effective performance of proposed solutions in both accuracy and speed aspects.     

Registering real and virtual imagery

Registering a virtual scene with a real scene captured by a video camera has a number of applications including visually guided robotic navigation, surveillance, military training and operation. The fundamental problem involves several challenging research issues including finding corresponding points between the virtual and the real scene and camera calibration. This paper presents our research in defining and mapping a set of reliable image features for registering the two imageries, extracting and selecting reliable control points for the construction of intrinsic and extrinsic camera parameters. A number of innovative algorithms are presented followed by extensive experimental analysis. An application of registering virtual database image with video image is presented. The algorithms we developed for calculating and matching linear structured features and selecting of reliable control points are applicable to image registration beyond virtual and real imageries.     

综合利用纹理和中心特征的图像检索

基于特征的图像检索在多媒体数据库管理和多媒体通信传输中得到越来越多的重视。本文介绍了基于纹理特征的特征提取方法和中心特征的提取方法，并进而提出了一种综合利用上述两个特征共同进行检索的方法。对真实图像库的检索实验表明，综合特征检索比单一特征检索更符合人的视觉特性，检索效果更好。     

边缘增强图像互相关模板匹配的并行架构

基于归一化互相关测度(NCC)的模板匹配已经在图像处理领域得到了广泛的应用。对图像进行边缘检测然后进行模板匹配,可充分利用图像的空间相关性,锐化模板匹配结果的相关峰,提高匹配的准确度,可以获得更高的定位精度。为了有效提高定位精度,考虑到导弹制导系统的算法实时性、体积以及为适应战场不同任务阶段采用不同匹配策略的灵活性要求,基于FPGA,通过结合Sobel边缘检测,进一步改进了提出的图像归一化互相关模板匹配高速并行实现架构。对边缘检测前后图像模板匹配的仿真比较结果表明,边缘检测处理可有效锐化相关峰;基于Altera的FPGA芯片EP2S90和开发软件Quartus Ⅱ 8.0的并行实现架构功能与时序仿真及在实际目标识别系统中的应用表明,这种方案可有效地提高系统的运算速度和定位精度,FPGA实现本身也进一步缩小了系统的体积。     

基于SIFT和NCC的多源遥感影像配准方法

针对多源遥感影像的配准,提出了一种结合SIFT算法和归一化互相关（NCC）匹配算法的配准方法。该方法采用SIFT算法提取特征点并进行匹配得到一定数量的特征点对后,利用SIFT特征点的尺度和方向信息对NCC进行改进,进一步从未能匹配的特征点中获取匹配点对,经粗差滤除后得到有效的匹配特征点对,随之进行影像配准。方法结合了SIFT算法和NCC算法的优点,解决了多源遥感影像因辐射差异和几何差异造成的难以正确配准的问题。实验结果表明,算法具有较强的鲁棒性,并取得了较好的配准精度。     

一种几何攻击下的数字图像水印检测方法

几何攻击是数字图像水印众多攻击的一种，它不试图破坏图像中的水印信号，而是使水印检测器与水印信号失去同步，致使水印检测器检测不到水印信号，是一种有效的水印攻击方法。该文介绍了一种基于标准化的互相关NCC(normalized-cross correlation)图像固有特征提取及特征点匹配方法，进而利用图像固有特征恢复经几何变换的水印图像，使水印检测器与水印同步以达到有效检测水印的目的。实验表明，使用该方法能较好地恢复图像，有效地同步水印检测器与水印信号，从而使水印对仿射变换攻击具有鲁棒性。     

Template matching using an improved electromagnetism-like algorithm

Template matching (TM) plays an important role in several image-processing applications such as feature tracking, object recognition, stereo matching, and remote sensing. The TM approach seeks for the best-possible resemblance between a subimage known as template and its coincident region within a source image. TM involves two critical aspects: similarity measurement and search strategy. The simplest available TM method aims for the best-possible coincidence between the images through an exhaustive computation of the normalized cross-correlation (NCC) values (similarity measurement) for all elements of the source image (search strategy). Recently, several TM algorithms that are based on evolutionary approaches have been proposed to reduce the number of NCC operations by calculating only a subset of search locations. In this paper, a new algorithm based on the electromagnetism-like algorithm (EMO) is proposed to reduce the number of search locations in the TM process. The algorithm uses an enhanced EMO version, which incorporates a modification of the local search procedure to accelerate the exploitation process. As a result, the new EMO algorithm can substantially reduce the number of fitness function evaluations while preserving the good search capabilities of the original EMO. In the proposed approach, particles represent search locations, which move throughout the positions of the source image. The NCC coefficient, considered as the fitness value (charge extent), evaluates the matching quality presented between the template image and the coincident region of the source image, for a determined search position (particle). The number of NCC evaluations is also reduced by considering a memory, which stores the NCC values previously visited to avoid the re-evaluation of the same search locations (particles). Guided by the fitness values (NCC coefficients), the set of candidate positions are evolved through EMO operators until the best-possible resemblance is determined. The conducted simulations show that the proposed method achieves the best balance over other TM algorithms in terms of estimation accuracy and computational cost.     

Hierarchical palmprint identification via multiple feature extraction

Biometric computing offers an effective approach to identify personal identity by using individual's unique, reliable and stable physical or behavioral characteristics. This paper describes a new method to authenticate individuals based on palmprint identification and verification. Firstly, a comparative study of palmprint feature extraction is presented. The concepts of texture feature and interesting points are introduced to define palmprint features. A texture-based dynamic selection scheme is proposed to facilitate the fast search for the best matching of the sample in the database in a hierarchical fashion. The global texture energy, which is characterized with high convergence of inner-palm similarities and good dispersion of inter-palm discrimination, is used to guide the dynamic selection of a small set of similar candidates from the database at coarse level for further processing. An interesting point based image matching is performed on the selected similar patterns at fine level for the final confirmation. The experimental results demonstrate the effectiveness and accuracy of the proposed method.     

基于Hessian矩阵和Gabor函数的局部兴趣点检测

局部特征方法是基于内容的图像与视频检索的重要方法。提出一种新的基于Hessian矩阵和Gabor函数的尺度不变局部特征点检测方法(Hessian-Gabor Detector)。该方法首先利用基于Hessian矩阵的检测子定位图像在空间域上的候选特征点位置,然后用基于Gabor函数的算子检测候选兴趣点在尺度空间的特征尺度,从而获得具有尺度不变特性的局部特征点。实验证明,与DOG、Harris-Laplace等方法相比,计算简单。应用于图像匹配中,能够显著地提高匹配效率。     

Max-plus algebra-based wavelet transforms and their FPGA implementation for image coding

In the present paper, we propose new image coding methods based on max-plus algebra-based wavelet transforms (MP-Wavelets). Max-plus algebra is an algebraic structure on the set of integers, endowed with maximum, minimum and standard addition as operations. The proposed wavelet decomposition schemes are novel classes of morphological wavelets which, in contrast to the existing approaches, involve only integers and only max, min and addition in their computation. Since they do not involve floating point calculations and multiplications, MP-Wavelets have a very low computational complexity and they are suitable for efficient hardware implementation. Through image coding experiments using 10,000 images extracted from low resolution web-crawled misc database, the characteristics of the proposed MP-Wavelets are presented. Furthermore, we show a computational complexity analysis to compare MP-Wavelets to one of the fastest wavelet transforms, the conventional Haar wavelet. The applicability of MP-Wavelets in video coding is also confirmed by image coding experiments using residual frames. Finally, the field programmable gate arrays (FPGA) implementation of MP-Wavelets is presented to confirm suitability of MP-Wavelets for hardware implementation.     

SAR与可见光图象匹配方法的研究

由于SAR和可见光成象传感器图象的物理特征不同,因此进行匹配难度很大。本文在深入分析了这两种传感器图象物理特征的基础上,提出了一种图象匹配的新方法。该方法运用形态学滤波方法去除SAR图象斑点噪声,利用图象的边缘特征进行匹配,并采用多分辨率分级搜索技术减少计算量,在对实验结果进行分析的基础上提出了搜索真实匹配位置的原则,大大提高了匹配的准确率。通过实验对SAR图象斑点噪声滤波和SAR与可见光图象匹配算法进行了验证,实验证明本算法的SAR图象去噪效果明显优于传统的Frost方法,图象匹配精度高,稳定性好。     

用混合弹性模型解决图象变形匹配问题

由于用传统的刚体匹配方法难以解决待匹配图象之间的结构差异 ,因此需要引入变形模型来进行图象的非刚体匹配 .为此提出了一种利用混合弹性模型 (HEM)来解决图象变形匹配问题的新方法 .该方法不需要预先提取图象的特征 ,而是直接利用匹配图象之间的灰度信息来实现图象之间的匹配 .匹配时 ,首先通过基于主轴的方法来实现两幅图象之间的全局仿射匹配 ;然后利用线性弹簧网模型来求取两幅图象之间的相关性 ,并进一步利用薄板样条模型来实现两幅图象的变形匹配 .该方法在匹配过程中还采取了多分辨率匹配策略 ,合成图象和医学脑图象的实验结果表明 ,该方法是有效的     

固定多摄像头的视觉拼接技术

视频拼接技术是计算机图形学和计算机视觉的重要分支,它的发展基于静态图像的拼接技术,但由于视频信息的复杂性,视频拼接也有区别于图像拼接,针对实际运用中的实时拼接的需要,本文提出了一种基于控制帧的固定摄像头视频拼接方法。首先采集控制帧图像,对摄像头进行参数标定获得相机内参和光心坐标,再使用一种改进的畸变矫正方法去除摄像头畸变带来的成像失真。然后对控制帧图像进行SIFT特征提取并进行粗匹配,再用RANSAC的方法剔除误匹配点并拟合出图像变换单应阵。最后使用查表法将各摄像头的图像同步投影到大场景图片上,对重合区域进行光亮补偿和多带融合。最终实现速度可达25帧/秒的实时视频拼接。     

Blind quality assessment of night-time image

High-quality night-time imaging is crucial to video surveillance, automatic drive and consumer electronics. However, different from day-time imaging, night-time imaging suffers from some disadvantages, such as low light, uneven illumination, difficult focusing, etc., which raises a great concern to the night-time imaging quality. Accordingly, a practical night-time image quality evaluation method is very promising to control and improve the night-time imaging system. Toward this end, in this paper, we propose a blind image quality assessment (BIQA) method to quantify the night-time image quality. Specifically, in the proposed method, we measure the night-time image quality by investigating the fundamental image properties, which are highly relevant to the image quality, such as the brightness, saturation, sharpness, noiseness, contrast and the semantics. Specific features are designed to characterize the image properties properly. Then we employ the support vector regression (SVR) method to infer the image quality with the extracted quality-aware features. The proposed BIQA method for night-time images is thoroughly evaluated on a representative night-time image database. Experimental results demonstrate that the proposed BIQA method for night-time images achieves superior prediction performance to other state-of-the-art BIQA methods.