非线性理论在计算机视觉中的应用

针对目前计算机视领域中普遍存在的大量非线性问题以及此带来的鲁棒必差、效率低等问题，结合目前比较新的解决非线性问题的方法，诸如同伦法、吴方法以及Ａｄｏｍａｉｎ方法等，对一系列计算机视觉中的非线性问题给出了比较满意的解答。     

Neural architecture for robotic vision sensor that discounts uneven illumination in a manufacturing environment

Computer vision algorithms for inspection or pick-and-place operations often depend on spatially uniform illumination of a workplace. This necessitates expensive lighting fixtures. To discount effects of uneven illumination we designed and tested a neural network that can adaptively control light sensitivity at the photosensor level. Our neural network architecture consists of multiple layers with hexagonally arranged nodes. All nodes have partially overlapping receptive fields of different spatial frequencies. Feedforward connections are excitatory while feedback pathways subserve lateral inhibition. The outputs of these nodes are combined so as to maximize the signal-to-noise ratio while constantly resetting thresholds to maintain high sensitivity. Our connectionist architecture can account for many characteristics attributed to the lightness constancy phenomenon observed in biological systems. The results suggest that our module maintains high sensitivity over the whole domain of intensities without interfering with transmission of visual information embedded in spatial discontinuities of intensity.     

A geometry-based error estimation for cross-ratios

For choosing specific cross-ratios as 2D projective coordinates in various computer vision applications, a reasonable error analysis model is usually required. This investigation adopts the assumption of normal distribution for positioning errors of point features in an image to formulate the error variances of cross-ratios. Based on a geometry-based error analysis, a straightforward way of identifying the cross-ratios with minimum error variances is proposed. Simulation results show that the proposed approach, as well as a further simplified alternative, yield much better estimations of minimum error variances in terms of accuracy, cost, and stability compared with some other methods, e.g., the one based on the rule given by Georis et al. (IEEE Trans. Pattern Anal. Mach. Intell. 20 (4) (1998) 366). Some causes of the performance differences in the estimations are explained using a special configuration of point features.     

基于非接触精密视觉测量的硬盘磁头头偏测量系统

应用基于机器视觉的非接触测量技术对计算机硬盘磁头的头偏测量，不仅满足了磁头生产防污、防ESD的需要，而且实现了精密测量的需要；对测量数据的采集处理以及SPC实时监控，有效地实现了生产过程的控制和预报；与FIS系统的连接实现了生产数据的可追溯性，为生产管理现代化提供一个强有力的工具。系统已广泛应用于硬盘磁头生产线。     

Extending the color gamut and dynamic range of an sRGB image using a residual image

Image sources, such as digital camera captures and photographic negatives, typically have more information than can be reproduced on a photographic print or a video display. The information that is lost during the tone/color rendering process relates to both the extended dynamic range and color gamut of the original scene. In conventional photographic systems, most of this additional information is archived on the photographic negative and can be accessed by adjusting the way the negative is printed. However, most digital imaging systems have traditionally archived only a rendered video RGB image. As a result, it is not possible to make the same sorts of image manipulations that historically have been possible with conventional photographic systems. This suggests that there would be an advantage to storing images using an extended dynamic range/color gamut color encoding. However, because of file compatibility issues, digital imaging systems that store images using color encoding other than a standard video RGB representation (e.g., sRGB) would be significantly disadvantaged in the marketplace. In this article, we describe a solution that has been developed to maintain compatibility with existing file formats and software applications, while simultaneously retaining the extended dynamic range and color gamut information associated with the original scenes. With this approach, the input raw digital camera image or film scan is first transformed to the scene‐referred ERIMM RGB color encoding. Next, a rendered sRGB image is formed in the usual way and stored in a conventional image file (e.g., a standard JPEG file). A residual image representing the difference between the original extended dynamic range image and the final rendered image is formed and stored in the image file using proprietary metadata tags. This provides a mechanism for archiving the extended dynamic range/color gamut information, which is normally discarded during the rendering process, without sacrificing interoperability. Appropriately enabled applications can decode the residual image metadata and use it to reconstruct the ERIMM RGB image, whereas applications that are not aware of the metadata will ignore it and only have access to the sRGB image. The residual image is formed such that it will have negligible pixel values for those portions of the image that lie within the sRGB gamut, and will therefore be highly compressible. Tests on a population of 950 real customer images have demonstrated that the extended dynamic range scene information can be stored with an average file size overhead of about 8% compared to the sRGB images alone. © 2003 Wiley Periodicals, Inc. Col Res Appl, 28, 251–266, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10160     

基于轮廓线匹配的二维重迭物体的识别

本文提出一种基于轮廓线匹配的二维重迭物体的识别算法,并采用一种点模式匹配的快 速算法进行特征点序列的匹配,通过引进形状移位数的概念和利用问题中的各种约束条件,提 高了物体识别速度.     

Chromaticity contour map of the RGB cube: A simple algorithm

A simple algorithm (amenable to spreadsheets) is described for plotting a luminance contour map of the color gamut of an additive‐primary display given the chromaticities of its primaries and screen white. Each contour of the plot represents (in either r,g or x,y coordinates) the polygon of intersection of the RGB cube with a plane of constant Y. Each vertex on a contour is an intersection of 1 of the 12 edges of the RGB cube with the constant‐Y plane. Feasibility of an edge as a vertex of the polygon is tested through comparison of the computed r,g coordinates with functions of the luminosity coefficients of the primaries. By ordering the RGB‐cube edges in a particular way prior to scanning them for feasibility, one is guaranteed to retrieve the vertices of any constant‐Y polygon in convex order. This fact facilitates plotting of the polygons.     

Projection center calibration by motion

Camera calibration is the first step of three-dimensional machine vision. A fundamental parameter to be calibrated is the position of the camera projection center with respect to the image plane. This paper presents a method for the computation of the projection center position using images of a translating rigid object, taken by the camera itself.

Many works have been proposed in literature to solve the calibration problem, but this method has several desirable features. The projection center position is computed directly, independently of all other camera parameters. The dimensions and position of the object used for calibration can be completely unknown.

This method is based on a geometric relation between the projection center and the focus of expansion. The use of this property enables the problem to be split into two parts. First a suitable number of focuses of expansion are computed from the images of the translating object. Then the focuses of expansion are taken as landmarks to build a spatial back triangulation problem, the solution of which gives the projection center position.     

血管数字减影图象中的结构特征提取

基于Ｘ线数字减影图象的三维血管结构重建很大程度上依赖于图象中血管的有效检测和结构关系描述。本文给出一个在局部特征检测基础上的结构特征提取和组织方法，涉及结构分析之前的中间视觉处理层次，即将图象中主要结构分离出来供结构分析之用。首先用一个突出结构检测算法独立地组织和选择中轴曲线片断和边缘曲线片断，然后将两者信号相结合以验证突出结构算法获得的结果并滤除噪声结构。     

On the Yustova threshold plates

A response to criticism of threshold plates for the study of color vision developed at the Mendeleev All-Russia Research Institute of Metrology and published in 1994 is presented. The critics base their conclusions on colorimetric testing and the examination of the plates. In response to the article by M. V. Danilova and J. D. Mollon [4]. __________ Translated from Izmeritel’naya Tekhnika, No. 1, pp. 37–39, January, 2007.