Image Ratio Features for Facial Expression Recognition Application

Video-based facial expression recognition is a challenging problem in computer vision and human-computer interaction. To target this problem, texture features have been extracted and widely used, because they can capture image intensity changes raised by skin deformation. However, existing texture features encounter problems with albedo and lighting variations. To solve both problems, we propose a new texture feature called image ratio features. Compared with previously proposed texture features, e. g., high gradient component features, image ratio features are more robust to albedo and lighting variations. In addition, to further improve facial expression recognition accuracy based on image ratio features, we combine image ratio features with facial animation parameters (FAPs), which describe the geometric motions of facial feature points. The performance evaluation is based on the Carnegie Mellon University Cohn-Kanade database, our own database, and the Japanese Female Facial Expression database. Experimental results show that the proposed image ratio feature is more robust to albedo and lighting variations, and the combination of image ratio features and FAPs outperforms each feature alone. In addition, we study asymmetric facial expressions based on our own facial expression database and demonstrate the superior performance of our combined expression recognition system.

基于视觉的室内移动机器人自定位技术研究

在关于移动机器人的诸多研究领域中，机器人自定位是十分关键的技术，是实现机器人自主运动和其他任务的基础，而且涉及领域广泛，有很多难点有待解决，因而是一个具有重要研究价值的课题。 本论文以沈阳新松机器人股份有限公司自主研发的家庭服务机器人为研发平台，系统地研究了基于计算机视觉的室内移动机器人自定位问题，成功设计了基于单目视觉人工路标以及粒子滤波的室内移动机器人自定位系统。 本文首先根据室内移动机器人自主导航定位的要求，设计了一种简易美观的新型视觉人工路标，并且研究与实现了该路标的实时准确检测以及不同路标的识别。 其次，在位姿计算方面，本文研究了共面P4P（4点透视）问题的解法及其在位姿计算方面的应用，并分析比较了两种不同P4P解法的优缺点，成功地将两种算法结合起来用于机器人位姿计算。 最后，在机器人自定位方面，本文将单目彩色摄像机作为传感器，在基于贝叶斯滤波理论的自定位理论框架下，利用粒子滤波自定位方法融合视觉信息与码盘信息，实现了自主移动机器人的自定位。 实践证明，本文设计的基于单目视觉人工路标的自定位系统能够成功地应用在室内移动机器人上，具有较高的应用推广价值。

基于几何特征的快速高精度角点检测算法

角点检测应用十分广泛,是许多计算机视觉任务的基础。本文提出了一种快速、高精度的角点检测算法,算法简单新颖,角点条件和角点响应函数设计独特。和以往不同的是:算法在设计上考虑的是角点的局部几何特征,使得处理的数据量大为减少,同时能够很好地保证检测精度等其他性能指标。通过和广泛使用的SUSAN算法、Harris算法在正确率、漏检、精度、抗噪声、计算复杂度等方面进行综合比较,结果表明该算法无论对人工合成图像还是对自然图像均具有良好的性能。

基于OpenGL的交会对接仿真系统

针对基于视觉的空间载体位姿测量很难进行真实实验的特点,提出了一种基于OpenG1的半物理仿真实验方法。该方法通过计算机图形学技术和计算机视觉技术的结合,能够直观、快速地模拟空间载体位姿测量过程。

基于视觉的机器人轨迹误差测量技术

提出了一种基于视觉的机器人轨迹精度测量系统,该系统以计算机视觉为基础,结合激光测量等技术,可实时测量机器人的运动轨迹误差.完成了高精度图像快速采集与处理、系统标定、三维计算及计算结果可视化等关键技术研究及系统研制工作,并在机器人上进行了实验,大量的实验表明,该系统的测量精度和速度均可满足机器人的轨迹测量的需要.

具有训练、学习和零件识别能力的计算机视觉系统

本文提出的系统主要为了在自动化传输带上进行零件的自动识别、定位、定向。曾用该系统对三十多种钟表零件进行反复验证,效果甚佳。

一种基于三焦点张量的度量重建方法

从二维图像恢复物体的三维结构是计算视觉的一个重要研究方向。通过研究三视图之间的对极几何,提出了一种基于三线性关系的度量重建方法。不需要对相机的运动或者景物结构施加任何约束,使用直接重建方法,利用三焦点张量恢复场景的度量重构。仿真实验和真实图像实验表明,该度量重建方法具有很高的准确性和实用性。

基于LQS的基本矩阵计算方法

基本矩阵作为分析两视图对极几何的有力工具,在视觉领域中占用重要的地位。分析了传统鲁棒方法在基本矩阵的求解问题中存在的不足,引入了稳健回归分析中的LQS方法,并结合Bucket分割技术,提出一种鲁棒估计基本矩阵的新方法,克服了RANSAC方法和LMedS方法的缺陷。模拟数据和真实图像实验结果表明,本文方法具有更高的鲁棒性和精确度。

知觉组织在拥挤现象中的作用

Crowding, generally defined as the deleterious influence of nearby contours on visual discrimination, is ubiquitous in spatial vision. Specifically, long-range effects of non-overlapping distracters can alter the appearance of an object, making it unrecognizable. Theories in many domains, including vision computation and high-level attention, have been proposed to account for crowding. However, neither compulsory averaging model nor insufficient spatial esolution of attention provides an adequate explanation for crowding. The present study examined the effects of perceptual organization on crowding. We hypothesize that target-distractor segmentation in crowding is analogous to figure-ground segregation in Gestalt. When distractors can be grouped as a whole or when they are similar to each other but different from the target, the target can be distinguished from distractors. However, grouping target and distractors together by Gestalt principles may interfere with target-distractor separation. Six experiments were carried out to assess our theory. In experiments 1, 2, and 3, we manipulated the similarity between target and distractor as well as the configuration of distractors to investigate the effects of stimuli-driven grouping on target-distractor segmentation. In experiments 4, 5, and 6, we focused on the interaction between bottom-up and top-down processes of grouping, and their influences on target-distractor segmentation. Our results demonstrated that: (a) when distractors were similar to each other but different from target, crowding was eased; (b) when distractors formed a subjective contour or were placed regularly, crowding was also reduced; (c) both bottom-up and top-down processes could influence target-distractor grouping, mediating the effects of crowding. These results support our hypothesis that the figure-ground segregation and target-distractor segmentation in crowding may share similar processes. The present study not only provides a novel explanation for crowding, but also examines the processing bottleneck in object recognition. These findings have significant implications on computer vision and interface design as well as on clinical practice in amblyopia and dyslexia.

Face Recognition Under Varying Pose

While researchers in computer vision and pattern recognition have worked on automatic techniques for recognizing faces for the last 20 years, most systems specialize on frontal views of the face. We present a face recognizer that works under varying pose, the difficult part of which is to handle face rotations in depth. Building on successful template-based systems, our basic approach is to represent faces with templates from multiple model views that cover different poses from the viewing sphere. Our system has achieved a recognition rate of 98% on a data base of 62 people containing 10 testing and 15 modelling views per person.

Aeroacoustics on Non-Dedicated Workstations

The simulation of subsonic aeroacoustic problems such as the flow-generated sound of wind instruments is well suited for parallel computing on a cluster of non-dedicated workstations. Simulations are demonstrated which employ 20 non-dedicated Hewlett-Packard workstations (HP9000/715), and achieve comparable performance on this problem as a 64-node CM-5 dedicated supercomputer with vector units. The success of the present approach depends on the low communication requirements of the problem (low communication to computation ratio) which arise from the coarse-grain decomposition of the problem and the use of local-interaction methods. Many important problems may be suitable for this type of parallel computing including computer vision, circuit simulation, and other subsonic flow problems.

Vectorizing Face Images by Interpreting Shape and Texture Computations

The correspondence problem in computer vision is basically a matching task between two or more sets of features. In this paper, we introduce a vectorized image representation, which is a feature-based representation where correspondence has been established with respect to a reference image. This representation has two components: (1) shape, or (x, y) feature locations, and (2) texture, defined as the image grey levels mapped onto the standard reference image. This paper explores an automatic technique for "vectorizing" face images. Our face vectorizer alternates back and forth between computation steps for shape and texture, and a key idea is to structure the two computations so that each one uses the output of the other. A hierarchical coarse-to-fine implementation is discussed, and applications are presented to the problems of facial feature detection and registration of two arbitrary faces.

Surface Reflectance Estimation and Natural Illumination Statistics

Humans recognize optical reflectance properties of surfaces such as metal, plastic, or paper from a single image without knowledge of illumination. We develop a machine vision system to perform similar recognition tasks automatically. Reflectance estimation under unknown, arbitrary illumination proves highly underconstrained due to the variety of potential illumination distributions and surface reflectance properties. We have found that the spatial structure of real-world illumination possesses some of the statistical regularities observed in the natural image statistics literature. A human or computer vision system may be able to exploit this prior information to determine the most likely surface reflectance given an observed image. We develop an algorithm for reflectance classification under unknown real-world illumination, which learns relationships between surface reflectance and certain features (statistics) computed from a single observed image. We also develop an automatic feature selection method.

Recognition of Surface Reflectance Properties from a Single Image under Unknown Real-World Illumination

This paper describes a machine vision system that classifies reflectance properties of surfaces such as metal, plastic, or paper, under unknown real-world illumination. We demonstrate performance of our algorithm for surfaces of arbitrary geometry. Reflectance estimation under arbitrary omnidirectional illumination proves highly underconstrained. Our reflectance estimation algorithm succeeds by learning relationships between surface reflectance and certain statistics computed from an observed image, which depend on statistical regularities in the spatial structure of real-world illumination. Although the algorithm assumes known geometry, its statistical nature makes it robust to inaccurate geometry estimates.

1968-1969 Progress Report

This report mainly summarizes the Project MAC A.I. Group work between July 1968 and June 1969 but covers some work up to February 1970. The work on computer vision is described in detail. This summary should be read in conjunction with last year's A.I. Group Report which is included at the end of this Memo.