Effects of apparent image velocity and complexity on the dynamic visual field using a high-speed train driving simulator

Train driving is a highly visual task. The visual capabilities of the train driver affects driving safety and driving performance. Understanding the effects of train speed and background image complexity on the visual behavior of the high-speed train driver is essential for optimizing performance and safety. This study investigated the role of the apparent image velocity and complexity on the dynamic visual field of drivers. Participants in a repeated-measures experiment drove a train at nine different speeds in a state-of-the-art high-speed train simulator. Eye movement analysis indicated that the effect of image velocity on the dynamic visual field of high-speed train driver was significant while image complexity had no effect on it. The fixation range was increasingly concentrated on the middle of the track as the speed increased, meanwhile there was a logarithmic decline in fixation range for areas surrounding the track. The extent of the visual search field decreased gradually, both vertically and horizontally, as the speed of train increased, and the rate of decrease was more rapid in the vertical direction. A model is proposed that predicts the extent of this tunnel vision phenomenon as a function of the train speed.Relevance to industryThis finding can be used as a basis for the design of high-speed railway system and as a foundation for improving the operational procedures of high-speed train driver for safety.     

Model Selection for Unsupervised Learning of Visual Context

This study addresses the problem of choosing the most suitable probabilistic model selection criterion for unsupervised learning of visual context of a dynamic scene using mixture models. A rectified Bayesian Information Criterion (BICr) and a Completed Likelihood Akaike’s Information Criterion (CL-AIC) are formulated to estimate the optimal model order (complexity) for a given visual scene. Both criteria are designed to overcome poor model selection by existing popular criteria when the data sample size varies from small to large and the true mixture distribution kernel functions differ from the assumed ones. Extensive experiments on learning visual context for dynamic scene modelling are carried out to demonstrate the effectiveness of BICr and CL-AIC, compared to that of existing popular model selection criteria including BIC, AIC and Integrated Completed Likelihood (ICL). Our study suggests that for learning visual context using a mixture model, BICr is the most appropriate criterion given sparse data, while CL-AIC should be chosen given moderate or large data sample sizes.     

用金字塔图法匹配边界图象的三眼立体视觉方法

本文在分析了双眼立体视觉面临的本质困难之后，提出了用金字塔图法匹配特征基元的三眼立体视觉方法，这种方法由于建筑在透视投影几何原理的稳固基础上，无须任何先验限制，也不以图象相似性为基础，故可较好地解决精度与匹配难度的矛盾，且运用金字塔图法进行匹配传播使在保证精度的基础上，匹配速度大大提高，对计算机人工图象的匹配结果表明，误匹配率几乎为零，且有较高的匹配速度和精度。     

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.     

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.     

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.     

Color vision in horses (Equus caballus): Deficiencies identified using a pseudoisochromatic plate test.

In the past, equine color vision was tested with stimuli composed either of painted cards or photographic slides or through physiological testing using electroretinogram flicker photometry. Some studies produced similar results, but others did not, demonstrating that there was not yet a definitive answer regarding color vision in horses (Equus caballus). In this study, a pseudoisochromatic plate test--which is highly effective in testing color vision both in small children and in adult humans--was used for the first time on a nonhuman animal. Stimuli consisted of different colored dotted circles set against backgrounds of varying dots. The coloration of the circles corresponded to the visual capabilities of different types of color deficiencies (anomalous trichromacy and dichromacy). Four horses were tested on a 2-choice discrimination task. All horses successfully reached criterion for gray circles and demonstration circles. None of the horses were able to discriminate the protan-deutan plate or the individual protan or deutan plates. However, all were able to discriminate the tritan plate. The results suggest that horses are dichromats with color vision capabilities similar to those of humans with red-green color deficiencies. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Color changes in cyanosis and the significance of congenital dichromasy and lighting

There are anecdotal reports of color vision deficient medical practitioners (particularly deuteranopes) having difficulty in recognizing cyanosis. An understanding of the color changes in oxygenated blood has led to a better understanding of the lighting needs for hospitals. Using the same database, this article investigates the propensity for color vision deficient observers to make mistakes with cyanotic patients. The possibilities for helping the observer with appropriate color of lighting are considered. The spectral reflectances of blood at varying oxygenation levels previously reported were used 7 ; 8 to calculate chromaticity and lightness. The direction of the chromaticity change was compared with the protanopic and deuteranopic confusion lines. An attempt was made to identify sources that enhanced color changes for the color deficient. The color change of blood oxygenation is closely aligned with the confusion lines of congenital dichromats (particularly protanopia) showing the potential for difficulty in making this decision. No source could be demonstrated to solve the problem or even reduce it significantly. © 2007 Wiley Periodicals, Inc. Col Res Appl, 32, 428–432, 2007