Analyzing the factors that determine our choice of visual search strategy may shed light on visual behavior in everyday situations. Previous results suggest that increasing task difficulty leads to more systematic search paths. Here we analyze observers' eye movements in an "easy" conjunction search task and a "difficult" shape search task to study visual search strategies in stereoscopic search displays with virtual depth induced by binocular disparity. Standard eye-movement variables, such as fixation duration and initial saccade latency, as well as new measures proposed here, such as saccadic step size, relative saccadic selectivity, and x-y target distance, revealed systematic effects on search dynamics in the horizontal-vertical plane throughout the search process. We found that in the "easy" task, observers start with the processing of display items in the display center immediately after stimulus onset and subsequently move their gaze outwards, guided by extrafoveally perceived stimulus color. In contrast, the "difficult" task induced an initial gaze shift to the upper-left display corner, followed by a systematic left-right and top-down search process. The only consistent depth effect was a trend of initial saccades in the easy task with smallest displays to the items closest to the observer. The results demonstrate the utility of eye-movement analysis for understanding search strategies and provide a first step toward studying search strategies in actual 3D scenarios.

The adaptation of an observer’s saccadic eye movements to artificial post-saccadic visual error can lead to perceptual mislocalization of individual, transient visual stimuli. In this study, we demonstrate that simultaneous saccadic adaptation to a consistent error pattern across a large number of saccade vectors is accompanied by corresponding spatial distortions in the perception of persistent objects. To induce this adaptation, we artificially introduced several post-saccadic error patterns, which led to a systematic distortion in participants’ oculomotor space and a corresponding distortion in their perception of the relative dimensions of a cross-figure. The results indicate a tight coupling between the oculomotor and visual–perceptual spaces that is not limited to misperception of individual visual locations but also affects metrics in the visual–perceptual space. This coupling suggests that our visual perception is continuously recalibrated by the post-saccadic error signal.

Ataer-Cansizoglu, E.; Taguchi, Y.; Ramalingam, S.; Garaas, T. TR2013-106 December 2013Abstract Planes are dominant in most indoor and outdoor scenes and the development of a hybrid algorithm that incorporates both point and plane features provides numerous advantages. In this regard, we present a tracking algorithm for RGB-D cameras using both points and planes as primitives. We show how to extend the standard prediction-and-correction framework to include planes in addition to points. By fitting planes, we implicitly take care of the noise in the depth data that is typical in many commercially available 3D sensors. In comparison with the techniques that use only points, our tracking algorithm has fewer failure modes, and our reconstructed model is compact and more accurate. The tracking algorithm is supported by re-localization and bundle adjustment processes to demonstrate a real-time simultaneous localization and mapping (SLAM)system using a hand-held or robot-mounted RGB-D camera. Our experiments show large-scale indoor reconstruction results as point-based and plane-based 3D models, and demonstrate an improvement over the point-based tracking algorithms using a benchmark for RGB-D cameras. IEEE Workshop on Consumer Depth Cameas for Computer VisionThis work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved.

Inspection time (IT) is the most popular simple psychometric measure that is used to account for a large part of the variance in human mental ability, with the estimated corrected correlation between IT and IQ being -0.50. In this study, we investigate the relationship between IT and the performance and oculomotor variables measured during three simple visual tasks. Participants' ITs were first measured using a slight variation of the standard IT task, which was followed by the three simple visual tasks that were designed to test participants' visual-attentional control and visual working memory under varying degrees of difficulty; they included a visual search task, a comparative visual search task, and a visual memorization task. Significant correlations were found between IT and performance variables for each of the visual tasks. The implications of the correlation between IT and performance-related variables are discussed. Oculomotor variables on the other hand only correlated significantly with IT during the retrieval phase of the visual memorization task, which is likely a product of differences in participants' ability to memorize objects during the loading phase of the experiment. This leads us to the conclusion that the oculomotor variables we measured do not correlate with IT in general, but may in the case where a systematic benefit would be realized.

Zhu, M.; Ramalingam, S.; Taguchi, Y.; Garaas, T. AbstractMore and more on-road vehicles are equipped with cameras each day. This paper presents a novel method for estimating the relative motion of a vehicle from a sequence of images obtained using a single vehicle-mounted camera. Recently, several researchers in robotics and computer vision have studied the performance of motion estimation algorithms under non-holonomic constraints and planarity. The successful algorithms typically use the smallest number of feature correspondences with respect to the motion model. It has been strongly established that such minimal algorithms are efficient and robust to outliers when used in a hypothesize-and-test framework such as random sample consensus (RANSAC). In this paper, we show that the planar 2-point motion estimation can be solved analytically using a single quadratic equation, without the need of iterative techniques such as Newton-Raphson method used in existing work. Non-iterative methods are more efficient and do not suffer from local minima problems. Although 2-point motion estimation generates visually accurate on-road vehicle-trajectory, the motion is not precise enough to perform dense 3D reconstruction due to the nonplanarity of roads. Thus we use a 2-point relative motion algorithm for the initial images followed by 3-point 2D-to-3D camera pose estimation for the subsequent images. Using this hybrid approach, we generate accurate motion estimates for a plane-sweeping algorithm that produces dense depth maps for obstacle detection applications. European Conference on Computer Vision (ECCV)This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved.Abstract. More and more on-road vehicles are equipped with cameras each day. This paper presents a novel method for estimating the relative motion of a vehicle from a sequence of images obtained using a single vehicle-mounted camera. Recently, several researchers in robotics and computer vision have studied the performance of motion estimation algorithms under non-holonomic constraints and planarity. The successful algorithms typically use the smallest number of feature correspondences with respect to the motion model. It has been strongly established that such minimal algorithms are efficient and robust to outliers when used in a hypothesize-and-test framework such as random sample consensus (RANSAC). In this paper, we show that the planar 2-point motion e...