Indoor and outdoor depth imaging of leaves with time-of-flight and stereo vision sensors: analysis and comparison

In this article we analyze the response of Time-of-Flight (ToF) cameras (active sensors) for close range imaging under three different illumination conditions and compare the results with stereo vision (passive) sensors. ToF cameras are sensitive to ambient light and have low resolution but deliver high frame rate accurate depth data under suitable conditions. We introduce metrics for performance evaluation over a small region of interest. Based on these metrics, we analyze and compare depth imaging of leaf under indoor (room) and outdoor (shadow and sunlight) conditions by varying exposure times of the sensors. Performance of three different ToF cameras (PMD CamBoard, PMD CamCube and SwissRanger SR4000) is compared against selected stereo-correspondence algorithms (local correlation and graph cuts). PMD CamCube has better cancelation of sunlight, followed by CamBoard, while SwissRanger SR4000 performs poorly under sunlight. Stereo vision is comparatively more robust to ambient illumination and provides high resolution depth data but is constrained by texture of the object along with computational efficiency. Graph cut based stereo correspondence algorithm can better retrieve the shape of the leaves but is computationally much more expensive as compared to local correlation. Finally, we propose a method to increase the dynamic range of ToF cameras for a scene involving both shadow and sunlight exposures at the same time by taking advantage of camera flags (PMD) or confidence matrix (SwissRanger). (C) 2013 International Society for Photogrammetly and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.Peer ReviewedPostprint (author’s final draft

Range Segmentation Using Visibility Constraints

Visibility constraints can aid the segmentation of foreground objects observed with multiple range images. In our approach, points are defined as foreground if they can be determined to occlude some {em empty space} in the scene. We present an efficient algorithm to estimate foreground points in each range view using explicit epipolar search. In cases where the background pattern is stationary, we show how visibility constraints from other views can generate virtual background values at points with no valid depth in the primary view. We demonstrate the performance of both algorithms for detecting people in indoor office environments

Quick and energy-efficient Bayesian computing of binocular disparity using stochastic digital signals

Reconstruction of the tridimensional geometry of a visual scene using the binocular disparity information is an important issue in computer vision and mobile robotics, which can be formulated as a Bayesian inference problem. However, computation of the full disparity distribution with an advanced Bayesian model is usually an intractable problem, and proves computationally challenging even with a simple model. In this paper, we show how probabilistic hardware using distributed memory and alternate representation of data as stochastic bitstreams can solve that problem with high performance and energy efficiency. We put forward a way to express discrete probability distributions using stochastic data representations and perform Bayesian fusion using those representations, and show how that approach can be applied to diparity computation. We evaluate the system using a simulated stochastic implementation and discuss possible hardware implementations of such architectures and their potential for sensorimotor processing and robotics.Comment: Preprint of article submitted for publication in International Journal of Approximate Reasoning and accepted pending minor revision