Towards segmentation into surfaces

Image segmentation is a fundamental problem of low level computer vision and is also used as a preprocessing step for a number of higher level tasks (e.g. object detection and recognition, action classification, optical flow and stereo computation etc). In this dissertation we study the image segmentation problem focusing on the task of segmentation into surfaces. First we present our unifying framework through which mean shift, bilateral filtering and anisotropic diffusion can be described. Three new methods are also described and implemented and the most prominent of them, called Color Mean Shift (CMS), is extensively tested and compared against the existing methods. We experimentally show that CMS outperforms the other methods i.e., creates more uniform regions and retains equally well the edges between segments. Next we argue that color based segmentation should be a two stage process; edge preserving filtering, followed by pixel clustering. We create novel segmentation algorithms by coupling the previously described filtering methods with standard grouping techniques. We compare all the segmentation methods with current state of the art grouping methods and show that they produce better results on the Berkeley and Weizmann segmentation datasets. A number of other interesting conclusions are also drawn from the comparison. Then we focus on surface normal estimation techniques. We present two novel methods to estimate the parameters of a planar surface viewed by a moving robot when the odometry is known. We also present a way of combining them and integrate the measurements over time using an extended Kalman filter. We test the estimation accuracy by demonstrating the ability of the system to navigate in an indoor environment using exclusively vision. We conclude this dissertation with a discussion on how color based segmentation can be integrated into a structure from motion framework that computes planar surfaces using homographies

A Distributed Algorithm for Constructing a Generalization of de Bruijn Graphs

De Bruijn graphs possess many characteristics that make them a suitable choice for the topology of an overlay network. These include constant degree at each node, logarithmic diameter and a highly-regular topology that permits nodes to make strong assumptions about the global structure of the network. We propose a distributed protocol that constructs an approximation of a de Bruijn graph in the presence of an arbitrary number of nodes. We show that the degree of each node is constant and that the diameter of the network is no worse than 2logN, where N is the number of nodes. The cost of the join and the departure procedures are O(logN) in the worst case. To the best of our knowledge, this is the first distributed protocol that provides such deterministic guarantees

Biol Cybern DOI 10.1007/s00422-006-0097-1 ORIGINAL PAPER Depth estimation using the compound eye of dipteran flies

Abstract In the neural superposition eye of a dipteran fly every ommatidium has eight photoreceptors, each associated with a rhabdomere, two central and six peripheral, which altogether result in seven functional light guides. Groups of eight rhabdomeres in neighboring ommatidia have largely overlapping fields of view. Based on the hypothesis that the light signals collected by these rhabdomeres can be used individually, we investigated the feasibility of estimating 3D scene information. According to Pick (Biol Cybern 26:215–224, 1977) the visual axes of these rhabdomeres are not parallel, but converge to a point 3–6 mm in front of the cornea. Such a structure theoretically could estimate depth in a very simple way by assuming that locally the image intensity is well approximated by a linear function of the spatial coordinates. Using the measurements of Pick (Biol Cybern 26:215–224, 1977) we performed simulation experiments to find whether this is practically possible. Our results indicate that depth estimation at small distances (up to about 1.5–2 cm) is reasonably accurate. This would allow the insect to obtain at least an ordinal spatial layout of its operational space when walking.

Combining Motion from Texture and Lines for Visual Navigation

Abstract — Two novel methods for computing 3D structure information from video for a piecewise planar scene are presented. The first method is based on a new line constraint, which clearly separates the estimation of distance from the estimation of slant. The second method exploits the concepts of phase correlation to compute from the change of image frequencies of a textured plane, distance and slant information. The two different estimates together with structure estimates from classical image motion are combined and integrated over time using an extended Kalman filter. The estimation of the scene structure is demonstrated experimentally in a motion control algorithm that allows the robot to move along a corridor. We demonstrate the efficacy of each individual method and their combination and show that the method allows for visual navigation in textured as well as un-textured environments. I

A Framework for Distributed Human Tracking

Abstract — Today, more than ever, monitoring and surveillance systems play an important role in many aspects of our lives. Technology plays a vital role in our efforts to create, store and analyze vast amounts of data for both security and commercial purposes. In this paper, we propose an application which combines research performed in computer networks, multimedia databases and computer vision. We consider the problem where a number of networks are interconnected. Each of the individual nodes (networks) are collecting, processing and storing data from several sensors (cameras). Specifically, we emphasize on how the data (images) are processed by the individual nodes and how the information is transmitted, so that queries involving multiple nodes can be answered. During this process, we also identify several challenges related to sharing voluminous content provided by visual surveillance devices