ISR3: Communication and Data Storage for an Unmanned Ground Vehicle*

Computer vision researchers working in mobile robotics and other real-time domains are forced to con- front issues not normally addressed in the computer vision literature. Among these are communication, or how to get data from one process to another, data storage and retrieval, primarily for transient, image- based data, and database management, for maps, ob- ject models and other permanent (typically 3D) data. This paper reviews eorts at CMU, SRI and UMass to build real-time computer vision systems for mobile robotics, and presents a new tool, called ISR3, for com- munication, data storage/retrieval and database man- agement on the UMass Mobile Perception Laboratory (MPL), a NAVLAB-like autonomous vehicle

Computer Vision and Image Understanding xxx

Abstract 13 This paper presents a panoramic virtual stereo vision approach to the problem of detecting 14 and localizing multiple moving objects (e.g., humans) in an indoor scene. Two panoramic 15 cameras, residing on different mobile platforms, compose a virtual stereo sensor with a flexible 16 baseline. A novel ''mutual calibration'' algorithm is proposed, where panoramic cameras on 17 two cooperative moving platforms are dynamically calibrated by looking at each other. A de-18 tailed numerical analysis of the error characteristics of the panoramic virtual stereo vision 19 (mutual calibration error, stereo matching error, and triangulation error) is given to derive 20 rules for optimal view planning. Experimental results are discussed for detecting and localizing 21 multiple humans in motion using two cooperative robot platforms. 2

RAPID : research on automated plankton identification

Author Posting. © Oceanography Society, 2007. This article is posted here by permission of Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 20, 2 (2007): 172-187.When Victor Hensen deployed the first true plankton1 net in 1887, he and his colleagues were attempting to answer three fundamental questions: What planktonic organisms are present in the ocean? How many of each type are present? How does the plankton’s composition change over time? Although answering these questions has remained a central goal of oceanographers, the sophisticated tools available to enumerate planktonic organisms today offer capabilities that Hensen probably could never have imagined.This material is based upon work supported by the National Science Foundation under Grants OCE-0325018, OCE-0324937, OCE-0325167 and OCE-9423471, and the European Union under grants Q5CR-2002-71699, MAS3-ct98-0188, and MAS2-ct92-0015

How Easy is Matching 2D Line Models Using Local Search?

Local search is a well established and highly effective method for solving complex combinatorial optimization problems. Here, local search is adapted to solve difficult geometric matching problems. Matching is posed as the problem of finding the optimal many-to-many correspondence mapping between a line segment model and image line segments. Image data is assumed to be fragmented, noisy and cluttered. The algorithms presented have been used for robot navigation, photo-interpretation and scene understanding. This paper explores how local search performs as model complexity increases, image clutter increases, and additional model instances are added to the image data. Expected run-times to find optimal matches with 95% confidence are determined for 48 distinct problems involving 6 models. Non-linear regression is used to estimate run-time growth as a function of problem size. Both polynomial and exponential growth models are fit to the run-time data. For problems with random clutter the..

Knowledge-Directed Vision: Control, Learning, and Integration

The knowledge-directed approach to image interpretation, popular in the 1980\u27s, sought to identify objects in unconstrained two-dimensional images and to determine the threedimensional relationships between these objects and the camera by applying large amounts of object- and domain-specific knowledge to the interpretation problem. Among the primary issues faced by these systems were variations among instances of an object class and differences in how object classes were defined in terms of shape, color, function, texture, size, and/or substructures. This paper argues that knowledge-directed vision systems typically failed for two reasons. The first is that the low- and mid-level vision procedures that were relied upon to perform the basic tasks of vision were too immature at the time to support the ambitious interpretation goals of these systems. This problem, we conjecture, has been largely solved by recent advances in the field of 3D computer vision, particularly in stereo and shape reconstruction from multiple views. The other impediment was that the control problem for vision procedures was never properly addressed as an independent problem. This paper reviews the issues confronted by knowledge-directed vision systems, and concludes that inadequate vision procedures and the lack of a control formalism blocked their further development. We then briefly introduce several new projects which, although still in the early stage of development, are addressing the complex control issues that continue to obstruct the development of robust knowledgedirected vision systems

TextFinder: An Automatic System To Detect And Recognize Text In Images

There are many applications in which the automatic detection and recognition of text embedded in images is useful. These applications include digital libraries, multimedia systems, Information Retrievial Systems, and Geographical Information Systems. When machine generated text is printed against clean backgrounds, it can be converted to a computer readable form (ASCII) using current Optical Character Recognition (OCR) technology. However, text is often printed against shaded or textured backgrounds or is embedded in images. Examples include maps, advertisements, photographs, videos and stock certificates. Current document segmentation and recognition technologies cannot handle these situations well. In this paper, a four-step system which automatically detects and extracts text in images is proposed. First, a texture segmentation scheme is used to focus attention on regions where text may occur. Second, strokes are extracted from the segmented text regions. Using reasonable heuristics..