Jeff L. DeCurtins

Verification-Based Approach for Automated Text and Feature Extraction from Raster-Scanned Maps

Existing systems for converting maps to an object-oriented form suitable for a geographic information system (GIS) are only partially automated. Most published approaches for automated interpretation of raster-scanned maps assume that the map is composed of various graphic entities, and that the vast majority of pixel positions on the map each belong to only one type of graphic entity and can therefore be geometrically segmented. However, complex color topographic maps contain several layers of information that overlap substantially (often within a single color plane), making it impossible to geometrically segment the map data into distinct regions containing a single class of graphic object. Here we describe a verification-based approach that uses various knowledge bases to detect, extract, and attribute map features without requiring the presegmentation of graphical entities. This approach builds on SRI Internationals (SRIs) verification-based computer vision and character recognition methodologies. The approach can also be applied to other types of documents containing a mix of text and graphics, such as engineering drawings, electrical schematics, and technical illustrations.

Keyword spotting via word shape recognition

With the advent of on-line access to very large collections of document images, electronic classification into areas of interest has become possible. A first approach to classification might be the use of OCR on each document followed by analysis of the resulting ASCII text. But if the quality of a document is poor, the format unconstrained, or time is critical, complete OCR of each image is not appropriate. An alternative approach is the use of word shape recognition (as opposed to individual character recognition) and the subsequent classification of documents by the presence or absence of selected keywords. Use of word shape recognition not only provides a more robust collection of features but also eliminates the need for character segmentation (a leading cause of error in OCR). In this paper we describe a system we have developed for the detection of isolated words, word portions, as well as multi-word phrases in images of documents. It is designed to be used with large, changeable, keyword sets and very large document sets. The system provides for automated training of desired keywords and creation of indexing filters to speed matching.

Understanding object configurations using range images

The authors describe techniques that generate multiple interpretations from dense range images of piles of unknown objects and methods that use physical law, such as object stability, to rank the interpretations. Each of the interpretations completely accounts for the observed range data, but the interpretations differ in the ways visible portions of objects are extended into the occluded portions of the scene. Experiments with 100 range images indicate that the techniques are fairly robust when the scenes consist of shapes that are approximately prismatic or cylindrical. These techniques are based on novel approaches in several key areas, including explicit use of sensor geometry, generic shape models to synthesize scene descriptions, spatial-reasoning techniques that incorporate knowledge about the laws of physics, direct estimation of the physical properties of the objects in the scene, and detection and refinement of descriptions of approximately planar or cylindrical surfaces. >

Automatic light-source placement for detecting object features

The selection and placement of cameras and light sources for a specific task (e.g., locating a part in a tray or inspecting an object) is one of the most important steps in creating a successful vision system, because obtaining high-quality images can greatly simplify the vision algorithms and improve their reliability. We will describe techniques that use a visual task description stated in terms of features to be detected, and derive a range of light-source locations that satisfy the task requirements. In particular, given a task description that specifies particular object edges to be detected with a given edge detector (e.g., a Sobel edge operator), our techniques determine the constraints on light-source location such that the edge is detected.

Scene description for object manipulation in unstructured environments

Sensing and reasoning techniques for the data-driven description of objects in unstructured environments are given. One of the techniques presented is for extrapolating the shape of individual objects using symmetries, while the other is applicable to objects jumbled in a pile. No a priori object models are needed for these techniques. A hypothesize and test approach is used to infer complete shape information from range images that represent partial knowledge about an objects shape. Using explicit knowledge about the geometrical relationship of the sensor to the scene, hypotheses are verified to ensure that they do not violate physical laws such as object transparency and solidity. In addition, basic physical rules such as stability and contact are used to improve the estimates of the shapes of objects that may be partially visible or partially outside the field of view of the sensor.<<ETX>>

Sketch: A simple-to-use programming system for visually guided robotic arc welding

Programmability, the principal advantage of a robot over traditional hard automation, may become more difficult to achieve when vision systems are used to enhance robot capabilities because such systems may add a significant level of complexity to the programming task. CAD/CAM data on which to base automatic program generation are expensive and unavailable in many applications, particularly in small batch or low volume manufacturing. The SKETCH automatic programming facility, described in this paper, is designed for use in programming a visually guided robotic arc-welding system in those applications for which CAD/CAM-based off-line robot programming is either infeasable or unavailable. This facility was developed as part of SRIs ongoing research into machine-vision-based guidance and control of robots for arc welding and is designed to allow a skilled welder to program the vision system quickly and easily by sketching on a graphics screen the cross section of the seam to be welded.

Segmentation-free approach to optical character recognition

This paper presents a segmentation-free approach to optical character recognition (OCR) based on the concept of occluded object recognition, in which objects are recognized and then segmented out from the image. In applying the concept of occluded object recognition to the problem of OCR, we treat characters as touching or occluded objects that are subject to special constraints on their poses, i.e., they are juxtaposed with little or no freedom in rotation. Based on these characteristics, we combine two very powerful techniques used in occluded object recognition -- indexing and voting (pose clustering) -- and tailor them to the problem of OCR. This results in a segmentation-free OCR approach that is both highly efficient and robust. We note that recently some techniques have been proposed for handwritten OCR that conceptually are also segmentation-free, although these techniques are quite different from ours.

Scene description: interactive computation of stability with friction

This paper describes a technique for hypothesizing the shape of hidden portions of unknown objects within a pile of such objects, using a dense range image of the pile. The technique employs symmetry, stability, viewpoint independence, and object impenetrability to hypothesize the unknown shape and dimension of each visible object. The process constructs alternative hypotheses, which differ in the way the visible portions of objects are extended into the occluded regions within the scene. To ensure that each interpretation is consistent with the observed range data, the known geometry of the range sensor is used in forming the hypotheses. The final result is one or more hypothesized object configurations, each of which is consistent with both the sensed range data and the physical constraints between objects in contact. For each resulting hypothesis, a free-body analysis is performed to determine if the hypothesized configuration is stable. The hypothesis with the highest stability rating is chosen as the most likely correct interpretation.

Visual registration for robotic operations on space shuttle tiles

Refurbishing the thermal-protection tiles on a space shuttle before each mission is a lengthy and labor-intensive process. A mobile robot is being developed (described elsewhere) to perform two of the required maintenance operations on the bottom side of the shuttle: (1) injection of a hydrophobic fluid, to prevent tiles from absorbing water, and (2) visual inspection, to detect anomalous tile conditions. Both operations depend on precise positioning of the robot end effector with respect to each tile. We describe our method for precise visual registration. The technique first detects the edges of the tile (whose approximate shape and dimensions are given from CAD data) and then uses correspondence between visual features in the post- and pre-flight images to improve the registration accuracy. Results on actual tile images are presented.

Scene description: an iterative approach

Analyzing sensor data to describe the shape of unknown three-dimensional objects randomly jumbled together is an area of great research interest. It is encountered in a large variety of industrial tasks of the bin-picking type. Classical approaches to bin-picking use strong object models. However a priori models are not available in many unstructured material handling applications such as mailpiece singulation random or mixed part feeding scavenging and other similar tasks. In such applications the key vision problem is determining how the partially visible objects relate to each other and to other invisible objects that may be underneath. The shapes of the partially visible objects are constrained by the invisible contacts between the objects the forces such as friction and gravity acting at these contacts and the assumed solidity (impenetrability) of the objects. This paper shows how heuristics such as object symmetry and assumptions such as general viewpoint can be used to generate initial hypotheses about the shapes of partially visible objects. These hypotheses are then iteratively expanded to determine the possible extents ofthe objects using criteria such as coplanarity ofdisconnected surfaces and intersection of swept volumes. A detailed example that illustrates the methods is described. 1. 0