Constructing Simple Stable Descriptions for Image Partitioning

A new formulation of the image partitioning problem is presented: construct a complete and stable description of an image, in terms of a specified descriptive language, that is simplest in the sense of being shortest. We show that a descriptive language limited to a low-order polynomial description of the intensity variation within each region and a chain-code-like description of the region boundaries yields intuitively satisfying partitions for a wide class of images. The advantage of this formulation is that it can be extended to deal with subsequent steps of the image-understanding problem (or to deal with other image attributes, such as texture) in a natural way by augmenting the descriptive language. Experiments performed on a variety of both real and synthetic images demonstrate the superior performance of this approach over partitioning techniques based on clustering vectors of local image attributes and standard edge-detection techniques. 1 Introduction The partitioning proble..

An Optimization-Based Approach to the Interpretation of Single Line Drawings as 3-D Wire Frames

Line drawings provide an effective means of communication about the geometry of 3-D objects. An understanding of how to duplicate the way humans interpret line drawings is extremely important in enabling man-machine communication with respect to images, diagrams, and spatial constructs. In particular, such an understanding could be used to provide the human with the capability to create a line-drawing sketch of a polyhedral object that the machine can automatically convert into the intended 3-D model. A recently published paper (Marill 1991) presented a simple optimization procedure supposedly able to duplicate human judgment in recovering the 3-D "wire frame" geometry of objects depicted in line drawings. Marill provided some impressive examples, but no theoretical justification for his approach. In this paper we introduce our own work by first critically examining Marill's algorithm. We provide an explanation for why Marill's algorithm was able to perform as well as it did on the exa..

Abstract Characterizing the Performance of Multiple-image Point-correspondence Algorithms using Self-Consistency

A new approach to characterizing the performance of point-correspondence algorithms is presented. Instead of relying on any \ground truth', it uses the self-consistency of the outputs of an algorithm independently applied to di erent sets of views of a static scene. It allows one to evaluate algorithms for a given class of scenes, as well as to estimate the accuracy of every element of the output of the algorithm for a given set of views. Experiments to demonstrate the usefulness of the methodology are presented.

A Framework for Robust 3-D Change Detection

We present an application of our framework for 3-D object-centered change detection 1–4 to combined satellite and aerial imagery. In this framework, geometry is compared to geometry, allowing us to compare image sets with different acquisition conditions and even different sensors. By working in this framework, we do not encounter the restrictions and short-comings of conventional image-based change detection, which requires that the images being compared have similar acquisition geometry, photometry, scene illumination, and so forth. The contributions of our framework are: (1) using a geometric basis for change detection, allowing image sets acquired under different conditions to be compared; (2) explicit modeling of image geometry to be able to numerically characterize significant and insignificant change. The contributions of this paper are: (1) the algorithms are embedded in an integrated cartographic modeling and image processing system, which can ingest and make use of a variety of government and commercial imagery and geospatial data products; (2) experimentation with a variety of imagery and scene content. Modifications to the algorithms specific to their use with satellite imagery are discussed and the results from several experiments with both aerial and satellite images urban domains are described and analyzed