Recognizing Three-Dimensional Objects without the Use of Models

We present an approach to the problem of recognizing three-dimensional objects from line-drawings. In this approach there are no models. The system needs only to be given a single picture of an object; it can then recognize the object in arbitrary orientations.

Intelligence Without Reason

Computers and Thought are the two categories that together define Artificial Intelligence as a discipline. It is generally accepted that work in Artificial Intelligence over the last thirty years has had a strong influence on aspects of computer architectures. In this paper we also make the converse claim; that the state of computer architecture has been a strong influence on our models of thought. The Von Neumann model of computation has lead Artificial Intelligence in particular directions. Intelligence in biological systems is completely different. Recent work in behavior-based Artificial Intelligenge has produced new models of intelligence that are much closer in spirit to biological systems. The non-Von Neumann computational models they use share many characteristics with biological computation.

Multi-Scale Vector-Ridge-Detection for Perceptual Organization Without Edges

We present a novel ridge detector that finds ridges on vector fields. It is designed to automatically find the right scale of a ridge even in the presence of noise, multiple steps and narrow valleys. One of the key features of such ridge detector is that it has a zero response at discontinuities. The ridge detector can be applied to scalar and vector quantities such as color. We also present a parallel perceptual organization scheme based on such ridge detector that works without edges; in addition to perceptual groups, the scheme computes potential focus of attention points at which to direct future processing. The relation to human perception and several theoretical findings supporting the scheme are presented. We also show results of a Connection Machine implementation of the scheme for perceptual organization (without edges) using color.

Feature Extraction Without Edge Detection

Information representation is a critical issue in machine vision. The representation strategy in the primitive stages of a vision system has enormous implications for the performance in subsequent stages. Existing feature extraction paradigms, like edge detection, provide sparse and unreliable representations of the image information. In this thesis, we propose a novel feature extraction paradigm. The features consist of salient, simple parts of regions bounded by zero-crossings. The features are dense, stable, and robust. The primary advantage of the features is that they have abstract geometric attributes pertaining to their size and shape. To demonstrate the utility of the feature extraction paradigm, we apply it to passive navigation. We argue that the paradigm is applicable to other early vision problems.

Physicochemical properties of silk fibroin after solubilization using calcium chloride with or without ethanol

To elucidate the physicochemical properties of silk protein, we studied the effects of calcium chloride and ethanol on the gelation of fibroin. Fibroin was treated with 5.0 M calcium chloride in water (Ca/W) or 5.0 M calcium chloride in 20% (v/v) ethanol (Ca/Et) and the rheological properties of colloidal fibroin were investigated. The Ca/W-treatment promoted an increased rate of gelation and gave higher gel strength than the Ca/Et-treatment. The maximum gel strengths of Ca/W- and Ca/Et-treated fibroins were obtained at pH 7.0 and pH 5.5, respectively. Scanning electron micrographs showed that the Ca/W-treated fibroin gel had a more developed three-dimensional molecular network than the Ca/Et-treated gel. Further, FT-IR spectra suggested that Ca/W-treated fibroin has more of a beta-structure than Ca/Et-treated one in colloidal conditions. This study indicated that the use of calcium chloride alone was more beneficial to the gelation of fibroin than combined use with ethanol.