Optical Flow From 1D Correlation: Application to a Simple Time-To-Crash Detector

In the first part of this paper we show that a new technique exploiting 1D correlation of 2D or even 1D patches between successive frames may be sufficient to compute a satisfactory estimation of the optical flow field. The algorithm is well-suited to VLSI implementations. The sparse measurements provided by the technique can be used to compute qualitative properties of the flow for a number of different visual tsks. In particular, the second part of the paper shows how to combine our 1D correlation technique with a scheme for detecting expansion or rotation ([5]) in a simple algorithm which also suggests interesting biological implications. The algorithm provides a rough estimate of time-to-crash. It was tested on real image sequences. We show its performance and compare the results to previous approaches.

The Three-Dimensional Interpretation of a Class of Simple Line-Drawings

We provide a theory of the three-dimensional interpretation of a class of line-drawings called p-images, which are interpreted by the human vision system as parallelepipeds ("boxes"). Despite their simplicity, p-images raise a number of interesting vision questions: *Why are p-images seen as three-dimensional objects? Why not just as flatimages? *What are the dimensions and pose of the perceived objects? *Why are some p-images interpreted as rectangular boxes, while others are seen as skewed, even though there is no obvious distinction between the images? *When p-images are rotated in three dimensions, why are the image-sequences perceived as distorting objects---even though structure-from-motion would predict that rigid objects would be seen? *Why are some three-dimensional parallelepipeds seen as radically different when viewed from different viewpoints? We show that these and related questions can be answered with the help of a single mathematical result and an associated perceptual principle. An interesting special case arises when there are right angles in the p-image. This case represents a singularity in the equations and is mystifying from the vision point of view. It would seem that (at least in this case) the vision system does not follow the ordinary rules of geometry but operates in accordance with other (and as yet unknown) principles.

Visual Navigation: Constructing and Utilizing Simple Maps of an Indoor Environment

The goal of this work is to navigate through an office environmentsusing only visual information gathered from four cameras placed onboard a mobile robot. The method is insensitive to physical changes within the room it is inspecting, such as moving objects. Forward and rotational motion vision are used to find doors and rooms, and these can be used to build topological maps. The map is built without the use of odometry or trajectory integration. The long term goal of the project described here is for the robot to build simple maps of its environment and to localize itself within this framework.

A Simple Model of Circuit Design

A simple analog circuit designer has been implemented as a rule based system. The system can design voltage followers. Miller integrators, and bootstrap ramp generators from functional descriptions of what these circuits do. While the designer works in a simple domain where all components are ideal, it demonstrates the abilities of skilled designers. While the domain is electronics, the design ideas are useful in many other engineering domains, such as mechanical engineering, chemical engineering, and numerical programming. Most circuit design systems are given the circuit schematic and use arithmetic constraints to select component values. This circuit designer is different because it designs the schematic. The designer uses a unidirectional CONTROL relation to find the schematic. The circuit designs are built around this relation; it restricts the search space, assigns purposes to components and finds design bugs.

Understanding Simple Picture Programs

What are the characteristics of the process by which an intent is transformed into a plan and then a program? How is a program debugged? This paper analyzes these questions in the context of understanding simple turtle programs. To understand and debug a program, a description of its intent is required. For turtle programs, this is a model of the desired geometric picture. a picture language is provided for this purpose. Annotation is necessary for documenting the performance of a program in such a way that the system can examine the procedures behavior as well as consider hypothetical lines of development due to tentative debugging edits. A descriptive framework representing both causality and teleology is developed. To understand the relation between program and model, the plan must be known. The plan is a description of the methodology for accomplishing the model. Concepts are explicated for translating the global intent of a declarative model into the local imperative code of a program. Given the plan, model and program, the system can interpret the picture and recognize inconsistencies. The description of the discrepancies between the picture actually produced by the program and the intended scene is the input to a debugging system. Repair of the program is based on a combination of general debugging techniques and specific fixing knowledge associated with the geometric model primitives. In both the plan and repairing the bugs, the system exhibits an interesting style of analysis. It is capable of debugging itself and reformulating its analysis of a plan or bug in response to self-criticism. In this fashion, it can qualitatively reformulate its theory of the program or error to account for surprises or anomalies.

Safe Distributed Coordination of Heterogeneous Robots through Dynamic Simple Temporal Networks

Research on autonomous intelligent systems has focused on how robots can robustly carry out missions in uncertain and harsh environments with very little or no human intervention. Robotic execution languages such as RAPs, ESL, and TDL improve robustness by managing functionally redundant procedures for achieving goals. The model-based programming approach extends this by guaranteeing correctness of execution through pre-planning of non-deterministic timed threads of activities. Executing model-based programs effectively on distributed autonomous platforms requires distributing this pre-planning process. This thesis presents a distributed planner for modelbased programs whose planning and execution is distributed among agents with widely varying levels of processor power and memory resources. We make two key contributions. First, we reformulate a model-based program, which describes cooperative activities, into a hierarchical dynamic simple temporal network. This enables efficient distributed coordination of robots and supports deployment on heterogeneous robots. Second, we introduce a distributed temporal planner, called DTP, which solves hierarchical dynamic simple temporal networks with the assistance of the distributed Bellman-Ford shortest path algorithm. The implementation of DTP has been demonstrated successfully on a wide range of randomly generated examples and on a pursuer-evader challenge problem in simulation.

Information Dissemination and Aggregation in Asset Markets with Simple Intelligent Traders

Various studies of asset markets have shown that traders are capable of learning and transmitting information through prices in many situations. In this paper we replace human traders with intelligent software agents in a series of simulated markets. Using these simple learning agents, we are able to replicate several features of the experiments with human subjects, regarding (1) dissemination of information from informed to uninformed traders, and (2) aggregation of information spread over different traders.

Face processing in humans is compatible with a simple shape-based model of vision

Understanding how the human visual system recognizes objects is one of the key challenges in neuroscience. Inspired by a large body of physiological evidence (Felleman and Van Essen, 1991; Hubel and Wiesel, 1962; Livingstone and Hubel, 1988; Tso et al., 2001; Zeki, 1993), a general class of recognition models has emerged which is based on a hierarchical organization of visual processing, with succeeding stages being sensitive to image features of increasing complexity (Hummel and Biederman, 1992; Riesenhuber and Poggio, 1999; Selfridge, 1959). However, these models appear to be incompatible with some well-known psychophysical results. Prominent among these are experiments investigating recognition impairments caused by vertical inversion of images, especially those of faces. It has been reported that faces that differ "featurally" are much easier to distinguish when inverted than those that differ "configurally" (Freire et al., 2000; Le Grand et al., 2001; Mondloch et al., 2002) ??finding that is difficult to reconcile with the aforementioned models. Here we show that after controlling for subjects' expectations, there is no difference between "featurally" and "configurally" transformed faces in terms of inversion effect. This result reinforces the plausibility of simple hierarchical models of object representation and recognition in cortex.