John R. Kender

AIJ Journal 1995 Journal Article

Topological direction-giving and visual navigation in large environments

• Il-Pyung Park
• John R. Kender

In this paper, we propose and investigate a new model for robot navigation in large unstructured environments. Current models, which depend on metric information, have to deal with inherent mechanical and sensory errors. Instead we supply the navigator with qualitative information. Our model consists of two parts, a map-maker and a navigator. Given a source and a goal, the mapmaker derives a navigational path based on the topological relationships between landmarks. A navigational path is generated as a combination of “parkway” and “trajectory” paths, both of which are abstractions of the real world into topological data structures. Traversing within a parkway enables the navigator to follow landmarks that are continuously visible. Traversing on a trajectory enables the navigator to move reliably into featureless space, based on local headings formed by visible landmarks that are robust to positional and orientational errors. Reliability measures of parkway and trajectory traversals are defined by appropriate error models that account for the sensory errors of the navigator, the population of neighboring objects, and the rotational and translational errors of the navigator. The optimal path is further abstracted into a “custom map”, which consists of a list of symbolic directional instructions, the vocabulary of which is defined by our environmental description language. Based on the custom map generated by the map-maker, the navigating robot looks for events that are characterized by spatial properties of the environment. The map-maker and the navigator are implemented using two cameras, an IBM 7575 robot arm, and a PIPE (Pipelined Image Processing Engine.)

IJCAI Conference 1991 Conference Paper

On Seeing Spaghetti: A Novel Self-Adjusting Seven Parameter Hough Space for Analyzing Flexible Extruded Objects

• John R. Kender
• Rick Kjeldsen

IJCAI Conference 1987 Conference Paper

What is a 'Degenerate' View?

• John R. Kender
• David G. Freudenstein

AAAI Conference 1986 Conference Paper

Shape from Darkness: Deriving Surface Information from Dynamic Shadows

• John R. Kender

We present a new method, shape from darkness, for extracting surface shape information based on object self-shadowing under moving light sources. It is motivated by the problem of human perception of fractal textures under perspective. One-dimensional dynamic shadows are analyzed in the continuous case, and their behavior is categorized into three exhaustive shadow classes. The continuous problem is shown to be solved by the integration of ordinary differential equations, using information captured in a new image representation called the suntrace. The discretization of the one-dimensional problem introduces uncertainty in the discrete suntrace; however it is successfully recast as the satisfaction of 8n constraint equations in 2n unknowns. A form of relaxation appears to quickly converge these constraints to accurate surface reconstructions; we give several examples on simulated images. The shape from darkness method has two advantages: it does not require a reflectance map, and it works on non-smooth surfaces. We conclude with a discussion on the method’s accuracy and practicality, its relation to human perception, and its future extensions.

AAAI Conference 1983 Conference Paper

Surface Constraints from Linear Extents

• John R. Kender

This pa. per demonstrates how image features of linear extent (lengths and spacings) image-independent constraints on un erlying surface cf encrate nearly orientations. General constraints are derived from the shape-from-texture paradigm; then. certain special cases are shown to be especially useful. Under orthography, the assumption that two extents are equal is shown to be identical to the assumption that an ima angle (i. e. orthogra hit e extent is a s e angle is a right skewed symmetry. are assumed equa 1 nder and para Ii erspective, orm of slope or if image extents into slope. In the * eneral cl, extent again de enerates constraints are usua y fi perspective case, t i! e shape complex fourth-order equationas, but they often simplify--even to graphic constructions m the image space itself. assumed e If image est ents are colinear and order, wit ual, the constraint equations reduce to second ‘ fi several graphic analogs. If extents are adjacent as well, the e uations are first order and the derived construction 4the “‘acli-knife particularly straightforward an d method”) is general. This method works not only on measures of extent per texel, but also on reciprocal measures: texels per extent. Several examples and discussion indicate that the methods are robust, derivin search, where otB surface informat ion cheaply, without er methods must fail. *

AAAI Conference 1982 Conference Paper

Why Perspective Is Difficult: How Two Algorithms Fail

• John R. Kender

Attempting to derive image algorithms solely under orthographic projection is deceptively easy. However, orthographic algorithms often fail when applied to the perspective case. More critically, since many things simplify under orthography, such algorithms often give no evidence for their proper extension. This paper gives two such examples, showing both the problems that arise for them under perspective, and the surprising extensions that they require.

AAAI Conference 1980 Conference Paper

Mapping Image Properties into Shape Constraints: Skewed Symmetry and Affine-Transformable Patterns, and the Shape-from-Texture Paradigm

• John R. Kender