Abstract not found

We present a robot localization system using biologically-inspired vision. Our system models two extensively studied human visual capabilities: (1) extracting the “gist” of a scene to produce a coarse localization hypothesis, and (2) refining it by locating salient landmark points in the scene. Gist is computed here as a holistic statistical signature of the image, yielding abstract scene classification and layout. Saliency is computed as a measure of interest at every image location, efficiently directing the time-consuming landmark identification process towards the most likely candidate locations in the image. The gist features and salient regions are then further processed using a Monte-Carlo localization algorithm to allow the robot to generate its position. We test the system in three different outdoor environments — building complex (38.4x54.86m area, 13966 testing images), vegetation-filled park (82.3x109.73m area, 26397 testing images), and open-field park (137.16x178.31m area, 34711 testing images) — each with its own challenges. The system is able to localize, on average, within 0.98, 2.63, and 3.46m, respectively, even with multiple kidnapped-robot instances.

Scenes are composed of numerous objects, textures and colors which are arranged in a variety of spatial layouts. This presents the question of how visual complexity is represented by a cognitive system. In this paper, we aim to study the representation of visual complexity for real-world scene images. Is visual complexity a perceptual property simple enough so that it can be compressed along a unique perceptual dimension? Or is visual complexity better represented by a multi-dimensional space? Thirty-four participants performed a hierarchical grouping task in which they divided scenes into successive groups of decreasing complexity, describing the criteria they used at each stage. Half of the participants were told that complexity was related to the structure of the image whereas the instructions in the other half were unspecified. Results are consistent with a multi-dimensional representation of visual complexity (quantity of objects, clutter, openness, symmetry, organization, variety of colors) with task constraints modulating the shape of the complexity space (e.g. the weight of a specific dimension).

We present context-based scene recognition for mobile robotics applications. Our classifier is able to differentiate outdoor scenes without temporal filtering relatively well from a variety of locations at a college campus using a set of features that together capture the “gist ” of the scene. We compare the classification accuracy of a set of scenes from 1551 frames filmed outdoors along a path and dividing them to four and twelve different legs while obtaining a classification rate of 67.96 percent and 48.61 percent, respectively. We also tested the scalability of the features by comparing the classification results from the previous scenes with four legs with a longer path with eleven legs while obtaining a classification rate of 55.08 percent. In the end, some ideas are put forth to improve the theoretical strength of the gist features. 1.

We propose an approach that allows a rigorous understanding of the visual categorization and recognition process without asking direct questions about unobservable memory representations. Our approach builds on the selective use of information and a new method (Gosselin & Schyns, 2000, Bubbles) to depict and measure what this information is. We examine three face recognition tasks (identity, gender, expressive or not) and establish the information responsible for recognition performance. We compare the human use of information to ideal observers confronted to similar tasks. We finally derive a gradient of probability for the allocation of attention to the different regions of the face.

Everyday, people flexibly perform different categorizations of common faces, objects and scenes. Intuition and scattered evidence suggest that these categorizations require the use of different visual information from the input. However, there is no unifying method, based on the categorization performance of subjects, that can isolate the information used. To this end, we developed Bubbles, a general technique that can assign the credit of human categorization performance to specific visual information. To illustrate the technique, we applied Bubbles on three categorization tasks (gender, expressive or not and identity) on the same set of faces, with human and ideal observers to compare the features they used. 2001 Elsevier Science Ltd. All rights reserved.

VERSION: 2010-2011: Document creation, done by O. Le Meur. 2011-2012: Correction, adding a slide on face/horizon line, done by O. Le Meur.