Neural nets for complex scenes understanding: simulation of a visual system with several cortical areas

Our study tries to combine scattered results in image processing, artificial intelligence, psychology, or neurobiology to improve our understanding of the cerebellar cortex and to realize systems that overstem the limitations of present systems . Our system emulates a little robot with a single eye . Its « brain » has several cortical areas . It is able to learn a given number of objects . We distinguish two sets of neural networks . The first one performs low level processing and extracts characteristic points . The second one processes a state space transformation of the input picture, tries to recognize the learning abjects and proposes a reconstruction to confirm the recognition .Dans cet article, nous présentons un système général d'interprétation d'images, basé sur des concepts neurobiologiques et psychologiques. L'ensemble des traitements est réalisé à l'aide de réseaux de neurones. Ce système est une sorte de robot simulé capable d'agir dans son environnement afin de reconnaître des objets déjà appris. L'un de ses principaux attraits est qu'il permet une communication simple entre les traitements de haut et de bas niveau. Enfin et surtout, il a été conçu pour montrer que l'on n'a pas besoin d'avoir des régions bien fermées ou des contours parfaits pour réaliser une bonne interprétatio

A mixed system of interpretation: neural networks/expert-system applied to aerial images

In this paper, we propose a complete system of analysis of images, which includes the whole sequence of treatments front the low level until the interpretation ŀ It uses neural networks as well as a rule-based system ŀ We show that the implementation of an expert-system gives useful information for the conception of the neural nets ŀ The mixed realisation allows us to use at best the specificities of each approach ŀ We also show how to make a neural network learn locally contradictory configurations.Dans cet article, nous proposons un système complet d'analyse d'images comprenant toute la chaîne de traitements depuis le bas-niveau jusqu'à l'interprétation. Il utilise à la fois un réseau de neurones et un système à base de règles. Nous montrons que la mise en oeuvre d'un système-expert fournit des informations précieuses pour la conception des réseaux. La réalisation mixte permet d'utiliser au mieux les spécificités de chacune des approches. Nous montrons également comment faire apprendre des configurations localement contradictoires à un réseau de neurone

Obstructions to embeddability into hyperquadrics and explicit examples

We give series of explicit examples of Levi-nondegenerate real-analytic hypersurfaces in complex spaces that are not transversally holomorphically embeddable into hyperquadrics of any dimension. For this, we construct invariants attached to a given hypersurface that serve as obstructions to embeddability. We further study the embeddability problem for real-analytic submanifolds of higher codimension and answer a question by Forstneri\v{c}.Comment: Revised version, appendix and references adde

A robot trace maker: modeling the fossil evidence of early invertebrate behavior.

The study of trace fossils, the fossilized remains of animal behavior, reveals interesting parallels with recent research in behavior-based robotics. This article reports robot simulations of the meandering foraging trails left by early invertebrates that demonstrate that such trails can be generated by mechanisms similar to those used for robot wall-following. We conclude with the suggestion that the capacity for intelligent behavior shown by many behavior-based robots is similar to that of animals of the late Precambrian and early Cambrian periods approximately 530 to 565 million years ago

Navigation visuelle dans un environnement ouvert : reconnaissance de vues panoramiques

Nous présentons un système de navigation pour robot autonome dans un environnement ouvert. Le robot rejoint un objectif en associant des mouvements aux informations visuelles provenant de l'environnement. Il utilise un apprentissage simple et en ligne. Il ne crée aucune carte complexe de son environnement. Le méchanisme s'avère efficace et robuste, de plus il semble en accord avec les observations animales. Enfin, notre implémentation dans un environnement réel supporte des perturbations importantes

Spatial Representation and Navigation in a Bio-inspired Robot

A biologically inspired computational model of rodent repre-sentation?based (locale) navigation is presented. The model combines visual input in the form of realistic two dimensional grey-scale images and odometer signals to drive the firing of simulated place and head direction cells via Hebbian synapses. The space representation is built incrementally and on-line without any prior information about the environment and consists of a large population of location-sensitive units (place cells) with overlapping receptive fields. Goal navigation is performed using reinforcement learning in continuous state and action spaces, where the state space is represented by population activity of the place cells. The model is able to reproduce a number of behavioral and neuro-physiological data on rodents. Performance of the model was tested on both simulated and real mobile Khepera robots in a set of behavioral tasks and is comparable to the performance of animals in similar tasks