Methods for Reliable Robot Vision with a Dioptric System

Image processin

Visual Analysis of Robot and Animal Colonies

Graphical & digital media application

Chapter Visual Analysis of Robot and Animal Colonies

Graphical & digital media application

Chapter Methods for Reliable Robot Vision with a Dioptric System

Image processin

Development of an ecological decision support system

In this paper a knowledge-based decision support system is described that determines the abiotic (chemical and physical) characteristics of a site on the basis of in-homogeneous samples of plant species. Techniques from the area of non-monotonic reasoning are applied to model multi-interpretable input information

An Integrated Virtual Environment for Visual-based Reaching

An interactive application to test different visuo-motor interactions is presented. The long term goal is to obtain a perceptual agent capable of achieving a full 3D awareness for interaction control/planning in the peripersonal space by using the interplay between vision and motion. We have developed an integrated virtual reality environment that implements robotic reaching tasks based on stereo visual cues. Stereo processing adopts a generalized phase-based algorithm to handle the 2D disparities present in active stereo vision system in order to achieve high accuracy

Crowd emotion detection using dynamic probabilistic models

Detecting emotions of a crowd to control the situation is an area of emerging interest. The purpose of this paper is to present a novel idea to detect the emotions of the crowd. Emotions are defined as evolving quantities arising from the reaction to contextual situations in a set of dynamic pattern of events. These events depend on internal and external interaction states in an already mapped space. The emotions of multiple people constituting a crowd in any surveillance environment are estimated by their social and collective behaviors using sensor signals e.g., a camera, which captures and tracks their motion. The feature space is constructed based on local features to model the contextual situations and the different interactions corresponding to different emergent behaviors are modeled using bio-inspired dynamic model. The changes in emotions correspond to behavioral changes which are produced to regulate behaviors under different encountered situations. Proposed algorithm involves the probabilistic signal processing modelling techniques for analysis of different types of collective behaviors based on interactions among people and classification models to estimate emotions as positive or negative. The evaluations are performed on simulated data show the proposed algorithm effectively recognizes the emotions of the crowd under specific situations. © 2014 Springer International Publishing Switzerland