Movement and Color Detection of a Dynamic Object: An application to a Mobile Robot

Abstract

The paper describes the integration of several image processing algorithms necessary to recognize a particular color and the movement of an object. The main objective is to detect the object by its color and track it by a mobile robot. Mean filter is applied to soften and sharpen the input image. Then, RGB filter is applied to calculate the center of mass and area of the object and to locate its position in a real environment to develop the robot motion. These algorithms are applied to a mobile robot, in a tested scenario, tracking an object.

Automatic Parameter Configuration: A Case Study on a Rehabilitation Oriented Human Limb Tracking Algorithm

“…Also hyperheuristics [5,24] and reactive algorithms [2] have been proposed as an alternative to algorithm configuration. In the field of computer vision, little attention has been given to automate parameter tuning; the few efforts reported are more focused on operator or function selection than parameter selection [4,15,23]. In our concern, there is no approach to the subject of parameter configuration applied to any tracking algorithm.…”

Query Optimization on Random Databases

“…These distributions are usually specified by means of a complex stochastic model. Various system characteristics of interest to the analyst can be viewed as answers to queries over the uncertain data values [9], [10]. Because the data is uncertain, there is a probability distribution over the possible results of running a given query, and the analysis of the underlying stochastic model is equivalent to studying the features (mean, variance, and so forth) of the query-result distribution.…”