Intelligent Data Analysis

Abstract

In this text, we demonstrate one of the most crucial and challenging areas in computer vision and intelligent data analysis (IDA), based on manipulating the convergence zone. This subject is divided into two parts: (1) a deep learning paradigm for object segmentation in computer vision and (2) a visualization paradigm for using efficiently incremental interpretation in manipulating the data sets for supervised and unsupervised learning and online or offline training in reinforcement learning. This topic recently has had a large impact in robotics and autonomous systems, food detection, recommendation systems, and medical applications. One of the prominent examples is an object segmentation that has to segment each object separately to its charging station assuming its map, or robotics surgery vision, is guided in open known and unknown environments by planting the most beneficial processes (in many aspects) based on perception and navigation layer data.