Object detection using feature subset selection

TL;DR: The previous work of object detection is utilized to get the position of an obstacle and refocus windows on the selected target to develop a quick obstacle detection and measurement algorithm for the image-based autonomous vehicle (AV) or computer assisted driving system.

TL;DR: This book will be of particular interest to academics, researchers, and graduate students at universities and industrial practitioners seeking to apply mobile and pervasive computing systems to improve construction industry productivity.

TL;DR: Wang et al. as discussed by the authors proposed a feature selection method based on improved genetic algorithm (GA), which sort the features by feature evaluation criteria to exclude irrelevant features and then use the simulated annealing algorithm (SA) to solve the problem that GA jumps out of the local optimal solution.

TL;DR: The Object Detection and Behaviour Classification (ODBC) system created from this PhD research, is ground-breaking and concentrates solely on the domain of people and behaviour detection in low resolution beach scene imagery.

TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.

TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?

TL;DR: A machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates and the introduction of a new image representation called the "integral image" which allows the features used by the detector to be computed very quickly.

TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.