Expert systems: Special issue on “Machine Learning Methods Neural Networks applied to Vision and Robotics (MLMVR)”

Abstract

The International Joint Conference on Neural Networks (IJCNN) was held in Anchorage (Alaska) in May 2017. This top conference in the field of neural networks included many tracks and special sessions. In particular, a special session on Machine Learning Methods Neural Networks applied to Vision and Robotics (MLMVR) was organized by the authors receiving a large volume of excellent contributions. Only a small set of outstanding papers presented at this special session were invited to submit extended versions of their work. After a rigorous revision process, four of these papers were accepted. Over the last decades, there has been an increasing interest in using machine learning methods combined with computer vision techniques to create autonomous systems that solve vision problems in different fields. This special issue was designed to serve researchers and developers to publish original, innovative, and state-of-the art machine learning algorithms and architectures for applications in the areas of computer vision, image processing, biometrics, virtual and augmented reality, robot vision, intelligent interfaces, and biomimetic object-vision recognition. This issue is composed of four papers selected from the original 15 submissions received in the special session MLMVR. The papers are divided in two groups: a set of two papers that applied deep learning methods to solve computer vision challenging problems. A second set of two papers that deal with image enhancement by using a cellular automata and competitive neural networks. In the first group, Yao et al. revisit the trajectory clustering problem by developing a method that can detect space- and time-invariant trajectory clusters. The method uses recurrent neural networks (RNNs) to convert trajectories into a fixed-length representation that well encodes the object's moving behaviours. Oprea et al. present a Schaeffer language recognition system with the purpose of teaching children with autism disorder the correct way to communicate using gestures in combination with speech reproduction. The purpose is to accelerate the learning process and increase children interest using a technological approach. A Long Short-Term Memory (LSTM) model has been implemented for this purpose reporting a 93.13% classification success rate over a subset of 25 Schaeffer gestures. In the second group, Priego et al. describe a novel spatio-temporal cellular automata-based filtering algorithm (st-CAF) intended for performing image sequence denoising processes. The approach presents several advantages over more traditional single-frame denoising techniques or even over their adaptation to sequences. Especially the fact that the cellular automaton used is able to contemplate information concerning the type of noise through the use of specific sequences to tune the algorithm, as well as temporal information by means of a spatio-temporal neighbourhood when processing each pixel of the sequence. These two elements lead to significant improvements in the results with respect to simple spatial or temporal sets of neighbours. Finally, Thurnhofer-Hemsi et al. present a novel approach for constructing a panoramic background model based on competitive learning neural networks and a subsequent piecewise linear interpolation by Delaunay triangulation. The approach can handle arbitrary camera directions and zooms for a Pan-Tilt-Zoom (PTZ) camera-based surveillance system. In summary, the selected papers present recent developments of machine learning and deep learning methods applied to the very active research fields like mobile robotics and computer vision.