We focus on gesture recognition based on 3D information in the form of a point cloud of the observed scene. A descriptor of the scene is built on the basis of a Viewpoint Feature Histogram (VFH). To increase the distinctiveness of the descriptor the scene is divided into smaller 3D cells and VFH is calculated for each of them. A verification of the method on publicly available Polish and American sign language datasets containing dynamic gestures as well as hand postures acquired by a time-of-flight (ToF) camera or Kinect is presented. Results of cross-validation test are given. Hand postures are recognized using a nearest neighbour classifier with city-block distance. For dynamic gestures two types of classifiers are applied: (i) the nearest neighbour technique with dynamic time warping and (ii) hidden Markov models. The results confirm the usefulness of our approach.

The paper considers Polish sign language (PSL) words recognition with sensor Kinect. The nearest neighbour classifier with dynamic time warping technique was examined. The classifier was using two sets of features, the first produced by Kinect (in a form of 3D positions of most important joints of observed person body - a skeletal image or a skeleton) and the second describing hands, which were tracked as skin colour regions in the images acquired by Kinect. Obtained feature vectors representing PSL words are clustered in order to discover natural data divisions among them. This step is helpful in revealing possible problems which could occur during recognition and provides additional information about processed data. Results of ten-fold cross-validation tests for the classifier with two feature sets are given. The classifier with data provided by Kinect yielded promising recognition accuracy (89.33%) but in order to obtain better results (98.33%) adding new features responsible for hand shape description should be considered.

In this paper we present an approach to recognition of signed expressions based on visual sequences obtained with Kinect sensor. Two variants of time series representing the expressions are considered: the first based on skeletal images of the body, and the second describing shape and position of hands extracted as skin coloured regions. Time series characterising isolated Polish sign language words are examined using three clustering algorithms and popular clustering quality indices which reveal natural gesture data division and indicate gesture samples difficult in further recognition. Ten-fold cross-validation recognition tests for the k-nearest neighbour classifier with dynamic time warping technique are shown. Recognition rate obtained with the skeletal image based features were improved from 89% to 95% by changing gesture representation from time series to a vector containing pairwise distances between gesture samples. The approach with skin colour based features involving utilisation of depth information of each pixel obtained by Kinect yielded 98% recognition rate.

The paper presents a method for recognizing sequences of static letters of the Polish finger alphabet using the point cloud descriptors: viewpoint feature histogram, eigenvalues-based descriptors, ensemble of shape functions, and global radius-based surface descriptor. Each sequence is understood as quick highly coarticulated motions, and the classification is performed by networks of hidden Markov models trained by transitions between postures corresponding to particular letters. Three kinds of the left-to-right Markov models of the transitions, two networks of the transition models—independent and dependent on a dictionary—as well as various combinations of point cloud descriptors are examined on a publicly available dataset of 4200 executions (registered as depth map sequences) prepared by the authors. The hand shape representation proposed in our method can also be applied for recognition of hand postures in single frames. We confirmed this using a known, challenging American finger alphabet dataset with about 60,000 depth images.

Purpose: This paper aims to present a vision-based method for determination of the position of a fixed-wing aircraft that is approaching a runway.Design methodology/approach: The method determines the location of an aircraft based on positions of precision approach path indicator lights and approach light system with sequenced flashing lights in the image captured by an on-board camera.Findings: As the relation of the lighting systems to the touchdown area on the considered runway is known in advance, the detected lights, seen as glowing lines or highlighted areas, in the image can be mapped onto the real-world coordinates and then used to estimate the position of the aircraft. Furthermore, the colours of lights are detected and can be used as auxiliary information.Practical implications: The presented method can be considered as a potential source of flight data for autonomous approach and for augmentation of manual approach.Originality/value: In this paper, a feasibility study of this concept is presented and primarily validated.

A new scheduling problem, the continuous flow flexible job shop (CF-FJS) is proposed. The formulation combines the well-known flexible job shop (FJS) problem and a dedicated continuous material flow model (MFM). In the MFM, operations are represented by material flow functions derived by integration of arbitrarily defined speed patterns. Two main concepts of the MFM formalism, i.e. variable speed of processing and continuous material flow, lead to position-dependent processing times and overlapping in operations which extend standard FJS formulation. Properties of the CF-FJS are investigated. A tabu search sched uling algorithm utilising these properties is proposed. Effective neighbourhood functions are defined based on elimination approaches. Two auxiliary procedures: search intensification level switching and fast feasibility detection are added to improve algorithm efficiency. The algorithm is verified using dedicated benchmark instances which comprise non-trivial representations of the CF-FJS specific features, i.e. machine efficiency patterns and minimum inter-operation buffers. The research is motivated by task scheduling in a fastener factory, but the presented results can be useful in many domains, such as production of granular goods, steel details, glass and fluids. The solution can be used in real-world applications. The published results can be helpful in testing new CF-FJS scheduling algorithms.