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One in every six persons in the UK suffers a hearing loss, either as a condition they have been born with, or they developed during their life. Nine hundred thousand people in the UK are severely or profoundly deaf. Based on a study by Action on Hearing Loss UK in 2013 only 17 percent of this population, can use the British Sign Language (BSL). That leaves a massive proportion of people with a hearing impediment who do not use sign language struggling in social interaction and suffering from emotional distress. It also leaves even a larger proportion of Hearing people who cannot communicate with those of the deaf community. This paper presents a Serious Game (SG) that aims to close the communication gap between able hearing people and people with hearing impairment by providing a tool that facilitates BSL learning targeting the adult population. The paper presents the theoretical framework supporting adult learning based on which a SG game using Virtual Reality (VR) technology has been developed. The paper explains the experimental framework of the study. It presents the creation of the research instruments to facilitate the study comprising of a SG that integrates video and conventional video-based educational material. It reports and analyses the study results that demonstrate the advantage of the SG in effectively supporting users learning a set of BSL signs. It also presents qualitative outcomes that inform the further development of the game to serve learning needs. The paper closes with conclusions, directions for further development of this educational resource, and future studies.

One in every six persons in the UK suffers a hearing loss, either as a condition they have been born with or a disorder they acquired during their life. 900,000 people in the UK are severely or profoundly deaf and based on a study by Action On Hearing Loss UK in 2013 only 17 percent of this population, can use the British Sign Language (BSL). That leaves a massive proportion of people with a hearing impediment who do not use sign language struggling in social interaction and suffering from emotional distress, and an even larger proportion of Hearing people who cannot communicate with those of the deaf community. This paper presents a theoretical framework for the design of interactive games to support learning BSL supporting the entire learning cycle, instruction, practice and assessment. It then describes the proposed design of a game based on this framework aiming to close the communication gap between able hearing people and people with a hearing impediment, by providing a tool that facilitates BSL learning targeting adult population. The paper concludes with the planning of a large scale study and directions for further development of this educational resource.

This paper presents a semi-parametric Algorithm for parsing football video structures. The approach works on a two interleaved based process that closely collaborate towards a common goal. The core part of the proposed method focus perform a fast automatic football video annotation by looking at the enhance entropy variance within a series of shot frames. The entropy is extracted on the Hue parameter from the HSV color system, not as a global feature but in spatial domain to identify regions within a shot that will characterize a certain activity within the shot period. The second part of the algorithm works towards the identification of dominant color regions that could represent players and playfield for further activity recognition. Experimental Results shows that the proposed football video segmentation algorithm performs with high accuracy.

We propose the design of a real-time system to recognize and interprethand gestures. The acquisition devices are low cost 3D sensors. 3D hand pose will be segmented, characterized and track using growing neural gas (GNG) structure. The capacity of the system to obtain information with a high degree of freedom allows the encoding of many gestures and a very accurate motion capture. The use of hand pose models combined with motion information provide with GNG permits to deal with the problem of the hand motion representation. A natural interface applied to a virtual mirrorwriting system and to a system to estimate hand pose will be designed to demonstrate the validity of the system.

Automatic video segmentation plays a vital role in sports videos annotation. This paper presents a fully automatic and computationally efficient algorithm for analysis of sports videos. Various methods of automatic shot boundary detection have been proposed to perform automatic video segmentation. These investigations mainly concentrate on detecting fades and dissolves for fast processing of the entire video scene without providing any additional feedback on object relativity within the shots. The goal of the proposed method is to identify regions that perform certain activities in a scene. The model uses some low-level feature video processing algorithms to extract the shot boundaries from a video scene and to identify dominant colours within these boundaries. An object classification method is used for clustering the seed distributions of the dominant colours to homogeneous regions. Using a simple tracking method a classification of these regions to active or static is performed. The efficiency of the proposed framework is demonstrated over a standard video benchmark with numerous types of sport events and the experimental results show that our algorithm can be used with high accuracy for automatic annotation of active regions for sport videos.

Growing models have been widely used for clustering or topology learning. Traditionally these models work on stationary environments, grow incrementally and adapt their nodes to a given distribution based on global parameters. In this paper, we present an enhanced unsupervised self-organising network for the modelling of visual objects. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product.