Fast 2D/3D object representation with growing neural gas

This work presents the design of a real-time system to model visual objects with the use of self-organising networks. The architecture of the system addresses multiple computer vision tasks such as image segmentation, optimal parameter estimation and object representation. 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 faces, and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. The proposed method is easily extensible to 3D objects, as it offers similar features for efficient mesh reconstruction.

Students who self-harm: coping style, rumination and alexithymia

Counsellors working with students or other young adults may encounter individuals who have self-harmed, either with suicidal or non-suicidal intent. Recent US studies reported rates of self-injury of up to 37% of the student population, but studies in the UK have focussed primarily on younger adolescents. This study examined reported self-harm incidents (scratching, cutting, poisoning, overdose etc) from a sample of 617 university students. A total of 27% reported at least one incident of self-harm, with almost 10% having harmed themselves while at university. Gender differences were not significant but psychology students reported significantly more self-harm than other students. Participants reporting self-harm scored significantly higher on maladaptive coping styles, rumination, and alexithymia (specifically difficulty in identifying emotions) and these differences were most marked for students reporting repetitive and recent self-harm. Rumination and Alexithymia factor 1 (difficulty identifying feelings) emerged as the most robust factors predicting self-harm status. Comments from students who self-harmed at university highlighted the importance of accessible services and academic staff support. The implications of these findings for counselling interventions are discussed, including challenging negative rumination tendencies and developing mindfulness skills.

A framework for extracting and representing project knowledge contexts using topic models and dynamic knowledge maps

There is still a lack of effective paradigms and tools for analysing and discovering the contents and relationships of project knowledge contexts in the field of project management. In this paper, a new framework for extracting and representing project knowledge contexts using topic models and dynamic knowledge maps under big data environments is proposed and developed. The conceptual paradigm, theoretical underpinning, extended topic model, and illustration examples of the ontology model for project knowledge maps are presented, with further research work envisaged.

3D Reconstruction of Medical Images from Slices Automatically Landmarked with Growing Neural Models

In this study, we utilise a novel approach to segment out the ventricular system in a series of high resolution T1-weighted MR images. We present a brain ventricles fast reconstruction method. The method is based on the processing of brain sections and establishing a fixed number of landmarks onto those sections to reconstruct the ventricles 3D surface. Automated landmark extraction is accomplished through the use of the self-organising network, the growing neural gas (GNG), which is able to topographically map the low dimensionality of the network to the high dimensionality of the contour manifold without requiring a priori knowledge of the input space structure. Moreover, our GNG landmark method is tolerant to noise and eliminates outliers. Our method accelerates the classical surface reconstruction and filtering processes. The proposed method offers higher accuracy compared to methods with similar efficiency as Voxel Grid.