Search results1 – 3 of 3
Adults with autism spectrum disorder (ASD) present with a range of psychiatric disorders. However, making an accurate diagnosis is challenging. It is important to follow a…
Adults with autism spectrum disorder (ASD) present with a range of psychiatric disorders. However, making an accurate diagnosis is challenging. It is important to follow a robust and informed process in the assessment of psychopathology that is centred on the individual and their neurodevelopmental difficulties. The purpose of this paper is to provide clinicians with an evidence-based approach to the assessment process for adults with ASD presenting with a possible co-occurrent psychiatric disorder.
A review of the recent literature was undertaken focusing on key papers that describe the assessment of mental health problems in adults with ASD.
The presentation of psychiatric symptoms is influenced by the underlying developmental disorder and it is often quite different from the one of the general population. Thus, it is essential to undertake a comprehensive psychopathological assessment including a diagnostic assessment of ASD. There is a very small evidence base on the use of diagnostic tools in the assessment of adults with ASD.
This is a practice review paper applying recent evidence from the literature.
This paper aims to introduce an unsupervised modular approach for eye centre localisation in images and videos following a coarse-to-fine, global-to-regional scheme. The…
This paper aims to introduce an unsupervised modular approach for eye centre localisation in images and videos following a coarse-to-fine, global-to-regional scheme. The design of the algorithm aims at excellent accuracy, robustness and real-time performance for use in real-world applications.
A modular approach has been designed that makes use of isophote and gradient features to estimate eye centre locations. This approach embraces two main modalities that progressively reduce global facial features to local levels for more precise inspections. A novel selective oriented gradient (SOG) filter has been specifically designed to remove strong gradients from eyebrows, eye corners and self-shadows, which sabotage most eye centre localisation methods. The proposed algorithm, tested on the BioID database, has shown superior accuracy.
The eye centre localisation algorithm has been compared with 11 other methods on the BioID database and six other methods on the GI4E database. The proposed algorithm has outperformed all the other algorithms in comparison in terms of localisation accuracy while exhibiting excellent real-time performance. This method is also inherently robust against head poses, partial eye occlusions and shadows.
The eye centre localisation method uses two mutually complementary modalities as a novel, fast, accurate and robust approach. In addition, other than assisting eye centre localisation, the SOG filter is able to resolve general tasks regarding the detection of curved shapes. From an applied point of view, the proposed method has great potentials in benefiting a wide range of real-world human-computer interaction (HCI) applications.