Using pose estimation to identify regions and points on natural history specimens.

A key challenge in mobilising growing numbers of digitised biological specimens for scientific research is finding high-throughput methods to extract phenotypic measurements on these datasets. In this paper, we test a pose estimation approach based on Deep Learning capable of accurately placing point labels to identify key locations on specimen images. We then apply the approach to two distinct challenges that each requires identification of key features in a 2D image: (i) identifying body region-specific plumage colouration on avian specimens and (ii) measuring morphometric shape variation in Littorina snail shells.

CompoundRay, an open-source tool for high-speed and high-fidelity rendering of compound eyes.

Revealing the functioning of compound eyes is of interest to biologists and engineers alike who wish to understand how visually complex behaviours (e.g. detection, tracking, and navigation) arise in nature, and to abstract concepts to develop novel artificial sensory systems.

Deep learning image segmentation reveals patterns of UV reflectance evolution in passerine birds.

Ultraviolet colouration is thought to be an important form of signalling in many bird species, yet broad insights regarding the prevalence of ultraviolet plumage colouration and the factors promoting its evolution are currently lacking. In this paper, we develop a image segmentation pipeline based on deep learning that considerably outperforms classical (i.e.

A corpus of audio-visual Lombard speech with frontal and profile views.

This paper presents a bi-view (front and side) audiovisual Lombard speech corpus, which is freely available for download. It contains 5400 utterances (2700 Lombard and 2700 plain reference utterances), produced by 54 talkers, with each utterance in the dataset following the same sentence format as the audiovisual "Grid" corpus [Cooke, Barker, Cunningham, and Shao (2006). J.

A PCA-Based method for determining craniofacial relationship and sexual dimorphism of facial shapes.

Previous studies have used principal component analysis (PCA) to investigate the craniofacial relationship, as well as sex determination using facial factors. However, few studies have investigated the extent to which the choice of principal components (PCs) affects the analysis of craniofacial relationship and sexual dimorphism. In this paper, we propose a PCA-based method for visual and quantitative analysis, using 140 samples of 3D heads (70 male and 70 female), produced from computed tomography (CT) images.