Analysis of individual mouse activity in group housed animals of different inbred strains using a novel automated home cage analysis system.

Central nervous system disorders such as autism as well as the range of neurodegenerative diseases such as Huntington's disease are commonly investigated using genetically altered mouse models. The current system for characterizing these mice usually involves removing the animals from their home-cage environment and placing them into novel environments where they undergo a battery of tests measuring a range of behavioral and physical phenotypes. These tests are often only conducted for short periods of times in social isolation. However, human manifestations of such disorders are often characterized by multiple phenotypes, presented over long periods of time and leading to significant social impacts. Here, we have developed a system which will allow the automated monitoring of individual mice housed socially in the cage they are reared and housed in, within established social groups and over long periods of time. We demonstrate that the system accurately reports individual locomotor behavior within the group and that the measurements taken can provide unique insights into the effects of genetic background on individual and group behavior not previously recognized

Colour Constrained 4D Flow

The addition of colour information to the computation of range/scene flow is proposed to improve its accuracy and robustness to ambiguities. This is applied in the form of additional optical flow constraints from aligned colour image data. Combining constraints gives improved velocity displacement fields for both synthetic and real datasets over using depth alone, or in using depth plus intensity. This ultimately has benefits for the processing of dense, temporal depth data obtainable from novel video-rate 3D capture systems

Automating progress measurement of construction projects

The accurate and up to date measurement of work in progress on construction sites is vital for project management functions like schedule and cost control. Currently, it takes place using traditional building surveying techniques and visual inspections. The usually monthly measurements are error prone and not frequent enough for reliable and effective project controls. This paper explores the potential of using computer vision technology in assisting the project management task. In particular, it examines the development of an integrated building information system that aims to determine the progress of construction from digital images captured on site in order to semi-automate the work in progress measurement and calculation of interim payments as well as function as an early warning system of potential delays. The study focuses on the quantity rather than quality aspect of work and is limited to the superstructure of buildings