@conference { , title = {Overhead spine arch analysis of dairy cows from three-dimensional video}, abstract = {We present a spine arch analysis method in dairy cows using overhead 3D video data. This method is aimed for early stage lameness detection. That is important in order to allow early treatment; and thus, reduce the animal suffering and minimize the high forecasted financial losses, caused by lameness. Our physical data collection setup is non-intrusive, covert and designed to allow full automation; therefore, it could be implemented on a large scale or daily basis with high accuracy. We track the animal’s spine using shape index and curvedness measure from the 3D surface as she walks freely under the 3D camera. Our spinal analysis focuses on the thoracic vertebrae region, where we found most of the arching caused by lameness. A cubic polynomial is fitted to analyze the arch and estimate the locomotion soundness. We have found more accurate results by eliminating the regular neck/head movements’ effect from the arch. Using 22-cow data set, we are able to achieve an early stage lameness detection accuracy of 95.4\%.}, conference = {Eighth International Conference on Graphic and Image Processing (ICGIP 2016)}, publicationstatus = {Unpublished}, url = {https://uwe-repository.worktribe.com/output/898527}, keyword = {Centre for Machine Vision, 3D vision, curvature, dairy cow, early lameness, lameness detection, spine arch, vertebrate}, year = {2017}, author = {Abdul Jabbar, Khalid and Hansen, Mark F and Smith, Melvyn and Smith, Lyndon} }