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Melvyn L. Smith

Researcher at University of the West of England

Publications -  141
Citations -  2377

Melvyn L. Smith is an academic researcher from University of the West of England. The author has contributed to research in topics: Photometric stereo & Facial recognition system. The author has an hindex of 25, co-authored 138 publications receiving 1904 citations. Previous affiliations of Melvyn L. Smith include University of the West.

Papers
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Journal ArticleDOI

Towards on-farm pig face recognition using convolutional neural networks

TL;DR: The results show that accurate individual pig recognition is possible with accuracy rates of 96.7% on 1553 images, and Class Activated Mapping using Grad-CAM is used to show the regions that the network uses to discriminate between pigs.
Patent

Overhead dimensioning system and method

TL;DR: In this paper, a system for dimensioning large or palletized freight, of one or more pieces determines the dimensions of a rectangular prism having the smallest volume, but which would contain the freight.
Journal ArticleDOI

Automated monitoring of dairy cow body condition, mobility and weight using a single 3D video capture device.

TL;DR: 3D imaging for concurrently monitoring cow body condition, lameness and weight and original moving spine segmentation/modelling approach in 3D postulated for body condition assessment are presented.
Journal ArticleDOI

Object surface recovery using a multi-light photometric stereo technique for non-Lambertian surfaces subject to shadows and specularities

TL;DR: It is shown that six lights is the minimum needed in order to apply photometric stereo to the entire visible surface of any convex object and offers advantages in the recovery of dichromatic surfaces possessing rough texture or deeply relieved topographic features, with applications in reverse engineering and industrial surface inspection.
Journal ArticleDOI

3D face reconstructions from photometric stereo using near infrared and visible light

TL;DR: A modified four-source PS algorithm is presented which enhances the surface normal estimates by assigning a likelihood measure for each pixel being in a shadowed region, determined by the discrepancies between measured pixel brightnesses and expected values.