ArticleFrontiers in bioinformatics2023
A computer vision framework for quantification of feather growth patterns.
Article in Frontiers in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed, 1 citations in OpenAlex.
- Progress and trends of non-contact detection methods for poultry growth information: A review.Poultry science · 2025Review
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4 authors at 1 institution in 1 country.
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Abstract
Feather growth patterns are important anatomical phenotypes for investigating the underlying genomic regulation of skin and epidermal appendage development. However, characterization of feather growth patterns previously relied on manual examination and visual inspection, which are both subjective and practically prohibitive for large sample sizes. Here, we report a new high-throughput technique to quantify the location and spatial extent of reversed feathers that comprise head crests in domestic pigeons. Phenotypic variation in pigeon feather growth patterns were rendered by computed tomography (CT) scans as point clouds. We then developed machine learning based, feature extraction techniques to isolate the feathers, and map the growth patterns on the skin in a quantitative, automated, and non-invasive way. Results from five test animals were in excellent agreement with "ground truth" results obtained
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