Charles V. Stewart

From collection trays to AI-ready data: An operational framework for automated batch entomological specimen processing

Alyson East, S M Rayeed, Elizabeth G. Campolongo, Nathan Cain et autres

Natural history collections house over three billion specimens critical to biodiversity research, yet fewer than 2% of North American arthropod specimens have been imaged, creating a bottleneck for trait-based analyses at broad temporal and spatial scales. Batch photography of multiple specimens improves …

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On Combining Animal Re-Identification Models to Address Small Datasets

Aleksandr Algasov, Ekaterina Nepovinnykh, Fedor Zolotarev, Tuomas Eerola et autres

Abstract Recent advancements in the automatic re-identification of animal individuals from images have opened up new possibilities for studying wildlife through camera traps and citizen science projects. Existing methods leverage distinct and permanent visual body markings, such as fur patterns or scars, …

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BeetleFlow: An Integrative Deep Learning Pipeline for Beetle Image Processing

Fangxun Liu, S M Rayeed, Samuel Stevens, Alyson East et autres

In entomology and ecology research, biologists often need to collect a large number of insects, among which beetles are the most common species. A common practice for biologists to organize beetles is to place them on trays and take a picture of …

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Fine-Grained Beetle Taxonomy with Vision Models: A Benchmark on Long-Tailed and Domain-Adaptive Classification

S M Rayeed, Alyson East, Samuel Stevens, Sydne Record et autres

Ground beetles are a highly sensitive and speciose biological indicator, critical for biodiversity monitoring, yet their taxonomic classification remains underutilized due to the manual effort required for species differentiation based on subtle morphological variations. In this paper, we present a benchmark for …

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kabr-tools: Automated Framework for Multi-Species Behavioral Monitoring

Jenna Kline, Maksim Kholiavchenko, Samuel Stevens, Nina van Tiel et autres

A comprehensive understanding of animal behavior ecology depends on scalable approaches to quantify and interpret complex, multidimensional behavioral patterns. Traditional field observations are often limited in scope, time-consuming, and labor-intensive, hindering the assessment of behavioral responses across landscapes. To address this, we …

Optimizing image capture for computer vision‐powered taxonomic identification and trait recognition of biodiversity specimens

Alyson East, Elizabeth Campolongo, Luke Meyers, S M Rayeed et autres

Abstract Biological collections house millions of specimens with digital images increasingly available through open‐access platforms. However, most imaging protocols were developed for human interpretation without considering automated analysis requirements. As computer vision applications revolutionize taxonomic identification and trait extraction, a critical gap …

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Studying collective animal behaviour with drones and computer vision

Jenna Kline, Saadia Afridi, Edouard G. A. Rolland, Guy Maalouf et autres

Abstract Drones are increasingly popular for collecting behaviour data of group‐living animals, offering inexpensive and minimally disruptive observation methods. Imagery collected by drones can be rapidly analysed using computer vision techniques to extract information, including behaviour classification, habitat analysis and identification of …

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Correction: BaboonLand Dataset: Tracking Primates in the Wild and Automating Behaviour Recognition from Drone Videos

Isla Duporge, Maksym Kholiavchenko, Roi Harel, Scott Wolf et autres

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Fine-Grained Taxonomy with Vision Models: A Benchmark on Long-Tailed and Domain-Adaptive Classification

S M Rayeed, Alyson East, Samuel Stevens, Sydne Record et autres

Ground beetles are a highly sensitive and speciose biological indicator, critical for biodiversity monitoring, yet their taxonomic classification remains underutilized due to the manual effort required for species differentiation based on subtle morphological variations. In this paper, we present a benchmark for …

Unsupervised Pelage Pattern Unwrapping for Animal Re-identification

Aleksandr Algasov, Ekaterina Nepovinnykh, Fedor Zolotarev, Tuomas Eerola et autres

Existing individual re-identification methods often struggle with the deformable nature of animal fur or skin patterns which undergo geometric distortions due to body movement and posture changes. In this paper, we propose a geometry-aware texture mapping approach that unwarps pelage patterns, the …

BaboonLand Dataset: Tracking Primates in the Wild and Automating Behaviour Recognition from Drone Videos

Isla Duporge, Maksym Kholiavchenko, Roi Harel, Scott Wolf et autres

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Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis

Arpita Roy Chowdhury, Dipanjyoti Paul, Zheda Mai, Jianyang Gu et autres

We present a simple approach to make pre-trained Vision Transformers (ViTs) interpretable for fine-grained analysis, aiming to identify and localize the traits that distinguish visually similar categories, such as bird species. Pretrained ViTs, such as DINO, have demonstrated remarkable capabilities in extracting …

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