Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Classification of Systems-I01:26

Classification of Systems-I

194
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
194
Methods of Classification and Identification01:28

Methods of Classification and Identification

21
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
21
Classification of Systems-II01:31

Classification of Systems-II

154
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
154
Aggregates Classification01:29

Aggregates Classification

332
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
332
Force Classification01:22

Force Classification

1.2K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.2K
Classification of Bones01:18

Classification of Bones

5.6K
The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
5.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Linking prey biomass and Neanderthal population in a challenging upland landscape in the Late Pleistocene: the upper valley of the Lozoya River (Spain).

Landscape ecology·2026
Same author

Earliest evidence of elephant butchery at Olduvai Gorge (Tanzania) reveals the evolutionary impact of early human megafaunal exploitation.

eLife·2026
Same author

Bone weathering in a Mediterranean climate region: An experimental case study from Doñana National Park (Spain).

PloS one·2025
Same author

Meta-learning provides a robust framework to discern taxonomic carnivore agency from the analysis of tooth marks on bone: reassessing the role of felids as predators of <i>Homo habilis</i>.

Royal Society open science·2025
Same author

Early humans and the balance of power: Homo habilis as prey.

Annals of the New York Academy of Sciences·2025
Same author

Evidence for deliberate burial of the dead by <i>Homo naledi</i>.

eLife·2025
Same journal

Multiomics Profiling During Autoimmune Demyelination Highlights a Complex Regulatory Role for Ataxin-1 in B Cells.

Annals of the New York Academy of Sciences·2026
Same journal

Global Trends in Light Pollution and Their Relationship With Socioeconomic Factors.

Annals of the New York Academy of Sciences·2026
Same journal

Wired for Corruption: Inter-Brain Synchrony Encodes Bribery-Related Value Information and Predicts Bribery Agreement.

Annals of the New York Academy of Sciences·2026
Same journal

LM-YOLO: A Lightweight Multi-Scale Enhanced Model for Forest Smoke Detection Using Unmanned Aerial Vehicles.

Annals of the New York Academy of Sciences·2026
Same journal

Polyrhythm Perception and Production: A Scoping Review.

Annals of the New York Academy of Sciences·2026
Same journal

DARTS-CNN-BiLSTM: Intelligent Fault Diagnosis for Computer Numerical Control Machine Tool Feed System.

Annals of the New York Academy of Sciences·2026
See all related articles

Related Experiment Video

Updated: Jul 14, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K

African bovid tribe classification using transfer learning and computer vision.

Manuel Domínguez-Rodrigo1,2,3, Juliet Brophy4,5, Gregory J Mathews6

  • 1Institute of Evolution in Africa (IDEA), University of Alcalá de Henares, Madrid, Spain.

Annals of the New York Academy of Sciences
|October 7, 2023
PubMed
Summary
This summary is machine-generated.

Computer vision methods accurately identify African bovid species from teeth images, achieving 92% classification accuracy. This offers an objective tool for paleoecological interpretation and reconstructing ancient environments.

Keywords:
African bovidsartificial intelligencecomputer visionecologypalaeoecology

More Related Videos

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.5K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K

Related Experiment Videos

Last Updated: Jul 14, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.5K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K

Area of Science:

  • Paleobiology
  • Paleoecology
  • Computer Science

Background:

  • Objective identification methods are rare in paleobiology, with subjective interpretation dominating fossil analysis.
  • Accurate identification of African bovids is vital for reconstructing paleo-landscapes, ungulate paleoecology, and hominin adaptation.
  • Previous analytical methods using Fourier analysis showed promise but were limited.

Purpose of the Study:

  • To implement computer vision methods for objective classification of African bovid teeth.
  • To assess the accuracy of artificial intelligence in identifying bovid taxa from fossilized teeth.
  • To provide a reliable tool for paleoecological interpretations.

Main Methods:

  • Utilized computer vision techniques, including transfer learning and ensemble analysis.
  • Applied methods to bidimensional images of African bovid teeth.
  • Tested the model on a large dataset of bovid tribe images.

Main Results:

  • Achieved 92% accuracy in correctly classifying African bovid tribes from tooth images.
  • Demonstrated the effectiveness of computer vision in objective fossil identification.
  • Showcased the potential of AI to outperform human experts in taphonomic analysis.

Conclusions:

  • Computer vision provides an objective and accurate tool for identifying African bovids.
  • This technology enhances the reliability of paleoecological interpretations.
  • The study paves the way for more confident reconstructions of ancient ecosystems and hominin environments.