The Cow Gait Recognition Using CHLAC

Mimura, Shingo; Itoh, Kazuyoshi; Kobayashi, Takumi; Takigawa, Tomohiro; Tajima, Akira; Sawamura, A.; Otsu, Nobuyuki

doi:10.1109/bliss.2008.31

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…S t+1 is actual grayscale data at time t + 1, I t+1 is actual RGB data at time t + 1, St+1 is grayscale image aligned by STN, Ĩt+1 is RGB image aligned by STN, and L 1 is L1 norm metric function. T. Kim et al [35] adds a correction term L FG derived from F obtained from the image with the background removed to the loss function in Equation (5). L FG is given by the L1 norm of the Hadamard product of S t+1 and I t+1 and Ft+1 (= θ t+1 (F t+1 )) as…”

Section: Stn (Spatial Transformer Network)mentioning

confidence: 99%

“…Identification of individual animals poses a different problem from the usual image classification because the physical characteristics of the same individual can change significantly over time, such as horn growth, injuries, and body defects due to fighting. Animal identification techniques encompass various methods for distinguishing individuals, such as those based on the nose [1][2][3][4], which exhibits minimal change in physical characteristics, differentiation through gait analysis [5][6][7], and identification using tags [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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Computer Vision Techniques for Growth Prediction: A Prisma-Based Systematic Literature Review

2023

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Growth prediction technology is not only a practical application but also a crucial approach that strengthens the safety of image processing techniques. By supplementing the growth images obtained from the original images, especially in insufficient data sets, we can increase the robustness of machine learning. Therefore, predicting the growth of living organisms is an important technology that increases the safety of existing applications that target living organisms and can extend to areas not yet realized. This paper is a systematic literature review (SLR) investigating biological growth prediction based on the PRISMA 2020 guidelines. We systematically survey existing studies from 2017 to 2022 to provide other researchers with current trends. We searched four digital libraries—IEEE Xplore, ACM Digital Library, Science Direct, and Web of Science—and finally analyzed 47 articles. We summarize the methods used, year, features, accuracy, and dataset of each paper. In particular, we explained LSTM, GAN, and STN, the most frequently used methods among the 20 papers related to machine learning (40% of all papers).

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Section: Stn (Spatial Transformer Network)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computer Vision Techniques for Growth Prediction: A Prisma-Based Systematic Literature Review

2023

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“…To identify species type, model-based approaches are more applicable as they consider body shape, while more precise identification of animals at the individual level requires model-free approaches. In wildlife monitoring, subjects such as classifying species [32] and identification of individual cows [33] through gait recognition of visual images have also been studied. A common characteristic in all these methods is that they consist of two main stages, i.e.…”

Section: Technologies For Eulerian Approachmentioning

confidence: 99%

Sensing Solutions for Collecting Spatio-Temporal Data for Wildlife Monitoring Applications: A Review

Baratchi

Meratnia

Havinga

et al. 2013

Sensors

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Movement ecology is a field which places movement as a basis for understanding animal behavior. To realize this concept, ecologists rely on data collection technologies providing spatio-temporal data in order to analyze movement. Recently, wireless sensor networks have offered new opportunities for data collection from remote places through multi-hop communication and collaborative capability of the nodes. Several technologies can be used in such networks for sensing purposes and for collecting spatio-temporal data from animals. In this paper, we investigate and review technological solutions which can be used for collecting data for wildlife monitoring. Our aim is to provide an overview of different sensing technologies used for wildlife monitoring and to review their capabilities in terms of data they provide for modeling movement behavior of animals.

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Bleeding and Tumor Detection for Capsule Endoscopy Images Using Improved Geometric Feature

Sakanashi

Nosato

et al. 2016

J. Med. Biol. Eng.

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The Cow Gait Recognition Using CHLAC

Cited by 5 publications

References 2 publications

Computer Vision Techniques for Growth Prediction: A Prisma-Based Systematic Literature Review

Computer Vision Techniques for Growth Prediction: A Prisma-Based Systematic Literature Review

Sensing Solutions for Collecting Spatio-Temporal Data for Wildlife Monitoring Applications: A Review

Bleeding and Tumor Detection for Capsule Endoscopy Images Using Improved Geometric Feature

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