CORF3D contour maps with application to Holstein cattle recognition from RGB and thermal images

Bhole, Amey; Udmale, Sandeep S.; Falzon, Owen; Azzopardi, George

doi:10.1016/j.eswa.2021.116354

Cited by 20 publications

(9 citation statements)

References 38 publications

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“…In [12], it was also demonstrated that a ConvNet classification model trained with CORF contour maps is more robust to high-frequency noise than one that is trained with RGB images. [6] demonstrated that ConvNets fed with CORF3D feature maps outperform those that use the original RGB channels. Further analysis showed that a fusion of both RGB and CORF3D features can achieve the most superior performance on a Holstein cow recognition problem in a farm with 383 cows.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Recently, the CORF3D feature set was introduced as part of the automatic recognition of Holstein cattle from their coat pattern based on RGB and infrared images [6]. The CORF3D feature set is a stack of three contour maps generated by the inhibition-augmented CORF operator using different strengths of the inhibition term for each layer.…”

Section: Background and Related Workmentioning

confidence: 99%

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Biometric Recognition of African Clawed Frogs

Prins,

Tomanin,

Kamenz

et al. 2023

Lecture Notes in Computer Science

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The African clawed frog (Xenopus laevis) is a commonly used model organism for cell biological, developmental, and biomedical research. For health monitoring and experimental quality control purposes, it is desirable to identify individual frogs regularly throughout their life. Current methods for identification are often invasive and associated with significant investment costs. Identification based on images of the biometric pattern on a frog's back has been implemented in some laboratories, but so far has been performed manually and therefore is time-consuming and limited to small group sizes. This work proposes a novel pipeline for data acquisition, pre-processing, and training of a classification model based on pattern recognition. The pipeline is structured around laboratory frog colonies and smartphone usage. In order to achieve a lightweight system in our evaluation we consider a MobileNet ConvNet pre-trained on ImageNet. Two feature sets are evaluated on a new data set of 1,647 image samples collected from 160 frogs: RGB images, and 3-channel contour maps (i.e. CORF3D). The results indicate that the CORF3D feature set is favoured over RGB. CORF3D achieved the best performance of 99.94% average accuracy, while RGB had the best performance of 98.79%. Analysis of misclassifications shows that bad predictions are often caused by bad lens focus, light reflections, and positional inconsistency in pattern extraction, which can be addressed during data acquisition. The proposed methodology is, therefore, an effective solution for the recognition of Xenopus laevis.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Biometric Recognition of African Clawed Frogs

Prins,

Tomanin,

Kamenz

et al. 2023

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…The use of a thermal sensor might even improve the tracking capabilities since it will make it easier to detect animals against the background and distinguish between overlapping individuals [156]. Computer vision relies on visual information only and thus is entirely non-invasive and non-intrusive [157]. Outdoors, cameras could be mounted on drones for locating and counting animals in the field [111].…”

Section: Computer Vision Technologymentioning

confidence: 99%

Mapping Welfare: Location Determining Techniques and Their Potential for Managing Cattle Welfare—A Review

Hofstra

Roelofs

Rutter

et al. 2022

Dairy

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Several studies have suggested that precision livestock farming (PLF) is a useful tool for animal welfare management and assessment. Location, posture and movement of an individual are key elements in identifying the animal and recording its behaviour. Currently, multiple technologies are available for automated monitoring of the location of individual animals, ranging from Global Navigation Satellite Systems (GNSS) to ultra-wideband (UWB), RFID, wireless sensor networks (WSN) and even computer vision. These techniques and developments all yield potential to manage and assess animal welfare, but also have their constraints, such as range and accuracy. Combining sensors such as accelerometers with any location determining technique into a sensor fusion system can give more detailed information on the individual cow, achieving an even more reliable and accurate indication of animal welfare. We conclude that location systems are a promising approach to determining animal welfare, especially when applied in conjunction with additional sensors, but additional research focused on the use of technology in animal welfare monitoring is needed.

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“…In recent years, the use of intelligent sensing devices for data collection has emerged as a promising solution to mitigate the problems associated with manual data collection. For instance, deploying devices like infrared sensors [18][19][20], 3D point cloud cameras [21,22], and RGB cameras [23,24] in farming or animal research settings facilitates the capture of livestock images and 3D coordinate points on their surfaces. Subsequently, employing 3D reconstruction techniques generates complete 3D models of livestock.…”

Section: Introductionmentioning

confidence: 99%

Computer Vision-Based Measurement Techniques for Livestock Body Dimension and Weight: A Review

Ma,

Qi,

Sun

et al. 2024

Agriculture

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Acquiring phenotypic data from livestock constitutes a crucial yet cumbersome phase in the breeding process. Traditionally, obtaining livestock phenotypic data primarily involves manual, on-body measurement methods. This approach not only requires extensive labor but also induces stress on animals, which leads to potential economic losses. Presently, the integration of next-generation Artificial Intelligence (AI), visual processing, intelligent sensing, multimodal fusion processing, and robotic technology is increasingly prevalent in livestock farming. The advantages of these technologies lie in their rapidity and efficiency, coupled with their capability to acquire livestock data in a non-contact manner. Based on this, we provide a comprehensive summary and analysis of the primary advanced technologies employed in the non-contact acquisition of livestock phenotypic data. This review focuses on visual and AI-related techniques, including 3D reconstruction technology, body dimension acquisition techniques, and live animal weight estimation. We introduce the development of livestock 3D reconstruction technology and compare the methods of obtaining 3D point cloud data of livestock through RGB cameras, laser scanning, and 3D cameras. Subsequently, we explore body size calculation methods and compare the advantages and disadvantages of RGB image calculation methods and 3D point cloud body size calculation methods. Furthermore, we also compare and analyze weight estimation methods of linear regression and neural networks. Finally, we discuss the challenges and future trends of non-contact livestock phenotypic data acquisition. Through emerging technologies like next-generation AI and computer vision, the acquisition, analysis, and management of livestock phenotypic data are poised for rapid advancement.

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CORF3D contour maps with application to Holstein cattle recognition from RGB and thermal images

Cited by 20 publications

References 38 publications

Biometric Recognition of African Clawed Frogs

Biometric Recognition of African Clawed Frogs

Mapping Welfare: Location Determining Techniques and Their Potential for Managing Cattle Welfare—A Review

Computer Vision-Based Measurement Techniques for Livestock Body Dimension and Weight: A Review

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