Visual animal biometrics: survey
Visual animal biometrics: survey
复制标题
视觉动物生物识别：调查
作者：
Santosh Kumar;S. Singh
视觉动物生物识别是计算机视觉、模式识别和认知科学领域的一门新兴研究学科。这是一个有前景的研究领域，鼓励量化算法和方法的新发展，用于表示、检测物种、个体的可见特征、表型外观以及形态和动物生物特征的识别。此外，它还有助于动物轨迹研究和物种行为分析。目前，由于广泛的生态数据收集和处理的数量和质量的提高，视觉动物生物识别系统的实际应用正在日益普及。然而，要推进视觉动物生物识别技术，需要整合所涉及的科学学科之间的方法。这种有价值的努力是值得的，因为这种方法的巨大前景取决于表型组学的形式抽象，可以在生命的不同组织层面之间建立完善的界面。本研究根据形态图像模式和生物特征对不同物种和个体动物的视觉动物生物识别系统和识别方法进行了全面的调查。这篇综合综述论文鼓励多学科研究人员、科学家、生物学家和不同的研究团体设计更好的平台来开发高效的算法和学习模型，以解决海量数据处理、分类和识别不同物种的相关问题。
Visual animal biometrics is an emerging research discipline in computer vision, pattern recognition and cognitive science. It is a promising research field that encourages new development of quantified algorithms and methodologies for representing, detection of visible features, phenotypic appearances of species, individuals and recognition of morphological and animal biometric characteristics. Furthermore, it also assists the study of animal trajectory and behaviours analysis of species. Currently, real-world applications of visual animal biometric systems are gaining more proliferation due to a variety of applications and use, enhancement of quantity and quality of the collection of extensive ecological data and processing. However, to advance visual animal biometrics will require integration of methodologies among the scientific disciplines involved. Such valuable efforts will be worthwhile due to the enormous perspective of this approach rests with the formal abstraction of phenomics, to build well-developed interfaces between different organisational levels of life. This study provides a comprehensive survey of visual animal biometric systems and recognition approaches for various species and individual animal based on their morphological image pattern and biometric characteristics. This comprehensive review paper encourages the multidisciplinary researchers, scientists, biologists and different research communities to design the better platforms for the development of efficient algorithms and learning models to solve the massive data processing, classification and identification of different species related problems.
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DOI：
10.1016/j.patrec.2015.08.015
发表时间：
2016-10
期刊：
Pattern Recognit. Lett.
影响因子：
--
作者：
John Atanbori;Wenting Duan;J. Murray;Kofi Appiah;P. Dickinson
通讯作者：
John Atanbori;Wenting Duan;J. Murray;Kofi Appiah;P. Dickinson
DOI：
10.1049/iet-bmt.2012.0043
发表时间：
2014-06
期刊：
IET Biom.
影响因子：
--
作者：
A. Baig;A. Bouridane;F. Kurugollu;B. Albesher
通讯作者：
A. Baig;A. Bouridane;F. Kurugollu;B. Albesher
DOI：
--
发表时间：
2016
期刊：
--
影响因子：
--
作者：
Karol J. Piczak
通讯作者：
Karol J. Piczak
RFID技术连续监测小动物生理信号
影响因子：
4.6
作者：
T. Volk;S. Gorbey;M. Bhattacharyya;W. Gruenwald;B. Lemmer;L. M. Reindl;T. Stieglitz;D. Jansen
通讯作者：
D. Jansen