Abstract
Computer vision (CV) is a branch of artificial intelligence that educates and assists computers in recognizing and comprehending the content of digital images. It is primarily concerned with replicating attributes of a human vision system and empowering computer systems to process and categorize artifacts in digital images similar to humans. CV can be applied in various domains, including robotics, autonomous vehicles, remote sensing, medical diagnosis, pattern recognition, etc. Extracting image features has become a key element in CV applications. For this purpose, we are using shape feature detectors and descriptors. Motivated by the need to understand shape feature detector fundamentals and applications in CV, the present work aims to explore various feature extraction techniques and shape detection approaches required for image retrieval. In addition, real-time applications of shape feature extraction and object recognition techniques are also discussed with examples.
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Raj, E.F.I., Balaji, M. (2023). Shape Feature Extraction Techniques for Computer Vision Applications. In: Kumar, B.V., Sivakumar, P., Surendiran, B., Ding, J. (eds) Smart Computer Vision. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-20541-5_4
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