Abstract
Object recognition seems like a sci-fi prediction to a tech future, widely used in computer vision tasks. Computer vision plays a significant role in developing various object recognition applications like biometric regulation, image retrieval, security, machine inspection, medical imaging, and digital watermarking. It also plays a substantial role in the invention of autonomous automobiles, to have safe driving by detecting objects and road signals. Computer vision is an essential aspect of machine learning and deep learning, which enables new medical diagnostic methods to analyze the X-rays and internal body scanning. In this paper, we discussed a few deep learning architectures like ConvNet, Region-based CNN, Fast RCNN, Faster RCNN, YOLO family for object recognition. The perception of recognition of objects is changing from model to model. Computer-Aided Medical Diagnosis (CAMD) has brought a drastic change in the real-time gland (tissue) recognition, and assists the radiologists to interpret a medical pathology image or video in seconds. After analyzing various object recognition algorithms, we have provided a detailed report on the different object recognition algorithms used to recognize objects in images, videos, and live feeds from the webcams.
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Mahanty, M., Bhattacharyya, D., Midhunchakkaravarthy, D. (2022). A Review on Deep Learning-Based Object Recognition Algorithms. In: Bhattacharyya, D., Saha, S.K., Fournier-Viger, P. (eds) Machine Intelligence and Soft Computing. Advances in Intelligent Systems and Computing, vol 1419. Springer, Singapore. https://doi.org/10.1007/978-981-16-8364-0_7
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