Deep Convolutional Neural Network for Object Classification: Under Constrained and Unconstrained Environments

Deep Convolutional Neural Network for Object Classification: Under Constrained and Unconstrained Environments

Amira Ahmad Al-Sharkawy, Gehan A. Bahgat, Elsayed E. Hemayed, Samia Abdel-Razik Mashali
ISBN13: 9781799866909|ISBN10: 1799866904|EISBN13: 9781799866923
DOI: 10.4018/978-1-7998-6690-9.ch016
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MLA

Al-Sharkawy, Amira Ahmad, et al. "Deep Convolutional Neural Network for Object Classification: Under Constrained and Unconstrained Environments." Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments, edited by Alex Noel Joseph Raj, et al., IGI Global, 2021, pp. 317-343. https://doi.org/10.4018/978-1-7998-6690-9.ch016

APA

Al-Sharkawy, A. A., Bahgat, G. A., Hemayed, E. E., & Mashali, S. A. (2021). Deep Convolutional Neural Network for Object Classification: Under Constrained and Unconstrained Environments. In A. Raj, V. Mahesh, & R. Nersisson (Eds.), Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments (pp. 317-343). IGI Global. https://doi.org/10.4018/978-1-7998-6690-9.ch016

Chicago

Al-Sharkawy, Amira Ahmad, et al. "Deep Convolutional Neural Network for Object Classification: Under Constrained and Unconstrained Environments." In Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments, edited by Alex Noel Joseph Raj, Vijayalakshmi G. V. Mahesh, and Ruban Nersisson, 317-343. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-6690-9.ch016

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Abstract

Object classification problem is essential in many applications nowadays. Human can easily classify objects in unconstrained environments easily. Classical classification techniques were far away from human performance. Thus, researchers try to mimic the human visual system till they reached the deep neural networks. This chapter gives a review and analysis in the field of the deep convolutional neural network usage in object classification under constrained and unconstrained environment. The chapter gives a brief review on the classical techniques of object classification and the development of bio-inspired computational models from neuroscience till the creation of deep neural networks. A review is given on the constrained environment issues: the hardware computing resources and memory, the object appearance and background, and the training and processing time. Datasets that are used to test the performance are analyzed according to the images environmental conditions, besides the dataset biasing is discussed.

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