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Books on the topic 'Deep learning,face detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Ratha, Nalini K., Vishal M. Patel, and Rama Chellappa, eds. Deep Learning-Based Face Analytics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74697-1.

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2

Kim, Kwangjo, Muhamad Erza Aminanto, and Harry Chandra Tanuwidjaja. Network Intrusion Detection using Deep Learning. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1444-5.

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3

Jiang, Xiaoyue, Abdenour Hadid, Yanwei Pang, Eric Granger, and Xiaoyi Feng, eds. Deep Learning in Object Detection and Recognition. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-10-5152-4.

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4

Alla, Sridhar, and Suman Kalyan Adari. Beginning Anomaly Detection Using Python-Based Deep Learning. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5177-5.

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5

Escriva, David Millan, Roy Shilkrot, Prateek Joshi, and Vinicius G. Mendonca. Building Computer Vision Projects with OpenCV 4 and C++: Implement complex computer vision algorithms and explore deep learning and face detection. Packt Publishing, 2019.

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6

Jiang, Xiaoyue, Eric Granger, Abdenour Hadid, Yanwei Pang, and Xiaoyi Feng. Deep Learning in Object Detection and Recognition. Springer, 2019.

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7

Jiang, Xiaoyue, Abdenour Hadid, and Yanwei Pang. Deep Learning in Object Detection and Recognition. Springer, 2019.

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8

Jiang, Xiaoyue, Eric Granger, Abdenour Hadid, Yanwei Pang, and Xiaoyi Feng. Deep Learning in Object Detection and Recognition. Springer, 2021.

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9

Kim, Kwangjo, Muhamad Erza Aminanto, and Harry Chandra Tanuwidjaja. Network Intrusion Detection using Deep Learning: A Feature Learning Approach. Springer, 2018.

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10

Kim, Kwangjo, Muhamad Erza Aminanto, and Harry Chandra Tanuwidjaja. Network Intrusion Detection using Deep Learning: A Feature Learning Approach. Springer, 2018.

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11

Michelucci, Umberto. Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection. Apress, 2019.

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12

Alla, Sridhar, and Suman Kalyan Adari. Beginning Anomaly Detection Using Python-Based Deep Learning: With Keras and PyTorch. Apress, 2019.

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13

Manaswi, Navin Kumar. Deep Learning with Applications Using Python: Chatbots and Face, Object, and Speech Recognition With TensorFlow and Keras. Apress, 2018.

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14

Bindemann, Markus, ed. Forensic Face Matching. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198837749.001.0001.

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Person identification at passport control, at borders, in police investigations, and in criminal trials relies critically on the identity verification of people via image-to-image or person-to-image comparison. While this task is known as ‘facial image comparison’ in forensic settings, it has been studied as ‘unfamiliar face matching’ in cognitive science. This book brings together expertise from practitioners, and academics in psychology and law, to draw together what is currently known about these tasks. It explains the problem of identity impostors and how within-person variability and betw

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15

Leibo, Joel Z., and Tomaso Poggio. Perception. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0025.

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This chapter provides an overview of biological perceptual systems and their underlying computational principles focusing on the sensory sheets of the retina and cochlea and exploring how complex feature detection emerges by combining simple feature detectors in a hierarchical fashion. We also explore how the microcircuits of the neocortex implement such schemes pointing out similarities to progress in the field of machine vision driven deep learning algorithms. We see signs that engineered systems are catching up with the brain. For example, vision-based pedestrian detection systems are now a

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16

Charon, Rita. Close Reading: The Signature Method of Narrative Medicine. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199360192.003.0008.

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Teaching healthcare professionals how to be close readers assures that they can listen with attention and empathy to what their patients tell them. The close reader pays attention to such narrative features as temporality, narrative situation, voice, metaphor, and mood. This chapter describes the origins of close reading in the 1920s and its subsequent contentious development within literary studies. It describes the salience of the skills learned from close reading for the practice of narrative medicine. The chapter examines such consequences of close reading as relationship-building among le

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