45 Result(s)
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Chapter
Reinforcement Learning
In this chapter, we will introduce the fundamental concepts of reinforcement learning such as , , deep Q- , etc. We will introduce why reinforcement is thought as a method of deep learning. Then, mathemati...
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Chapter
Boltzmann Machines
In this chapter, we will introduce , restricted Boltzmann , and deep Boltzmann . We will generalize our deep neural networks from networks to general graphs, we will use probabilistic graphical to model t...
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Chapter and Conference Paper
Traffic-Sign Recognition Using Deep Learning
Traffic-sign recognition (TSR) has been an essential part of driver-assistance systems, which is able to assist drivers in avoiding a vast number of potential hazards and improve the experience of driving. How...
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Chapter
Deep Learning Platforms
There are many deep learning platforms available such as , , MXNet, , and Theano. Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework, which originally was developed a...
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Chapter
Autoencoder and GAN
In this chapter, we will emphasize on computational iterations in autoencoder and GAN (generative adversarial learning). Additionally, we will deeply learn deep learning and interpret how information theory ha...
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Chapter and Conference Paper
Fruit Detection from Digital Images Using CenterNet
In this paper, CenterNet is chosen as the model to settle fruit detection problem from digital images. Three CenterNet models with various backbones were implemented, namely, ResNet-18, DLA-34, and Hourglass. ...
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Chapter
Introduction
This chapter covers the fundamentals of deep learning, we present the relevant knowledge in chronological order so as to fully introduce the history and development of deep learning; meanwhile, we review how t...
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Chapter
CNN and RNN
In this chapter, we will introduce the typical deep neural networks from the viewpoint of family, especially region-based , , and . Meanwhile, from the viewpoint of time series analysis, we depict the f...
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Article
Gait recognition using multichannel convolution neural networks
Human gait recognition has a wide range of applications in multiple fields, such as video surveillance, digital security, and forensics. In this paper, we investigate the challenging problem of cross-view gait...
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Article
Cross-view gait recognition through ensemble learning
Gait has been well known as an unobtrusive promising biometric to identify a person from a distance. However, the effectiveness of silhouette-based approaches in gait recognition is diluted due to variations o...
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Article
Overview of currency recognition using deep learning
The human visual system could be used for recognizing and authenticating currency notes. However, the observation powers of our eyes are limited, and it is often difficult for us to recognize genuine currency ...
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Chapter and Conference Paper
A Sequential CNN Approach for Foreign Object Detection in Hyperspectral Images
This paper reports about potentials of hyperspectral imaging for object detection, especially on an application of foreign object detection (FOD) in meat products
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Chapter
Visual Event Computing I
In surveillance, we need present a story of a moving object. This story is called event which is the best way to describe the motion of this object. In this chapter, we will critically compare and evaluate the...
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Chapter
Visual Event Computing II
In this chapter, we will continue event computing within the life cycle, we will emphasize it from the aspect of artificial intelligence in observation, learning, presentation and reasoning. We will critically...
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Chapter
Surveillance Data Capturing and Compression
In this chapter, we will introduce surveillance data capturing using finite state machine (FSM) and critically evaluate the major technology of surveillance data compression. has been used in the case of tra...
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Chapter
Surveillance Data Analytics
In surveillance, object refers to treat an object as a whole, while object analytics means taking each object into multiple parts and analyzing the components from various aspects. In this chapter, we mainly...
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Chapter
Surveillance Alarm Making
Surveillance systems are monitoring human behaviors (walking, running, jum**, etc.) as well as natural disasters of this world (wide fire, flooding, tsunami, earthquake on earth, etc.) for the sake of safety...
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Chapter
Introduction
This chapter covers the fundamentals in intelligent surveillance, i.e., what intelligent surveillance is, what elements should be included in a surveillance system, and how such a surveillance system will be i...
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Chapter
Surveillance Data Secure Transmissions
In this chapter, we will introduce fundamental knowledge of network communications including the infrastructure of computer networks as well as network security, monitoring, and forensics; we also analyze scra...
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Chapter
Biometrics for Surveillance
Our human always bring biometric information such as face, fingerprints, palms, and iris; no matter where we are, but is discriminative from one to another which has essential and unique characteristics. In ...
