Pattern Recognition
5th Asian Conference, ACPR 2019, Auckland, New Zealand, November 26–29, 2019, Revised Selected Papers, Part II
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**wei Qi
Machine Learning
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Book and Conference Proceedings
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Book and Conference Proceedings
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Chapter and Conference Paper
Vehicle-Related Scene Understanding Using Deep Learning
Automated driving is an inevitable trend in future transportation, it is also one of the eminent achievements in the matter of artificial intelligence. Deep learning produces a significant contribution to the ...
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Chapter and Conference Paper
A Web-Based Augmented Reality Approach to Instantly View and Display 4D Medical Images
In recent years, development in non-invasive and painless medical imaging such as Computed Tomography (CT) or MRI (magnetic resonance imaging), has improved the process of diseases diagnosis and clarification,...
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Chapter and Conference Paper
Red-Green-Blue Augmented Reality Tags for Retail Stores
In this paper, we introduce a new Augmented Reality (AR) Tag to enhance detection rates, accuracy and also user experiences in marker-based AR technologies. The tag is a colour printed card, divided into three...
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Chapter and Conference Paper
Early Diagnosis of Alzheimer’s Disease Based on Selective Kernel Network with Spatial Attention
Alzheimer’s disease (AD) is a neurodegenerative disorder which leads to memory and behaviour impairment. Early discovery and diagnosis can delay the progress of this disease. In this paper, we propose a new de...
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Book
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Chapter
CapsNet and Manifold Learning
is one of the relatively new methods in deep learning, which has taken topological of a scene into consideration. The output will be a vector to reflect this relationship. Meanwhile, manifold , which is em...
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Chapter
Transfer Learning and Ensemble Learning
In this chapter, we start from transfer learning and introduce the relationship between different learners; we use ensemble learning to combine them together and hope to get a strong learner from a weak learne...
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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
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
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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Chapter and Conference Paper
A Survey of Deep Learning Based Fully Automatic Bone Age Assessment Algorithms
Bone age assessment (BAA) is a technique for assessing the maturity of individual skeletal development, and it is the most accurate and objective method for assessing the deviation of individual development in...
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Chapter
Planning and Management of Charging Facilities for Electric Vehicle Sharing
Electric vehicle (EV) sharing has experienced rapid development and has served as a flexible and environmental friendly means for urban transportation. However, charging an EV sharing fleet is still a challeng...
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Chapter and Conference Paper
Small Visual Object Detection in Smart Waste Classification Using Transformers with Deep Learning
Smart object waste classification is relatively essential for protecting the environment and saving resources. This is considered a vital pathway towards sustainability. In waste classification, we see that it...
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Chapter and Conference Paper
A Real-Time Kiwifruit Detection Based on Improved YOLOv7
In New Zealand (NZ), agriculture is an essential industry, Kiwifruits contribute significantly to the country’s overall exports. Traditionally Kiwifruits require manually picking up and heavily relies on human...
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Chapter and Conference Paper
Automatic Bleeding Risk Rating System of Gastric Varices
An automated bleeding risk rating system of gastric varices (GV) aims to predict the bleeding risk and severity of GV, in order to assist endoscopists in diagnosis and decrease the mortality rate of patients w...
