Deep learning and image processing are two areas that interest many academics and industry professionals. The main objective of this book is to provide concepts about these two areas in the same platform. Professionals from academia and research labs have shared ideas, problems and solutions relating to the multifaceted aspects of these areas.
The first chapter deals with an interesting introduction to deep learning: the relation between man, mind and intelligence, which is dealt with here. This provides an excellent foundation for subsequent chapters. The second chapter demonstrates the application of deep neural networks for image classification. A wide range of images are used in this application, proving the robustness of the proposed approach. Hand gesture recognition with deep neural networks is analyzed in the third chapter. An interesting aspect of this chapter explains how recognized hand gestures are used to control the robotic arm.
Deep learning techniques for image retrieval are discussed in the fourth chapter. The significance of increasing multimedia data in real time and the necessity for efficient search processes is also stressed in this chapter. The fifth chapter concentrates on from human images using deep learning techniques. The sample disease used in this approach is a form of diabetes commonly found in humans. The sixth chapter deals with the application of tuberculosis detection in the human body through deep learning approaches. Experimental results show promising results for the proposed technique.
Object retrieval from images using deep convolutional features are discussed in the seventh chapter. Convolutional neural networks are used for the experimental analysis in this work. The eighth chapter highlights the application of hierarchical object detection with deep reinforcement learning approaches using different variations of the images. A comparative analysis of deep data and big data are discussed in the ninth chapter which adds a different dimension to the preceding content.
Vehicle type recognition using sparse filtered convolutional neural networks is discussed in the tenth chapter. Images from publicly available database are used for the experimental analysis in this work. The application of deep learning approaches for surveillance and security applications is discussed in the eleventh chapter. The final chapter talks about the possibility of enhancing the quality of images captured from a long distance using deep learning approaches. The variety of content in these chapters provides an excellent platform for researchers working in these areas.
We would like to express our gratitude to all of the authors who submitted chapters for their contributions. We also acknowledge the great efforts of the reviewers who have spent their valuable time working on the contents of this book. We would also like to thank Prof. Gerhard Joubert, Editor-in-Chief, Advances in Parallel Computing series and IOS Press for their constant guidance throughout this book project.
D. Jude Hemanth
Vania Viera Estrela