Titlebook: Reconstruction-Free Compressive Vision for Surveillance Applications; Henry Braun,Pavan Turaga,Cihan Tepedelenlioglu Book 2019 Springer Na

燒瓶

Constitution

星球的光亮度

Book 2019es of high-performance sensors including infrared cameras and magnetic resonance imaging systems. Advances in computer vision and deep learning have enabled new applications of automated systems. In this book, we introduce reconstruction-free compressive vision, where image processing and computer v

壓迫

Introduction, as a model, we discuss inference algorithms for CS data. Two broad categories of inference problems are considered: video-based target tracking, and image-based detection/classification [2]. In particular, we focus on the field of . where image processing and computer vision algorithms are develope

多產(chǎn)魚

Compressed Sensing Fundamentals,as by devoting this chapter to CS theory and literature and Chapter 3 to related work in image processing and computer vision that we utilize for reconstruction-free compressive vision. We begin by introducing the CS sensing matrix, . reconstruction, and other fundamental background information in S

增強(qiáng)

Computer Vision and Image Processing for Surveillance Applications,s in machine learning and computer vision. The line between image processing and computer vision is sometimes difficult to define, as the image processing community adopts machine learning and artificial intelligence approaches to the problems of the field. However, an overview of all of computer vi

向下

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Conclusion,he work within the context of compressive sensing and computer vision, as well as showing new research papers pushing the boundaries of what is possible in the field. Compressive vision has two main goals: first to improve the accuracy of inference on compressive measurements, and second to reduce t

Apraxia

Compressed Sensing Fundamentals,ection 2.1. Next, Section 2.3 discusses existing CS imaging hardware in order to provide real-life motivation for the work. CS reconstruction algorithms are then discussed in Section 2.4 and bounds on CS sensing performance are covered in Section 2.6. Finally, deep learning for CS reconstruction is presented in Section 2.8.

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