Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world

Energy efficient enabling technologies for semantic video processing on mobile devices

Semantic object-based processing will play an increasingly important role in future multimedia systems due to the ubiquity of digital multimedia capture/playback technologies and increasing storage capacity. Although the object based paradigm has many undeniable benefits, numerous technical challenges remain before the applications becomes pervasive, particularly on computational constrained mobile devices. A fundamental issue is the ill-posed problem of semantic object segmentation. Furthermore, on battery powered mobile computing devices, the additional algorithmic complexity of semantic object based processing compared to conventional video processing is highly undesirable both from a real-time operation and battery life perspective. This thesis attempts to tackle these issues by firstly constraining the solution space and focusing on the human face as a primary semantic concept of use to users of mobile devices. A novel face detection algorithm is proposed, which from the outset was designed to be amenable to be offloaded from the host microprocessor to dedicated hardware, thereby providing real-time performance and reducing power consumption. The algorithm uses an Artificial Neural Network (ANN), whose topology and weights are evolved via a genetic algorithm (GA). The computational burden of the ANN evaluation is offloaded to a dedicated hardware accelerator, which is capable of processing any evolved network topology. Efficient arithmetic circuitry, which leverages modified Booth recoding, column compressors and carry save adders, is adopted throughout the design. To tackle the increased computational costs associated with object tracking or object based shape encoding, a novel energy efficient binary motion estimation architecture is proposed. Energy is reduced in the proposed motion estimation architecture by minimising the redundant operations inherent in the binary data. Both architectures are shown to compare favourable with the relevant prior art

Mobile personal authentication using fingerprint.

Cheng Po Sum.Thesis submitted in: July 2003.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 64-67).Abstracts in English and Chinese.List of Figures --- p.iList of Tables --- p.iiiAcknowledgments --- p.iv摘要 --- p.vThesis Abstract --- p.viChapter 1. --- Mobile Commerce --- p.1Chapter 1.1 --- Introduction to Mobile Commerce --- p.1Chapter 1.2 --- Mobile commence payment systems --- p.2Chapter 1.3 --- Security in mobile commerce --- p.5Chapter 2. --- Mobile authentication using Fingerprint --- p.10Chapter 2.1 --- Authentication basics --- p.10Chapter 2.2 --- Fingerprint basics --- p.12Chapter 2.3 --- Fingerprint authentication using mobile device --- p.15Chapter 3. --- Design of Mobile Fingerprint Authentication Device --- p.19Chapter 3.1 --- Objectives --- p.19Chapter 3.2 --- Hardware and software design --- p.21Chapter 3.2.1 --- Choice of hardware platform --- p.21Chapter 3.3 --- Experiments --- p.25Chapter 3.3.1 --- Design methodology I - DSP --- p.25Chapter 3.3.1.1 --- Hardware platform --- p.25Chapter 3.3.1.2 --- Software platform --- p.26Chapter 3.3.1.3 --- Implementation --- p.26Chapter 3.3.1.4 --- Experiment and result --- p.27Chapter 3.3.2 --- Design methodology II ´ؤ SoC --- p.28Chapter 3.3.2.1 --- Hardware components --- p.28Chapter 3.3.2.2 --- Software components --- p.29Chapter 3.3.2.3 --- Implementation Department of Computer Science and Engineering --- p.29Chapter 3.3.2.4 --- Experiment and result --- p.30Chapter 3.4 --- Observation --- p.30Chapter 4. --- Implementation of the Device --- p.31Chapter 4.1 --- Choice of platforms --- p.31Chapter 4.2 --- Implementation Details --- p.31Chapter 4.2.1 --- Hardware implementation --- p.31Chapter 4.2.1.1 --- Atmel FingerChip --- p.32Chapter 4.2.1.2 --- Gemplus smart card and reader --- p.33Chapter 4.2.2 --- Software implementation --- p.33Chapter 4.2.2.1 --- Operating System --- p.33Chapter 4.2.2.2 --- File System --- p.33Chapter 4.2.2.3 --- Device Driver --- p.35Chapter 4.2.2.4 --- Smart card --- p.38Chapter 4.2.2.5 --- Fingerprint software --- p.41Chapter 4.2.2.6 --- Graphical user interface --- p.41Chapter 4.3 --- Results and observations --- p.44Chapter 5. --- An Application Example 一 A Penalty Ticket Payment System (PTPS) --- p.47Chapter 5.1 --- Requirement --- p.47Chapter 5.2 --- Design Principles --- p.48Chapter 5.3 --- Implementation --- p.52Chapter 5.4 --- Results and Observation --- p.57Chapter 6. --- Conclusions and future work --- p.62Chapter 7. --- References --- p.6

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers

Image Registration Workshop Proceedings

Automatic image registration has often been considered as a preliminary step for higher-level processing, such as object recognition or data fusion. But with the unprecedented amounts of data which are being and will continue to be generated by newly developed sensors, the very topic of automatic image registration has become and important research topic. This workshop presents a collection of very high quality work which has been grouped in four main areas: (1) theoretical aspects of image registration; (2) applications to satellite imagery; (3) applications to medical imagery; and (4) image registration for computer vision research

Automatic Car Registration Plate Recognition Using the Hough Transform

The development of automatic car registration plate recognition systems will provide greater efficiency for vehicle monitoring in automatic access control, and will avoid the need to equip vehicles with special RF tags for identification since all vehicles possess a unique registration plate. Thus this study is an attempt to introduce an automatic car registration plate recognition system based on identifying the plate characters by using the Hough transform. However, the proposed recognition system can be used in conjunction with a tag system for higher security access control. The automatic registration plate recognition could also have considerable potential in a wide range of applications especially in the identification of vehicle-based offences and with law enforcement. Recent advances in computer vision technology and the falling price of the related devices has contributed in making it practical to build an automatic, registration plate recognition systems. There have been a number of Optical Character Recognition (OCR) techniques, which have been used in the recognition of car registration plate characters. These systems include the character details matching process (Lotufo, et al. 1990), BAM (Bi-directional Associative Memories) neural network (Fahmy 1994) neural network (Tindall, 1995) and cross correlation pattern matching character matching techniques (Cornelli, et al. 1995). All of these systems recognized the characters by matching the full image of every character with a character\u27s template database which requires considerable processing time and large memory for the database. The purpose of this study is to explore the potential for using Hough transform (Hough 1962) in vehicle registration plate recognition. The OCR technique used in this project is unlike the other systems where the character recognition was based on matching the character\u27s full image; However the OCR technique in this system used Hough transform to identify the characters, where the recognition of a character is based on matching its identification array to the database. To validate the research, a car registration plate recognition system was developed to locate the registration plate from the full image of a vehicle and then extrar.t the plate characters by using image processing techniques. A Hough transform algorithm was applied to every character within the registration plate image to produce an identification array for these characters, and the plate characters were recognized by matching their identification array to the database. The system has been applied to a number of video recorded car images to recognize their registration plates. The rate of correctly recognized characters was 82.7% of the extracted characters, but improvement can be granted by using a faster digital camera and taking some precautions in the registration plate frames. However, the research indicated that the optical character recognition technique used in the study is an efficient and simple algorithm to identify characters, without requiring a relatively large processing memory