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

Algoritmo de estimação de movimento e sua arquitetura de hardware para HEVC

Doutoramento em Engenharia EletrotécnicaVideo coding has been used in applications like video surveillance, video conferencing, video streaming, video broadcasting and video storage. In a typical video coding standard, many algorithms are combined to compress a video. However, one of those algorithms, the motion estimation is the most complex task. Hence, it is necessary to implement this task in real time by using appropriate VLSI architectures. This thesis proposes a new fast motion estimation algorithm and its implementation in real time. The results show that the proposed algorithm and its motion estimation hardware architecture out performs the state of the art. The proposed architecture operates at a maximum operating frequency of 241.6 MHz and is able to process 1080p@60Hz with all possible variables block sizes specified in HEVC standard as well as with motion vector search range of up to ±64 pixels.A codificação de vídeo tem sido usada em aplicações tais como, vídeovigilância, vídeo-conferência, video streaming e armazenamento de vídeo. Numa norma de codificação de vídeo, diversos algoritmos são combinados para comprimir o vídeo. Contudo, um desses algoritmos, a estimação de movimento é a tarefa mais complexa. Por isso, é necessário implementar esta tarefa em tempo real usando arquiteturas de hardware apropriadas. Esta tese propõe um algoritmo de estimação de movimento rápido bem como a sua implementação em tempo real. Os resultados mostram que o algoritmo e a arquitetura de hardware propostos têm melhor desempenho que os existentes. A arquitetura proposta opera a uma frequência máxima de 241.6 MHz e é capaz de processar imagens de resolução 1080p@60Hz, com todos os tamanhos de blocos especificados na norma HEVC, bem como um domínio de pesquisa de vetores de movimento até ±64 pixels

Fast Acquisition and Reconstruction Techniques in MRI

The aim of this thesis was to develop fast reconstruction and acquisition techniques for MRI that can support clinical applications where time is a limiting factor. In general, fast acquisition techniques were realized by undersampling k-space, while fast reconstruction techniques were achieved by using efficient numerical algorithms. In particular, undersampled acquisitions were processed in a CS and MRF framework. Preconditioning techniques were used to accelerate CS reconstructions, and a number of challenges encountered in MRF were addressed using appropriate post-processing techniques. European Research Council (ERC) Advanced Grant (670629 NOMA MRI)LUMC / Geneeskund

Novel source coding methods for optimising real time video codecs.

The quality of the decoded video is affected by errors occurring in the various layers of the protocol stack. In this thesis, disjoint errors occurring in different layers of the protocol stack are investigated with the primary objective of demonstrating the flexibility of the source coding layer. In the first part of the thesis, the errors occurring in the editing layer, due to the coexistence of different video standards in the broadcast market, are addressed. The problems investigated are ‘Field Reversal’ and ‘Mixed Pulldown’. Field Reversal is caused when the interlaced video fields are not shown in the same order as they were captured. This results in a shaky video display, as the fields are not displayed in chronological order. Additionally, Mixed Pulldown occurs when the video frame-rate is up-sampled and down-sampled, when digitised film material is being standardised to suit standard televisions. Novel image processing algorithms are proposed to solve these problems from the source coding layer. In the second part of the thesis, the errors occurring in the transmission layer due to data corruption are addressed. The usage of block level source error-resilient methods over bit level channel coding methods are investigated and improvements are suggested. The secondary objective of the thesis is to optimise the proposed algorithm’s architecture for real-time implementation, since the problems are of a commercial nature. The Field Reversal and Mixed Pulldown algorithms were tested in real time at MTV (Music Television) and are made available commercially through ‘Cerify’, a Linux-based media testing box manufactured by Tektronix Plc. The channel error-resilient algorithms were tested in a laboratory environment using Matlab and performance improvements are obtained

Neuromorphic Systems for Pattern Recognition and Uav Trajectory Planning

Detection and control are two essential components in an intelligent system. This thesis investigates novel techniques in both areas with a focus on the applications of handwritten text recognition and UAV flight control. Recognizing handwritten texts is a challenging task due to many different writing styles and lack of clear boundary between adjacent characters. The difficulty is greatly increased if the detection algorithms is solely based on pattern matching without information of dynamics of handwriting trajectories. Motivated by the aforementioned challenges, this thesis first investigates the pattern recognition problem. We use offline handwritten texts recognition as a case study to explore the performance of a recurrent belief propagation model. We first develop a probabilistic inference network to post process the recognition results of deep Convolutional Neural Network (CNN) (e.g. LeNet) and collect individual characters to form words. The output of the inference network is a set of words and their probability. A series of post processing and improvement techniques are then introduced to further increase the recognition accuracy. We study the performance of proposed model through various comparisons. The results show that it significantly improves the accuracy by correcting deletion, insertion and replacement errors, which are the main sources of invalid candidate words. Deep Reinforcement Learning (DRL) has widely been applied to control the autonomous systems because it provides solutions for various complex decision-making tasks that previously could not be solved solely with deep learning. To enable autonomous Unmanned Aerial Vehicles (UAV), this thesis presents a two-level trajectory planning framework for UAVs in an indoor environment. A sequence of waypoints is selected at the higher-level, which leads the UAV from its current position to the destination. At the lower-level, an optimal trajectory is generated analytically between each pair of adjacent waypoints. The goal of trajectory generation is to maintain the stability of the UAV, and the goal of the waypoints planning is to select waypoints with the lowest control thrust throughout the entire trip while avoiding collisions with obstacles. The entire framework is implemented using DRL, which learns the highly complicated and nonlinear interaction between those two levels, and the impact from the environment. Given the pre-planned trajectory, this thesis further presents an actor-critic reinforcement learning framework that realizes continuous trajectory control of the UAV through a set of desired waypoints. We construct a deep neural network and develop reinforcement learning for better trajectory tracking. In addition, Field Programmable Gate Arrays (FPGA) based hardware acceleration is designed for energy efficient real-time control. If we are to integrate the trajectory planning model onto a UAV system for real-time on-board planning, a key challenge is how to deliver required performance under strict memory and computational constraints. Techniques that compress Deep Neural Network (DNN) models attract our attention because they allow optimized neural network models to be efficiently deployed on platforms with limited energy and storage capacity. However, conventional model compression techniques prune the DNN after it is fully trained, which is very time-consuming especially when the model is trained using DRL. To overcome the limitation, we present an early phase integrated neural network weight compression system for DRL based waypoints planning. By applying pruning at an early phase, the compression of the DRL model can be realized without significant overhead in training. By tightly integrating pruning and retraining at the early phase, we achieve a higher model compression rate, reduce more memory and computing complexity, and improve the success rate compared to the original work

Complexity management for video encoders.

Software implementation of block-based video coding standards has been used in a wide range of applications. In many cases, such as real-time multimedia systems or power-constrained systems, the coding performance of software-only video encoders and decoders is limited by computational complexity. This thesis presents research work to develop techniques to manage computational complexity of video encoders. These techniques aim to provide significant complexity saving as well as adaptively controlling the computational complexity. This thesis first investigates experimentally the most computationally intensive functions in a video encoder. Based on the results of profile tests, several functions are selected as candidates, on which complexity reduction algorithms will be performed. These functions include discrete cosine transform and related functions as well as motion estimation. Adaptive complexity-reduction algorithms are proposed for computationally expensive functions: discrete cosine transform and motion estimation functions respectively. It is shown that these algorithms can flexibly control the computational complexity of each function with negligible loss of video quality. The inherent characteristics of coded macroblocks are investigated through experimental tests and they are categorized into "skipped" and" unskipped" macroblocks based on two parameters. An innovative algorithm is developed to reduce the computational complexity by predicting "skipped" macroblock prior to encoding and not carrying out the coding process on these macroblocks. The approaches described in this thesis can not only achieve adaptive control of the computational complexity of a video encoder, but also can manage the trade-off between complexity and distortion. These proposed algorithms are evaluated in terms of complexity reduction performance, rate-distortion performance and subjective and objective visual quality by experimental testing.The advantages and disadvantages of each algorithm are discussed

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