CED: Color Event Camera Dataset

Event cameras are novel, bio-inspired visual sensors, whose pixels output asynchronous and independent timestamped spikes at local intensity changes, called 'events'. Event cameras offer advantages over conventional frame-based cameras in terms of latency, high dynamic range (HDR) and temporal resolution. Until recently, event cameras have been limited to outputting events in the intensity channel, however, recent advances have resulted in the development of color event cameras, such as the Color-DAVIS346. In this work, we present and release the first Color Event Camera Dataset (CED), containing 50 minutes of footage with both color frames and events. CED features a wide variety of indoor and outdoor scenes, which we hope will help drive forward event-based vision research. We also present an extension of the event camera simulator ESIM that enables simulation of color events. Finally, we present an evaluation of three state-of-the-art image reconstruction methods that can be used to convert the Color-DAVIS346 into a continuous-time, HDR, color video camera to visualise the event stream, and for use in downstream vision applications.Comment: Conference on Computer Vision and Pattern Recognition Workshop

An FPGA-based versatile development system for endoscopic capsule design optimization

This work presents a development system, based on Field Programmable Gate Array (FPGA), that was specifically designed for testing the entire electronics to be integrated in an endoscopic capsule, such as a camera, an image compression engine, a high-speed telemetric system, illumination and inertial sensors. Thanks to its high flexibility, several features were tested and evaluated, thus allowing to find the optimal configuration, in terms of power consumption, performances and size, to be fit in a capsule. As final result, an average frame rate of 19 frame per second (fps) over a transmission channel of 1.5 Mbit/s was chosen as the best choice for the development of a miniaturized endoscopic capsule prototype

Camera functionality in modern mobile terminals : software FIR filter for demosaicing with RISC

Masteroppgave i informasjons- og kommunikasjonsteknologi 2001 - Høgskolen i Agder, GrimstadThis thesis discusses implementation and optimizing of the image pre-processing operation demosaicing on a typical hardware architecture for mobile terminals. Work has shown that the demosaicing operation is equivalent to the mathematical operation of interpolation, and that optimizing potential lies within the implementation of the anti-imaging filter for the interpolator. This filter can be realized as an FIR filter with the filter coefficients obtained from a suitable interpolation function (kernel). Through literature review, nearest neighbor replication, bilinear and cubic convolution kernels were implemented and optimized according to a typical hardware architecture found in mobile terminals. Typical hardware architecture used consisted of ARM710T RISC processor with 8 KB on-chip cache and external SDRAM memory. The work has shown that the cubic convolution interpolation kernel offers the best image quality requiring only moderate computational effort. Though, dependent the application area, and taking the whole camera system into consideration, bilinear interpolation might be better suited as interpolation kernel for the demosaicing operation in the mobile terminal. The bilinear interpolation offers a good trade-off between the visual result and computational effort. The nearest neighbor replication interpolation kernel offers the poorest image quality, but is the most computational efficient of the kernels

Efficient Encoding of Wireless Capsule Endoscopy Images Using Direct Compression of Colour Filter Array Images

Since its invention in 2001, wireless capsule endoscopy (WCE) has played an important role in the endoscopic examination of the gastrointestinal tract. During this period, WCE has undergone tremendous advances in technology, making it the first-line modality for diseases from bleeding to cancer in the small-bowel. Current research efforts are focused on evolving WCE to include functionality such as drug delivery, biopsy, and active locomotion. For the integration of these functionalities into WCE, two critical prerequisites are the image quality enhancement and the power consumption reduction. An efficient image compression solution is required to retain the highest image quality while reducing the transmission power. The issue is more challenging due to the fact that image sensors in WCE capture images in Bayer Colour filter array (CFA) format. Therefore, standard compression engines provide inferior compression performance. The focus of this thesis is to design an optimized image compression pipeline to encode the capsule endoscopic (CE) image efficiently in CFA format. To this end, this thesis proposes two image compression schemes. First, a lossless image compression algorithm is proposed consisting of an optimum reversible colour transformation, a low complexity prediction model, a corner clipping mechanism and a single context adaptive Golomb-Rice entropy encoder. The derivation of colour transformation that provides the best performance for a given prediction model is considered as an optimization problem. The low complexity prediction model works in raster order fashion and requires no buffer memory. The application of colour transformation yields lower inter-colour correlation and allows the efficient independent encoding of the colour components. The second compression scheme in this thesis is a lossy compression algorithm with a integer discrete cosine transformation at its core. Using the statistics obtained from a large dataset of CE image, an optimum colour transformation is derived using the principal component analysis (PCA). The transformed coefficients are quantized using optimized quantization table, which was designed with a focus to discard medically irrelevant information. A fast demosaicking algorithm is developed to reconstruct the colour image from the lossy CFA image in the decoder. Extensive experiments and comparisons with state-of-the-art lossless image compression methods establish the superiority of the proposed compression methods as simple and efficient image compression algorithm. The lossless algorithm can transmit the image in a lossless manner within the available bandwidth. On the other hand, performance evaluation of lossy compression algorithm indicates that it can deliver high quality images at low transmission power and low computation costs

SoDaCam: Software-defined Cameras via Single-Photon Imaging

Reinterpretable cameras are defined by their post-processing capabilities that exceed traditional imaging. We present "SoDaCam" that provides reinterpretable cameras at the granularity of photons, from photon-cubes acquired by single-photon devices. Photon-cubes represent the spatio-temporal detections of photons as a sequence of binary frames, at frame-rates as high as 100 kHz. We show that simple transformations of the photon-cube, or photon-cube projections, provide the functionality of numerous imaging systems including: exposure bracketing, flutter shutter cameras, video compressive systems, event cameras, and even cameras that move during exposure. Our photon-cube projections offer the flexibility of being software-defined constructs that are only limited by what is computable, and shot-noise. We exploit this flexibility to provide new capabilities for the emulated cameras. As an added benefit, our projections provide camera-dependent compression of photon-cubes, which we demonstrate using an implementation of our projections on a novel compute architecture that is designed for single-photon imaging.Comment: Accepted at ICCV 2023 (oral). Project webpage can be found at https://wisionlab.com/project/sodacam

High Speed and High Dynamic Range Video with an Event Camera

Event cameras are novel sensors that report brightness changes in the form of a stream of asynchronous "events" instead of intensity frames. They offer significant advantages with respect to conventional cameras: high temporal resolution, high dynamic range, and no motion blur. While the stream of events encodes in principle the complete visual signal, the reconstruction of an intensity image from a stream of events is an ill-posed problem in practice. Existing reconstruction approaches are based on hand-crafted priors and strong assumptions about the imaging process as well as the statistics of natural images. In this work we propose to learn to reconstruct intensity images from event streams directly from data instead of relying on any hand-crafted priors. We propose a novel recurrent network to reconstruct videos from a stream of events, and train it on a large amount of simulated event data. During training we propose to use a perceptual loss to encourage reconstructions to follow natural image statistics. We further extend our approach to synthesize color images from color event streams. Our quantitative experiments show that our network surpasses state-of-the-art reconstruction methods by a large margin in terms of image quality (>20%), while comfortably running in real-time. We show that the network is able to synthesize high framerate videos (> 5,000 frames per second) of high-speed phenomena (e.g. a bullet hitting an object) and is able to provide high dynamic range reconstructions in challenging lighting conditions. As an additional contribution, we demonstrate the effectiveness of our reconstructions as an intermediate representation for event data. We show that off-the-shelf computer vision algorithms can be applied to our reconstructions for tasks such as object classification and visual-inertial odometry and that this strategy consistently outperforms algorithms that were specifically designed for event data. We release the reconstruction code, a pre-t..

How to See with an Event Camera

Seeing enables us to recognise people and things, detect motion, perceive our 3D environment and more. Light stimulates our eyes, sending electrical impulses to the brain where we form an image and extract useful information. Computer vision aims to endow computers with the ability to interpret and understand visual information - an artificial analogue to human vision. Traditionally, images from a conventional camera are processed by algorithms designed to extract information. Event cameras are bio-inspired sensors that offer improvements over conventional cameras. They (i) are fast, (ii) can see dark and bright at the same time, (iii) have less motion-blur, (iv) use less energy and (v) transmit data efficiently. However, it is difficult for humans and computers alike to make sense of the raw output of event cameras, called events, because events look nothing like conventional images. This thesis presents novel techniques for extracting information from events via: (i) reconstructing images from events then processing the images using conventional computer vision and (ii) processing events directly to obtain desired information. To advance both fronts, a key goal is to develop a sophisticated understanding of event camera output including its noise properties. Chapters 3 and 4 present fast algorithms that process each event upon arrival to continuously reconstruct the latest image and extract information. Chapters 5 and 6 apply machine learning to event cameras, letting the computer learn from a large amount of data how to process event data to reconstruct video and estimate motion. I hope the algorithms presented in this thesis will take us one step closer to building intelligent systems that can see with event cameras