CMOS Vision Sensors: Embedding Computer Vision at Imaging Front-Ends

CMOS Image Sensors (CIS) are key for imaging technol-ogies. These chips are conceived for capturing opticalscenes focused on their surface, and for delivering elec-trical images, commonly in digital format. CISs may incor-porate intelligence; however, their smartness basicallyconcerns calibration, error correction and other similartasks. The term CVISs (CMOS VIsion Sensors) definesother class of sensor front-ends which are aimed at per-forming vision tasks right at the focal plane. They havebeen running under names such as computational imagesensors, vision sensors and silicon retinas, among others. CVIS and CISs are similar regarding physical imple-mentation. However, while inputs of both CIS and CVISare images captured by photo-sensors placed at thefocal-plane, CVISs primary outputs may not be imagesbut either image features or even decisions based on thespatial-temporal analysis of the scenes. We may hencestate that CVISs are more “intelligent” than CISs as theyfocus on information instead of on raw data. Actually,CVIS architectures capable of extracting and interpretingthe information contained in images, and prompting reac-tion commands thereof, have been explored for years inacademia, and industrial applications are recently ramp-ing up.One of the challenges of CVISs architects is incorporat-ing computer vision concepts into the design flow. Theendeavor is ambitious because imaging and computervision communities are rather disjoint groups talking dif-ferent languages. The Cellular Nonlinear Network Univer-sal Machine (CNNUM) paradigm, proposed by Profs.Chua and Roska, defined an adequate framework forsuch conciliation as it is particularly well suited for hard-ware-software co-design [1]-[4]. This paper overviewsCVISs chips that were conceived and prototyped at IMSEVision Lab over the past twenty years. Some of them fitthe CNNUM paradigm while others are tangential to it. Allthem employ per-pixel mixed-signal processing circuitryto achieve sensor-processing concurrency in the quest offast operation with reduced energy budget.Junta de Andalucía TIC 2012-2338Ministerio de Economía y Competitividad TEC 2015-66878-C3-1-R y TEC 2015-66878-C3-3-

The Fifth NASA Symposium on VLSI Design

The fifth annual NASA Symposium on VLSI Design had 13 sessions including Radiation Effects, Architectures, Mixed Signal, Design Techniques, Fault Testing, Synthesis, Signal Processing, and other Featured Presentations. The symposium provides insights into developments in VLSI and digital systems which can be used to increase data systems performance. The presentations share insights into next generation advances that will serve as a basis for future VLSI design

Bio-Inspired Computer Vision: Towards a Synergistic Approach of Artificial and Biological Vision

To appear in CVIUStudies in biological vision have always been a great source of inspiration for design of computer vision algorithms. In the past, several successful methods were designed with varying degrees of correspondence with biological vision studies, ranging from purely functional inspiration to methods that utilise models that were primarily developed for explaining biological observations. Even though it seems well recognised that computational models of biological vision can help in design of computer vision algorithms, it is a non-trivial exercise for a computer vision researcher to mine relevant information from biological vision literature as very few studies in biology are organised at a task level. In this paper we aim to bridge this gap by providing a computer vision task centric presentation of models primarily originating in biological vision studies. Not only do we revisit some of the main features of biological vision and discuss the foundations of existing computational studies modelling biological vision, but also we consider three classical computer vision tasks from a biological perspective: image sensing, segmentation and optical flow. Using this task-centric approach, we discuss well-known biological functional principles and compare them with approaches taken by computer vision. Based on this comparative analysis of computer and biological vision, we present some recent models in biological vision and highlight a few models that we think are promising for future investigations in computer vision. To this extent, this paper provides new insights and a starting point for investigators interested in the design of biology-based computer vision algorithms and pave a way for much needed interaction between the two communities leading to the development of synergistic models of artificial and biological vision

Propuesta de arquitectura y circuitos para la mejora del rango dinámico de sistemas de visión en un chip diseñados en tecnologías CMOS profundamente submicrométrica

El trabajo presentado en esta tesis trata de proponer nuevas técnicas para la expansión del rango dinámico en sensores electrónicos de imagen. En este caso, hemos dirigido nuestros estudios hacia la posibilidad de proveer dicha funcionalidad en un solo chip. Esto es, sin necesitar ningún soporte externo de hardware o software, formando un tipo de sistema denominado Sistema de Visión en un Chip (VSoC). El rango dinámico de los sensores electrónicos de imagen se define como el cociente entre la máxima y la mínima iluminación medible. Para mejorar este factor surgen dos opciones. La primera, reducir la mínima luz medible mediante la disminución del ruido en el sensor de imagen. La segunda, incrementar la máxima luz medible mediante la extensión del límite de saturación del sensor. Cronológicamente, nuestra primera opción para mejorar el rango dinámico se basó en reducir el ruido. Varias opciones se pueden tomar para mejorar la figura de mérito de ruido del sistema: reducir el ruido usando una tecnología CIS o usar circuitos dedicados, tales como calibración o auto cero. Sin embargo, el uso de técnicas de circuitos implica limitaciones, las cuales sólo pueden ser resueltas mediante el uso de tecnologías no estándar que están especialmente diseñadas para este propósito. La tecnología CIS utilizada está dirigida a la mejora de la calidad y las posibilidades del proceso de fotosensado, tales como sensibilidad, ruido, permitir imagen a color, etcétera. Para estudiar las características de la tecnología en más detalle, se diseñó un chip de test, lo cual permite extraer las mejores opciones para futuros píxeles. No obstante, a pesar de un satisfactorio comportamiento general, las medidas referentes al rango dinámico indicaron que la mejora de este mediante sólo tecnología CIS es muy limitada. Es decir, la mejora de la corriente oscura del sensor no es suficiente para nuestro propósito. Para una mayor mejora del rango dinámico se deben incluir circuitos dentro del píxel. No obstante, las tecnologías CIS usualmente no permiten nada más que transistores NMOS al lado del fotosensor, lo cual implica una seria restricción en el circuito a usar. Como resultado, el diseño de un sensor de imagen con mejora del rango dinámico en tecnologías CIS fue desestimado en favor del uso de una tecnología estándar, la cual da más flexibilidad al diseño del píxel. En tecnologías estándar, es posible introducir una alta funcionalidad usando circuitos dentro del píxel, lo cual permite técnicas avanzadas para extender el límite de saturación de los sensores de imagen. Para este objetivo surgen dos opciones: adquisición lineal o compresiva. Si se realiza una adquisición lineal, se generarán una gran cantidad de datos por cada píxel. Como ejemplo, si el rango dinámico de la escena es de 120dB al menos se necesitarían 20-bits/píxel, log2(10120/20)=19.93, para la representación binaria de este rango dinámico. Esto necesitaría de amplios recursos para procesar esta gran cantidad de datos, y un gran ancho de banda para moverlos al circuito de procesamiento. Para evitar estos problemas, los sensores de imagen de alto rango dinámico usualmente optan por utilizar una adquisición compresiva de la luz. Por lo tanto, esto implica dos tareas a realizar: la captura y la compresión de la imagen. La captura de la imagen se realiza a nivel de píxel, en el dispositivo fotosensor, mientras que la compresión de la imagen puede ser realizada a nivel de píxel, de sistema, o mediante postprocesado externo. Usando el postprocesado, existe un campo de investigación que estudia la compresión de escenas de alto rango dinámico mientras se mantienen los detalles, produciendo un resultado apropiado para la percepción humana en monitores convencionales de bajo rango dinámico. Esto se denomina Mapeo de Tonos (Tone Mapping) y usualmente emplea solo 8-bits/píxel para las representaciones de imágenes, ya que éste es el estándar para las imágenes de bajo rango dinámico. Los píxeles de adquisición compresiva, por su parte, realizan una compresión que no es dependiente de la escena de alto rango dinámico a capturar, lo cual implica una baja compresión o pérdida de detalles y contraste. Para evitar estas desventajas, en este trabajo, se presenta un píxel de adquisición compresiva que aplica una técnica de mapeo de tonos que permite la captura de imágenes ya comprimidas de una forma optimizada para mantener los detalles y el contraste, produciendo una cantidad muy reducida de datos. Las técnicas de mapeo de tonos ejecutan normalmente postprocesamiento mediante software en un ordenador sobre imágenes capturadas sin compresión, las cuales contienen una gran cantidad de datos. Estas técnicas han pertenecido tradicionalmente al campo de los gráficos por ordenador debido a la gran cantidad de esfuerzo computacional que requieren. Sin embargo, hemos desarrollado un nuevo algoritmo de mapeo de tonos especialmente adaptado para aprovechar los circuitos dentro del píxel y que requiere un reducido esfuerzo de computación fuera de la matriz de píxeles, lo cual permite el desarrollo de un sistema de visión en un solo chip. El nuevo algoritmo de mapeo de tonos, el cual es un concepto matemático que puede ser simulado mediante software, se ha implementado también en un chip. Sin embargo, para esta implementación hardware en un chip son necesarias algunas adaptaciones y técnicas avanzadas de diseño, que constituyen en sí mismas otra de las contribuciones de este trabajo. Más aún, debido a la nueva funcionalidad, se han desarrollado modificaciones de los típicos métodos a usar para la caracterización y captura de imágenes

Error-efficient computing systems

This survey explores the theory and practice of techniques to make computing systems faster or more energy-efficient by allowing them to make controlled errors. In the same way that systems which only use as much energy as necessary are referred to as being energy-efficient, you can think of the class of systems addressed by this survey as being error-efficient: They only prevent as many errors as they need to. The definition of what constitutes an error varies across the parts of a system. And the errors which are acceptable depend on the application at hand. In computing systems, making errors, when behaving correctly would be too expensive, can conserve resources. The resources conserved may be time: By making some errors, systems may be faster. The resource may also be energy: A system may use less power from its batteries or from the electrical grid by only avoiding certain errors while tolerating benign errors that are associated with reduced power consumption. The resource in question may be an even more abstract quantity such as consistency of ordering of the outputs of a system. This survey is for anyone interested in an end-to-end view of one set of techniques that address the theory and practice of making computing systems more efficient by trading errors for improved efficiency

Tools for interfacing, extracting, and analyzing neural signals using wide-field fluorescence imaging and optogenetics in awake behaving mice

Imaging of multiple cells has rapidly multiplied the rate of data acquisition as well as our knowledge of the complex dynamics within the mammalian brain. The process of data acquisition has been dramatically enhanced with highly affordable, sensitive image sensors enable high-throughput detection of neural activity in intact animals. Genetically encoded calcium sensors deliver a substantial boost in signal strength and in combination with equally critical advances in the size, speed, and sensitivity of image sensors available in scientific cameras enables high-throughput detection of neural activity in behaving animals using traditional wide-field fluorescence microscopy. However, the tremendous increase in data flow presents challenges to processing, analysis, and storage of captured video, and prompts a reexamination of traditional routines used to process data in neuroscience and now demand improvements in both our hardware and software applications for processing, analyzing, and storing captured video. This project demonstrates the ease with which a dependable and affordable wide-field fluorescence imaging system can be assembled and integrated with behavior control and monitoring system such as found in a typical neuroscience laboratory. An Open-source MATLAB toolbox is employed to efficiently analyze and visualize large imaging data sets in a manner that is both interactive and fully automated. This software package provides a library of image pre-processing routines optimized for batch-processing of continuous functional fluorescence video, and additionally automates a fast unsupervised ROI detection and signal extraction routine. Further, an extension of this toolbox that uses GPU programming to process streaming video, enabling the identification, segmentation and extraction of neural activity signals on-line is described in which specific algorithms improve signal specificity and image quality at the single cell level in a behaving animal. This project describes the strategic ingredients for transforming a large bulk flow of raw continuous video into proportionally informative images and knowledge

Toward robust and efficient physically-based rendering

Le rendu fondé sur la physique est utilisé pour le design, l'illustration ou l'animation par ordinateur. Ce type de rendu produit des images photo-réalistes en résolvant les équations qui décrivent le transport de la lumière dans une scène. Bien que ces équations soient connues depuis longtemps, et qu'un grand nombre d'algorithmes aient été développés pour les résoudre, il n'en existe pas qui puisse gérer de manière efficace toutes les scènes possibles. Plutôt qu'essayer de développer un nouvel algorithme de simulation d'éclairage, nous proposons d'améliorer la robustesse de la plupart des méthodes utilisées à ce jour et/ou qui sont amenées à être développées dans les années à venir. Nous faisons cela en commençant par identifier les sources de non-robustesse dans un moteur de rendu basé sur la physique, puis en développant des méthodes permettant de minimiser leur impact. Le résultat de ce travail est un ensemble de méthodes utilisant différents outils mathématiques et algorithmiques, chacune de ces méthodes visant à améliorer une partie spécifique d'un moteur de rendu. Nous examinons aussi comment les architectures matérielles actuelles peuvent être utilisées à leur maximum afin d'obtenir des algorithmes plus rapides, sans ajouter d'approximations. Bien que les contributions présentées dans cette thèse aient vocation à être combinées, chacune d'entre elles peut être utilisée seule : elles sont techniquement indépendantes les unes des autres.Physically-based rendering is used for design, illustration or computer animation. It consists in producing photorealistic images by solving the equations which describe how light travels in a scene. Although these equations have been known for a long time and many algorithms for light simulation have been developed, no algorithm exists to solve them efficiently for any scene. Instead of trying to develop a new algorithm devoted to light simulation, we propose to enhance the robustness of most methods used nowadays and/or which can be developed in the years to come. We do this by first identifying the sources of non-robustness in a physically-based rendering engine, and then addressing them by specific algorithms. The result is a set of methods based on different mathematical or algorithmic methods, each aiming at improving a different part of a rendering engine. We also investigate how the current hardware architectures can be used at their maximum to produce more efficient algorithms, without adding approximations. Although the contributions presented in this dissertation are meant to be combined, each of them can be used in a standalone way: they have been designed to be internally independent of each other

Physical principles for scalable neural recoding

Simultaneously measuring the activities of all neurons in a mammalian brain at millisecond resolution is a challenge beyond the limits of existing techniques in neuroscience. Entirely new approaches may be required, motivating an analysis of the fundamental physical constraints on the problem. We outline the physical principles governing brain activity mapping using optical, electrical, magnetic resonance, and molecular modalities of neural recording. Focusing on the mouse brain, we analyze the scalability of each method, concentrating on the limitations imposed by spatiotemporal resolution, energy dissipation, and volume displacement. Based on this analysis, all existing approaches require orders of magnitude improvement in key parameters. Electrical recording is limited by the low multiplexing capacity of electrodes and their lack of intrinsic spatial resolution, optical methods are constrained by the scattering of visible light in brain tissue, magnetic resonance is hindered by the diffusion and relaxation timescales of water protons, and the implementation of molecular recording is complicated by the stochastic kinetics of enzymes. Understanding the physical limits of brain activity mapping may provide insight into opportunities for novel solutions. For example, unconventional methods for delivering electrodes may enable unprecedented numbers of recording sites, embedded optical devices could allow optical detectors to be placed within a few scattering lengths of the measured neurons, and new classes of molecularly engineered sensors might obviate cumbersome hardware architectures. We also study the physics of powering and communicating with microscale devices embedded in brain tissue and find that, while radio-frequency electromagnetic data transmission suffers from a severe power–bandwidth tradeoff, communication via infrared light or ultrasound may allow high data rates due to the possibility of spatial multiplexing. The use of embedded local recording and wireless data transmission would only be viable, however, given major improvements to the power efficiency of microelectronic devices