CMOS-3D smart imager architectures for feature detection

This paper reports a multi-layered smart image sensor architecture for feature extraction based on detection of interest points. The architecture is conceived for 3-D integrated circuit technologies consisting of two layers (tiers) plus memory. The top tier includes sensing and processing circuitry aimed to perform Gaussian filtering and generate Gaussian pyramids in fully concurrent way. The circuitry in this tier operates in mixed-signal domain. It embeds in-pixel correlated double sampling, a switched-capacitor network for Gaussian pyramid generation, analog memories and a comparator for in-pixel analog-to-digital conversion. This tier can be further split into two for improved resolution; one containing the sensors and another containing a capacitor per sensor plus the mixed-signal processing circuitry. Regarding the bottom tier, it embeds digital circuitry entitled for the calculation of Harris, Hessian, and difference-of-Gaussian detectors. The overall system can hence be configured by the user to detect interest points by using the algorithm out of these three better suited to practical applications. The paper describes the different kind of algorithms featured and the circuitry employed at top and bottom tiers. The Gaussian pyramid is implemented with a switched-capacitor network in less than 50 μs, outperforming more conventional solutions.Xunta de Galicia 10PXIB206037PRMinisterio de Ciencia e Innovación TEC2009-12686, IPT-2011-1625-430000Office of Naval Research N00014111031

A Bio-Inspired Vision Sensor With Dual Operation and Readout Modes

This paper presents a novel event-based vision sensor with two operation modes: intensity mode and spatial contrast detection. They can be combined with two different readout approaches: pulse density modulation and time-to-first spike. The sensor is conceived to be a node of an smart camera network made up of several independent an autonomous nodes that send information to a central one. The user can toggle the operation and the readout modes with two control bits. The sensor has low latency (below 1 ms under average illumination conditions), low power consumption (19 mA), and reduced data flow, when detecting spatial contrast. A new approach to compute the spatial contrast based on inter-pixel event communication less prone to mismatch effects than diffusive networks is proposed. The sensor was fabricated in the standard AMS4M2P 0.35-um process. A detailed system-level description and experimental results are provided.Office of Naval Research (USA) N00014-14-1-0355Ministerio de Economía y Competitividad TEC2012- 38921-C02-02, P12-TIC-2338, IPT-2011-1625-43000

Advances on CMOS image sensors

This paper offers an introduction to the technological advances of image sensors designed using complementary metal–oxide–semiconductor (CMOS) processes along the last decades. We review some of those technological advances and examine potential disruptive growth directions for CMOS image sensors and proposed ways to achieve them. Those advances include breakthroughs on image quality such as resolution, capture speed, light sensitivity and color detection and advances on the computational imaging. The current trend is to push the innovation efforts even further as the market requires higher resolution, higher speed, lower power consumption and, mainly, lower cost sensors. Although CMOS image sensors are currently used in several different applications from consumer to defense to medical diagnosis, product differentiation is becoming both a requirement and a difficult goal for any image sensor manufacturer. The unique properties of CMOS process allows the integration of several signal processing techniques and are driving the impressive advancement of the computational imaging. With this paper, we offer a very comprehensive review of methods, techniques, designs and fabrication of CMOS image sensors that have impacted or might will impact the images sensor applications and markets

Smart cmos image sensor for 3d measurement

3D measurements are concerned with extracting visual information from the geometry of visible surfaces and interpreting the 3D coordinate data thus obtained, to detect or track the position or reconstruct the profile of an object, often in real time. These systems necessitate image sensors with high accuracy of position estimation and high frame rate of data processing for handling large volumes of data. A standard imager cannot address the requirements of fast image acquisition and processing, which are the two figures of merit for 3D measurements. Hence, dedicated VLSI imager architectures are indispensable for designing these high performance sensors. CMOS imaging technology provides potential to integrate image processing algorithms on the focal plane of the device, resulting in smart image sensors, capable of achieving better processing features in handling massive image data. The objective of this thesis is to present a new architecture of smart CMOS image sensor for real time 3D measurement using the sheet-beam projection methods based on active triangulation. Proposing the vision sensor as an ensemble of linear sensor arrays, all working in parallel and processing the entire image in slices, the complexity of the image-processing task shifts from O (N 2 ) to O (N). Inherent also in the design is the high level of parallelism to achieve massive parallel processing at high frame rate, required in 3D computation problems. This work demonstrates a prototype of the smart linear sensor incorporating full testability features to test and debug both at device and system levels. The salient features of this work are the asynchronous position to pulse stream conversion, multiple images binarization, high parallelism and modular architecture resulting in frame rate and sub-pixel resolution suitable for real time 3D measurements

A Robust Analog VLSI Reichardt Motion Sensor

Silicon imagers with integrated motion-detection circuitry have been developed and tested for the past 15 years. Many previous circuits estimate motion by identifying and tracking spatial or temporal features. These approaches are prone to failure at low SNR conditions, where feature detection becomes unreliable. An alternate approach to motion detection is an intensity-based spatiotemporal correlation algorithm, such as the one proposed by Hassenstein and Reichardt in 1956 to explain aspects of insect vision. We implemented a Reichardt motion sensor with integrated photodetectors in a standard CMOS process. Our circuit operates at sub-microwatt power levels, the lowest reported for any motion sensor. We measure the effects of device mismatch on these parallel, analog circuits to show they are suitable for constructing 2-D VLSI arrays. Traditional correlation-based sensors suffer from strong contrast dependence. We introduce a circuit architecture that lessens this dependence. We also demonstrate robust performance of our sensor to complex stimuli in the presence of spatial and temporal noise

Applications of Silicon Retinas: from Neuroscience to Computer Vision

Traditional visual sensor technology is firmly rooted in the concept of sequences of image frames. The sequence of stroboscopic images in these "frame cameras" is very different compared to the information running from the retina to the visual cortex. While conventional cameras have improved in the direction of smaller pixels and higher frame rates, the basics of image acquisition have remained the same. Event-based vision sensors were originally known as "silicon retinas" but are now widely called "event cameras." They are a new type of vision sensors that take inspiration from the mechanisms developed by nature for the mammalian retina and suggest a different way of perceiving the world. As in the neural system, the sensed information is encoded in a train of spikes, or so-called events, comparable to the action potential generated in the nerve. Event-based sensors produce sparse and asynchronous output that represents in- formative changes in the scene. These sensors have advantages in terms of fast response, low latency, high dynamic range, and sparse output. All these char- acteristics are appealing for computer vision and robotic applications, increasing the interest in this kind of sensor. However, since the sensor’s output is very dif- ferent, algorithms applied for frames need to be rethought and re-adapted. This thesis focuses on several applications of event cameras in scientific scenarios. It aims to identify where they can make the difference compared to frame cam- eras. The presented applications use the Dynamic Vision Sensor (event camera developed by the Sensors Group of the Institute of Neuroinformatics, University of Zurich and ETH). To explore some applications in more extreme situations, the first chapters of the thesis focus on the characterization of several advanced versions of the standard DVS. The low light condition represents a challenging situation for every vision sensor. Taking inspiration from standard Complementary Metal Oxide Semiconductor (CMOS) technology, the DVS pixel performances in a low light scenario can be improved, increasing sensitivity and quantum efficiency, by using back-side illumination. This thesis characterizes the so-called Back Side Illumination DAVIS (BSI DAVIS) camera and shows results from its application in calcium imaging of neural activity. The BSI DAVIS has shown better performance in the low light scene due to its high Quantum Efficiency (QE) of 93% and proved to be the best type of technology for microscopy application. The BSI DAVIS allows detecting fast dynamic changes in neural fluorescent imaging using the green fluorescent calcium indicator GCaMP6f. Event camera advances have pushed the exploration of event-based cameras in computer vision tasks. Chapters of this thesis focus on two of the most active research areas in computer vision: human pose estimation and hand gesture classification. Both chapters report the datasets collected to achieve the task, fulfilling the continuous need for data for this kind of new technology. The Dynamic Vision Sensor Human Pose dataset (DHP19) is an extensive collection of 33 whole-body human actions from 17 subjects. The chapter presents the first benchmark neural network model for 3D pose estimation using DHP19. The network archives a mean error of less than 8 mm in the 3D space, which is comparable with frame-based Human Pose Estimation (HPE) methods using frames. The gesture classification chapter reports an application running on a mobile device and explores future developments in the direction of embedded portable low power devices for online processing. The sparse output from the sensor suggests using a small model with a reduced number of parameters and low power consumption. The thesis also describes pilot results from two other scientific imaging applica- tions for raindrop size measurement and laser speckle analysis presented in the appendices