Jorge Fernández-Berni

FLIP-Q: A QCIF Resolution Focal-Plane Array for Low-Power Image Processing

This paper reports a 176×144-pixel smart image sensor designed and fabricated in a 0.35 CMOS-OPTO process. The chip implements a massively parallel focal-plane processing array which can output different simplified representations of the scene at very low power. The array is composed of pixel-level processing elements which carry out analog image processing concurrently with photosensing. These processing elements can be grouped into fully-programmable rectangular-shape areas by loading the appropriate interconnection patterns into the registers at the edge of the array. The targeted processing can be thus performed block-wise. Readout is done pixel-by-pixel in a random access fashion. On-chip 8b ADC is provided. The image processing primitives implemented by the chip, experimentally tested and fully functional, are scale space and Gaussian pyramid generation, fully-programmable multiresolution scene representation-including foveation-and block-wise energy-based scene representation. The power consumption associated to the capture, processing and A/D conversion of an image flow at 30 fps, with full-frame processing but reduced frame size output, ranges from 2.7 mW to 5.6 mW, depending on the operation to be performed.

Early forest fire detection by vision-enabled wireless sensor networks

Wireless sensor networks constitute a powerful technology particularly suitable for environmental monitoring. With regard to wildfires, they enable low-cost fine-grained surveillance of hazardous locations like wildland–urban interfaces. This paper presents work developed during the last 4 years targeting a vision-enabled wireless sensor network node for the reliable, early on-site detection of forest fires. The tasks carried out ranged from devising a robust vision algorithm for smoke detection to the design and physical implementation of a power-efficient smart imager tailored to the characteristics of such an algorithm. By integrating this smart imager with a commercial wireless platform, we endowed the resulting system with vision capabilities and radio communication. Numerous tests were arranged in different natural scenarios in order to progressively tune all the parameters involved in the autonomous operation of this prototype node. The last test carried out, involving the prescribed burning of a 95 × 20-m shrub plot, confirmed the high degree of reliability of our approach in terms of both successful early detection and a very low false-alarm rate.

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.

Focal-Plane Sensing-Processing: A Power-Efficient Approach for the Implementation of Privacy-Aware Networked Visual Sensors

The capture, processing and distribution of visual information is one of the major challenges for the paradigm of the Internet of Things. Privacy emerges as a fundamental barrier to overcome. The idea of networked image sensors pervasively collecting data generates social rejection in the face of sensitive information being tampered by hackers or misused by legitimate users. Power consumption also constitutes a crucial aspect. Images contain a massive amount of data to be processed under strict timing requirements, demanding high-performance vision systems. In this paper, we describe a hardware-based strategy to concurrently address these two key issues. By conveying processing capabilities to the focal plane in addition to sensing, we can implement privacy protection measures just at the point where sensitive data are generated. Furthermore, such measures can be tailored for efficiently reducing the computational load of subsequent processing stages. As a proof of concept, a full-custom QVGA vision sensor chip is presented. It incorporates a mixed-signal focal-plane sensing-processing array providing programmable pixelation of multiple image regions in parallel. In addition to this functionality, the sensor exploits reconfigurability to implement other processing primitives, namely block-wise dynamic range adaptation, integral image computation and multi-resolution filtering. The proposed circuitry is also suitable to build a granular space, becoming the raw material for subsequent feature extraction and recognition of categorized objects.

All-MOS implementation of RC networks for time-controlled Gaussian spatial filtering

This paper addresses the design and VLSI implementation of MOS-based RC networks capable of performing time-controlled Gaussian filtering. In these networks, all the resistors are substituted one by one by a single MOS transistor biased in the ohmic region. The design of this elementary transistor is carefully realized according to the value of the ideal resistor to be emulated. For a prescribed signal range, the MOSFET in triode region delivers an interval of instantaneous resistance values. We demonstrate that, for the elementary 2-node network, establishing the design equation at a particular point within this interval guarantees minimum error. This equation is then corroborated for networks of arbitrary size by analyzing them from a stochastic point of view. Following the design methodology proposed, the error committed by an MOS-based grid when compared with its equivalent ideal RC network is, despite the intrinsic nonlinearities of the transistors, below 1% even under mismatch conditions of 10%. In terms of image processing, this error hardly affects the outcome, which is perceptually equivalent to that of the ideal network. These results, extracted from simulation, are verified in a prototype vision chip with QCIF resolution manufactured in the AMS 0.35µm CMOS-OPTO process. This prototype incorporates a focal-plane MOS-based RC network that performs fully programmable Gaussian filtering. Copyright

Wi-FLIP: A wireless smart camera based on a focal-plane low-power image processor

This paper presents Wi-FLIP, a vision-enabled WSN node resulting from the integration of FLIP-Q, a prototype vision chip, and Imotel, a commercial WSN platform. In Wi-FLIP, image processing is not only constrained to the digital domain like in conventional architectures. Instead, its image sensor — the FLIP-Q prototype — incorporates pixel-level processing elements (PEs) implemented by analog circuitry. These PEs are interconnected, rendering a massively parallel SIMD-based focal-plane array. Low-level image processing tasks fit very well into this processing scheme. They feature a heavy computational load composed of pixel-wise repetitive operations which can be realized in parallel with moderate accuracy. In such circumstances, analog circuitry, not very precise but faster and more area- and power-efficient than its digital counterpart, has been extensively reported to achieve better performance. The Wi-FLIPs image sensor does not therefore output raw but pre-processed images that make the subsequent digital processing much lighter. The energy cost of such pre-processing is really low — 5.6mW for the worst-case scenario. As a result, for the configuration where the Imote2s processor works at minimum clock frequency, the maximum power consumed by our prototype represents only the 5.2% of the whole system power consumption. This percentage gets even lower as the clock frequency increases. We report experimental results for different algorithms, image resolutions and clock frequencies. The main drawback of this first version of Wi-FLIP is the low frame rate reachable due to the non-standard GPIO-based FLIPQ-to-Imote2 interface.

Bottom-up performance analysis of focal-plane mixed-signal hardware for Viola–Jones early vision tasks

Summary Focal-plane mixed-signal arrays have traditionally been designed according to the general claim that moderate accuracy in processing is affordable. The performance of their circuitry has been analyzed in these terms without a comprehensive study of the ultimate consequences of such moderate accuracy. In this paper, for the first time to the best of our knowledge, we do carry out this study. We move expectable performance of mixed-signal image processing hardware directly into the vision algorithm making use of it. This permits to close a wider design loop, enabling a more aggressive design of this kind of hardware provided that the algorithm, at the highest level—semantic interpretation of the scene—, can afford it. Thus, we present a thorough analysis of the non-idealities associated with the implementation of a QVGA array tailored for the distinctive characteristics of the Viola–Jones processing framework. The resulting deviation models are then introduced in the processing flow of this framework provided by the OpenCV library. We have found, contrary to what could be expected, that these deviations do not necessarily degrade the performance of the Viola–Jones algorithm. They could be even beneficial for certain high-level specifications. Additionally, we demonstrate the architectural advantages of our approach: exploitation of focal-plane distributed memory and ultra-low-power operation. Copyright

Single-Exposure HDR Technique Based on Tunable Balance Between Local and Global Adaptation

This brief describes a high-dynamic-range technique that compresses wide ranges of illuminations into the available signal range with a single exposure. An online analysis of the image histogram provides the sensor with the necessary feedback to dynamically accommodate changing illumination conditions. This adaptation is accomplished by properly weighing the influence of local and global illuminations on each pixel response. The main advantages of this technique with respect to similar approaches previously reported are as follows: 1) standard active-pixel-sensor circuitry can be used to render the pixel values and 2) the resulting compressed image representation is ready either for readout or for early vision processing at the very focal plane without requiring any additional peripheral circuit block. Experimental results from a prototype smart image sensor achieving a dynamic range of 102 dB are presented.

A 26.5 nJ/px 2.64 Mpx/s CMOS vision sensor for Gaussian pyramid extraction

This paper introduces a CMOS vision sensor to extract the Gaussian pyramid with an energy cost of 26.5 nJ/px at 2.64 Mpx/s, thus outperforming conventional solutions employing an imager and a separate digital processor. The chip, manufactured in a 0.18 μm CMOS technology, consists of an arrangement of 88 × 60 processing elements (PEs) which captures images of 176 × 120 resolution and performs concurrent parallel processing right at pixel level. The Gaussian pyramid is generated by using a switched-capacitor network. Every PE includes four photodiodes, four MiM capacitors, one 8-bit single-slope ADC and one CDS circuit, occupying 44 × 44 μm2. Suitability of the chip is assessed by using metrics pertaining to visual tracking.

A hierarchical vision processing architecture oriented to 3D integration of smart camera chips

This paper introduces a vision processing architecture that is directly mappable on a 3D chip integration technology. Due to the aggregated nature of the information contained in the visual stimulus, adapted architectures are more efficient than conventional processing schemes. Given the relatively minor importance of the value of an isolated pixel, converting every one of them to digital prior to any processing is inefficient. Instead of this, our system relies on focal-plane image filtering and key point detection for feature extraction. The originally large amount of data representing the image is now reduced to a smaller number of abstracted entities, simplifying the operation of the subsequent digital processor. There are certain limitations to the implementation of such hierarchical scheme. The incorporation of processing elements close to the photo-sensing devices in a planar technology has a negative influence in the fill factor, pixel pitch and image size. It therefore affects the sensitivity and spatial resolution of the image sensor. A fundamental tradeoff needs to be solved. The larger the amount of processing conveyed to the sensor plane, the larger the pixel pitch. On the contrary, using a smaller pixel pitch sends more processing circuitry to the periphery of the sensor and tightens the data bottleneck between the sensor plane and the memory plane. 3D integration technologies with a high density of through-silicon-vias can help overcome these limitations. Vertical integration of the sensor plane and the processing and memory planes with a fully parallel connection eliminates data bottlenecks without compromising fill factor and pixel pitch. A case study is presented: a smart vision chip designed on a 3D integration technology provided by MIT Lincoln Labs, whose base process is 0.15@mm FD-SOI. Simulation results advance performance improvements with respect to the state-of-the-art in smart vision chips.