Advantages of Selective Change-Driven Vision for Resource-Limited Systems

IEEE Transactions on Circuits and Systems for Video Technology (Impact Factor: 2.62). 11/2011; 21(10):1415 - 1423. DOI: 10.1109/TCSVT.2011.2162761
Source: IEEE Xplore


Selective change-driven (SCD) vision is a capture/processing strategy especially suited for vision systems with limited resources and/or vision applications with real-time constraints. SCD vision capture essentially involves delivering only the pixels that have undergone the greatest change in illumination since the last time they were read-out. SCD vision processing involves processing a limited pixel flow with similar results to the usual image flow, but with far lower bandwidth and processing requirements. SCD vision is based on pixel flow processing instead of traditional image flow processing. This complete change in the way video is processed and has a direct impact on the processing hardware required to deal with visual information. In this paper, we present the first CMOS sensor using the SCD strategy, along with a highly resource-limited system implementing an object tracking experiment. Results show that SCD vision outperforms traditional vision systems by at least one order of magnitude, with limited hardware requirements for the specific tracking experiment being tested.

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    • "Figure 1 shows the sensor die which has an area of 7.87 mm2 (2.8 mm × 2.8 mm). The sensor has a resolution of 32×32 pixels and, although this resolution can be considered low for most applications, it is adequate for demonstration purposes and may even be useful in several cases, such as in resource-limited systems [50]. As shown in Figure 1, most of the sensor layout is hidden, since the pixel circuitry is covered by a metal layer to protect it from the light. "
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    ABSTRACT: Selective change driven (SCD) Vision is a biologically inspired strategy for acquiring, transmitting and processing images that significantly speeds up image sensing. SCD vision is based on a new CMOS image sensor which delivers, ordered by the absolute magnitude of its change, the pixels that have changed after the last time they were read out. Moreover, the traditional full frame processing hardware and programming methodology has to be changed, as a part of this biomimetic approach, to a new processing paradigm based on pixel processing in a data flow manner, instead of full frame image processing.
    Full-text · Article · Dec 2011 · Sensors
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    ABSTRACT: This article deals with the application of the principles of SCD (Selective Change Driven) vision to 3D laser scanning. Two experimental sets have been implemented: one with a classical CMOS (Complementary Metal-Oxide Semiconductor) sensor, and the other one with a recently developed CMOS SCD sensor for comparative purposes, both using the technique known as Active Triangulation. An SCD sensor only delivers the pixels that have changed most, ordered by the magnitude of their change since their last readout. The 3D scanning method is based on the systematic search through the entire image to detect pixels that exceed a certain threshold, showing the SCD approach to be ideal for this application. Several experiments for both capturing strategies have been performed to try to find the limitations in high speed acquisition/processing. The classical approach is limited by the sequential array acquisition, as predicted by the Nyquist.
    Full-text · Article · Oct 2013 · Sensors
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    ABSTRACT: In this paper, we investigate the multi-resource allocation problem, a unique feature of which is that the multiple resources can compensate each other while achieving the desired system performance. In particular, power and time allocations are jointly optimized with the target of energy efficiency under the resource-limited constraints. Different from previous studies on the power-time tradeoff, we consider a multi-server case where the concurrent serving users are quantitatively restricted. Therefore user selection is investigated accompanying the resource allocation, making the power-time tradeoff occur not only between the users in the same server but also in different servers. The complex multivariate optimization problem can be modeled as a variant of 2-Dimension Bin Packing Problem (V2D-BPP), which is a joint non-linear and integer programming problem. Though we use state decomposition model to transform it into a convex optimization problem, the variables are still coupled. Therefore, we propose an Iterative Dual Optimization (IDO) algorithm to obtain its optimal solution. Simulations show that the joint multi-resource allocation algorithm outperforms two existing non-joint algorithms from the perspective of energy efficiency.
    No preview · Article · Mar 2015 · KSII Transactions on Internet and Information Systems
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