2018
DOI: 10.1109/mcas.2018.2821772
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CMOS Vision Sensors: Embedding Computer Vision at Imaging Front-Ends

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Cited by 27 publications
(19 citation statements)
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“…In the field of material wear detection, three-dimensional (3D) sensors are also used, which represent new technical means for obtaining information. Three-dimensional data provides more information and at the same time, reduces the deviation of the measured data [ 130 ]. A large group of sensors consists of chemical character sensors.…”
Section: Resultsmentioning
confidence: 99%
“…In the field of material wear detection, three-dimensional (3D) sensors are also used, which represent new technical means for obtaining information. Three-dimensional data provides more information and at the same time, reduces the deviation of the measured data [ 130 ]. A large group of sensors consists of chemical character sensors.…”
Section: Resultsmentioning
confidence: 99%
“…. , N}, but is part of the 3 × 3 neighborhood of a cell, which belongs to the cellular network, being listed in the set 5 It is important to note that only CNN hardware realizations with such a basic coupling configuration have been developed so far (Vázquez et al, 2018). v xi,j across a capacitor with capacitance C xi,j R > 0 , expressing the state, and the output voltage v yi,j .…”
Section: Theory Of Cellular Nonlinear Networkmentioning
confidence: 99%
“…Besides constitutive the ideal framework for modeling biological systems (Chua, 1998;Chua and Roska, 2002), Cellular Nonlinear Networks (CNNs) (Chua and Yang, 1988a;Chua and Yang, 1988b) represent a powerful multi-variate signal processing paradigm, which, featuring a bio-inspired architecture, operates in a massively parallel fashion, allowing to process data at very high rates, as necessary in time-critical Internet-of-Things (IoT) applications, nowadays. Purely CMOS analogue hardware implementations of the CNN signal processing paradigm are typically co-integrated with highly selective equal-sized sensor arrays to allow the solution of complex computing tasks directly where the acquisition of specific data takes place (Vázquez et al, 2018). A technological issue, which limits the applicability scope of these sensor-processor arrays, is related to the huge difference between the typically small minimum size of an element of the sensor matrix, and the relatively large minimum integrated circuit (IC) area, which a processing element of the CNN hardware realization usually occupies, due to the fact that it needs to accommodate memory units, which endow the resulting computing machine with local stored programmability on board, allowing to harness thoroughly the advantages associated with the massive parallelism of the CNN signal processing paradigm.…”
Section: Introductionmentioning
confidence: 99%
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“…Early machine vision, on the other hand primarily relied on developing better photodetectors and in some instances integrating CMOS-based preprocessing modules for image filtering, feature extraction, light-level adaptation, noise elimination etc. bridging the gap between "sensing" and "compute" [24][25][26][27][28]. More recent developments in the area of brain-inspired machine vision also attempts to combine "sensing" with "memory" [13,23,29].…”
mentioning
confidence: 99%