A CMOS Vision System On-Chip with Multi-Core, Cellular Sensory-Processing Front-End

Rodríguez-Vázquez, Á.; Domínguez-Castro, R.; Jimenez-Garrido, F.; Morillas, Sergio; García-García, Alberto; Utrera, Cayetana; Pardo, Ma. Dolores; Listán, Juan; Romay, Rafael

doi:10.1007/978-1-4419-1011-0_6

Cited by 36 publications

(37 citation statements)

References 22 publications

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“…Such systems are very efficient for real time image processing which is necessary for a robust FPH detection in highly dynamic processes [10]. Here, a system called Q-Eye was used consisting of 176 × 144 cells [11]. Each cell consists of a single photo sensor, processor and memory elements, and interconnections to the 8 neighboring cells.…”

Section: System Setupmentioning

confidence: 99%

The full penetration hole as a stochastic process: controlling penetration depth in keyhole laser-welding processes

Blug

Abt²,

Nicolosi³

et al. 2012

Appl. Phys. B

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Although laser-welding processes are frequently used in industrial production the quality control of these processes is not satisfactory yet. Until recently, the "full penetration hole" was presumed as an image feature which appears when the keyhole opens at the bottom of the work piece. Therefore it was used as an indicator for full penetration only. We used a novel camera based on "cellular neural networks" which enables measurements at frame rates up to 14 kHz. The results show that the occurrence of the full penetration hole can be described as a stochastic process. The probability to observe it increases near the full penetration state. In overlap joints, a very similar image feature appears when the penetration depth reaches the gap between the sheets. This stochastic process is exploited by a closedloop system which controls penetration depth near the bottom of the work piece ("full penetration") or near the gap in overlap joints ("partial penetration"). It guides the welding process at the minimum laser power necessary for the required penetration depth. As a result, defects like spatters are reduced considerably and the penetration depth becomes independent of process drifts such as feeding rate or pollution on protection glasses.

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Section: System Setupmentioning

confidence: 99%

The full penetration hole as a stochastic process: controlling penetration depth in keyhole laser-welding processes

Blug

Abt²,

Nicolosi³

et al. 2012

Appl. Phys. B

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“…The model was implemented on a standalone vision system, Eye-RIS [4]. It is a small embedded industrial vision system, based on a general purpose focal-plan sensor-processor (FPSP) chip, called Q-Eye.…”

Section: Implementation On a Focal-plane Sensor-processor Devicementioning

confidence: 99%

“…4), developed by AnaFocus Ltd, Seville, Spain [5] is constructed of an FPSP chip (Q-Eye) [4], a general purpose processor, which is used for driving the chip and for external communication. The Eye-RIS system…”

Section: The Eye-ris Systemmentioning

confidence: 99%

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Implementation and Validation of a Looming Object Detector Model Derived from Mammalian Retinal Circuit

Zarándy

Fülöp

2011

Focal-Plane Sensor-Processor Chips

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The model of a recently identified mammalian retina circuit, responsible for identifying looming or approaching objects, is implemented on mixed-signal focal-plane sensor-processor array. The free parameters of the implementation are characterized; their effects to the model are analyzed. The implemented model is calibrated with real stimuli with known kinetic and geometrical properties. As the calibration shows, the identified retina channel is responsible for last minute detection of approaching objects.

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“…Thus, for instance, consumer applications seek for minimum possible in-pixel circuitry [12]- [14], while high-end machine vision applications may call for larger amounts of in-pixel circuitry to increase the speed in the extraction of image features and in the reaction thereof [15] [16]. As a general rule of thumb, the incorporation of circuitry at pixel level enables images being processed as they are acquired thus increasing speed and reducing power consumption in the realization of vision tasks [17]- [21]. Sensors composed of these smart pixels, makes the next evolutionary step of CMOS pixels, following passive pixels and active pixels [1], by embedding within the pixel resources for mixed-signal processing, memory and the programming and control of information flows.…”

Section: Introductionmentioning

confidence: 99%

Form factor improvement of smart-pixels for vision sensors through 3-D vertically-integrated technologies

Rodríguez-Vázquez

Carmona-Galán

Berni

et al. 2014

2014 IEEE 5th Latin American Symposium on Circuits and Systems

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While conventional CMOS active pixel sensors embed only the circuitry required for photo-detection, pixel addressing and voltage buffering, smart pixels incorporate also circuitry for data processing, data storage and control of data interchange. This additional circuitry enables data processing be realized concurrently with the acquisition of images which is instrumental to reduce the number of data needed to carry to information contained into images. This way, more efficient vision systems can be built at the cost of larger pixel pitch. Vertically-integrated 3D technologies enable to keep the advnatges of smart pixels while improving the form factor of smart pixels.

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A CMOS Vision System On-Chip with Multi-Core, Cellular Sensory-Processing Front-End

Cited by 36 publications

References 22 publications

The full penetration hole as a stochastic process: controlling penetration depth in keyhole laser-welding processes

The full penetration hole as a stochastic process: controlling penetration depth in keyhole laser-welding processes

Implementation and Validation of a Looming Object Detector Model Derived from Mammalian Retinal Circuit

Form factor improvement of smart-pixels for vision sensors through 3-D vertically-integrated technologies

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