The Eye-RIS CMOS Vision System

Rodríguez-Vázquez, Á.; Domínguez-Castro, R.; Jimenez-Garrido, F.; Morillas, Sergio; Listán, Juan; Alba, Luis González de; Utrera, Cayetana; Espejo, S.; Romay, Rafael

doi:10.1007/978-1-4020-8263-4_2

Cited by 46 publications

(41 citation statements)

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“…To validate the proposed algorithms, we implemented them with the Eye-RIS vision system [12]. In the following sections, we will present an evaluation of our algorithms and a comparison with the baseline method based on threshold decomposition.…”

Section: Resultsmentioning

confidence: 99%

“…To implement our algorithm, we use the Eye-RIS vision system [12]. A conceptual block diagram of this system is presented in Fig.…”

Section: The Eye-ris Vision Systemmentioning

confidence: 99%

“…1. To test our approach, we implemented it in the Eye-RIS vision system [12], which is a smart camera that contains a mixed-signal FPP called Q-Eye.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical stack filtering: a bitplane-based algorithm for massively parallel processors

Frias-Velazquez

Morros

García

et al. 2017

J Real-Time Image Proc

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With the development of novel parallel architectures for image processing, the implementation of well-known image operators needs to be reformulated to take advantage of the so-called massive parallelism. In this work, we propose a general algorithm that implements a large class of nonlinear filters, called stack filters, with a 2D-array processor. The proposed method consists of decomposing an image into bitplanes with the bitwise decomposition and then processes every bitplane hierarchically. The filtered image is reconstructed by simply stacking the filtered bitplanes according to their order of significance. Owing to its hierarchical structure, our algorithm allows us to trade-off between image quality and processing time, and to significantly reduce the computation time of low-entropy images. Also, experimental tests show that the processing time of our method is substantially lower than that of classical methods when using large structuring elements. All these features are of interest to a variety of real-time applications based on morphological operations such as video segmentation and video enhancement.Peer ReviewedPostprint (author's final draft

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Section: Resultsmentioning

confidence: 99%

“…To implement our algorithm, we use the Eye-RIS vision system [12]. A conceptual block diagram of this system is presented in Fig.…”

Section: The Eye-ris Vision Systemmentioning

confidence: 99%

See 1 more Smart Citation

Hierarchical stack filtering: a bitplane-based algorithm for massively parallel processors

Frias-Velazquez

Morros

García

et al. 2017

J Real-Time Image Proc

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show abstract

“…Thus, for instance, in-pixel processing circuitry can be used to obtain high-speed through parallel processing in tasks where many data are involved such as spatial filtering, image feature extraction, motion estimation, … [12]. Then, off-pixel embedded digital processors can be used for control and high-level processing tasks involving a reduced data set [13].…”

Section: Second Quarter 2008 Ieee Circuits and Systems Magazinementioning

confidence: 99%

“…To untie the gordian knot of this architectural choice the following question should be answered: where to place the border between the parallel mixel-signal circuitry and the serial digital processing circuitry? This question arises when one confronts the design of any on-chip sensory-processing system [13]. Its answer involves issues regarding the application (for instance whether complete images or only image features must be downloaded from the sensor); considerations on the computing paradigm to adopt [14]; and issues regarding feasibility of the available technologies and circuit primitives [41].…”

Section: From the Locust To The Car: Locust Cmos Chipsmentioning

confidence: 99%

Insect-vision inspired collision warning vision processor for automobiles

Liñán‐Cembrano

Carranza

Rind

et al. 2008

IEEE Circuits Syst. Mag.

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FeatureDigital Object Identifier 10. 1109 /MCAS.2008 Vision is expected to play important roles for car safety enhancement. Imaging systems can be used to enlarging the vision field of the driver. For instance capturing and displaying views of hidden areas around the car which the driver can analyze for safer decision-making. Vision systems go a step further. They can autonomously analyze the visual information, identify dangerous situations and prompt the delivery of warning signals. For instance in case of road lane departure, if an overtaking car is in the blind spot, if an object is approaching within collision course, etc. Processing capabilities are also needed for applications viewing the car interior such as "intelligent airbag systems" that base deployment decisions on passenger features.On-line processing of visual information for car safety involves multiple sensors and views, huge amount of data per view and large frame rates. The associated computational load may be prohibitive for conventional processing architectures. Dedicated systems with embedded local processing capabilities may be needed to confront the challenges. This paper describes a dedicated sensory-processing architecture for collision warning which is inspired by insect vision. Particularly, the paper relies on the exploitation of the knowledge about the behavior of Locusta Migratoria to develop dedicated chips and systems which are integrated into model cars as well as into a commercial car (Volvo XC90) and tested to deliver collision warnings in real traffic scenarios.Authorized licensed use limited to: Newcastle University. Downloaded on March 16,2010 at 10:30:53 EDT from IEEE Xplore. Restrictions apply.

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

Fernández-Berni

Carmona-Galán

Rı́o

et al. 2014

Circuit Theory & Apps

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SUMMARYFocal-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.

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The Eye-RIS CMOS Vision System

Cited by 46 publications

References 1 publication

Hierarchical stack filtering: a bitplane-based algorithm for massively parallel processors

Hierarchical stack filtering: a bitplane-based algorithm for massively parallel processors

Insect-vision inspired collision warning vision processor for automobiles

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

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