Efficient Smart CMOS Camera Based on FPGAs Oriented to Embedded Image Processing

Bravo, Ignácio; Baliñas, Javier; Gardel, Alfredo; Lázaro, José; Espinosa, Felipe; García, Jorge

doi:10.3390/s110302282

Cited by 23 publications

(13 citation statements)

References 22 publications

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“…Bravo et al [4] proposed an intelligent ad-hoc camera with embedded processing. A Virtex 4 FPGA device for internal image processing was used.…”

Section: Related Workmentioning

confidence: 99%

Design of Embedded Vision System based on FPGA-SoC

Alsheikhy¹,

Fahem²

2019

IJACSA

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The advanced microelectronics in the last decades provide each year new tools and devices making it possible to design more and more efficient artificial vision systems capable of meeting the constraints imposed. All the elements are thus brought together to make artificial vision one of the most promising, even unifying scientific "challenges" of our time. This is because the development of a vision system requires knowledge from several disciplines, from signal processing to computer architecture, through theories of probability, linear algebra, computer science, artificial intelligence and analog and digital electronics. The work proposed in this paper is located at the intersection of embedded systems and image processing domains. The objective is to propose an embedded vision system for video acquisition and processing by adding hardware accelerators in order to extract some image characteristics. With the introduction of reconfigurable platforms, such as new All Programmable System on Chip (APSoC) platforms and the advent of new high-level Electronic Design Automation (EDA) tools to configure them, FPGA-SoC based image processing has emerged as a practical solution for most computer vision problems. In this paper, we are interested in the design and implementation of an embedded vision system. This design facilitates video streaming from the camera to the monitor and hardware processing over real-time FPGA-SoC.

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“…Bravo et al [4] proposed an intelligent ad-hoc camera with embedded processing. A Virtex 4 FPGA device for internal image processing was used.…”

Section: Related Workmentioning

confidence: 99%

Design of Embedded Vision System based on FPGA-SoC

Alsheikhy¹,

Fahem²

2019

IJACSA

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“…For instance, algorithms for smart video surveillance could detect and track pedestrians [23], but for a robotic application, algorithms could be edge and feature detection [2]. In recent years, advances in embedded vision systems such as progress in microprocessor power and FPGA technology allowed the creation of compact smart cameras with low cost and, this increased the smart camera applications performance, as shown in [11,6,7,8]. In current embedded vision applications, smart cameras represent a promising on-board solution under different application domains: motion detection [25], object detection/tracking [32,31], inspection and surveillance [16], human behavior recognition [20], etc.…”

Section: Introductionmentioning

confidence: 99%

Robust feature extraction algorithm suitable for real-time embedded applications

Aguilar-González

Arias-Estrada

Berry

2017

J Real-Time Image Proc

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International audienceSmart cameras integrate processing close to the image sensor, so they can deliver high-level information to a host computer or high-level decision process. One of the most common processing is the visual features extraction since many vision-based use-cases are based on such algorithm. Unfortunately, in most of cases, features detection algorithms are not robust or do not reach real-time processing. Based on these limitations, a feature detection algorithm that is robust enough to deliver robust features under any type of indoor / outdoor scenarios is proposed. This was achieved by applying a non-textured corner filter combined to a subpixel refinement. Furthermore, an FPGA architecture is proposed. This architecture allows compact system design, real-time processing for Full HD images (it can process up to 44 frames/91.238.400 pixels per second for Full HD images), and high efficiency for smart camera implementations (similar hardware resources than previous formulations without subpixel refinement and without non-textured corner filter). For accuracy/robustness, experimental results for several real world scenes are encouraging and show the feasibility of our algorithmic approach

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“…This embedded system accelerates the basic image processing algorithms required in a mobile robot application, such as low-level image processing, spatial filtering, feature extraction, and block matching operations. In [10], a FPGA was proposed to process the images from a CMOS camera to create an embedded and autonomous image processing system. In [11], an FPGA was also proposed for integrated navigation by combining GPS, gyroscopes, and vehicle odometry.…”

Section: Introductionmentioning

confidence: 99%

An Embedded Real-Time Red Peach Detection System Based on an OV7670 Camera, ARM Cortex-M4 Processor and 3D Look-Up Tables

Teixidó

Font

Pallejà

et al. 2012

Sensors

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This work proposes the development of an embedded real-time fruit detection system for future automatic fruit harvesting. The proposed embedded system is based on an ARM Cortex-M4 (STM32F407VGT6) processor and an Omnivision OV7670 color camera. The future goal of this embedded vision system will be to control a robotized arm to automatically select and pick some fruit directly from the tree. The complete embedded system has been designed to be placed directly in the gripper tool of the future robotized harvesting arm. The embedded system will be able to perform real-time fruit detection and tracking by using a three-dimensional look-up-table (LUT) defined in the RGB color space and optimized for fruit picking. Additionally, two different methodologies for creating optimized 3D LUTs based on existing linear color models and fruit histograms were implemented in this work and compared for the case of red peaches. The resulting system is able to acquire general and zoomed orchard images and to update the relative tracking information of a red peach in the tree ten times per second.

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Efficient Smart CMOS Camera Based on FPGAs Oriented to Embedded Image Processing

Cited by 23 publications

References 22 publications

Design of Embedded Vision System based on FPGA-SoC

Design of Embedded Vision System based on FPGA-SoC

Robust feature extraction algorithm suitable for real-time embedded applications

An Embedded Real-Time Red Peach Detection System Based on an OV7670 Camera, ARM Cortex-M4 Processor and 3D Look-Up Tables

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