A 0.5 V, 14.28-kframes/s, 96.7-dB Smart Image Sensor With Array-Level Image Signal Processing for IoT Applications

Yin, Chin; Chiu, Chin‐Fong; Hsieh, Chih-Cheng

doi:10.1109/ted.2016.2521168

Cited by 17 publications

(8 citation statements)

References 11 publications

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“…Such defects are categorized as 'pixel processing defects'. Image defects caused by pixel manufacturing process defects mainly include 'dark spots' and 'bright spots' [16]. The gray value of the pixel output image corresponding to the bright and dark points is higher or lower than the normal value by a certain percentage, and the specific percentage is specified by the test plan.…”

Section: Image Quality Evaluationmentioning

confidence: 99%

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Retracted: Explosive Growth of Image Sensors in Smart Government Technology and Economic Scale

Zhu

Zhou³

et al. 2024

IEEE Access

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CMOS image sensor is a rapidly developing image sensor product in the past decade. Relying on the advantages of process compatibility with standard CMOS process, CMOS image sensor integrates analog photoelectric photosensitive circuit and digital signal processing circuit to become a small and powerful system on chip. This article mainly studies the explosive growth of image sensors in smart government technology and economic scale. The main purpose of this article is to study the technical application of image sensors, reduce the administrative cost of social governance, improve the level of government governance, benefit the public, and realize the requirements of smart government affairs and rapid economic development of the city. This study tested the static performance of the analog-to-digital conversion circuit by inputting a limited number of sampling points, and its integral nonlinearity and differential nonlinearity were both less than 1LSB. The bandgap reference circuit provides a reference level for the entire system. It uses two PNP-type bipolar transistors parasitic in the CMOS process to achieve a temperature and voltage-independent bandgap reference voltage through the feedback of the operational amplifier. It is ideal for use In the case of resistance simulation, its accuracy reaches 16.6ppm. The scanning method of the pixel array uses multi-sampling technology to increase the dynamic range of the sensor by N 2 times. The correlated sub-sampling technology eliminates the fixed pattern noise between pixel units. The use of pipelined analog-to-digital converters can achieve good resolution, speed and area compromise.Through the realization and simulation of transistor-level circuits, the performance of each part of the system meets the requirements of design and application.

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Section: Image Quality Evaluationmentioning

confidence: 99%

“…The test program first calls Set_PLL (16,43) to initialize the CPU core clock to 400Mhz and the system clock to 133Mhz. Then call SPI_Init to initialize the SPI controller, set the SPI to work in the master mode, the SPI baud rate is 16.625Mhz, select the SPISEL2 interface as the chip selection signal, and enable the SPI interface.…”

Section: System Testmentioning

confidence: 99%

Retracted: Explosive Growth of Image Sensors in Smart Government Technology and Economic Scale

Zhu

Zhou³

et al. 2024

IEEE Access

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“…Yin et al [ 31 ] presented a smart image sensor with Array Level Image Signal Processing (ALISP) and Event-Driven Peripherals (EDP) to achieve multi-point tracking (MPT) with edge extraction. The authors presented a prototype setup for an optical handwriting recognition application.…”

Section: Cmos Image Sensor Applicationsmentioning

confidence: 99%

CMOS Image Sensors in Surveillance System Applications

Sukhavasi

Elleithy

Abuzneid

et al. 2021

Sensors

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Recent technology advances in CMOS image sensors (CIS) enable their utilization in the most demanding of surveillance fields, especially visual surveillance and intrusion detection in intelligent surveillance systems, aerial surveillance in war zones, Earth environmental surveillance by satellites in space monitoring, agricultural monitoring using wireless sensor networks and internet of things and driver assistance in automotive fields. This paper presents an overview of CMOS image sensor-based surveillance applications over the last decade by tabulating the design characteristics related to image quality such as resolution, frame rate, dynamic range, signal-to-noise ratio, and also processing technology. Different models of CMOS image sensors used in all applications have been surveyed and tabulated for every year and application.

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“…Detection should work without the use of complex algorithms, using convolutional methods [ 22 , 23 ] such as neural networks [ 24 , 25 , 26 ], using neural classifiers for the purpose of detecting persons as in Reference [ 27 ], or methods of detection of persons using the Haar-cascade classifier as in Reference [ 28 ], with the simplest possible subsequent implementation on built-in devices [ 29 , 30 , 31 ]. Built-in devices for detecting persons can be based on the use of image sensors, similarly as described in solutions [ 32 , 33 , 34 ], in the form of a smart camera sensor with the function of preprocessing image data into binary images with white dots indicating the position of a person in the scene, a smart camera sensor for detecting the background of the scene and foreground of the scene as the position of the found person as in Reference [ 35 ], smart camera sensor with the function of neglecting the dynamic background as in Reference [ 36 ], a smart camera sensor for Histogram of Oriented Gradients (HOG) image data processing as in Reference [ 37 ], or a specialized solution of the System on Chip (SOC) coping with basic image processing tasks such as edge detection in References [ 38 , 39 ], as an edge detector [ 40 ], or a solution with a low-power smart CMOS image sensor used to detect persons for indoor and outdoor use as in Reference [ 41 ]. Other image processing solutions, such as edge detection using digital parallel pulse computation [ 42 ], a non-parallel Sobel edge detector addressed by a smart camera sensor [ 43 ], discuss similar solutions that serve as sources of information providing a wide range of possible alternative solutions.…”

Section: Introductionmentioning

confidence: 99%

A System for the Detection of Persons in Intelligent Buildings Using Camera Systems—A Comparative Study

Schneider

Macháček

Martínek

et al. 2020

Sensors

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This article deals with the design and implementation of a prototype of an efficient Low-Cost, Low-Power, Low Complexity–hereinafter (L-CPC) an image recognition system for person detection. The developed and presented methods for processing, analyzing and recognition are designed exactly for inbuilt devices (e.g., motion sensor, identification of property and other specific applications), which will comply with the requirements of intelligent building technologies. The paper describes detection methods using a static background, where, during the search for people, the background image field being compared does not change, and a dynamic background, where the background image field is continually adjusted or complemented by objects merging into the background. The results are compared with the output of the Horn-Schunck algorithm applied using the principle of optical flow. The possible objects detected are subsequently stored and evaluated in the actual algorithm described. The detection results, using the change detection methods, are then evaluated using the Saaty method in order to determine the most successful configuration of the entire detection system. Each of the configurations used was also tested on a video sequence divided into a total of 12 story sections, in which the normal activities of people inside the intelligent building were simulated.

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A 0.5 V, 14.28-kframes/s, 96.7-dB Smart Image Sensor With Array-Level Image Signal Processing for IoT Applications

Cited by 17 publications

References 11 publications

Retracted: Explosive Growth of Image Sensors in Smart Government Technology and Economic Scale

Retracted: Explosive Growth of Image Sensors in Smart Government Technology and Economic Scale

CMOS Image Sensors in Surveillance System Applications

A System for the Detection of Persons in Intelligent Buildings Using Camera Systems—A Comparative Study

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