An embedded system for an eye-detection sensor

Amir, Arnon; Zimet, Lior; Sangiovanni-Vincentelli, A.; Kao, Sean

doi:10.1016/j.cviu.2004.07.009

Cited by 50 publications

(31 citation statements)

References 24 publications

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“…There have been several studies on anomaly detection [15][16][17]; nevertheless, they are not capable of detecting anomalies in categorical data like TMR (Triple Modular Redundancy) and DWC (Duplication With Comparison); moreover, some studies have this ability only for speci c types of categorical data such as eye-detection sensors [18]. Markov [8], Stide [8], probability-based [19] and bu er-based methods [19] are some of the most signi cant anomaly detection methods presented for categorical data.…”

Section: Related Workmentioning

confidence: 99%

Comparison and Analysis of Three Anomaly Correction Methods in Embedded Systems

Mojarad

Zarandi

2017

Scientia Iranica

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Abstract. This paper proposes and compares three anomaly correction methods in embedded systems: 1) Markov-based; 2) Stide-based (sequence time-delay embedding); 3) Cluster-based correction methods. All these methods work online on data streams coming from sensors of embedded systems. In these methods, detection is rst obtained using training on normal data, and next in runtime, the correction mechanisms can be applied. Markov-based method is based on probabilities between states, Stide-based method is based on storing common events, and Cluster-based one is based on clustering similar members. In the detection phase, these methods check normal behavior of input data based on what is learned at the training phase. Evaluation is performed using 7000 data sets. The window size and number of injected anomalies vary between 3 and 5, 1 and 7, respectively. Correction coverage for Markov-based, Stide-based, and Cluster-based methods is on average 77.66%, 60.9%, and 70.36%, respectively. Therefore, Markov-based method is the best in terms of correction coverage. Moreover, area overheads of these methods are 249.64, 63.35, and 2.08 m 2 , respectively. As a trade-o , Cluster-based method shows the best correction coverage compared to area, power, and delay overheads.

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Section: Related Workmentioning

confidence: 99%

Comparison and Analysis of Three Anomaly Correction Methods in Embedded Systems

Mojarad

Zarandi

2017

Scientia Iranica

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“…Amir et al developed a hardware implementation of a labeling algorithm [20] with a single image scan that processes each scan line as it becomes available to obtain only the 0th and 1st moment features and bounding boxes of connected components in an image for the pupil tracking of human eyes. They also developed an embedded system for human eye detection by hardware-implementing their algorithm; this system can process a 640 × 480 pixel image in real-time at 60 fps.…”

Section: Copyright C 2012 the Institute Of Electronics Information Amentioning

confidence: 99%

A Fast Multi-Object Extraction Algorithm Based on Cell-Based Connected Components Labeling

Takaki

Ishii

2012

IEICE Trans. Inf. & Syst.

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Qingyi GU†a) , Takeshi TAKAKI †b) , Nonmembers, and Idaku ISHII †c) , Member SUMMARYWe describe a cell-based connected component labeling algorithm to calculate the 0th and 1st moment features as the attributes for labeled regions. These can be used to indicate their sizes and positions for multi-object extraction. Based on the additivity in moment features, the cell-based labeling algorithm can label divided cells of a certain size in an image by scanning the image only once to obtain the moment features of the labeled regions with remarkably reduced computational complexity and memory consumption for labeling. Our algorithm is a simple-onetime-scan cell-based labeling algorithm, which is suitable for hardware and parallel implementation. We also compared it with conventional labeling algorithms. The experimental results showed that our algorithm is faster than conventional raster-scan labeling algorithms. key words: fast multi-object extraction, connected component labeling

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“…It is difficult to achieve real-time performance with CPU-based eye detection in an embedded environment. A. Amir et al reduce processing time by using FPGA [15]. However, the eye detection algorithm they used is relatively simple; hence, the eye miss rate is relatively high.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, A. Amir et al implemented an embedded system for eye detection using a CMOS sensor and FPGA to overcome the shortcomings of CPU-based eye detection [15]. Their hardware-based embedded system for eye detection was implemented using simple logic gates, with no CPU and no addressable frame buffers.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An FPGA-based Parallel Hardware Architecture for Real-time Eye Detection

Kim¹,

Jung²,

Nguyen³

et al. 2012

JSTS:Journal of Semiconductor Technology and Science

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Abstract-Eye detection is widely used in applications, such as face recognition, driver behavior analysis, and human-computer interaction. However, it is difficult to achieve real-time performance with software-based eye detection in an embedded environment. In this paper, we propose a parallel hardware architecture for real-time eye detection. We use the AdaBoost algorithm with modified census transform(MCT) to detect eyes on a face image. We parallelize part of the algorithm to speed up processing. Several downscaled pyramid images of the eye candidate region are generated in parallel using the input face image. We can detect the left and the right eye simultaneously using these downscaled images. The sequential data processing bottleneck caused by repetitive operation is removed by employing a pipelined parallel architecture. The proposed architecture is designed using Verilog HDL and implemented on a Virtex-5 FPGA for prototyping and evaluation. The proposed system can detect eyes within 0.15 ms in a VGA image.

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An embedded system for an eye-detection sensor

Cited by 50 publications

References 24 publications

Comparison and Analysis of Three Anomaly Correction Methods in Embedded Systems

Comparison and Analysis of Three Anomaly Correction Methods in Embedded Systems

A Fast Multi-Object Extraction Algorithm Based on Cell-Based Connected Components Labeling

An FPGA-based Parallel Hardware Architecture for Real-time Eye Detection

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