Energy Efficient Canny Edge Detector for Advanced Mobile Vision Applications

Lee, Juseong; Tang, Hoyoung; Park, Jong Sun

doi:10.1109/tcsvt.2016.2640038

Cited by 77 publications

(35 citation statements)

References 20 publications

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“…In the circumference identification, the Hough transform implemented by [1] [15], and [9] was used. Because images were obtained under a controlled medium in terms of the distance that was taken, a standard radio measurement was used as a parameter in the Hough transform, as well as the detection thresholds, looking for only one circumference to be located: the one corresponding to the radio.…”

Section: Resultsmentioning

confidence: 99%

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Implementation of the Hough Transform for Iris Detection and Segmentation

Paulín-Martínez¹,

Lara-Guevara²,

Romero-González³

et al. 2019

AMI

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The iris is used as a reference for the study of unique biometric marks in people. The analysis of how to extract the iris characteristic information represents a fundamental challenge in image analysis, due to the implications it presents: detection of relevant information, data coding schemes, etc. For this reason, in the search for extraction of useful and characteristic information, approximations have been proposed for its analysis. In this article, it is presented a scheme to extract the relevant information based on the Hough transform. This transform helps to find primitive geometries in the irises, which are used to characterize each one of these. The results of the implementation of the algorithm of the Hough transform applied to the location and segmentation of the iris by means of its circumference are presented in the paper. Two public databases of iris images were used: UBIRIS V2 and CASIA-IrisV4, which were acquired under the same conditions and controlled environments. In the pre-processing stage the edges are found from the noise elimination in the image through the Canny detector. Subsequently, to the images of the detected edges, the Hough transform is applied to the disposition of the geometries detected.

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Section: Resultsmentioning

confidence: 99%

“…In order to carry out the identification, some figures can be formed by the edges that compose them. [8] and [9] use the Canny's method with the first derivative for edge detection, based on the variation of intensity between pixels.…”

Section: Introductionmentioning

confidence: 99%

Implementation of the Hough Transform for Iris Detection and Segmentation

Paulín-Martínez¹,

Lara-Guevara²,

Romero-González³

et al. 2019

AMI

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show abstract

“…This subsection demonstrates how to identify the eyes, and how to obtain whether the eyes are open or closed. The system deals with using information obtained for the binary version of the image and apply HAAR cascaded classifier [15,16] to detect eyes. Upon a time the eyes are identified, a series of algorithms can be applied to mark whether the eyes are open or closed.…”

Section: Fatigue Detectionmentioning

confidence: 99%

“…. The high detection accuracy is achieved through the use of cascaded classifier which is applied on a small number of important features, called Haar-like features that are selected by AdaBoost [15,16]. Fig.…”

Section: Fatigue Detectionmentioning

confidence: 99%

“…Road traffic injuries are currently the ninth leading source of death globally, and essential reason of death for youth people between the ages of [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. Existing trends propose that by 2030 road traffic deaths will turn into the fifth leading cause of death unless crucial action is taken [1].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Assistive Drive System using Mobile Cloud Computing

Sameh¹

2019

IJCA

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No one can deny that mobile devices are increasingly becoming an essential part of our lives, and being used for information delivery, access and communication. In this paper, a novel assistive drive system with mobile offloading is proposed. Three effective measures are integrated for reliable and early drowsiness detection, namely behavioral, vehicle, and physiological measures. These measures give higher quality and relevant information. Additionally, the proposed system uses mobile devices to process readings. However, with huge amount of data and intensive computations, mobiles cannot deliver results in reasonable times. A possible approach is to offload computations onto the cloud.

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Automated object tracking, event detection, and recognition for high‐speed video of drop formation phenomena

Radcliffe

Reklaitis

2021

AIChE Journal

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Optical imaging technologies have the potential to provide detailed information which can inform process design decisions via modeling of critical phenomena or provide innovative process sensors for use in online monitoring and control strategies. In this work, novel algorithms are developed for automated object tracking, event detection, and classification in high‐speed imaging sequences of drop breakup and coalescence. Using generalization of the physical patterns, a combined strategy extracts quantitative information about the spatiotemporal trajectory and events of every fluid‐object, mapping its lineage and relations to other objects without the need for a training dataset. This high‐level reference frame makes the extracted information easily accessible for evaluation of quantities over sets of objects or events of interest. Included results demonstrate the capability of the software for handling complex multievent scenarios at acceptable execution times, processing 2.7 GB image data (22,000 images/30 videos) in 110–115 s in MATLAB on a standard desktop computer.

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Energy Efficient Canny Edge Detector for Advanced Mobile Vision Applications

Cited by 77 publications

References 20 publications

Implementation of the Hough Transform for Iris Detection and Segmentation

Implementation of the Hough Transform for Iris Detection and Segmentation

Optimal Assistive Drive System using Mobile Cloud Computing

Automated object tracking, event detection, and recognition for high‐speed video of drop formation phenomena

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