Pupil Detection System Using Intensity Labeling Algorithm in Field Programmable Gate Array

2020 ◽  
Vol 17 (12) ◽  
pp. 5364-5367
Author(s):  
S. Baskaran ◽  
L. Mubark Ali ◽  
A. Anitharani ◽  
E. Annal Sheeba Rani ◽  
N. Nandhagopal
Keyword(s):  
Real Time ◽  
Detection System ◽  
Median Filter ◽  
Diagnostic Technique ◽  
Image Process ◽  
Eye Tracker ◽  
Eye Detection ◽  
Detection Techniques ◽  
Pupil Detection ◽  
Labeling Algorithm

Pupil detection techniques are an essential diagnostic technique in medical applications. Pupil detection becomes more complex because of the dynamic movement of the pupil region and it’s size. Eye-tracking is either the method of assessing the point of focus (where one sees) or the orientation of an eye relative to the head. An instrument used to control eye positions and eye activity is the eye tracker. As an input tool for human-computer interaction, eye trackers are used in research on the visual system, in psychology, psycholinguistics, marketing, and product design. Eye detection is one in all the applications in the image process. This is very important in human identification and it will improve today’s identification technique that solely involves the eye detection to spot individuals. This technology is still new, only a few domains are applying this technology as their medical system. The proposed work is developing an eye pupil detection method in real-time, stable, using an intensity labeling algorithm. The proposed hardware architecture is designed using the median filter, segmentation using the threshold process, and morphology to detect pupil shape. Finally, an intensity Labeling algorithm is done to locate an exact eye pupil region. A Real-time FPGA implementation is done by Altera Quartus II software with cyclone IV FPGA.

scholarly journals Real–Time Eye Tracking and Iris Localization

2012 ◽  
Author(s):  
Husniza Razalli ◽  
Rahmita Wirza O. K. Rahmat ◽  
Ramlan Mahmud
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Vision System ◽  
Main Task ◽  
Circle Fitting ◽  
Eye Tracker ◽  
Eye Detection ◽  
Iris Localization ◽  
Point Detection ◽  
Human Eyes

Masalah sistem pengesanan mata yang tegar tanpa sebarang gangguan adalah satu isu yang penting dan mencabar di dalam bidang visi komputer. Masalah ini bukan hanya mengurangkan masalah dalam carian ciri–ciri paras rupa untuk proses pengecaman tetapi juga boleh digunakan untuk memudahkan tugas pengenalpastian dan interaksi antara manusia dan sistem komputer. Walaupun kebanyakan hasil kerja terdahulu telah pun mempunyai keupayaan menentukan lokasi mata manusia tetapi objektif utama rencana ini bukan tertumpu kepada pengesanan mata sahaja. Objektif kajian adalah untuk merekabentuk sebuah sistem masa nyata dan terperinci, iaitu sistem pengesanan muka berskala dengan ciri–ciri petunjuk pergerakan mata berdasarkan pergerakan anak mata (iris) dengan mengunakan teknik penempatan yang terhasil daripada teknik pemprosesan imej dan teknik muatan bulatan. Hasil daripada kajian ini telah pun berjaya diimplimentasikan menggunakan kamera web dengan ralat yang minimum. Kata kunci: Pengesanan mata masa nyata; penempatan anak mata; pemprosesan imej; pengesanan bucu; muatan bulatan Robust, non–intrusive human eye detection problem has been a fundamental and challenging problem for computer vision area. Not only it is a problem of its own, it can be used to ease the problem of finding the locations of other facial features for recognition tasks and human–computer interaction purposes as well. Many previous works have the capability of determining the locations of the human eyes but the main task in this paper is not only a vision system with eye detection capability. Our aim is to design a real–time face tracker system and iris localization using edge point detection method indicates from image processing and circle fitting technique. As a result, our eye tracker system was successfully implemented using non–intrusive webcam with less error. Key words: Real–time face tracking; iris localization; image processing; edge detection; circle fitting

An FPGA-Based Real-Time Human Eye Pupil Detection System Using E2V Smart Camera

2019 ◽  
Vol 16 (2) ◽  
pp. 649-654
Author(s):  
S. Navaneethan ◽  
N. Nandhagopal ◽  
V. Nivedita
Keyword(s):  
Real Time ◽  
Detection System ◽  
Detection Algorithm ◽  
Video Frame ◽  
Smart Camera ◽  
Verilog Hdl ◽  
Real Time System ◽  
Human Eye ◽  
Pupil Detection ◽  
Main Motive

Threshold based pupil detection algorithm was found tobe most efficient method to detect human eye. An implementation of a real-time system on an FPGA board to detect and track a human's eye is the main motive to obtain from proposed work. The Pupil detection algorithm involved thresholding and image filtering. The Pupil location was identified by computing the center value of the detected region. The proposed hardware architecture is designed using Verilog HDL and implemented on aAltera DE2 cyclone II FPGA for prototyping and logic utilizations are compared with Existing work. The overall setup included Cyclone II FPGA, a E2V camera, SDRAM and a VGA monitor. Experimental results proved the accuracy and effectiveness of the hardware realtime implementation as the algorithm was able to manage various types of input video frame. All calculation was performed in real time. Although the system can be furthered improved to obtain better results, overall the project was a success as it enabled any inputted eye to be accurately detected and tracked.

Real Time Defects of Beer Bottle Detection System Based on DSP

2008 ◽  
Vol 392-394 ◽  
pp. 414-418 ◽  
Author(s):  
B. Ren ◽  
Tan Cheng Xie ◽  
X. Nan
Keyword(s):  
Real Time ◽  
Video Processing ◽  
Detection System ◽  
Processing System ◽  
Ccd Camera ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Experimental Result ◽  
Detection Techniques ◽  
Video Images

The paper analyses the problem of beer bottles detection techniques on the beer bottles production line, uses digital image processing technique on the beer bottles online defect detection. The paper puts forward the designing ideas of the hardware, developing flow of the software and the algorithm of beer bottles detection. TMSDM642 is used to set up the real-time video processing system of the hardware .The hardware system is mainly composed of three parts: the part of memory, the part of the input and the part of the output. When beer bottles are put into the work area, the video images of the bottle-mouth and bottle-bottom will be gained by the CCD camera, firstly, preprocessing is used to eliminate video image noise. Secondly, the image segmentation algorithm is used to detect defects in video images. Lastly the goal of extracting defects will be accomplished. The experimental result indicated that this system may effectively exam the flaw or the unqualified beer bottles.

Real-time error detection techniques based on FPGA

2013 ◽  
Vol 33 (5) ◽  
pp. 1459-1462
Author(s):  
Xiaoming JU ◽  
Jiehao ZHANG ◽  
Yizhong ZHANG
Keyword(s):  
Real Time ◽  
Error Detection ◽  
Time Error ◽  
Detection Techniques

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

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