Fingerprint Identification Using the Hybrid Thresholding and Edge detection for the Room Security

Sumijan, Sumijan; Arlis, Syafri; Purnama, Pradani Ayu Widya

doi:10.18421/tem94-10

Cited by 3 publications

(2 citation statements)

References 10 publications

(9 reference statements)

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“…Thresholding Hybrid Laplacian from Otsu and Gaussian to obtain the results of background and object separation to identify smart fingerprint photos, with an average level of similarity of 87.94 percent. The calculating results demonstrate an extremely high degree of precision [40].…”

Section: Introductionmentioning

confidence: 84%

Hybrid Canny Zerocross Method for Edge Detection in Retina Identification Cases

Andini¹,

Wanto²,

Devita³

et al. 2022

International Journal on Advanced Science, Engineering and Information Technology

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Edge detection is fundamental to Figure processing. Edges include much information in a figure, including the object's location, shape, size, and information about its texture. Since edge detection is a critical component of Figure processing for object detection, comprehend algorithms for edge detection. This is because the edges define an item's contours, serve as a demarcation between the object and its backdrop, and serve as a demarcation between overlapping objects. That is, if the edges of an image can be identified accurately, all things can be found. The proposal of this paper is the use of the Canny Zerocross hybrid method to perform better edge detection based on comparative studies and the incorporation of the Canny way, which is considered one of the best edge detection methods, with the Zerocross way (cross zero) which is a derivative of the laplacian. In this paper, the research data used is the retinal image dataset-data obtained from STARE (Structured Analysis of the Retina). The Veterans Administration Medical Center in San Diego and the Shiley Eye Center (ECS) at the University of California provided Figures and clinical data from the retinal images. The experimental results of the comparative study show that the Zerocross edge detection technique is better than the Canny edge detection technique. Meanwhile, edge detection and image identification would be better when combining the two methods (hybrid) based on merging studies.

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Section: Introductionmentioning

confidence: 84%

Hybrid Canny Zerocross Method for Edge Detection in Retina Identification Cases

Andini¹,

Wanto²,

Devita³

et al. 2022

International Journal on Advanced Science, Engineering and Information Technology

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“…Data diperoleh dari wawancara dan hasil dokumentasi alat rontgen X-Ray. [11]. Rumus untuk mengubah citra kebentuk grayscale(keabuabuan) disajikan pada Persamaan (1).…”

Section: Mengumpulkan Data Dan Informasi Tentang Objek Penelitianunclassified

Identifikasi Citra Paru-Paru pada Pasien COVID-19 dengan Teknik Edge Detection

Pertiwi

2022

jsisfotek

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X-ray chest radiographs produce digital radiographic images of the chest area such as the lungs, heart and ribs. This image can visualize the lung condition of patients with COVID-19. The Edge Detection technique can see the edges of objects in the lungs of COVID-19 patients more clearly. The lungs of patients with COVID-19 are damaged due to the GGO (Ground Glass Opacity) COVID-19, namely there is white fog, the lungs look blurry at the edges or areas affected by the disease and also affect the area of the area. This technique can make it easier for health workers to see X-ray results on objects in the lungs of COVID-19 patients and assist doctors in handling COVID-19 patients. With advances in the computer field in the application of image processing techniques, this Edge Detection Technique uses Matlab software to obtain edge and area images of clean lungs in COVDI-19 patients. The data used in this study were 30 samples of lung images of COVID patients and 10 images of healthy patients' lungs as a comparison sourced from Embung Fatimah Hospital Batam, which were processed in the grayscale stage, then the image was preprocessed with Intensity Adjustment then segmented with Active Contour and then using Edge Detection Technique. The results of this study were as many as 22 lung images produced an accuracy value of 73%. The image of the test results with the Edge Detection Technique to identify the edges of the object's lungs of COVID-19 patients which are quite clear by producing white pixels that are so visible. The perimeter of the right lung of COVID-19 patients ranged from 110,897 - 261,254 mm and Area 267,719 - 940,668 mm2, Perimeter of the left lung ranged from 114,613 - 262,943 and Area 170.616 - 856,993 mm2, while healthy patients as comparison had the right lung Perimeter range of 187,598 -270,624 and Area 514,947 - 1025.44 mm2, Perimeter of the left lung 182,226 - 287,358 and Area 480,592 - 901,418 mm2 means that COVID-19 virus infection reduces lung area, the range of COVID-19 lungs is lower than healthy lungs.

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