scholarly journals Analisis Pendeteksian Pola Wajah Menggunakan Metode Haar-Like Feature

2019 ◽  
Vol 2 (2) ◽  
pp. 69 ◽  
Author(s):  
Sugandi Chau ◽  
Jepri Banjarnahor ◽  
Dikky Irfansyah ◽  
Sinta Kumala
Keyword(s):  
Edge Detection ◽  
Bilateral Filter ◽  
Cascade Classifier ◽  
Integral Image ◽  
Canny Edge Detection ◽  
Canny Edge

Pencatatan kehadiran mahasiswa merupakan salah satu faktor penting dalam pengolahan kedisiplinan, kewajiban, dan ketaatan mahasiswa dalam mengikuti proses perkuliahan. Pencatatan kehadiran mahasiswa sebelumnya dilakukan dengan cara manual yaitu dengan menggunkan tanda tangan, pencatatan kehadiran dengan cara manual dapat menjadi penghambat pemantauan kedisiplinan, ketaatan mahasiswa dalam hal ketepatan waktu datang mahasiswa. Pencatatan kehadiran mahasiswa secara manual dapat diganti dengan pencatatan kehadiran mahasiswa secara terkomputerisasi yang menggunakan proses indentifikasi teknologi biometrik, untuk mengidentifikasi pola wajah mahasiswa digunakan metode <em>bilateral filter</em>, <em>canny edge detection</em>, <em>haar-like feature</em>, <em>integral image</em>, <em>cascade classifier adaboost</em>. Dari hasil pengujian, tingkat keberhasilan pencatatan kehadiran mahasiswa berdasarkan pola wajah dari masing-masing mahasiswa sebesar 70.43%.

Application of Improved Canny Edge Detection Algorithm in Coal-Rock Interface Recognition

2012 ◽  
Vol 220-223 ◽  
pp. 1279-1283 ◽  
Author(s):  
Li Hong Dong ◽  
Peng Bing Zhao
Keyword(s):  
Edge Detection ◽  
Detection Algorithm ◽  
Canny Edge Detection ◽  
Edge Detection Algorithm ◽  
Canny Edge ◽  
Rock Interface ◽  
Coal Rock ◽  
Image Edge ◽  
Canny Algorithm ◽  
Canny Edge Detection Algorithm

The coal-rock interface recognition is one of the critical automated technologies in the fully mechanized mining face. The poor working conditions underground result in the seriously polluted edge information of the coal-rock interface, which affects the positioning precision of the shearer drum. The Gaussian filter parameters and the high-low thresholds are difficult to select in the traditional Canny algorithm, which causes the information loss of gradual edge and the phenomenon of false edge. Consequently, this paper presents an improved Canny edge detection algorithm, which adopts the adaptive median filtering algorithm to calculate the thresholds of Canny algorithm according to the grayscale mean and variance mean. This algorithm can protect the image edge details better and can restrain the blurred image edge. Experimental results show that this algorithm has improved the edge extraction effect under the case of noise interference and improved the detection precision and accuracy of the coal-rock image effectively.

Canny edge detection enhancement by general auto-regression model and bi-dimensional maximum conditional entropy

Optik ◽  
2014 ◽  
Vol 125 (15) ◽  
pp. 3946-3953 ◽  
Author(s):  
Fei Hao ◽  
Jinfei Shi ◽  
Zhisheng Zhang ◽  
Ruwen Chen ◽  
Songqing Zhu
Keyword(s):  
Edge Detection ◽  
Regression Model ◽  
Conditional Entropy ◽  
Canny Edge Detection ◽  
Canny Edge ◽  
Detection Enhancement ◽  
Auto Regression

Performance evaluation of canny edge detection on a tiled multicore architecture

2011 ◽  
Author(s):  
Andrew Z. Brethorst ◽  
Nehal Desai ◽  
Douglas P. Enright ◽  
Ronald Scrofano
Keyword(s):  
Performance Evaluation ◽  
Edge Detection ◽  
Multicore Architecture ◽  
Canny Edge Detection ◽  
Canny Edge
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