scholarly journals PERBANDINGAN ALGORITMA CANNY EDGE DETECTION DAN PREWITT PADA DETEKSI STADIUM DIABETIK RETINOPATI

2021 ◽  
Vol 9 (02) ◽  
pp. 87-94
Author(s):  
Vina Ardelia Effendy ◽  
Febri Maspiyanti
Keyword(s):  
Diabetic Retinopathy ◽  
Edge Detection ◽  
Causes Of Death ◽  
Detection Method ◽  
Canny Edge Detection ◽  
Network Algorithms ◽  
A Value ◽  
Long Time ◽  
Canny Edge ◽  
Edge Detection Method

Diabetes is a serious threat to human health. In 2016, non-communicable diseases including Diabetes accounted for 70% of the total causes of death in the world. Diabetes if left unchecked will cause complications that can attack other organs to cause blindness called Diabetic Retinopathy (DR). Ophthalmologists make a grouping of diabetic characteristics of retinopathy by observing the retinal images of the eye taken using a fundus camera. This method requires a long time in the observation that allows errors in making observations, so image processing is needed to detect and classify the stage of diabetic retinopathy suffered by the patient. Thus, this research aims to help the process of early treatment of patients with diabetic retinopathy so as not to cause blindness. The data used in this study is DB0 Diaret data with a pixel size of 128 x 104 and the amount of data is 131. The methods used in this system include Canny Edge Detection, Prewitt, and stadium readings using Artificial Neural Network Algorithms. In this study the highest accuracy results obtained on the Canny Edge Detection method with a value of 90% while the Prewitt method has a 79% result. So, we get the conclusion that Canny Edge Detection is considered better.

A Comparison of Two Approaches for Collision Avoidance of an Automated Guided Vehicle Using Monocular Vision

2011 ◽  
Vol 145 ◽  
pp. 547-551 ◽  
Author(s):  
Zahari Taha ◽  
Jessnor Arif Mat Jizat
Keyword(s):  
Edge Detection ◽  
Collision Avoidance ◽  
Detection Method ◽  
Monocular Vision ◽  
Automated Guided Vehicle ◽  
False Detection ◽  
Canny Edge Detection ◽  
Canny Edge ◽  
Edge Detection Method ◽  
Robot Platform

In this paper a comparison of two approaches for collision avoidance of an automated guided vehicle (AGV) using monocular vision is presented. The first approach is by floor sampling. The floor where the AGV operates, is usually monotone. Thus, by sampling the floor, the information can be used to search similar pixels and establish the floor plane in its vision. Therefore any other objects are considered as obstacles and should be avoided. The second approach employs the Canny edge detection method. The Canny edge detection method allows accurate detection, close to real object, and minimum false detection by image noise. Using this method, every edge detected is considered to be part of an obstacle. This approach tries to avoid the nearest obstacle to its vision. Experiments are conducted in a control environment. The monocular camera is mounted on an ERP-42 Unmanned Solution robot platform and is the sole sensor providing information for the robot about its environment.

scholarly journals Identifikasi Kupu-Kupu Menggunakan Ekstraksi Fitur Deteksi Tepi (Edge Detection) dan Klasifikasi K-Nearest Neighbor (KNN)

2019 ◽  
Vol 5 (2) ◽  
Author(s):  
Rico Andrian ◽  
Saipul Anwar ◽  
Meizano Ardhi Muhammad ◽  
Akmal Junaidi
Keyword(s):  
Edge Detection ◽  
Nearest Neighbor ◽  
Detection Method ◽  
Butterfly Species ◽  
K Nearest Neighbor ◽  
A Value ◽  
Edge Strength ◽  
Canny Edge ◽  
Edge Detection Method

Lampung has the only breeding of in situ butterflies engineered in Indonesia namely Gita Persada Butterfly Park, which has approximately 211 butterfly species. Butterflies can be classified according to patterns found on the wings of a butterfly. The weakness of the human eye in distinguishing patterns on butterflies is a foundation in building butterfly identification based on pattern recognition. This study uses 6 species of butterflies: Papilio memnon, Troides helena, Papilio nephelus, Cethosia penthesilea, Papilio peranthus, and Pachliopta aristolochiae. The butterfly dataset used is 600 images. The butterfly image used is in the form of the upper wing side. The pre-processing stage uses the method of scaling, segmentation, and grayscale. The feature extraction stage uses the canny edge detection method by applying smoothing, edge strength, edge direction, non maximum suppression, and hyterisis thresholding. The classification phase uses the K-Nearest Neighbor (KNN) method with values k = 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21 and 23 obtained under the Rule of Thumb. The identification of butterfly require a classification time of 8 seconds. The highest accuracy is obtained from testing with a value of k = 5 by 80%.

scholarly journals Processing of Drone’s Digital Image for Determining Border of Rice Fields with Edge Detection Method

2019 ◽  
Vol 2 (2) ◽  
pp. 139-144
Author(s):  
Suhardiman Diman ◽  
Zahir Zainuddin ◽  
Salama Manjang
Keyword(s):  
Edge Detection ◽  
Rice Field ◽  
Detection Method ◽  
Computing Time ◽  
Threshold Value ◽  
Error Rates ◽  
Detection Methods ◽  
Canny Edge Detection ◽  
Canny Edge ◽  
Edge Detection Method

Edge detection was the basic thing used in most image processing applications to get information from the image frame as a beginning for extracting the features of the segmentation object that will be detected. Nowadays, many edge detection methods create doubts in choosing the right edge detection method and according to image conditions. Based on the problems, a study was conducted to compare the performance of edge detection using methods of canny, Sobel and laplacian by using object of rice field. The program was created by using the Python programming language on OpenCV.  The result of the study on one image test that the Canny method produces thin and smooth edges and did not omit the important information on the image while it has required a lot of computing time. Classification is generally started from the data acquisition process; pre-processing and post-processing. Canny edge detection can detect actual edges with minimum error rates and produce optimal image edges. The threshold value obtained from the Canny method was the best and optimal threshold value for each method. The result of a test by comparing the three methods showed that the Canny edge detection method gives better results in determining the rice field boundary, which was 90% compared to Sobel 87% and laplacian 89%.

scholarly journals Implementasi Penggabungan Prewitt dan Canny Edge Detection untuk Identifikasi Ikan Air Tawar

KREA-TIF ◽  
2018 ◽  
Vol 6 (2) ◽  
pp. 120
Author(s):  
Gibtha Fitri Laxmi ◽  
Puspa Eosina ◽  
Fety Fatimah
Keyword(s):  
Edge Detection ◽  
Image Retrieval ◽  
Freshwater Fish ◽  
Detection Method ◽  
Image Data ◽  
Content Based Image Retrieval ◽  
A Value ◽  
Edge Points ◽  
Canny Edge ◽  
Edge Detection Method

<p align="center"><strong>Abstrak</strong></p><p class="IsiAbstrak">Indonesia merupakan negara yang memiliki keanekaragaman hayati yang besar, salah satunya jenisnya ialah keanekaragaman ikan air tawar. Ikan air tawar yang layak konsumsi saat ini pun banyak jenisnya, sehingga bagi masyarakat yang kurang pengetahuan untuk mengenali jenis ikan sangatlah sulit. Teknologi identifikasi pengenalan citra dengan berbasis konten citra (Content Based Image Retrieval) dengan fitur bentuk berdasarkan titik tepi yang dihasilkan dapat membantu mengenali jenis ikan yang ada. Citra ikan yang digunakan diubah dari RGB menjadi grayscale yang diproses dengan metode deteksi tepi menjadi matriks nilai biner sehingga membentuk titik tepi dari ikan. Data citra ikan air tawar dalam penelitian berjumlah sepuluh jenis ikan, yang akan diproses untuk mendapatkan ekstraksi fitur deteksi tepinya. Deteksi tepi yang digunakan ialah penggabungan metode prewitt dan canny. Penelitian ini tidak memiliki hasil yang akurat dengan nilai 25%. Dimana penggabungan fitur lain akan sangat membantu dalam identifikasi.</p><p align="center"><strong>Abstract</strong></p><p><em>Indonesia is a country that has a great biodiversity, one of which is the diversity of freshwater fish. Freshwater fish that are suitable for consumption today are of many kinds, so that people who lack knowledge to recognize fish species are very difficult. Image recognition identification technology with Content Based Image Retrieval with shape features based on the resulting edge points can help identify the types of fish that exist. The fish image used is converted from RGB to grayscale which is processed by edge detection method into a binary value matrix so that it forms the edge points of the fish. Image data of freshwater fish in the study amounted to ten types of fish, which will be processed to obtain extraction of the edge detection features. The edge detection used is the merging of the prewitt and canny methods. This study did not have accurate results with a value of 25%. Where combining other features will be very helpful in identification.</em></p>

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