scholarly journals Deteksi Posisi Plat Nomor Kendaraan Menggunakan Metode Transformasi Hough dan Hit or Miss

Electrician ◽  
2018 ◽  
Vol 12 (3) ◽  
pp. 118
Author(s):  
Yuda Puspito ◽  
FX Arinto Setyawan ◽  
Helmy Fitriawan
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Detection Rate ◽  
Detection System ◽  
Threshold Value ◽  
Transformation Method ◽  
Gray Level ◽  
Average Value ◽  
Average Recall

Abstrak: Penelitian ini dikembangkan sebuah sistem pendeteksi posisi plat nomor kendaraan yang ditampilkan pada GUI Matlab. Pendeteksian posisi plat nomor kendaraan menggunakan dua metode, yaitu metode transformasi hough dan transformasi hit or miss. Tahap pengolahan citra yang digunakan meliputi: binerisasi, aras keabuan, deteksi tepi, pemotongan citra, filtering, dan resizing. Keefektifan sistem ini diukur dengan perhitungan terhadap nilai perolehan (recall) dan nilai ketepatan (precision). Berdasarkan hasil penelitian didapatkan bahwa sistem berhasil mendeteksi posisi plat nomor kendaraan dengan tingkat keberhasilan pendeteksian sebesar 76% untuk nilai threshold 0,75, 72% untuk nilai threshold 0,8 dan 48% untuk nilai threshold 0,85. Hasil penelitian juga menunjukkan nilai rata-rata recall sebesar 54% untuk nilai threshold 0,75, 50% untuk nilai threshold 0,8 dan 40% untuk nilai threshold 0,85, sedangkan nilai rata-rata precision sebesar 14% untuk nilai threshold 0,75, 14% untuk nilai threshold 0,8 dan 12% untuk nilai threshold 0,85.Kata kunci: Trasnformasi Hough, Transformasi Hit Or Miss, Recall, Precission. Abstract: This research was developed a detection system of vehicle license plates that displayed at the GUI Matlab. The detecting the number plate position of the vehicle uses two methods, namely the transformation method of hough and the transformation of hit or miss. The image processing stages used include: binerization, gray level, edge detection, image cutting, filtering, and resizing. The effectiveness of this system is measured by calculating the value of the recall and the precision. Based on the results of the study it was found that the system successfully detected the number plate position of the vehicle with a detection rate of 76% for the threshold value of 0.75, 72% for the threshold value of 0.8 and 48% for the threshold value of 0.85. The results also showed an average recall value of 54% for the threshold value of 0.75, 50% for the threshold value of 0.8 and 40% for the threshold value of 0.85, while the average value of precision was 14% for the threshold value of 0.75, 14% for the threshold value of 0.85, while the average value of precision was 14% for the threshold value of 0.75, 14% for the threshold value of 0.8 and 12% for the threshold value of 0.85.Keywords: Trasnformasi Hough, Transformasi Hit Or Miss, Recall, Precission. 

scholarly journals Early warning flood detector adopting camera by Sobel Canny edge detection algorithm method

2021 ◽  
Vol 22 (3) ◽  
pp. 1796
Author(s):  
Satryo B. Utomo ◽  
Januar Fery Irawan ◽  
Rizqi Renafasih Alinra
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Water Level ◽  
Real Time ◽  
Early Warning ◽  
Detection System ◽  
Water Levels ◽  
Level Gauge ◽  
Canny Edge Detection ◽  
Canny Edge

Early warning of floods is an essential part of disaster management. Various automatic detectors have been developed in flood mitigation, including cameras. But reliability and accuracy have not been improved. Besides, the use of monitoring devices has been employed to monitor water levels in various water building facilities. The early warning flood detector was carried out with a sensor camera using an orange ball that floats near the water level gauge in a bounding box. This approach uses the integration of computer vision and image processing, namely digital image processing techniques, with Sobel Canny edge detection (SCED) algorithms to detect quickly and accurately water levels in real-time. After the water level is measured, a flood detection process is carried out based on the specified water level. According to the results of experiments in the laboratory, it has been shown that the proposed approach can detect objects accurately and fast in real-time. Besides, from the water level detection experiment, good results were obtained. Therefore, the object detection system and water level can be used as an efficient and accurate early detection system for flood disasters.

A Sub-Pixel Edge Detection Method Combining the Interpolation with the Moment Theory

2013 ◽  
Vol 475-476 ◽  
pp. 351-354
Author(s):  
Ya Zhou Zhou ◽  
Qiu Cheng Sun ◽  
Hao Chen
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Detection Method ◽  
Detection Accuracy ◽  
Gray Level ◽  
Two Dimensional ◽  
Moment Theory ◽  
One Dimensional ◽  
Edge Detection Method ◽  
The Moment

A new sub-pixel edge detection method is proposed to improve the detection accuracy. Firstly, using the theory of interpolation to acquire the continuous gray level distribution in one-dimensional .Therefore, the location of edge is determined. Secondly, in view of the two-dimensional edge detection, the moment spatial is taken into account. At last, the two-dimensional edge detection simplified as one-dimensional. From the test ,its known that the accuracy of the this algorithm is higher, especially for images with noise. So, the proposed algorithm has good applicability in image processing.

An Advanced Image Processing Prototype for Corrosion Finding Using Image Processing

2021 ◽  
Vol 18 (4) ◽  
pp. 1251-1255
Author(s):  
M. Malathi ◽  
P. Sinthia
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Detection Method ◽  
Research Work ◽  
Threshold Value ◽  
Fundamental Operation ◽  
Colour Changes ◽  
Edge Detection Method ◽  
Edge Recognition ◽  
Segmentation Threshold

The main objective of the research work is to recognize the rust of the substance with the help of Image Processing. The recognition of the rust portion of an image is carried out by quantizing of image in matrix form. The quantization process helps to perform the fundamental operation on image and also helps to identify the desired oxidation portion of an image. The corrosion portion was identified through the threshold operation, edge detection and segmentation. Threshold value assists to describe the types of the rust. Further the abrupt modification of colour in the images was captured by the edge detection method. Consequently partitioning of an image find the colour changes in the oxidized image. The corrosion portion was recognized by combining the edge recognition and partitioning process. Finally recommended methods provide the 98% accuracy to detect the rust.

The Application of MATLAB in Welding Image Processing

2012 ◽  
Vol 201-202 ◽  
pp. 300-303 ◽  
Author(s):  
Yuan Peng Liu ◽  
Xin Sun ◽  
Zhen Hua Wen
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Detection Methods ◽  
Gray Level ◽  
The Real ◽  
Edge Points ◽  
Welding Image ◽  
Margin Detection ◽  
Maximal Points

Firstly the margin-detection methods commonly used are presented in this paper. The algorithm idea is that the edge points correspond to the local maximal points of original image’s gray-level gradient. These algorithms are very sensitive to noises if these images mixed much noise, which usually leads to wrongly detect noise points as marginal points, and the real edge can not be detected as the interference action of noise. However, we perform kinds of pretreatments on these images under the MATLAB environment and adequately make use of the functions of image processing toolbox to satisfy the need of edge detection. Lastly, Combined with practical examples, the specific application of MATLAB in edge detection is analyzed in detail.

A Novel Extracting Algorithm of the Low Gray Fuzzy Edges for Infrared Images

2014 ◽  
Vol 1037 ◽  
pp. 411-415
Author(s):  
Dong Xing Li ◽  
Liang Geng ◽  
Qin Jun Du ◽  
Han Ren ◽  
Ai Jun Li ◽  
...  
Keyword(s):  
Edge Detection ◽  
Infrared Image ◽  
Threshold Value ◽  
Detection Algorithm ◽  
Conversion Function ◽  
Gray Level ◽  
Infrared Images ◽  
Computation Complexity ◽  
Edge Information ◽  
Edge Detection Algorithm

The fuzzy edge detection algorithm proposed by Pal-King has some disadvantages for extracting the low gray level edge information for the infrared images, such as high computation complexity, low threshold segmentation inaccuracy and the leakage edge information. For overcoming the disadvantages, the improved image fuzzy edge detection algorithm is proposed in this paper. First, redefining membership function to simplify computation complexity, the new conversion function enable the function transform interval is [0, 1], thus the value of the low gray level edge is not to be set to 0. Second, Ostu's algorithm is used in the selection of segmentation threshold named as transit point. The traditional threshold value is improved in order to make the segmentation accurate. The experimental results show that the lower gray infrared image edge information is preserved via proposed algorithm in this paper. The detecting results are more accurate. The run time is decreased obviously than the traditional Pal - king algorithm.

scholarly journals The object detection system of balinese script on traditional Balinese manuscript with findcontours method

2021 ◽  
Vol 11 (3) ◽  
pp. 177-184
Author(s):  
Putra Manuaba ◽  
◽  
Komang Ayu Triana Indah ◽  
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Edge Detection ◽  
Object Detection ◽  
Detection System ◽  
Main Issue ◽  
Age Factor

Lontar is a traditional Balinese manuscript with a Balinese script in it. Balinese traditional manuscripts can be more than 100 years old. The age factor of the Balinese manuscript has an impact on the Balinese script in it. Balinese script that has been written more than 10 years tends to be darker. This makes Balinese script not visible well, and this affects the image quality of the manuscript. This thing becomes the main issue in this research, Balinese script detection on Balinese manuscript images. the first of all is image processing using edge detection, canny and Sobel becomes the main algorithm of this process. After image processing, the Balinese manuscript will be processed with the findcontour method to detect an object that contains in it. The final process of this detection system is to separate detected objects into three main groups namely noise object, Balinese script object, and hole object. Application (Balinese script object detection system) is more accurate in detecting Balinese script objects in Balinese script under 1 year (new script), it tends to be more likely to find noise/dirt. This is because the writing of the lontar using a pencil first before using the knife media. This adds to the noise or dirt detected by the application The findcontour method can detect Balinese script objects with a detection result of 30% - 70% Balinese script objects.

scholarly journals Algorithm based on Histogram and Entropy for Edge Detection in Gray Level Images

2013 ◽  
Vol 11 (1) ◽  
pp. 2207-2215
Author(s):  
Mohamed A. El-Sayed
Keyword(s):  
Edge Detection ◽  
Threshold Value ◽  
Tsallis Entropy ◽  
Gray Level ◽  
Traditional Methods ◽  
Novel Approach ◽  
Computer Vision Applications ◽  
Derivatives Of ◽  
Gray Level Images

Edge detection and feature extraction are widely used in image processing and computer vision applications. Most of the traditional methods for edge detection are based on the first and second order derivatives of gray levels of the pixels of the original image utilizing 2D spatial convolution masks to approximate  the derivative. In this paper we present an algorithm for edge detection in gray level images. The main objective is to solve the previous problem of traditional methods with generate suitable quality of edge detection. Our new algorithm is based on two definitions of entropy: Shannon’s classical concept and a variation called Tsallis entropy. The  novel approach utilizing Subextensive Tsallis entropy rather than the evaluation of derivatives of the image in detecting edges in gray level images has been proposed. Here, we have used a suitable threshold value to segment the image and achieve the binary image. The effectiveness is demonstrated by using many different kinds of test images from the real-world and synthetic images. The results of this study were quite promising.

Algorithms for segmentation and recognition of objects on medical images based on chiarlet transformation and neural networks

2020 ◽  
pp. 35-45
Author(s):  
Y.A. Hamad ◽  
K.V. Simonov ◽  
A.S. Kents
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Edge Detection ◽  
Medical Images ◽  
Classification Algorithms ◽  
Visual Data ◽  
Recognition Of Objects

The paper considers general approaches to image processing, analysis of visual data and computer vision. The main methods for detecting features and edges associated with these approaches are presented. A brief description of modern edge detection and classification algorithms suitable for isolating and characterizing the type of pathology in the lungs in medical images is also given.

Traffic incident detection system based on video image processing

2008 ◽  
Vol 28 (7) ◽  
pp. 1886-1889 ◽  
Author(s):  
Qin WANG ◽  
Shan HUANG ◽  
Hong-bin ZHANG ◽  
Quan YANG ◽  
Jian-jun ZHANG
Keyword(s):  
Image Processing ◽  
Detection System ◽  
Video Image ◽  
Incident Detection ◽  
Traffic Incident ◽  
Video Image Processing

scholarly journals Fractional-Order Colour Image Processing

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 457
Author(s):  
Manuel Henriques ◽  
Duarte Valério ◽  
Paulo Gordo ◽  
Rui Melicio
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Fractional Derivative ◽  
Fractional Order ◽  
Fractional Derivatives ◽  
Grey Scale ◽  
Colour Image ◽  
Colour Image Processing ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Many image processing algorithms make use of derivatives. In such cases, fractional derivatives allow an extra degree of freedom, which can be used to obtain better results in applications such as edge detection. Published literature concentrates on grey-scale images; in this paper, algorithms of six fractional detectors for colour images are implemented, and their performance is illustrated. The algorithms are: Canny, Sobel, Roberts, Laplacian of Gaussian, CRONE, and fractional derivative.

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