Detection of Chicken Egg Embryos using BW Image Segmentation and Edge Detection Methods

2021 ◽  
Vol 5 (6) ◽  
pp. 1062-1069
Author(s):  
Shoffan Saifullah ◽  
Andiko Putro Suryotomo ◽  
Yuhefizar
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Image Enhancement ◽  
Histogram Equalization ◽  
Detection Methods ◽  
Adaptive Histogram Equalization ◽  
Chicken Egg ◽  
Enhancement Method ◽  
Edge Detection Method

This study aims to identify chicken egg embryos with the concept of image processing. This concept uses input and output in images. Thus the identification process, which was originally carried out using manual observation, was developed by computerization. Digital images are applied in identification by various image preprocessing, image segmentation, and edge detection methods. Based on these three methods, image processing has three processes: image grayscaling (convert to a grayscale image), image adjustment, and image enhancement. Image adjustment aims to clarify the image based on color correction. Meanwhile, image enhancement improves image quality, using histogram equalization (HE) and Contrast Limited Adaptive Histogram Equalization methods (CLAHE). Specifically for the image enhancement method, the CLAHE-HE combination is used for the improvement process. At the end of the process, the method used is edge detection. In this method, there is a comparison of various edge detection operators such as Roberts, Prewitt, Sobel, and canny. The results of edge detection using these four methods have the SSIM value respectively 0.9403; 0.9392; 0.9394; 0.9402. These results indicate that the SSIM values ​​of the four operators have the same or nearly the same value. Thus, the edge detection method can provide good edge detection results and be implemented because the SSIM value is close to 1.00 (more than 0.93). Image segmentation detected object (egg and embryo), and the continued process by edge detection showed clearly edge of egg and embryo.

scholarly journals Segmentasi Citra Menggunakan Metode Watershed Transform Berdasarkan Image Enhancement Dalam Mendeteksi Embrio Telur

2020 ◽  
Vol 5 (2) ◽  
pp. 53-60 ◽  
Author(s):  
Shoffan Saifullah
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Histogram Equalization ◽  
Watershed Transform ◽  
Adaptive Histogram Equalization

Image processing dapat diterapkan dalam proses deteksi embrio telur. Proses deteksi embrio telur dilakukan dengan menggunakan proses segmentasi, yang membagi citra sesuai dengan daerah yang dibagi. Proses ini memerlukan perbaikan citra yang diproses untuk memperoleh hasil optimal. Penelitian ini akan menganalisis deteksi embrio telur berdasarkan image processing dengan image enhancement dan konsep segmentasi menggunakan metode watershed transform. Image enhacement pada preprocessing dalam perbaikan citra menggunakan kombinasi metode Contrast Limited Adaptive Histogram Equalization (CLAHE) dan Histogram Equalization (HE). Citra grayscale telur diperbaiki dengan menggunakan metode CLAHE, dan hasilnya diproses kembali dengan menggunakan HE. Hasil perbaikan citra menunjukkan bahwa metode kombinasi CLAHE-HE memberikan gambar secara jelas daerah objek citra telur yang memiliki embrio. Proses segmentasi dengan menggunakan konversi citra ke citra hitam putih dan segmentasi watershed mampu menunjukkan secara jelas objek telur ayam yang memiliki embrio. Hasil segmentasi mampu membagi daerah telur memiliki embrio secara nyata dan akurat dengan persentase sebesar  » 98%.

A Study on Different Edge Detection Techniques in Digital Image Processing

2017 ◽  
pp. 100-122 ◽  
Author(s):  
Shouvik Chakraborty ◽  
Mousomi Roy ◽  
Sirshendu Hore
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Basic Feature ◽  
Detection Methods ◽  
Natural Image ◽  
Detection Techniques ◽  
Image Edges

Image segmentation is one of the fundamental problems in image processing. In digital image processing, there are many image segmentation techniques. One of the most important techniques is Edge detection techniques for natural image segmentation. Edge is a one of the basic feature of an image. Edge detection can be used as a fundamental tool for image segmentation. Edge detection methods transform original images into edge images benefits from the changes of grey tones in the image. The image edges include a good number of rich information that is very significant for obtaining the image characteristic by object recognition and analyzing the image. In a gray scale image, the edge is a local feature that, within a neighborhood, separates two regions, in each of which the gray level is more or less uniform with different values on the two sides of the edge. In this paper, the main objective is to study the theory of edge detection for image segmentation using various computing approaches.

An Image Enhancement Method Based on the Coal Mine Monitoring System

2012 ◽  
Vol 468-471 ◽  
pp. 204-207
Author(s):  
Zhen Chong Wang ◽  
Yan Qin Zhao
Keyword(s):  
Image Enhancement ◽  
Coal Mine ◽  
Impulse Noise ◽  
Histogram Equalization ◽  
Video Monitor ◽  
Low Contrast ◽  
Adaptive Histogram Equalization ◽  
Enhancement Method ◽  
Special Environment

For the low illumination and low contrast in the coal mine, images captured from the video monitor system sometimes are not so clear to help the related personal monitoring the production and safety of the mine. According to the special environment of coal mine, an image enhancement method was presented. In this method the impulse noise which is the mainly noise in the coal mine was first reduced with median filtering, then the low contrast and illumination was greatly improved with the improved adaptive histogram equalization. Experiments show that this method can improve the quality of images underground effectively.

scholarly journals Comparative Analysis of Image Quality Values on Edge Detection Methods

2020 ◽  
Vol 4 (2) ◽  
pp. 345-351
Author(s):  
Wicaksono Yuli Sulistyo ◽  
Imam Riadi ◽  
Anton Yudhana
Keyword(s):  
Image Processing ◽  
Comparative Analysis ◽  
Edge Detection ◽  
Computer Technology ◽  
Detection Method ◽  
Detection Methods ◽  
Sobel Operator ◽  
Image Edge Detection ◽  
Image Edge ◽  
Edge Detection Method

Identification of object boundaries in a digital image is developing rapidly in line with advances in computer technology for image processing. Edge detection becomes important because humans in recognizing the object of an image will pay attention to the edges contained in the image. Edge detection of an image is done because the edge of the object in the image contains very important information, the information obtained can be either size or shape. The edge detection method used in this study is Sobel operator, Prewitt operator, Laplace operator, Laplacian of Gaussian (LoG) operator and Kirsch operator which are compared and analyzed in the five methods. The results of the comparison show that the clear margins are the Sobel, Prewitt and Kirsch operators, with PSNR calculations that produce values ​​above 30 dB. Laplace and LoG operators only have an average PSNR value below 30 dB. Other quality comparisons use the histogram value and the contrast value with the highest value results in the Laplace and LoG operators with an average histogram value of 110 and a contrast value of 24. The lowest histogram and contrast value are owned by the Sobel and Prewitt operators.  

scholarly journals An image enhancement method based on gabor filtering in wavelet domain and adaptive histogram equalization

2021 ◽  
Vol 21 (1) ◽  
pp. 146
Author(s):  
Jeevan K M ◽  
Anne Gowda A B ◽  
Padmaja Vijay Kumar
Keyword(s):  
Image Enhancement ◽  
Histogram Equalization ◽  
Median Filtering ◽  
Wavelet Domain ◽  
Gabor Filtering ◽  
Low Contrast ◽  
Enhancement Technique ◽  
Adaptive Histogram Equalization ◽  
Enhancement Method ◽  
Two Parameters

<p><span>The images are not always good enough to convey the proper information. The image may be very bright or very dark sometime or it may be low contrast or high contrast. Because of these reasons image enhancement plays important role in digital image processing. In this paper we proposed an image enhancement technique in which Gabor and median filtering is performed in wavelet domain and Adaptive Histogram Equalization is performed in spatial domain. Brightness and contrast are the two parameters used for analyzing the performance of the proposed method</span></p>

scholarly journals An Extensive Review on Image Segmentation Techniques

2021 ◽  
Vol 1 (2) ◽  
pp. 1-5
Author(s):  
Kamlesh Sharma ◽  
Nidhi Garg
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Frequency Domain ◽  
Image Enhancement ◽  
Digital Images ◽  
Extensive Review ◽  
Current Image ◽  
Segmentation Image

Image processing is the use of algorithms to perform various operations on digital images. The techniques that are explained further are image segmentation and image enhancement. Image Segmentation is a method to partition an image into multiple segments, to change the presentation of an image into something more meaningful and easier to analyze. The current image segmentation techniques include region-based segmentation and edge detection segmentation. Image Enhancement is the process of improving the quality of image. Under this section there are two broad divisions- Spatial Domain Technique and Frequency Domain Technique.

scholarly journals Algorithmic Approaches For Image Edge Detection

2021 ◽  
Vol 10 (3) ◽  
pp. 1942-1948
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Image Enhancement ◽  
Digital Image Processing ◽  
Digital Image ◽  
Image Edge Detection ◽  
Image Edge ◽  
Algorithmic Approaches

Edge detection is most important technique in digital image processing. It play an important role in image segmentation and many other applications. Edge detection providesfoundation to many medical and military applications.It difficult to generate a generic code for edge detection so many kinds ofalgorithms are available. In this article 4 different approaches Global image enhancement with addition (GIEA), Global image enhancement with Multiplication (GIEM),Without Global image enhancement with Addition (WOGIEA),and without Global image enhancement with Multiplication (WOGIEM)for edge detection is proposed. These algorithms are validatedon 9 different images. The results showthat GIEA give us more accurate results as compare to other techniques.

scholarly journals An Extensive Review on Image Segmentation Techniques

2021 ◽  
pp. 1-5
Author(s):  
Dr. Kamlesh Sharma ◽  
◽  
Nidhi Garg ◽  
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Frequency Domain ◽  
Image Enhancement ◽  
Digital Images ◽  
Extensive Review ◽  
Current Image ◽  
Segmentation Image

Image processing is the use of algorithms to perform various operations on digital images. The techniques that are explained further are image segmentation and image enhancement. Image Segmentation is a method to partition an image into multiple segments, to change the presentation of an image into something more meaningful and easier to analyze. The current image segmentation techniques include region-based segmentation and edge detection segmentation. Image Enhancement is the process of improving the quality of image. Under this section there are two broad divisions- Spatial Domain Technique and Frequency Domain Technique.

Perbandingan Performa Histogram Equalization untuk Peningkatan Kualitas Gambar Minim Cahaya pada Android

2020 ◽  
Vol 12 (2) ◽  
pp. 80-88
Author(s):  
Claudia Kenyta ◽  
Daniel Martomanggolo Wonohadidjojo
Keyword(s):  
Image Enhancement ◽  
Light Condition ◽  
Histogram Equalization ◽  
Low Light ◽  
Adaptive Histogram Equalization ◽  
Enhancement Method ◽  
Better Than

When the photos are taken in low light condition, the quality of the results will not meet their expectation. Image Enhancement method can be used to enhance the quality of the photos taken in low light condition. One of the algorithms used is called Histogram Equalization (HE), that works using Histogram basis. The superiority of HE algorithm in enhancing the quality of the photos taken in low light condition is the simplicity of the algorithm itself and it does not need a high specification device for the algorithm to run. One variant of HE algorithm is Contrast Limited Adaptive Histogram Equalization (CLAHE). This paper shows the implementation of HE algorithm and its performance in enhancing the quality of photos taken in low light condition on Android based application and the comparison with CLAHE algorithm. The results show that, HE algorithm is better than CLAHE algorithm.

scholarly journals Automatic Leftover Weight Prediction in Tray Box Using Improved Image Segmentation Color Lighting Component

2020 ◽  
Vol 55 (1) ◽  
Author(s):  
Yuita Arum Sari ◽  
Ratih Kartika Dewi ◽  
Jaya Mahar Maligan ◽  
Luthfi Maulana ◽  
Sigit Adinugroho
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Food Waste ◽  
Histogram Equalization ◽  
Mean Square ◽  
Color Channel ◽  
Adaptive Histogram Equalization ◽  
Weight Prediction

The problem of food waste is experienced by many countries, including Indonesia. In the previous Comstock model, estimating food scraps required the expertise of the estimator, but this method has drawbacks because of subjective perspective of even skilled observers. Another weakness occurred when the observers were exhausted, which in turn negatively affected the measurement of leftover estimation. Therefore, in this paper, we propose an approach for automatic weight prediction using image processing in order to minimize the error forecasting caused by humans. Improved lighting component in image segmentation is also utilized. We apply this framework in the tray box images and estimate each compartment. Two types of tray box backgrounds are tested: gray and black backgrounds. The first part of the proposed method takes a lighting component from each color channel of LAB, HSV, YcbCr, YUV, and LUV. Each of those color channels are applied in contrast limited adaptive histogram equalization to adjust the contrast of each image. After that, the Otsu segmentation is applied, and some formulas to calculate leftover automatically are also presented. The result shows remarkable results when applied in the black background of the tray box with root mean square error around 6.67 using an L lighting component of LAB and Y lighting color component as well YcbCr and YUV. The proposed method is good for leftover forecasting since the estimation is not significantly different from one done by human observers.

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