Segmentation for embryonated Egg Images Detection using the K-Means Algorithm in Image Processing

Saifullah, Shoffan

doi:10.1109/icic50835.2020.9288648

Cited by 15 publications

(16 citation statements)

References 46 publications

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“…In the previous process, we captured an object (chicken egg) using candling process, and the design of this process is shown in Figure 2. (a) [28], [29]. Based on this process, the digital image was created and processed by other image preprocessing methods.…”

Section: Methodsmentioning

confidence: 99%

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

Saifullah

Suryotomo

Yuhefizar

2021

RESTI

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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.

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Section: Methodsmentioning

confidence: 99%

“…Figure 3 shows that the three color components converted into a grayscale image with a grayscale range between 0-255. The converted image is based on the number matrix of the image according to the size [40] and then calculated. The visible image is as shown in Figure 3.…”

Section: Convert To Grayscale Imagementioning

confidence: 99%

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

Saifullah

Suryotomo

Yuhefizar

2021

RESTI

Self Cite

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“…This image in each pixel has a color from white to black, where each pixel is represented by 8 bits. The grayscale process can be done using (5) [21], [22].…”

Section: Background Subtractionmentioning

confidence: 99%

Parking detection system using background subtraction and HSV color segmentation

et al. 2021

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Manual system vehicle parking makes finding vacant parking lots difficult, so it has to check directly to the vacant space. If many people do parking, then the time needed for it is very much or requires many people to handle it. This research develops a real-time parking system to detect parking. The system is designed using the HSV color segmentation method in determining the background image. In addition, the detection process uses the background subtraction method. Applying these two methods requires image preprocessing using several methods such as grayscaling, blurring (low-pass filter). In addition, it is followed by a thresholding and filtering process to get the best image in the detection process. In the process, there is a determination of the ROI to determine the focus area of the object identified as empty parking. The parking detection process produces the best average accuracy of 95.76%. The minimum threshold value of 255 pixels is 0.4. This value is the best value from 33 test data in several criteria, such as the time of capture, composition and color of the vehicle, the shape of the shadow of the object’s environment, and the intensity of light. This parking detection system can be implemented in real-time to determine the position of an empty place.

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“…This condition is growing along with the development of technology. Technology in identifying the fertility of chicken eggs has been developed in various ways, one of which is the application of image processing to identify the fertility of chicken egg images, either using image segmentation [8], [9] or classification or identification [10].…”

Section: Introductionmentioning

confidence: 99%

Chicken Egg Fertility Identification using FOS and BP-Neural Networks on Image Processing

Saifullah

Suryotomo

2021

RESTI

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This article aims to test FOS (first-order statistical) in extracting features of embryonated eggs. This test uses the initial step of image processing to get the best input image in feature extraction. The image processing method starts from the image acquisition process, then improves with image preprocessing and segmentation. Image acquisition in this study uses the concept of egg candling in a dark place captured with a smartphone camera. The acquisition results are improved by image preprocessing using gray scaling, image enhancement (by Histogram Equalization), and segmentation of chicken egg image. The segmentation results were extracted using FOS with five parameters: mean, entropy, variance, skewness, and kurtosis. Based on the calculation of these parameters, it is graphed and shows the difference in patterns between fertile and infertile eggs. However, some eggs have a similar pattern, thus affecting the identification process. The identification process used neural networks by the backpropagation method for training and testing. The training results provide an accuracy value of 100% of all training data; however, 80% of the new test data obtained test results at testing. This test is carried out with 100 data, 50 each for training and test data. Based on the test results, which significantly affect the level of accuracy is the feature extraction method. FOS pattern in detecting the fertility of chicken eggs by BP Neural Network is still categorized as low, so it is necessary to improve methods to get maximum results.

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Segmentation for embryonated Egg Images Detection using the K-Means Algorithm in Image Processing

Cited by 15 publications

References 46 publications

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

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

Parking detection system using background subtraction and HSV color segmentation

Chicken Egg Fertility Identification using FOS and BP-Neural Networks on Image Processing

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