2022
DOI: 10.26555/jiteki.v8i2.23724
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K-Means Segmentation Based-on Lab Color Space for Embryo Detection in Incubated Egg

Abstract: The quality of the hatching process influences the success of the hatch rate besides the inherent egg factors. Eliminating infertile or dead eggs and monitoring embryonic growth are very important factors in efficient hatchery practices. This process aims to sort eggs that only have embryos to remain in the incubator until the end of the hatching process. This process aims to sort eggs with embryos to remain hatched until the end. Maximum checking is done the first week in the hatching period. This study aims … Show more

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Cited by 10 publications
(6 citation statements)
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“…Data preprocessing is crucial for ensuring that the input medical images are optimal for subsequent segmentation and enhancement processes [84]. The preprocessing steps address image artifacts, enhance image quality, and simplify image representation [85]. In this section, we provide a comprehensive description of the data preprocessing pipeline, which includes the following key steps: image conversion to 8-bit, grayscaling, image enhancement with histogram equalization (HE), and image adjustment.…”
Section: Dataset Preprocessingmentioning
confidence: 99%
See 1 more Smart Citation
“…Data preprocessing is crucial for ensuring that the input medical images are optimal for subsequent segmentation and enhancement processes [84]. The preprocessing steps address image artifacts, enhance image quality, and simplify image representation [85]. In this section, we provide a comprehensive description of the data preprocessing pipeline, which includes the following key steps: image conversion to 8-bit, grayscaling, image enhancement with histogram equalization (HE), and image adjustment.…”
Section: Dataset Preprocessingmentioning
confidence: 99%
“…One of the central aspects of data preprocessing is the application of histogram equalization (HE). This enhancement technique redistributes pixel intensities to achieve a more uniform histogram [92]. Histogram equalization (HE) is employed to improve the contrast and visibility of structures within the images [93].…”
Section: Image Enhancement With Histogram Equalization (He)mentioning
confidence: 99%
“…Our ensemble CNN-transfer learning architecture [18] relies on the DeepLabV3+ with ResNet18 model, forming the backbone of our brain tumor prediction system. ResNet18, renowned for its deep architecture as shown in Figure 2…”
Section: Base Cnn Model: Resnet18mentioning
confidence: 99%
“…The system utilizes a custom-built application with a digital camera as an input source for capturing real-time video streams. Extensive training and testing on a comprehensive dataset are conducted, evaluating the system's performance with an 80:20 train-test split [12]. The trained CNN model processes real-time video frames [14], providing timely and accurate mask detection.…”
Section: Introductionmentioning
confidence: 99%