Automated image captioning with deep neural networks

Zarir, Abdullah Ahmad; Bashar, Saad Bin; Ismail, Amelia Ritahani

doi:10.31763/sitech.v1i1.31

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Red is multiplied by 0.2989, green is multiplied by 0.587, and blue is multiplied by 0.1141. 𝐺′ = 0.2989 * 𝑅 + 0.587 * 𝐺 + 0.1141 * 𝐵 (10) Image enhancement improves the image [33] before processing [34]. Image enhancement in this study uses a combination of Histogram Equalization (HE) and Contrast Limited Histogram Equalization (CLAHE) [35].…”

Section: Image Grayscaling and Enhancementmentioning

confidence: 99%

K-Means Segmentation Based-on Lab Color Space for Embryo Detection in Incubated Egg

Saifullah

Dreżewski

Khaliduzzaman

et al. 2022

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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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 to detect the presence of embryos in eggs. Detection of the existence of embryos is processed using segmentation. Egg images are segmented using the K-means algorithm based on Lab color images. The results of the image acquisition are converted into Lab color space images. The results of Lab color space images are processed using K-means for each color. The K-means process uses cluster k=3, where this cluster divides the image into three parts: background, eggs, and yolk. Egg yolks are part of eggs that have embryonic characteristics. This study applies the concept of color in the initial segmentation and grayscale in the final stages. The initial phase results show that the image segmentation results using k-means clustering based on Lab color space provide a grouping of three parts. At the grayscale image processing stage, the results of color image segmentation are processed with grayscaling, image enhancement, and morphology. Thus, it seems clear that the yolk segmented shows the presence of egg embryos. Based on this process and results, the initial stages of the embryo detection process used K-means segmentation based on Lab color space. The evaluation uses MSE and MSSIM, with values of 0.0486 and 0.9979; this can be used as a reference that the results obtained can detect embryos in egg yolk. This protocol could be used in a nondestructive quantitative study on embryos and their morphology in a precision poultry production system in the future.

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Section: Image Grayscaling and Enhancementmentioning

confidence: 99%

K-Means Segmentation Based-on Lab Color Space for Embryo Detection in Incubated Egg

Saifullah

Dreżewski

Khaliduzzaman

et al. 2022

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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“…Raw data (without labels) can be processed and accepted for the clustering process. This method is different from supervised learning [26] [27], which receives input in the form of vectors (x-1, y1), (x2, y2), …, (xi , yi), where xi is data of the training data and yi is the class label for xi. This method is very popular, fast, and simple [28].…”

Section: K-means Clusteringmentioning

confidence: 99%

K-Means Clustering for Egg Embryo's Detection Based-on Statistical Feature Extraction Approach of Candling Eggs Image

Saifullah

2020

Sinergi

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This research discusses the detection of embryonic eggs using the k-means clustering method based on statistical feature extraction. The processes that occur in detection are image acquisition, image enhancement, feature extraction, and identification/detection. The data used consisted of 200 egg image data, consisting of 100 test data and 100 new test data. The acquisition process uses a smartphone camera by capturing candled egg objects. The results of image acquisition become a reference in the process of image enhancement and feature extraction using Statistical Feature Extraction. The statistical feature extraction applied is the Gray Level Co-occurrence Matrix (GLCM) method, which consists of 6 features, namely Energy, Contrast, Entropy, Variance, Correlation, and Homogeneity. The results of feature extraction (6 features) are grouped by the K-means Clustering method. The clustering process uses Euclidean distance calculations to determine the proximity of features. The results of grouping and testing give the best average results with an accuracy of ≈ 74% from several test samples.

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

Saifullah

2020

2020 Fifth International Conference on Informatics and Computing (ICIC)

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Automated image captioning with deep neural networks

Cited by 3 publications

References 11 publications

K-Means Segmentation Based-on Lab Color Space for Embryo Detection in Incubated Egg

K-Means Segmentation Based-on Lab Color Space for Embryo Detection in Incubated Egg

K-Means Clustering for Egg Embryo's Detection Based-on Statistical Feature Extraction Approach of Candling Eggs Image

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

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