Paper Reading AI Learner

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

2021-02-08 14:03:51
Shoffan Saifullah

Abstract

Image processing can be applied in the detection of egg embryos. The egg embryos detection is processed using a segmentation process. The segmentation divides the image according to the area that is divided. This process requires improvement of the image that is processed to obtain optimal results. This study will analyze the detection of egg embryos based on image processing with image enhancement and the concept of segmentation using the watershed method. Image enhancement in preprocessing in image improvement uses a combination of Contrast Limited Adaptive Histogram Equalization (CLAHE) and Histogram Equalization (HE) methods. The grayscale egg image is corrected using the CLAHE method, and the results are reprocessed using HE. The image improvement results show that the CLAHE-HE combination method gives a clear picture of the object area of the egg image that has an embryo. The segmentation process using image conversion to black and white image and watershed segmentation can clearly show the object of a chicken egg that has an embryo. The results of segmentation can divide the area of the egg having embryos in a real and accurate way with a percentage \approx 98\%.

Abstract (translated)

URL

https://arxiv.org/abs/2102.04202

PDF

https://arxiv.org/pdf/2102.04202.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot