Review on the Methodologies for Image Segmentation Based on CNN

Sivanarayana, G V; Kumar, K. Naveen; Srinivas, Y.; Kumar, G.V.S. Raj

doi:10.1007/978-981-15-5397-4_18

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…As AIs have developed, CNNs have been used in speech recognition 19 , face recognition 20 , image recognition 14 , motion analysis 7 , motion analysis 21 , image segmentation 22 , natural language processing 23 and EEG analysis 5 .…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Table tennis motion recognition based on the bat trajectory using varying-length-input convolution neural networks

Zhang,

Ren,

Lin

et al. 2024

Sci Rep

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Action recognition has been applied in fields such as smart homes, gaming, traffic management, and security monitoring. Motion recognition is helpful for biomechanical analysis, auxiliary training systems, table tennis robots, motion-sensing games, virtual reality and other fields. In our study, we collected data on table tennis skill motion, created the TTMD6 dataset, and analyzed the characteristics of table tennis paddle trajectories. We propose a motion recognition algorithm to recognize paddle trajectories. Other research has used multijoint data to identify actions, while we use only the paddle trajectory to recognize table tennis skill motions, accelerating the speed of motion recognition. Therefore, it is feasible to use paddle trajectories to recognize table tennis skill motions.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Table tennis motion recognition based on the bat trajectory using varying-length-input convolution neural networks

Zhang,

Ren,

Lin

et al. 2024

Sci Rep

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“…Objective assessment techniques such as the Jaccard Coefficient (J.C.), Volume of Similarity (V.S. ), and others are used to evaluate segmentation models (8,9) . The result obtained using quality metrics for both the proposed model B.G.M.M and the existing G.M.M with the E.M. model is tabulated in Table 1.…”

Section: Uci and Bio-well Datasetmentioning

confidence: 99%

Image Segmentation Based on G.O.A for Finding Deformities in Medical and Aura Images

Poojary,

Srinivas

2024

IJST

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Objectives: The research aims to develop the segmentation model to identify the deformity in the medical images as accurately as possible and plan for better medical treatment. The study is extended to identify the disease before its appearance in the human body through human aura images to support aura imaging in medical diagnosis. Methods: The study used a brain image from the UCI data set and Aura images from the Biowell data set to identify the disease. The segmentation model Bivariate Gaussian Mixture Model (B.G.M.M) was developed. Model parameters are derived using the Expectation Maximization (E.M) Algorithm. The Grasshopper optimization Algorithm (G.O.A) extracts optimal features from the images. The chosen feature is fed as input to the classification model B.G.M.M. Segmentation accuracy is measured using the quality metrics. Findings: The developed approach shows 97% accuracy in identifying the damaged tissues in MRI images and high-intensity energy zones in the aura images, indicating the potential for deformities. Novelty: This study significantly contributes to the field by offering novel solutions for precise and comprehensive image analysis in medical and aura imaging contexts. Keywords: G.O.A, segmentation, G.M.M, E.M, quality metrics, deformity identification, Hue and saturation

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“…The indicator of the right image is a result of segmentation. Segmentation is a stage that aims to partition/divide the image [18] [19] into several main parts that contain important information. Recognition of objects in the image requires parameters that can characterize the object [6].…”

Section: Image Segmentationmentioning

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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Review on the Methodologies for Image Segmentation Based on CNN

Cited by 5 publications

References 5 publications

Table tennis motion recognition based on the bat trajectory using varying-length-input convolution neural networks

Table tennis motion recognition based on the bat trajectory using varying-length-input convolution neural networks

Image Segmentation Based on G.O.A for Finding Deformities in Medical and Aura Images

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

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