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Engineering Group Research Article 記事ID: igmin216

Deep Learning-based Multi-class Three-dimensional (3-D) Object Classification using Phase-only Digital Holographic Information

Data Science Image ProcessingMachine Learning
Uma Mahesh RN * 1 and
L Basavaraju 2
Affiliation

Affiliation

    Associate Professor, Dept of CSE (AI and ML), ATME College of Engineering, Mysore-570028, India

    Associate Professor, Dept of CSE (AI and ML), ATME College of Engineering, Mysore-570028, India

    Professor, Dept of ECE, ATME College of Engineering, Mysore-570028, India

DOI10.61927/igmin216 全文HTML 全文PDF この記事を引用する
DOI10.61927/igmin216 全文HTML 全文PDF この記事を引用する

要約

In this paper, we present a deep CNN-based approach for multi-class classification of three-dimensional (3-D) objects using phase-only digital holographic information. The 3-D objects considered for the multi-class (four-class) classification task are ‘triangle-square’, ‘circle-square’, ‘square-triangle’, and ‘triangle-circle’. The 3-D object ‘triangle-square’ is considered for Class-1 and the remaining 3-D objects ‘circle-square’, ‘square-circle’, and ‘triangle-circle’ are considered for Class-2, Class-3, and Class-4. The digital holograms of 3-D objects were created using the two-step Phase-Shifting Digital Holography (PSDH) technique and were computationally post-processed to obtain phase-only digital holographic data. Subsequently, a deep CNN was trained on a phase-only image dataset consisting of 2880 images to produce the results. The loss and accuracy curves are presented to validate the performance of the model. Additionally, the results are validated using metrics such as the confusion matrix, classification report, Receiver Operating Characteristic (ROC) curve, and precision-recall curve.

数字

Block diagram of Convolutional Neural Network (CNN). Figure 1: Block diagram of Convolutional Neural Network (CNN...
Schematic of the geometry for the recording of the digital hologram of 3-D object volume with different features in the first and second planes and separating distances z =10 cm and d = 2 cm. (a) triangle-square. BS: beam splitter CCD: charge-coupled device. Figure 2: Schematic of the geometry for the recording of the...
Reconstructed phase-only images of 3-D objects (a) circle-square (b) triangle-circle (c) triangle-square. Figure 3: Reconstructed phase-only images of 3-D objects (a)...
Loss and accuracy curves on training/validation sets for phase-only image dataset (a) loss (b) accuracy. Figure 4: Loss and accuracy curves on training/validation se...
Multi-Class Confusion Matrix on Phase-only image dataset obtained from Deep CNN Model. Figure 5: Multi-Class Confusion Matrix on Phase-only image d...
ROC curve obtained from deep CNN model on phase-only image dataset. Figure 6: ROC curve obtained from deep CNN model on phase-on...
Positive predictive value – sensitivity characteristic obtained from deep CNN model on phase-only image dataset. Figure 7: Positive predictive value – sensitivity characte...

参考文献

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