Deep convolutional neural network for binary regression of three-dimensional objects using information retrieved from digital Fresnel holograms

Mahesh, R.; Nelleri, Anith

doi:10.1007/s00340-022-07877-w

Cited by 2 publications

(3 citation statements)

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“…The construction of the remaining fourteen 3D objects was similar to that of the four 3D objects shown in Figure 1 but with different features on each plane [ 48 ]. In total, eighteen 3D objects were considered for the proposed five-class classification and regression tasks [ 49 ]. The 3D objects were characterized by their intensity and phase information.…”

Section: Theorymentioning

confidence: 99%

“…Section S1.2 presents the details of the digital recording and numerical reconstruction of the holograms to obtain the complex 3D object wave information. Figure 2 describes the experimental setup (of the Mach–Zehnder digital holographic recording geometry in an off-axis scheme) used for the recording of the holograms of the 3D objects [ 49 ]. A He-Ne laser source with a wavelength

was used here.…”

Section: Theorymentioning

confidence: 99%

“…In the present work, five-class classification and regression tasks using a deep CNN applied to holograms, reconstructed intensity and phase information combined in a single image, and phase (depth)-only image datasets of eighteen 3D objects are proposed. The advantage of this work compared to the previous works [ 28 , 29 , 37 , 45 , 46 , 47 , 48 , 49 ] was that the five-class classification and regression tasks were performed on datasets of holograms, concatenated intensity–phase images, and phase-only images of eighteen 3D objects. These three datasets of eighteen 3D objects were constructed by using recorded digital holograms and a complex-wave retrieval method [ 50 ].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Class Classification and Multi-Output Regression of Three-Dimensional Objects Using Artificial Intelligence Applied to Digital Holographic Information

Nelleri

2023

Sensors

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Digital holographically sensed 3D data processing, which is useful for AI-based vision, is demonstrated. Three prominent methods of learning from datasets such as sensed holograms, computationally retrieved intensity and phase from holograms forming concatenated intensity–phase (whole information) images, and phase-only images (depth information) were utilized for the proposed multi-class classification and multi-output regression tasks of the chosen 3D objects in supervised learning. Each dataset comprised 2268 images obtained from the chosen eighteen 3D objects. The efficacy of our approaches was validated on experimentally generated digital holographic data then further quantified and compared using specific evaluation matrices. The machine learning classifiers had better AUC values for different classes on the holograms and whole information datasets compared to the CNN, whereas the CNN had a better performance on the phase-only image dataset compared to these classifiers. The MLP regressor was found to have a stable prediction in the test and validation sets with a fixed EV regression score of 0.00 compared to the CNN, the other regressors for holograms, and the phase-only image datasets, whereas the RF regressor showed a better performance in the validation set for the whole information dataset with a fixed EV regression score of 0.01 compared to the CNN and other regressors.

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Section: Theorymentioning

confidence: 99%

was used here.…”

Section: Theorymentioning

confidence: 99%