Portable and low-cost hologram verification module using a snapshot-based hyperspectral imaging algorithm

Abstract

One of the challenges in differentiating a duplicate hologram from an original one is reflectivity. A slight change in lighting condition will completely change the reflection pattern exhibited by a hologram, and consequently, a standardized duplicate hologram detector has not yet been created. In this study, a portable and low-cost snapshot hyperspectral imaging (HSI) algorithm-based housing module for differentiating between original and duplicate holograms was proposed. The module consisted of a Raspberry Pi 4 processor, a Raspberry Pi camera, a display, and a light-emitting diode lighting system with a dimmer. A visible HSI algorithm that could convert an RGB image captured by the Raspberry Pi camera into a hyperspectral image was established. A specific region of interest was selected from the spectral image and mean gray value (MGV) and reflectivity were measured. Results suggested that shorter wavelengths are the most suitable for differentiating holograms when using MGV as the parameter for classification, while longer wavelengths are the most suitable when using reflectivity. The key features of this design include low cost, simplicity, lack of moving parts, and no requirement for an additional decoding key.

Mechanical Integrity of All-on-Four Dental Implant Systems: Finite Element Simulation of Material Properties of Zirconia, Titanium, and PEEK

“…A visual representation of the results is shown in Fig. (4). Overall, the results indicate that zirconia had a much lower maximum deformation and a lower average deformation, PEEK had the highest maximum deformation and a higher average deformation, and titanium performed reasonably.…”

“…One such method is hyperspectral imaging (HSI) where a spectrometer can be used to detect the biofilm contaminations on dental implants [1,2]. Specifically, with the improvement in snapshot-based HSI, which has been used in many other medical and other applications, dental implant performance analysis in the future will be much more convenient [3][4][5][6][7][8][9]. In recent years, many types of biosensors have also been developed [10][11][12].…”

Automatic Counterfeit Currency Detection Using a Novel Snapshot Hyperspectral Imaging Algorithm

“…HSI acquires the spectrum for each pixel in an image [ 18 , 19 , 20 , 21 ]. It has been used in many applications, such as cancer detection [ 22 , 23 , 24 , 25 ], air pollution monitoring [ 26 , 27 ], nanostructure identification [ 28 , 29 , 30 , 31 ], aerospace [ 32 , 33 , 34 ], food quality maintenance [ 35 ], verification [ 36 , 37 , 38 ], military [ 39 ], remote sensing [ 40 , 41 , 42 ], and agriculture [ 43 ].…”

Early esophageal detection using hyperspectral engineering and convolutional neural network

“…The use of hyperspectral imaging (HSI) technology in conjunction with artificial intelligence (AI) deep learning (DL) techniques for the analysis of spectral data pertaining to esophageal cancer (EC) has the potential to enhance the efficiency and precision of diagnostic procedures [5]. Hyperspectral pictures possess spectral intervals at the nanoscale level, resulting in a much greater capacity to detect spectrum information compared to multispectral images [6][7][8][9]. Spectrum conversion refers to the application of an imaging spectrometer to capture a picture including a broad range of wavelengths [10,11].…”