XSpectra<sup>®</sup>: The most advanced real time food contaminants detector

Garavelli, Bruno; Mencarelli, Alessandra; Zanotti, L.

doi:10.1109/biocas.2017.8325176

Cited by 5 publications

(3 citation statements)

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“…1, an example for vein detection is depicted that was recorded utilizing the proposed Camera Array for Multi-Spectral Imaging (CAMSI). Recently, also the food industry is exploring solutions to enhance quality controls using multi-spectral acquisition systems [3] Figure 1. Liquids detection and security feature tracking are typical applications for multi-spectral imaging.…”

Section: A Applicationsmentioning

confidence: 99%

“…14 shows the recorded scene with highlighted marker regions and the estimated disparity map that is obtained after applying CAMSI. The highlighted regions are used to calculate an average depth µ z as well as depth deviation σ z using the relationship shown in (3). This procedure is repeated by generating further disparity maps by applying two state-of-the-art cross-spectral stereo matching algorithms, namely Census + SGM [58], [64], [44] and CCNG+SGM [47], [64].…”

Section: A Registration Performancementioning

confidence: 99%

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Camera Array for Multi-Spectral Imaging

Genser

Seiler

Kaup

2020

IEEE Trans. on Image Process.

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Recently, many new applications arose for multispectral and hyper-spectral imaging. Besides modern biometric systems for identity verification, also agricultural and medical applications came up, which measure the health condition of plants and humans. Despite the growing demand, the acquisition of multi-spectral data is up to the present complicated. Often, expensive, inflexible, or low resolution acquisition setups are only obtainable for specific professional applications. To overcome these limitations, a novel camera array for multi-spectral imaging is presented in this article for generating consistent multispectral videos. As differing spectral images are acquired at various viewpoints, a geometrically constrained multi-camera sensor layout is introduced, which enables the formulation of novel registration and reconstruction algorithms to globally set up robust models. On average, the novel acquisition approach achieves a gain of 2.5 dB PSNR compared to recently published multi-spectral filter array imaging systems. At the same time, the proposed acquisition system ensures not only a superior spatial, but also a high spectral, and temporal resolution, while filters are flexibly exchangeable by the user depending on the application. Moreover, depth information is generated, so that 3D imaging applications, e.g., for augmented or virtual reality, become possible. The proposed camera array for multi-spectral imaging can be set up using off-the-shelf hardware, which allows for a compact design and employment in, e.g., mobile devices or drones, while being cost-effective.

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Section: A Applicationsmentioning

confidence: 99%

Section: A Registration Performancementioning

confidence: 99%

Camera Array for Multi-Spectral Imaging

Genser

Seiler

Kaup

2020

IEEE Trans. on Image Process.

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“…For example, multispectral imaging can acquire dozens of spectral images of targets. These are extensively used in biomedical imaging, medical diagnostics, and remote sensing [1][2][3][4][5][6]. Multispectral or hyperspectral imaging technologies can be categorized into spatial-scanning or spectral-scanning spectrometers, snapshot multispectral imaging systems, and computational multispectral imaging systems.…”

Section: Introductionmentioning

confidence: 99%

Spectral Clustering Super-Resolution Imaging Based on Multispectral Camera Array

Huang

Chen

Wang

et al. 2023

IEEE Trans. on Image Process.

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Although multispectral and hyperspectral imaging acquisitions are applied in numerous fields, the existing spectral imaging systems suffer from either low temporal or spatial resolution. In this study, a new multispectral imaging systemcamera array based multispectral super resolution imaging system (CAMSRIS) is proposed that can simultaneously achieve multispectral imaging with high temporal and spatial resolutions. The proposed registration algorithm is used to align pairs of different peripheral and central view images. A novel, superresolution, spectral-clustering-based image reconstruction algorithm was developed for the proposed CAMSRIS to improve the spatial resolution of the acquired images and preserve the exact spectral information without introducing false information. The reconstructed results showed that the spatial and spectral quality and operational efficiency of the proposed system are better than those of a multispectral filter array (MSFA) based on different multispectral datasets. The PSNR of the multispectral super-resolution images obtained by the proposed method were respectively higher by 2.03 and 1.93 dB than those of GAP-TV and DeSCI, and the execution time was significantly shortened by approximately 54.55 s and 9820.19 s when the CAMSI dataset was used. The feasibility of the proposed system was verified in practical applications based on different scenes captured by the self-built system.

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