Strategies for Merging Hyperspectral Data of Different Spectral and Spatial Resoultion

Illmann, Raik; Rosenberger, Maik; Notni, Gunther

doi:10.1109/dicta.2018.8615875

Cited by 2 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The resolution, offset, and regions of interest (ROI) of the images must be adjusted by the data fusion algorithm so that each pixel of the RGB image gets a corresponding depth value in addition to the color values. This progress is difficult due to an offset in images both in the translational and rotational direction caused by different resolutions, non-commensurability, and missing or inconsistent data, as described by Illmann et al [ 43 ] and Lahat et al [ 44 ].…”

Section: Data Fusion Processmentioning

confidence: 99%

See 1 more Smart Citation

Data Fusion of RGB and Depth Data with Image Enhancement

Wunsch,

Tenorio,

Anding

et al. 2024

J. Imaging

Self Cite

View full text Add to dashboard Cite

Since 3D sensors became popular, imaged depth data are easier to obtain in the consumer sector. In applications such as defect localization on industrial objects or mass/volume estimation, precise depth data is important and, thus, benefits from the usage of multiple information sources. However, a combination of RGB images and depth images can not only improve our understanding of objects, capacitating one to gain more information about objects but also enhance data quality. Combining different camera systems using data fusion can enable higher quality data since disadvantages can be compensated. Data fusion itself consists of data preparation and data registration. A challenge in data fusion is the different resolutions of sensors. Therefore, up- and downsampling algorithms are needed. This paper compares multiple up- and downsampling methods, such as different direct interpolation methods, joint bilateral upsampling (JBU), and Markov random fields (MRFs), in terms of their potential to create RGB-D images and improve the quality of depth information. In contrast to the literature in which imaging systems are adjusted to acquire the data of the same section simultaneously, the laboratory setup in this study was based on conveyor-based optical sorting processes, and therefore, the data were acquired at different time periods and different spatial locations. Data assignment and data cropping were necessary. In order to evaluate the results, root mean square error (RMSE), signal-to-noise ratio (SNR), correlation (CORR), universal quality index (UQI), and the contour offset are monitored. With JBU outperforming the other upsampling methods, achieving a meanRMSE = 25.22, mean SNR = 32.80, mean CORR = 0.99, and mean UQI = 0.97.

show abstract

Section: Data Fusion Processmentioning

confidence: 99%

“…For this data registration, extracting the corresponding features in each image is necessary. Illmann et al [ 43 ] provide further details and strategies for merging data. Feature extraction and data registration also deal with the time delay between both imaging systems.…”

Section: Data Fusion Processmentioning

confidence: 99%