Friedhelm König, Werner Praefcke · 2003
The shortcomings of the conventional three-channel imaging are rooted in metamerism problems. To avoid these problems multispectral imaging is introduced as an complementary approach. This imaging technology uses the complete spectral information of the original image, and, hence, avoids the problems due to metamerism. A multispectral image requires much more data than the same image using conventional technology (e.g. 31 sample values instead of 3 colorimetric values, corresponding to a data enlargement factor of about 10). Efficient multispectral image encoding has therefore been developed. Furthermore, this multispectral image encoding is compatible to conventional image encoding. The multispectral image encoding is based on the decomposition of the spectral data into three conventional components (e.g. RGB) and additional metameric components. Using this approach, the data enlargement factor can be reduced to values between 2 and 3.
Cited by 10
Zukal, Martin, Jiří Mekyska, Číka, Petr et al. · 2013
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ROBERTSON, THOMAS VINCENT · Purdue e-Pubs (Purdue University System) · 1973
Abstract not available
Cited by 11
T Kuezina daniel, Nasir Memon, Charles A. Bouman · Elsevier eBooks · 2005
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2007
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山口 雅浩, Masahiro Yamaguchi, Markku Hauta-Kasari et al. · Tokyo Tech Research Repository (Tokyo Institute of Technology) · 2017
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Li, You · Brno University of Technology Digital Library (Brno University of Technology) · 2021
S rychlým rozvojem technologie multispektrálního zobrazování v posledních desetiletích obrázky získané zobrazovacími systémy obsahují nejen barevná pásma RGB v každodenním životě, ale také mají multispektrální barevná pásma a vysoké prostorové rozlišení v multispektrálních obrazových datech. Díky tomu obrázky obsahují bohaté informace o charakteristických cílových oblastech. Fúze obrazu je také důležitou větví v oblasti zpracování obrazu, kde je více obrázků ze stejné oblasti ve stejné výšce sloučeno do jednoho obrazu. Poté se zlepší korelace mezi spektrálními informacemi multispektrálních obrazů. Aby se informace na obrázku neztratily. Tato práce obsahuje popis návrhu a implementace multispektrálního obrazového systému, předzpracování multispektrálních obrazů, fúzi multispektrálních obrazů a analýzu hlavních komponent. Nakonec je představeno hodnocení celého systému.
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Pavel Zemčík, Michael Frydrych, Heikki Kälviäinen et al. · 2002
Advances in colour image sensors and computer technology have opened up new opportunities for colour-based image analysis by allowing it to use multispectral images - images that have tens or even hundreds of spectral colour channels. As multispectral images usually occupy large amounts of memory, some suitable way is needed to compress such images and to represent them efficiently. Image compression has been one of the mainstream research topics for a long time; however, the research usually focuses on compressing images that are intended to be finally seen by humans. While many traditional methods can also be reused for multispectral images, some features of the multispectral images can be addressed differently than in traditional images. This paper describes a way of how to represent the multispectral colour data as a linear combination of colours contained in the clusters of colours that most frequently occur in an image.
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Justin Edwards, Mohamed El‐Sharkawy · Journal of Low Power Electronics and Applications · 2023
Semantic segmentation is a machine learning task that is seeing increased utilization in multiple fields, from medical imagery to land demarcation and autonomous vehicles. A real-time autonomous system must be lightweight while maintaining reasonable accuracy. This research focuses on leveraging the fusion of long-wave infrared (LWIR) imagery with visual spectrum imagery to fill in the inherent performance gaps when using visual imagery alone. This approach culminated in the Fast Thermal Fusion Network (FTFNet), which shows marked improvement over the baseline architecture of the Multispectral Fusion Network (MFNet) while maintaining a low footprint.
Cited by 1Open access
Cristhian Aguilera, Fernando Barrera, Felipe Lumbreras et al. · Sensors · 2012
This paper presents a novel feature point descriptor for the multispectral image case: Far-Infrared and Visible Spectrum images. It allows matching interest points on images of the same scene but acquired in different spectral bands. Initially, points of interest are detected on both images through a SIFT-like based scale space representation. Then, these points are characterized using an Edge Oriented Histogram (EOH) descriptor. Finally, points of interest from multispectral images are matched by finding nearest couples using the information from the descriptor. The provided experimental results and comparisons with similar methods show both the validity of the proposed approach as well as the improvements it offers with respect to the current state-of-the-art.
Cited by 169Open access
Todd N. Tsakiris, Robert E. Hillman, Cardinal Warde · 1989
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Peter D. Burns, Roy S. Berns · Color and Imaging Conference · 1996
Multispectral image capture (i.e, more than three channels) facilitates both more accurate tristimulus estimation and possibilities for spectral reconstruction of each scene pixel. A seven-channel camera was assembled using approximately 50 nm bandwidth interference filters, manufactured by Melles Griot, in conjunction with a Kodak Professional DCS 200m digital camera. Multichannel images were recorded for the Macbeth ColorChecker chart as an illustrative example. Three methods of spectral reconstruction were evaluated: spline interpolation, modified-discrete-sine-transformation (MDST) interpolation, and an approach based on principal-component analysis (PCA). The spectral reconstruction accuracy was quantified both spectrally and by computing CIELAB coordinates for a single illuminant and observer. The PCA-based technique resulted in the best estimated spectral-reflectance-factor functions. These results were compared with a least-squares colorimetric model that does not include the spectral-reconstruction step. This direct mapping resulted in similar colorimetric performance to the PCA method. The multispectral camera had marked improvement compared with traditional three-channel devices.
Cited by 97Open access
RECERCAT (Consorci de Serveis Universitaris de Catalunya) · 2021
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Yufeng Zheng, Genshe Chen, Zhonghai Wang et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2016
Image compression is necessary for data transportation, which saves both transferring time and storage space. In this paper, we focus on our discussion on <i>lossy compression</i>. There are many standard image formats and corresponding compression algorithms, for examples, JPEG (DCT — discrete cosine transform), JPEG 2000 (DWT — discrete wavelet transform), BPG (better portable graphics) and TIFF (LZW — Lempel-Ziv-Welch). The image quality (IQ) of decompressed image will be measured by numerical metrics such as root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural Similarity (SSIM) Index. Given an image and a specified IQ, we will investigate how to select a compression method and its parameters to achieve an expected compression. Our scenario consists of 3 steps. The first step is to compress a set of interested images by varying parameters and compute their IQs for each compression method. The second step is to create several regression models per compression method after analyzing the IQ-measurement versus compression-parameter from a number of compressed images. The third step is to compress the given image with the specified IQ using the selected compression method (JPEG, JPEG2000, BPG, or TIFF) according to the <i>regressed models</i>. The IQ may be specified by a <i>compression ratio </i>(e.g., 100), then we will select the compression method of the highest IQ (SSIM, or PSNR). Or the IQ may be specified by a IQ metric (e.g., SSIM = 0.8, or PSNR = 50), then we will select the compression method of the highest compression ratio. Our experiments tested on thermal (long-wave infrared) images (in <i>gray scales</i>) showed very promising results.
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于珊珊, 村上百合, 小尾高史 et al. · Tokyo Tech Research Repository (Tokyo Institute of Technology) · 2006
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Preema Mole, M Mathurakani · Advanced Computing An International Journal · 2016
The availability of imaging sensors operating in multiple spectral bands has led to the requirement of image fusion algorithms that would combine the image from these sensors in an efficient way to give an image that is more perceptible to human eye. Multispectral Image fusion is the process of combining images optically acquired in more than one spectral band. In this paper, we present a pixel-level image fusion that combines four images from four different spectral bands namely near infrared(0.76-0.90um), mid infrared(1.55-1.75um),thermal-infrared(10.4-12.5um) and mid infrared(2.08-2.35um) to give a composite colour image. The work coalesces a fusion technique that involves linear transformation based on Cholesky decomposition of the covariance matrix of source data that converts multispectral source images which are in grayscale into colour image. This work is composed of different segments that includes estimation of covariance matrix of images, cholesky decomposition and transformation ones. Finally, the fused colour image is compared with the fused image obtained by PCA transformation.
Cited by 1Open access
K. C. Tam · IEEE Transactions on Nuclear Science · 1983
Limited-angle reconstruction algorithms utilize a priori information on the objects to compensate for the missing scannings in limited-angle imagings. In earlier papers we have shown qualitatively that decomposing a composite object through multiple energy scans enables better use of the a priori information and therefore is capable of bringing about improvement in limited-angle reconstructions. In this paper a more quantitative analysis is performed to study the conditions for such a improvement and its magnitude. In particular, it is shown that this technique favors reconstruction for the hot spot type of phantoms much more than the cold spot type.
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Johannes Jordan, Elli Angelopoulou · 2015
In recent years, graph-based methods have had a significant impact on image segmentation. They are especially noteworthy for supervised segmentation, where the user provides task-specific foreground and background seeds. We adapt the power watershed framework to multispectral and hyperspectral image data and incorporate similarity measures from the field of spectral matching. We also propose a new data-driven graph edge weighting. Our weights are computed by the topological information of a self-organizing map. We show that graph weights based on a simpleLp-norm, as used in other modalities, do not give satisfactory segmentation results for multispectral data, while similarity measures that were specifically designed for this domain perform better. Our new approach is competitive and has an advantage in some of the tested scenarios.
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Hollaus, Fabian, Diem, Markus, Sablatnig, Robert · reposiTUm (TU Wien) · 2020
The final publication is available via <a href="https://doi.org/10.1109/ICFHR-2018.2018.00105" target="_blank">https://doi.org/10.1109/ICFHR-2018.2018.00105</a>.
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Masahiro Yamaguchi · The Journal of The Institute of Image Information and Television Engineers · 2015
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