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Добірка наукової літератури з теми "Multi-dimensional image"

Автор: Grafiati

Опубліковано: 10 січня 2023

Оновлено: 28 січня 2023

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Статті в журналах з теми "Multi-dimensional image"

1

Legland, David, and Marie-Françoise Devaux. "ImageM: a user-friendly interface for the processing of multi-dimensional images with Matlab." F1000Research 10 (April 30, 2021): 333. http://dx.doi.org/10.12688/f1000research.51732.1.

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Modern imaging devices provide a wealth of data often organized as images with many dimensions, such as 2D/3D, time and channel. Matlab is an efficient software solution for image processing, but it lacks many features facilitating the interactive interpretation of image data, such as a user-friendly image visualization, or the management of image meta-data (e.g. spatial calibration), thus limiting its application to bio-image analysis. The ImageM application proposes an integrated user interface that facilitates the processing and the analysis of multi-dimensional images within the Matlab environment. It provides a user-friendly visualization of multi-dimensional images, a collection of image processing algorithms and methods for analysis of images, the management of spatial calibration, and facilities for the analysis of multi-variate images. ImageM can also be run on the open source alternative software to Matlab, Octave. ImageM is freely distributed on GitHub: https://github.com/mattools/ImageM.

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2

Lukac, Rastislav, Phillip A. Laplante, and Konstantinos N. Plataniotis. "Multi-dimensional image processing." Real-Time Imaging 11, no. 5-6 (October 2005): 355–57. http://dx.doi.org/10.1016/j.rti.2005.08.002.

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3

Park, Jong-Il, and Seiki Inoue. "Three-Dimensional Image Information. Image-Based Rendering from Multi-View Images." Journal of the Institute of Image Information and Television Engineers 52, no. 3 (1998): 371–76. http://dx.doi.org/10.3169/itej.52.371.

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4

Wang, Ke Feng, Su Zhuang, and Xiao Rong Zhao. "JPEG Image Encryption Algorithm Based on Three-Dimensional Multi-Chaotic System." Applied Mechanics and Materials 734 (February 2015): 554–57. http://dx.doi.org/10.4028/www.scientific.net/amm.734.554.

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The paper decribes the JPEG image encryption algorithm based on three-dimensional multi-chaotic system. The algorithm is designed to segment the image into three-demensional matrix system with a variety of chaoic encryption system. The images are scrambled and transformed in the three-dimensional space, and then by the three-dimensional chaotic sequence from multi-chaotic system, they are respectively transformed as airspace tricolor per-pixel alternative images. Reaearch results show that the algorithm has good confusion and diffusion properties of pixels and a powerful key space of greater resistance. The encrypted image pixels are distrubuted randomly and evenly with adjacent pixels of zero correlation properties, which proves the proposed scheme has a high security.

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5

Ibaroudene, Djaffer, and Raj Acharya. "Linear hypertree for multi-dimensional image representation." Information Sciences 68, no. 1-2 (February 1993): 123–54. http://dx.doi.org/10.1016/0020-0255(93)90025-h.

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6

Martin, Ingrid M., and Sevgin Eroglu. "Measuring a multi-dimensional construct: Country image." Journal of Business Research 28, no. 3 (November 1993): 191–210. http://dx.doi.org/10.1016/0148-2963(93)90047-s.

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7

de Oliveira, H. M., V. V. Vermehren, and R. J. Cintra. "Multi-dimensional wavelets for scalable image decomposition: Orbital wavelets." International Journal of Wavelets, Multiresolution and Information Processing 18, no. 05 (June 15, 2020): 2050038. http://dx.doi.org/10.1142/s0219691320500381.

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Wavelets are closely related to Schrödinger’s wave functions and the interpretation of Born. Similar to the appearance of atomic orbital, it is proposed to combine anti-symmetric wavelets into orbital wavelets. The proposed approach allows the increase of the dimension of wavelets through this process. New orbital 2D-wavelets are introduced for the decomposition of still images, showing that it is possible to perform an analysis simultaneously in two distinct scales. An example of such an image analysis is shown.

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8

Yu, Xia, Lin Bo, and Chen Xin. "Low light combining multiscale deep learning networks and image enhancement algorithm." Современные инновации, системы и технологии - Modern Innovations, Systems and Technologies 2, no. 4 (November 28, 2022): 0215–32. http://dx.doi.org/10.47813/2782-2818-2022-2-4-0215-0232.

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Aiming at the lack of reference images for low-light enhancement tasks and the problems of color distortion, texture loss, blurred details, and difficulty in obtaining ground-truth images in existing algorithms, this paper proposes a multi-scale weighted feature low-light based on Retinex theory and attention mechanism. An image enhancement algorithm is proposed. The algorithm performs multi-scale feature extraction on low-light images through the feature extraction module based on the Unet architecture, generates a high-dimensional multi-scale feature map, and establishes an attention mechanism module to highlight the feature information of different scales that are beneficial to the enhanced image, and obtain a weighted image. High-dimensional feature map, the final reflection estimation module uses Retinex theory to build a network model, and generates the final enhanced image through the high-dimensional feature map. An end-to-end network architecture is designed and a set of self-regular loss functions are used to constrain the network model, which gets rid of the constraints of reference images and realizes unsupervised learning. The final experimental results show that the algorithm in this paper maintains high image details and textures while enhancing the contrast and clarity of the image, has good visual effects, can effectively enhance low-light images, and greatly improves the visual quality. Compared with other enhanced algorithms, the objective indicators PSNR and SSIM have been improved.

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9

Patro, K. Abhimanyu Kumar, and Bibhudendra Acharya. "A novel multi-dimensional multiple image encryption technique." Multimedia Tools and Applications 79, no. 19-20 (January 24, 2020): 12959–94. http://dx.doi.org/10.1007/s11042-019-08470-8.

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Wang, Cailing, Hongwei Wang, Yinyong Zhang, Jia Wen, and Fan Yang. "High Dimensional Feature for Hyperspectral Image Classification." MATEC Web of Conferences 246 (2018): 03041. http://dx.doi.org/10.1051/matecconf/201824603041.

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Making a high dimensional (e.g., 100k-dim) feature for hyperspectral image classification seems not a good idea because it will bring difficulties on consequent training, computation, and storage. In this paper, we study the performance of a high-dimensional feature by texture feature. The texture feature based on multi-local binary pattern descriptor, can achieve significant improvements over both its tradition version and the one we proposed in our previous work. We also make the high-dimensional feature practical, we employ the PCA method for dimension reduction and support vector machine for hyperspectral image classification. The two real hyperspectral image datasets are employed. Our experimental results with real hyperspectral images indicate that the high dimensional feature can enhance the classification accuracy than some low dimensional.

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Дисертації з теми "Multi-dimensional image"

1

Bove, V. Michael. "Synthetic movies derived from multi-dimensional image sensors." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/64847.

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2

Larkin, Kieran Gerard. "Topics in Multi dimensional Signal Demodulation." University of Sydney. Physics, 2001. http://hdl.handle.net/2123/367.

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Problems in the demodulation of one, two, and three-dimensional signals are investigated. In one-dimensional linear systems the analytic signal and the Hilbert transform are central to the understanding of both modulation and demodulation. However, it is shown that an efficient nonlinear algorithm exists which is not explicable purely in terms of an approximation to the Hilbert transform. The algorithm is applied to the problem of finding the envelope peak of a white light interferogram. The accuracy of peak location is then shown to compare favourably with conventional, but less efficient, techniques. In two dimensions (2-D) the intensity of a wavefield yields to a phase demodulation technique equivalent to direct phase retrieval. The special symmetry of a Helmholtz wavefield allows a unique inversion of an autocorrelation. More generally, a 2-D (non-Helmholtz) fringe pattern can be demodulated by an isotropic 2-D extension of the Hilbert transform that uses a spiral phase signum function. The range of validity of the new transform is established using the asymptotic method of stationary phase. Simulations of the algorithm confirm that deviations from the ideal occur where the fringe pattern curvature is larger than the fringe frequency. A new self-calibrating algorithm for arbitrary sequences of phase-shifted interferograms is developed using the aforementioned spiral phase transform. The algorithm is shown to work even with discontinuous fringe patterns, which are known to seriously hamper other methods. Initial simulations of the algorithm indicate an accuracy of 5 milliradians is achievable. Previously undocumented connections between the demodulation techniques are uncovered and discussed.

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3

Schodl, Arno. "Multi-dimensional exemplar-based texture synthesis." Diss., Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/9166.

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4

Gessert, Nils Thorben [Verfasser]. "Deep learning with multi-dimensional medical image data / Nils Thorben Gessert." Hamburg : Universitätsbibliothek der Technischen Universität Hamburg-Harburg, 2020. http://d-nb.info/1224270851/34.

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5

Huang, Wei. "Automatic affine and elastic registration strategies for multi-dimensional medical images." Link to electronic thesis, 2007. http://www.wpi.edu/Pubs/ETD/Available/etd-050207-145713/.

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6

Boote, Jonathan. "Voiced and non-voiced consumer responses to primary dissatisfaction : a uni-dimensional and multi-dimensional study." Thesis, University of Bedfordshire, 2003. http://hdl.handle.net/10547/292478.

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It is proposed by scholars of consumer complaining behaviour that dissatisfaction occurs at two levels: (1) the primary level resulting from product or service failure; and (2) the secondary level resulting from an unsuccessful attempt at complaint resolution with the company held responsible. This thesis provides an integrated analysis of the triggers of voiced and non-voiced consumer responses to the level of primary dissatisfaction. A deductively derived two-factor taxonomy of consumer responses to dissatisfaction was developed out of the extant literature, to identify those responses to dissatisfaction analogous with both primary and secondary dissatisfaction. Based on this taxonomy, a deductively derived typology of consumer responses to primary dissatisfaction, together with two truncated alternatives, was also proposed. The proposed typologies facilitated two levels of analysis: (1) the unidimensional level relating to whether or not the dissatisfaction was voiced to the company held responsible; and (2) the multi-dimensional level of response style engaged in. At each level of analysis, the contribution to total explained variance of six trigger sets was assessed: pre-dissatisfaction situation, post-dissatisfaction situation, company/consumer relationship, marketplace/consumer relationship, psychographics and demographics. The validity of both the proposed two-factor taxonomy and the three typologies was examined through a cross-sectional survey of 1000 dissatisfied consumers across eight product and service categories. Findings supported the deductive basis of the taxonomy, where it was found that whilst exit behaviours and private negative word-of-mouth are analogous with the level of primary dissatisfaction, the incidence of public negative word-of-mouth, third party action and grudge holding was significantly greater among consumers experiencing secondary dissatisfaction. These findings also supported the deductive basis of the proposed typologies of responses to primary dissatisfaction, by empirically identifying those responses analogous with this level of dissatisfaction. At the unidimensional level of analysis, the trigger sets explained 54% ofthe variance between a voiced and a non-voiced instance of primary dissatisfaction. At the multidimensional level of the response style, 63% of variance was explained between response styles in the truncated typology with the most practical use for suppliers. On the basis of these findings, recommendations are made to practitioners on how to encourage supplier-friendly consumer response styles following primary dissatisfaction, and to discourage less friendly styles. The empirically supported truncated typology -distinguishing between passive, private responses, telling, and telling + private responses -represents an important strategic tool for suppliers to monitor and benchmark responses to primary dissatisfaction among their customer base.

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7

Yuen, P. C. "Multi-scale representation and recognition of three dimensional surfaces using geometric invariants." Thesis, University of Surrey, 2001. http://epubs.surrey.ac.uk/979/.

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8

Berent, Jesse. "Coherent multi-dimensional segmentation of multiview images using a variational framework and applications to image based rendering." Thesis, Imperial College London, 2008. http://hdl.handle.net/10044/1/1419.

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Анотація:

Image Based Rendering (IBR) and in particular light field rendering has attracted a lot of attention for interpolating new viewpoints from a set of multiview images. New images of a scene are interpolated directly from nearby available ones, thus enabling a photorealistic rendering. Sampling theory for light fields has shown that exact geometric information in the scene is often unnecessary for rendering new views. Indeed, the band of the function is approximately limited and new views can be rendered using classical interpolation methods. However, IBR using undersampled light fields suffers from aliasing effects and is difficult particularly when the scene has large depth variations and occlusions. In order to deal with these cases, we study two approaches: New sampling schemes have recently emerged that are able to perfectly reconstruct certain classes of parametric signals that are not bandlimited but characterized by a finite number of parameters. In this context, we derive novel sampling schemes for piecewise sinusoidal and polynomial signals. In particular, we show that a piecewise sinusoidal signal with arbitrarily high frequencies can be exactly recovered given certain conditions. These results are applied to parametric multiview data that are not bandlimited. We also focus on the problem of extracting regions (or layers) in multiview images that can be individually rendered free of aliasing. The problem is posed in a multidimensional variational framework using region competition. In extension to previous methods, layers are considered as multi-dimensional hypervolumes. Therefore the segmentation is done jointly over all the images and coherence is imposed throughout the data. However, instead of propagating active hypersurfaces, we derive a semi-parametric methodology that takes into account the constraints imposed by the camera setup and the occlusion ordering. The resulting framework is a global multi-dimensional region competition that is consistent in all the images and efficiently handles occlusions. We show the validity of the approach with captured light fields. Other special effects such as augmented reality and disocclusion of hidden objects are also demonstrated.

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9

Doshi, Niraj P. "Multi-dimensional local binary pattern texture descriptors and their application for medical image analysis." Thesis, Loughborough University, 2014. https://dspace.lboro.ac.uk/2134/17332.

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Texture can be broadly stated as spatial variation of image intensities. Texture analysis and classification is a well researched area for its importance to many computer vision applications. Consequently, much research has focussed on deriving powerful and efficient texture descriptors. Local binary patterns (LBP) and its variants are simple yet powerful texture descriptors. LBP features describe the texture neighbourhood of a pixel using simple comparison operators, and are often calculated based on varying neighbourhood radii to provide multi-resolution texture descriptions. A comprehensive evaluation of different LBP variants on a common benchmark dataset is missing in the literature. This thesis presents the performance for different LBP variants on texture classification and retrieval tasks. The results show that multi-scale local binary pattern variance (LBPV) gives the best performance over eight benchmarked datasets. Furthermore, improvements to the Dominant LBP (D-LBP) by ranking dominant patterns over complete training set and Compound LBP (CM-LBP) by considering 16 bits binary codes are suggested which are shown to outperform their original counterparts. The main contribution of the thesis is the introduction of multi-dimensional LBP features, which preserve the relationships between different scales by building a multi-dimensional histogram. The results on benchmarked classification and retrieval datasets clearly show that the multi-dimensional LBP (MD-LBP) improves the results compared to conventional multi-scale LBP. The same principle is applied to LBPV (MD-LBPV), again leading to improved performance. The proposed variants result in relatively large feature lengths which is addressed using three different feature length reduction techniques. Principle component analysis (PCA) is shown to give the best performance when the feature length is reduced to match that of conventional multi-scale LBP. The proposed multi-dimensional LBP variants are applied for medical image analysis application. The first application is nailfold capillary (NC) image classification. Performance of MD-LBPV on NC images is highest, whereas for second application, HEp-2 cell classification, performance of MD-LBP is highest. It is observed that the proposed texture descriptors gives improved texture classification accuracy.

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10

Jones, Michael Greystock. "An investigation of the potential of multi-modality imaging in three dimensional thick tissue microscopy." Thesis, University of Bristol, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387965.

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Книги з теми "Multi-dimensional image"

1

Zawieska, Dorota. Wieloobrazowe dopasowanie zdjęć bliskiego zasięgu do automatycznej rekonstrukcji fotorealistycznych modeli 3D: Multi-image matching of close range photographs for photorealistic 3D models reconstruction. Warszawa: Oficyna Wydawnicza Politechniki Warszawskiej, 2013.

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2

Stefano, Selleri, ed. The Official Blender 2.3 guide: Free 3D creation suite for modeling, animation, and rendering. San Francisco: No Starch Press, 2004.

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3

IEEE Workshop on Stereo and Multi-Baseline Vision (2001 Kauai, Hawaii). IEEE Workshop on stereo and Multi-Baseline Vision (SMBV 2001): Proceedings, 9-10 December 2001, Kkauai, Hawaii. Los Alamitos, Calif: IEEE Computer Society, 2001.

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4

United States. National Aeronautics and Space Administration., ed. Multi-dimensional measurements of combustion species in flame tube and sector gas turbine combustors. [Washington, DC]: National Aeronautics and Space Administration, 1996.

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5

Multi-dimensional measurements of combustion species in flame tube and sector gas turbine combustors. [Washington, DC]: National Aeronautics and Space Administration, 1996.

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6

United States. National Aeronautics and Space Administration., ed. Multi-dimensional measurements of combustion species in flame tube and sector gas turbine combustors. [Washington, DC]: National Aeronautics and Space Administration, 1996.

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7

Stereo and Multi-Baseline Vision (Smbv 2001): 2001 IEEE Workshop. Ieee, 2001.

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8

Oklopcic, Zoran. Territorial Isomorphs. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799092.003.0007.

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From the perspective of K-Universe, the formation of sovereign states is governed by recursive applications of K-Algorithm, and results in T-Isomorphs. Chapter 7 proposes Sierpinski recursion as an intuitive and commonsensical alternative to the existing ones, confronting it with those that can be gleaned from four constitutional theories––foundational constitutionalism, constitutional pluralism, para-constitutionalism, and radical pluralism—imagined also as potential problem-solving templates. In order to look at these theories more practically, what also needs to be reimagined is the conception of ultimate constitutional authority: away from the preoccupations with origins and narratives, and towards fuzzy, yet discernible mental image: a multi-dimensional matrix of presumed authority-challenges that informs the theorists’ attitudes towards a range of relevant issues in constitutional theory—from narrative identity and the character of political collectivity to constitutional pluralism and constitutionalism.

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9

Roosendaal, Ton, and Stefano Selleri. The Official Blender 2.3 Guide: Free 3D Creation Suite for Modeling, Animation, and Rendering. 3rd ed. No Starch Press, 2005.

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Частини книг з теми "Multi-dimensional image"

1

Son, Jung-Young, Chun-Hea Lee, Wook-Ho Son, Min-Chul Park, and Bahram Javidi. "Image Formats of Various 3-D Displays." In Multi-Dimensional Imaging, 267–301. Chichester, UK: John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118705766.ch12.

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2

Lorenzen, P. J., and S. C. Joshi. "High-Dimensional Multi-modal Image Registration." In Biomedical Image Registration, 234–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39701-4_25.

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3

Li, Li-Jia, Jun Zhu, Hao Su, Eric P. Xing, and Li Fei-Fei. "Multi-Level Structured Image Coding on High-Dimensional Image Representation." In Computer Vision – ACCV 2012, 147–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37444-9_12.

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4

Di Martino, Ferdinando, and Salvatore Sessa. "Multi-dimensional and High Degree Fuzzy Transform." In Fuzzy Transforms for Image Processing and Data Analysis, 15–25. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44613-0_2.

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5

Zhang, Tianshuo, Xiangyu Zhu, Feng Pan, Ke Xiang, and Zhen Lei. "Face Forgery Detection by Multi-dimensional Image Decomposition." In Biometric Recognition, 591–601. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20233-9_60.

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6

Xuan, Jiayu, Zhihui Xin, Xiaoqiao Huang, Zhixu Wang, and Yu Sun. "Overview of SAR Image Change Detection." In 3D Imaging Technologies—Multi-dimensional Signal Processing and Deep Learning, 29–38. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3391-1_4.

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7

Cyganek, Bogusław. "Overview of Tensor Methods for Multi-dimensional Signals Change Detection and Compression." In Image Processing and Communications, 3–5. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31254-1_1.

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8

Koprinkova-Hristova, Petia. "Multi-dimensional Data Clustering and Visualization via Echo State Networks." In New Approaches in Intelligent Image Analysis, 93–122. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32192-9_3.

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Yu, Gaihong, Dianfu Ma, Hualei Shen, and Yonggang Huang. "Web-Based Multi-Dimensional Medical Image Collaborative Annotation System." In Lecture Notes in Electrical Engineering, 511–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34531-9_54.

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de Mello, Rodrigo Fernandes, and Iker Gondra. "Multi-Dimensional Dynamic Time Warping for Image Texture Similarity." In Lecture Notes in Computer Science, 23–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88190-2_8.

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Тези доповідей конференцій з теми "Multi-dimensional image"

1

Keller, Y., Y. Shkolnisky, and A. Averbuch. "Accurate multi-dimensional alignment." In rnational Conference on Image Processing. IEEE, 2005. http://dx.doi.org/10.1109/icip.2005.1530576.

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2

yong, Liu, and Yang ping xian. "Multi-dimensional multi-scale image enhancement algorithm." In 2010 2nd International Conference on Signal Processing Systems (ICSPS). IEEE, 2010. http://dx.doi.org/10.1109/icsps.2010.5555431.

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Timchenko, L. I. "A multistage hierarchical approach to image processing." In IEE Colloquium on Multi-Dimensional Systems: Problems and Solutions. IEE, 1996. http://dx.doi.org/10.1049/ic:19960002.

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4

Arbeiter, James H. "A Multi-Dimensional Video Image Processing Architecture." In 29th Annual Technical Symposium, edited by Keith Bromley and William J. Miceli. SPIE, 1986. http://dx.doi.org/10.1117/12.949707.

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Heylen, Rob, and Paul Scheunders. "Multi-dimensional pixel purity index." In 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2013. http://dx.doi.org/10.1109/whispers.2013.8080725.

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Sha Lu, Huaideng Wang, Jing He, Zhentian Shao, and Jie Yuan. "Multi-dimensional Multi-resolution Fourier Analysis method in image processing." In 2013 15th IEEE International Conference on Communication Technology (ICCT). IEEE, 2013. http://dx.doi.org/10.1109/icct.2013.6820350.

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Sun, Tiancheng, and Huarong Gu. "Three-dimensional display using multi-layer translucencies." In Three-Dimensional Image Acquisition and Display Technology and Applications, edited by Byoungho Lee, Yongtian Wang, Liangcai Cao, and Guohai Situ. SPIE, 2018. http://dx.doi.org/10.1117/12.2504660.

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Datta, Aloke, and Anisha Chakravorty. "Hyperspectral Image Segmentation using Multi-dimensional Histogram over Principal Component Images." In 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN). IEEE, 2018. http://dx.doi.org/10.1109/icacccn.2018.8748388.

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Bartolini, Ilaria, and Paolo Ciaccia. "Multi-dimensional keyword-based image annotation and search." In the 2nd International Workshop. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1868366.1868371.

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Mendoza, Carlos S., German Bohorquez-Ruiz, Begona Acha, and Carmen Serrano. "Multi-dimensional earth mover's distance active contours." In 2011 18th IEEE International Conference on Image Processing (ICIP 2011). IEEE, 2011. http://dx.doi.org/10.1109/icip.2011.6116341.

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Звіти організацій з теми "Multi-dimensional image"

1

Anderson, Gerald L., and Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7585193.bard.

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Анотація:

This research report describes a methodology whereby multi-spectral and hyperspectral imagery from remote sensing, is used for deriving predicted field maps of selected plant growth attributes which are required for precision cropping. A major task in precision cropping is to establish areas of the field that differ from the rest of the field and share a common characteristic. Yield distribution f maps can be prepared by yield monitors, which are available for some harvester types. Other field attributes of interest in precision cropping, e.g. soil properties, leaf Nitrate, biomass etc. are obtained by manual sampling of the filed in a grid pattern. Maps of various field attributes are then prepared from these samples by the "Inverse Distance" interpolation method or by Kriging. An improved interpolation method was developed which is based on minimizing the overall curvature of the resulting map. Such maps are the ground truth reference, used for training the algorithm that generates the predicted field maps from remote sensing imagery. Both the reference and the predicted maps are stratified into "Prototype Plots", e.g. 15xl5 blocks of 2m pixels whereby the block size is 30x30m. This averaging reduces the datasets to manageable size and significantly improves the typically poor repeatability of remote sensing imaging systems. In the first two years of the project we used the Normalized Difference Vegetation Index (NDVI), for generating predicted yield maps of sugar beets and com. The NDVI was computed from image cubes of three spectral bands, generated by an optically filtered three camera video imaging system. A two dimensional FFT based regression model Y=f(X), was used wherein Y was the reference map and X=NDVI was the predictor. The FFT regression method applies the "Wavelet Based", "Pixel Block" and "Image Rotation" transforms to the reference and remote images, prior to the Fast - Fourier Transform (FFT) Regression method with the "Phase Lock" option. A complex domain based map Yfft is derived by least squares minimization between the amplitude matrices of X and Y, via the 2D FFT. For one time predictions, the phase matrix of Y is combined with the amplitude matrix ofYfft, whereby an improved predicted map Yplock is formed. Usually, the residuals of Y plock versus Y are about half of the values of Yfft versus Y. For long term predictions, the phase matrix of a "field mask" is combined with the amplitude matrices of the reference image Y and the predicted image Yfft. The field mask is a binary image of a pre-selected region of interest in X and Y. The resultant maps Ypref and Ypred aremodified versions of Y and Yfft respectively. The residuals of Ypred versus Ypref are even lower than the residuals of Yplock versus Y. The maps, Ypref and Ypred represent a close consensus of two independent imaging methods which "view" the same target. In the last two years of the project our remote sensing capability was expanded by addition of a CASI II airborne hyperspectral imaging system and an ASD hyperspectral radiometer. Unfortunately, the cross-noice and poor repeatability problem we had in multi-spectral imaging was exasperated in hyperspectral imaging. We have been able to overcome this problem by over-flying each field twice in rapid succession and developing the Repeatability Index (RI). The RI quantifies the repeatability of each spectral band in the hyperspectral image cube. Thereby, it is possible to select the bands of higher repeatability for inclusion in the prediction model while bands of low repeatability are excluded. Further segregation of high and low repeatability bands takes place in the prediction model algorithm, which is based on a combination of a "Genetic Algorithm" and Partial Least Squares", (PLS-GA). In summary, modus operandi was developed, for deriving important plant growth attribute maps (yield, leaf nitrate, biomass and sugar percent in beets), from remote sensing imagery, with sufficient accuracy for precision cropping applications. This achievement is remarkable, given the inherently high cross-noice between the reference and remote imagery as well as the highly non-repeatable nature of remote sensing systems. The above methodologies may be readily adopted by commercial companies, which specialize in proving remotely sensed data to farmers.

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