@article{kuehni_ramanath_2006, title={Sensing spectral stimuli: Sensor functions and number}, volume={31}, ISSN={["1520-6378"]}, DOI={10.1002/col.20171}, abstractNote={Using synthesized and real spectra this article addresses questions regarding the degree to which synthetic and real sensor systems with one, two, and three sensors can distinguish among stimuli and can reconstruct the original spectra. Based on 64 synthetic spectra, the trichromatic sensors L, M, S distinguish the spectra at the level of 85% of the optimal result but reconstruct them only at the level of 70%. For symmetrical and symmetrically placed sensors the corresponding values are 76 and 94%. The conclusion is drawn that distinguishability was a more important goal of evolution than reconstructability. © 2005 Wiley Periodicals, Inc. Col Res Appl, 31, 30–37, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20171}, number={1}, journal={COLOR RESEARCH AND APPLICATION}, author={Kuehni, Rolf G. and Ramanath, Rajeev}, year={2006}, month={Feb}, pages={30–37} }
@article{karacali_ramanath_snyder_2004, title={A comparative analysis of structural risk minimization by support vector machines and nearest neighbor rule}, volume={25}, ISSN={["1872-7344"]}, DOI={10.1016/j.patrec.2003.09.002}, abstractNote={Support vector machines (SVMs) are by far the most sophisticated and powerful classifiers available today. However, this robustness and novelty in approach come at a large computational cost. On the other hand, nearest neighbor (NN) classifiers provide a simple yet robust approach that is guaranteed to converge to a result. In this paper, we present a technique that combines these two classifiers by adopting a NN rule-based structural risk minimization classifier. Using synthetic and real data, the classification technique is shown to be more robust to kernel conditions with a significantly lower computational cost than conventional SVMs. Consequently, the proposed method provides a powerful alternative to SVMs in applications where computation time and accuracy are of prime importance. Experimental results indicate that the NNSRM formulation is not only computationally less expensive, but also much more robust to varying data representations than SVMs.}, number={1}, journal={PATTERN RECOGNITION LETTERS}, author={Karacali, B and Ramanath, R and Snyder, WE}, year={2004}, month={Jan}, pages={63–71} }
@article{kuehni_ramanath_2004, title={Comparing observers}, volume={29}, ISSN={["1520-6378"]}, DOI={10.1002/col.20004}, abstractNote={Color-matchingfunctions may be considered dimension reduction functions that project a spectral reflectance function into the desired space of colors. Using a gray metameric pair with maximal spectral difference we compare the abilities of various human and other observers with regard to the transition wavelengths for that metameric pair. Transition wavelengths are shown to be a convenient tool for comparing and classifying observers regardless of the number of dimension reduction functions. Four human observers were identified as differing in a comparable manner from the CIE 2° standard observer. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 183–186, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20004}, number={3}, journal={COLOR RESEARCH AND APPLICATION}, author={Kuehni, RG and Ramanath, R}, year={2004}, month={Jun}, pages={183–186} }
@article{ramanath_kuehni_snyder_hinks_2004, title={Spectral spaces and color spaces}, volume={29}, ISSN={["1520-6378"]}, DOI={10.1002/col.10211}, abstractNote={It has long been known that color experiences under controlled conditions may be ordered into a color space based on three primary attributes. It is also known that the color of an object depends on its spectral reflectance function, among other factors. Using dimensionality reduction techniques applied to reflectance measurements (in our case a published set of 1 nm interval reflectance functions of Munsell color chips) it is possible to construct 3D spaces of various kinds. In this article we compare color spaces, perceptual or based on dimensionality reduction using color matching functions and additional operations (uniform color space), to spectral spaces derived with a variety of dimensionality reduction techniques. Most spectral spaces put object spectra into the ordinal order of a psychological color space, but so do many random continuous functions. In terms of interval scales there are large differences between color and spectral spaces. In spectral spaces psychophysical metamers are located in different places. © 2003 Wiley Periodicals, Inc. Col Res Appl, 29, 29–37, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10211}, number={1}, journal={COLOR RESEARCH AND APPLICATION}, author={Ramanath, R and Kuehni, RG and Snyder, WE and Hinks, D}, year={2004}, month={Feb}, pages={29–37} }
@article{ramanath_snyder_2003, title={Adaptive demosaicking}, volume={12}, ISSN={["1017-9909"]}, DOI={10.1117/1.1606459}, abstractNote={The *Journal of Electronic Imaging* (JEI), copublished bimonthly with the Society for Imaging Science and Technology, publishes peer-reviewed papers that cover research and applications in all areas of electronic imaging science and technology.}, number={4}, journal={JOURNAL OF ELECTRONIC IMAGING}, author={Ramanath, R and Snyder, WE}, year={2003}, month={Oct}, pages={633–642} }
@article{ramanath_snyder_2002, title={Demosaicking as a bilateral filtering process}, volume={4667}, ISBN={["0-8194-4407-3"]}, ISSN={["0277-786X"]}, DOI={10.1117/12.467984}, abstractNote={Digital Still Color Cameras sample the visible spectrum using an array of color filters overlaid on a CCD such that each pixel samples only one color band. The resulting mosaic of color samples is processed to produce a high resolution color image such that a value of a color band not sampled at a certain location is estimated from its neighbors. This is often referred to as 'demosaicking.' In this paper, we approach the process of demosaicking as a bilateral filtering process which is a combination of spatial domain filtering and filtering based on similarity measures. Bilateral filtering smooths images while preserving edges by means of nonlinear combinations of neighboring image pixel values. A bilateral filter can enforce similarity metrics (such as squared error or error in the CIELAB space) between neighbors while performing the typical filtering operations. We have implemented a variety of kernel combinations while performing demosaicking. This approach provides us with a means to denoise, sharpen and demosaic the image simultaneously. We thus have the ability to represent demosaicking algorithms as spatial convolutions. The proposed method along with a variety of existing demosaicking strategies are run on synthetic images and real-world images for comparative purposes.}, journal={IMAGE PROCESSING: ALGORITHMS AND SYSTEMS}, author={Ramanath, R and Snyder, WE}, year={2002}, pages={236–244} }
@article{ramanath_snyder_bilbro_sander_2002, title={Demosaicking methods for Bayer color arrays}, volume={11}, ISSN={["1560-229X"]}, DOI={10.1117/1.1484495}, abstractNote={Digital Still Color Cameras sample the color spectrum using a monolithic array of color filters overlaid on a charge coupled device array such that each pixel samples only one color band. The resulting mosaic of color samples is processed to produce a high resolution color image such that the values of the color bands not sampled at a certain location are estimated from its neighbors. This process is often referred to as demosaicking. This paper introduces and compares a few commonly used demosaicking methods using error metrics like mean squared error in the RGB color space and perceived error in the CIELAB color space. © 2002 SPIE and IS&T.}, number={3}, journal={JOURNAL OF ELECTRONIC IMAGING}, author={Ramanath, R and Snyder, WE and Bilbro, GL and Sander, WA}, year={2002}, month={Jul}, pages={306–315} }
@inbook{ramanath_snyder_hinks_2002, title={Image comparison measure for digital still color cameras}, volume={1}, booktitle={2002 International Conference on Image Processing: proceedings: ICIP: 22-25 September, 2002, Rochester Riverside Convention Center, Rochester, New York, USA: Vol. 1}, publisher={Piscataway, NJ: IEEE}, author={Ramanath, A. R and Snyder, B. W. and Hinks, C. D.}, year={2002}, pages={629–632} }