Albedo estimation has traditionally been used to make computational simulations of real objects under different conditions, but as yet no device is capable of measuring albedo directly. The aim of this work is to introduce a photometric-based color imaging framework that can estimate albedo and can reproduce the appearance both indoors and outdoors of images under different lights and illumination geometry. Using a calibration sample set composed of chips made of the same material but different colors and textures, we compare two photometric-stereo techniques, one of them avoiding the effect of shadows and highlights in the image and the other ignoring this constraint. We combined a photometric-stereo technique and a color-estimation algorithm that directly relates the camera sensor outputs with the albedo values. The proposed method can produce illuminant-free images with good color accuracy when a three-channel red-green-blue (RGB) digital camera is used, even outdoors under solar illumination.
We have modified the Fuzzy C-Means algorithm for an application related to segmentation of hyperspectral images.
Classical fuzzy c-means algorithm uses Euclidean distance for computing sample membership to each cluster. We have
introduced a different distance metric, Spectral Similarity Value (SSV), in order to have a more convenient similarity
measure for reflectance information. SSV distance metric considers both magnitude difference (by the use of Euclidean
distance) and spectral shape (by the use of Pearson correlation). Experiments confirmed that the introduction of this
metric improves the quality of hyperspectral image segmentation, creating spectrally more dense clusters and increasing
the number of correctly classified pixels.
The profit of low-cost, multispectral imaging systems in estimating spectral power distributions has been widely studied. There are various mathematical methods available (PCA, Wiener's estimation method, spline interpolation, MDST, among others) which permit the accurate reconstruction of a spectrum from the response of a small set of sensors. One important issue in this task is the influence of noise, its propagation through mathematical transformations and how the selection of the sensors of the multispectral system, combined with the spectral estimation algorithm chosen, may reduce its influence. We report here on four different spectral recovery methods that reconstruct skylight spectra from the responses of three Gaussian sensors (the spectral profile of which is a Gaussian curve). The sensors are searched for using a simulated annealing algorithm, and they are optimized so that they give the best possible spectral and colorimetric reconstructions, even in the presence of noise. We show here how the accuracy of the reconstructions is influenced by the recovery method chosen.
The objective of the study was to examine the influence of the mean luminance level on the detection thresholds for red-green chromatic gratings of three different spatial frequencies. Data for chromatic sinusoidal gratings with higher mean luminance levels (within the photopic level) than those ones used in previous works were reported. The study analyzed the transition luminance between the DeVries- Rose law and the Weber law regions, and considered the validity of the constant-flux hypothesis for the three spatial frequencies tested. The results suggest that the 'flux' would not be a critical factor in the processing of chromatic gratings in the low spatial frequency range.
We study here the feasibility of a spectral daylight recovering algorithm using a linear model that takes advantage of the strong correlation among daylight curves. To test the algorithm we use the daylight eigenvectors obtained by a principal-value decomposition over 2600 daylight spectra recorded over a period of two years. A binary search, performed here, found the optimal spectral positions of a set of few narrow-band filters. We analyze, over the set of 2600 daylight curves, the algorithm accuracy when using three to six narrow filters, obtaining that such an daylight algorithm is not sufficiently accurate in comparison with similar linear models that recover objects spectral reflectances proposed in artificial-vision.
Proc. SPIE. 2730, Second Iberoamerican Meeting on Optics
KEYWORDS: Optical filters, Visual process modeling, Cameras, Image acquisition, Data processing, Image filtering, Human vision and color perception, Optical pattern recognition, Information operations, RGB color model
Two multichannel configurations different from the conventional RGB channels are proposed to improve color discrimination in optical pattern recognition. One configuration consists of n narrow-band channels which are selected depending on the colors of the objects to discriminate. The other configuration applies a linear transformation on the RGB information based on the color human vision models. The proposed configurations are particularly advantageous when the colors of the objects belong to the same range of hue.