We propose a practical schema for semiautomatic segmentation of images of Arctic charr. The goal is to separate differently colored parts of the fish, especially red abdominal areas from the other parts. The novelty and importance of the proposed system are in the reconstruction of a working schema rather than its components. The system is important to fisheries since the coloration of fish is connected to the genetic quality and is often used to evaluate the health status of the fish. Quantitative analysis of this kind of information gives follow-up data and a more realistic view of fish stock than the basic visual evaluation. The schema takes consideration of economical limitations of an ordinary fishery and educational aspects of personnel. The results are evaluated visually by the experts and against a neural network solution.
In this paper, we present results for optimizing images for an industrial show room. The light conditions are not very controllable and the projector is not a high quality one. The optimization is done using metameric reproduction and to do this we measure spectral information of the product, projector and the illumination at the show room. The spectral characteristic of the red channel of the projector was surprising: the range of possible red values was narrower than the green and blue range. This caused some limitations which needed to be taken into account in calculating the optimal images: optimal images can have either full contrast range with a reddish tint or correct hue with narrower contrast range.
When skin areas such as faces and hands are imaged under natural environments their color appearance is frequently affected by variations in illumination intensity and chromaticity. In color-based skin tracking and detection, changing intensity is often dismissed either with the use of normalized, intensity-invariant color coordinates or by additionally modeling possible skin intensities. Chromaticity variations are rarely considered, although they are common in practice. In most approaches considering chromaticity, the experiments are done with a small or undefined variation range. It is difficult to compare different approaches and assess their applicability range for this reason. To improve the situation, we evaluate the performance of four state-of-the-art methods under drastic but practically common illumination changes. The effect of illumination chromaticity for skin is clearly defined, and based on it we draw conclusion about the performance of these approaches.
The appearance of skin colors in the images depends among other things, on the camera, the calibration of the camera, and the illumination under which the image was taken. In this study, we investigate how the skin colors appear in the chromaticity coordinates of different color spaces like HSV/HSL, normalized rgb, YES and TSL. For this purpose, we have taken images of faces under 16 different illumination/camera calibration conditions using simulated illuminants (Horizon, A, fluorescent TL84 and daylight) with different RGB cameras (1CCD web cameras and a 3CCD camera). In the making of this series of 16 images, first the selected camera was calibrated to one of the four light sources and an image was taken. After that the light source was changed to the other light sources and at each time the person was imaged. The process was repeated to the other two light sources. The same procedure was done for all four light sources and for each camera. The skin regions were extracted from these images and this skin data was then converted to different color spaces. We inspected how the chromaticities of different skin color groups in these color spaces overlap in images taken in all 16 different cases and only in those cases in which the selected camera was calibrated to the current illuminant. These investigations were also made between different cameras. In addition to this, we examined the overlapping of all skin chromaticities from the different skin color groups between cameras.
The purpose of this paper is to investigate if it is possible to use estimation techniques to reduce the difference between predicted and actual RGB values. Images and spectral reflectances of two classes of objects were used: matte, 2D (Munsell chips and Macbeth chart) and natural, 3D objects (faces). In the prediction phase, a simple RGB model was evaluated which takes into account only the spectral power distributions of the current and calibration illuminants, spectral reflectances of the objects, and the spectral response of the RGB camera in the calculations to avoid the complexity of modeling other possible factors affecting image formation. The results show that an estimation can make the prediction results closer to the actual values.
Saturation here refers to electronic saturation of the camera sensors which produces clipped colors, and not the purity of color as in the hue-saturation and value scale. Saturated images are routinely discarded in image analysis yet there are situations when they cannot be avoided. This paper proposes two strategies to recover color information in facial images taken under non-ideal conditions to make them useful for further processing. The first assumes that the skin is matte and that there are parts of the image which are not clipped. Ratios between R, G and B values of unclipped pixels belonging to the same parts of the image may then be used to compute for lost channel values. The second approach uses color eigenfaces computed from our physics-based face database obtained under different illuminants and camera calibration conditions. Skin color is recovered by transforming the first few eigenface coefficients towards ideal condition values. Excellent color recovery for clipped images is achieved when these two techniques are combined and used on face images captured under daylight illuminant with a camera white balanced for incandescent light.
Machine vision systems based on a color camera are increasingly used for color measurements under industrial conditions. An important problem in the measurement of small color differences is related to metamerism. But because color cameras and human vision have slightly different response functions, they produce different sets of 'tristimulus values' for the same object viewed under the same illumination conditions, and it can happen that metameric pairs for a human are discriminated very well by a color camera and vice versa. The purpose of the present research was to evaluate the performance of color camera for measuring small color differences. The tests were performed for the whole collection of the NCS Color Block viewed under three different illuminants: standard illuminant A, standard illuminant D65, and illuminant F11, and for two options of color camera, 8-bit and 12-bit. The results show some limitations with lower bit camera for accurate colorimetric measurements and good performance with the 12-bit camera in discrimination of very similar colors.