In many tasks of machine vision applications, it is important that recorded colors remain constant, in the real world scene, even under changes of the illuminants and the cameras. Contrary to the human vision system, a machine vision system exhibits inadequate adaptability to the variation of lighting conditions. Automatic white balance control available in commercial cameras is not sufficient to provide reproducible color classification. We address this problem of color constancy on a large image database acquired with varying digital cameras and lighting conditions. A device-independent color representation may be obtained by applying a chromatic adaptation transform, from a calibrated color checker pattern included in the field of view. Instead of using the standard Macbeth color checker, we suggest selecting judicious colors to design a customized pattern from contextual information. A comparative study demonstrates that this approach ensures a stronger constancy of the colors-of-interest before vision control thus enabling a wide variety of applications.
Multispectral color imaging is a promising technology, which can solve many of the problems of traditional RGB color
imaging. However, it still lacks widespread and general use because of its limitations. State of the art multispectral imaging
systems need multiple shots making it not only slower but also incapable of capturing scenes in motion. Moreover, the
systems are mostly costly and complex to operate. The purpose of the work described in this paper is to propose a one-shot
six-channel multispectral color image acquisition system using a stereo camera or a pair of cameras in a stereoscopic
configuration, and a pair of optical filters. The best pair of filters is selected from among readily available filters such
that they modify the sensitivities of the two cameras in such a way that they get spread reasonably well throughout the
visible spectrum and gives optimal reconstruction of spectral reflectance and/or color. As the cameras are in a stereoscopic
configuration, the system is capable of acquiring 3D images as well, and stereo matching algorithms provide a solution to
the image alignment problem. Thus the system can be used as a "two-in-one" multispectral-stereo system. However, this
paper mainly focuses on the multispectral part. Both simulations and experiments have shown that the proposed system
performs well spectrally and colorimetrically.
Modern optical measuring systems are able to record objects with high spatial and spectral precision. The acquisition of
spatial data is possible with resolutions of a few hundredths of a millimeter using active projection-based camera
systems, while spectral data can be obtained using filter-based multispectral camera systems that can capture surface
spectral reflectance with high spatial resolution. We present a methodology for combining data from these two discrete
optical measuring systems by registering their individual measurements into a common geometrical frame. Furthermore,
the potential for its application as a tool for the non-invasive monitoring of paintings and polychromy is evaluated. The
integration of time-referenced spatial and spectral datasets is beneficial to record and monitor cultural heritage. This
enables the type and extent of surface and colorimetric change to be precisely characterized and quantified over time.
Together, these could facilitate the study of deterioration mechanisms or the efficacy of conservation treatments by
measuring the rate, type, and amount of change over time. An interdisciplinary team of imaging scientists and art
scholars was assembled to undertake a trial program of repeated data acquisitions of several valuable historic surfaces of
cultural heritage objects. The preliminary results are presented and discussed.
In this paper, we propose a simple method for wine color characterization, classification and
reproduction. The aim is to represent the colors of wines with limited number of hues that we call nuances.
Burgundy wines (France) constitute the wine samples in this study but the method remains general. The method
consists of four steps: spectral transmittance measures of a large number of wine samples. Then standard and
gamma corrected colors are reconstructed from spectral data. Afterwards, a ΔE-based classification is performed
in the CIELAB, which provides good visual uniformity and thus offers the best discrimination between the
different samples. The last step is a spectral-based color reproduction using synthetic liquids. The obtained
results are encouraging in that they permit an accurate characterization and reproduction of wine color.
We describe in this paper a stereoscopic system based on a multispectral camera and a projector. To be used, this system must be calibrated. This starts by a geometrical calibration of the stereoscopic set using a weak calibration. It is also necessary to know the spectral response of each element in the acquisition chain, from the projector to the camera. Then, image acquisition can begin. To acquire a multi-spectral image, we have just to use the projector to send a luminous pattern on the scene. The projection of the pattern in the image is detected and labeled since the projector was characterized during the calibration step. Finally, we can obtain the 3D position of the different parts of the luminous pattern on the scene by using triangulation. Moreover, a spectral reflectance can be associated to each of them. The colorimetric accuracy obtained by a multispectral camera is totally improved compared with a color camera.
We present a new approach to optically calibrate a multispectral imaging system based on interference filters. Such a system typically suffers from some blurring of its channel images. Because the effectiveness of spectrum reconstruction depends heavily on the quality of the acquired channel images, and because this blurring negatively affects them, a method for deblurring and denoising them is required. The blur is modeled as a uniform intensity distribution within a circular disk. It allows us to characterize, quantitatively, the degradation for each channel image. In terms of global reduction of the blur, it consists of the choice of the best channel for the focus adjustment according to minimal corrections applied to the other channels. Then, for a given acquisition, the restoration can be performed with the computed parameters using adapted Wiener filtering. This process of optical calibration is evaluated on real images and shows large improvements, especially when the scene is detailed.