Alamin Mansouri

Development of a Protocol for CCD Calibration: Application to a Multispectral Imaging System

In this paper we describe in detail a method for calibrating a CCD-based camera. The calibration aims to remove both temporal and systematic noises introduced by the sensor, electronics, and optics after which we can correct the non-linearity of its response. For the non-linearity correction we use a simple and powerful approach consisting on a complementary approach between a polynomial fitting and an LUT based algorithm. The proposed methodology is accurate in the sense that it takes into account individual characteristics of each pixel. In each pixel, systematic noises are measured through acquiring offset images, thermal images, and FlatField images. A rigorous protocol for acquiring these images based on experimentation is established. The method to acquire Flat-Field image is novel and is particularly efficient in that it can correct all defects due to non-uniform pixel responses, vignettage, blemishes on optic and/or filters, and perhaps even illumination nonuniformity. We notice that such a methodology of calibration is particularly efficient in the case of an optical filter based multispectral imaging system, although it remains valid for any imaging system based on a CCD sensor.

Optical calibration of a multispectral imaging system based on interference filters

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.

Integration of 3D and multispectral data for cultural heritage applications: Survey and perspectives

Cultural heritage is increasingly put through imaging systems such as multispectral cameras and 3D scanners. Though these acquisition systems are often used independently, they collect complementary information (spectral vs. spatial) used for the study, archiving and visualization of cultural heritage. Recording 3D and multispectral data in a single coordinate system enhances the potential insights in data analysis. We present the state of the art of such acquisition systems and their applications for the study of cultural heritage. We also describe existing registration techniques that can be used to obtain 3D models with multispectral texture and explore the idea of optically tracking acquisition systems to ensure an easy and precise registration.

Toward a 3D Multispectral Scanner: An Application to Multimedia

A stereoscopic system based on a multispectral camera and an LCD projector uses multispectral information for 3D object reconstruction. By linking 3D points to a curve representing the spectral reflectance, the system gives a physical representation of the matter thats independent from illuminant, observer, and acquisition devices

Neural networks in two cascade algorithms for spectral reflectance reconstruction

In this paper, we deal with the problem of the spectral reflectance curves reconstruction. Because of the reconstruction of such curves is an inverse problem, slight variations in input data completely skew the expected results. So, finding a robust reconstruction operator is highly required. We present a robust method based upon neural networks. This method takes advantage of that neural networks are generally robust to the noise. Furthermore, we propose two cascade algorithms of using these neural networks. The first algorithm allows faithful reconstruction of spectra that are previously learned. The second algorithm allows good generalization allowing for reconstructing a wide range of reflectance that are not learned in the training stage. The results confirm the robustness and the reliability of the proposed method compared to some classical ones.

Saliency for Spectral Image Analysis

We introduce a new feature extraction model for purposes of image comparison, visualization and interpretation. We define the notion of spectral saliency, as the extent to which a certain group of pixels stands out in an image and in terms of reflectance, rather than in terms of colorimetric attributes as it is the case in traditional saliency studies. The model takes as an input a multi- or hyper-spectral image with any dimensionality, any range of wavelengths, and it uses a series of dedicated feature extractions to output a single saliency map. We also present a local analysis of the image spectrum allowing to produce such maps in color, thus depicting not only which objects are salients, but also in which range of wavelengths. A variety of applications can be derived from the resulting maps, particularly under the scope of visualization, such as the saliency-driven evaluation of dimensionality reduction techniques. Results show that spectral saliency provides valuable information, which do not correlate neither with visual saliency, second-order statistics nor with naturalness, but serve however well for visualization-related applications.

A Constrained Band Selection Method Based on Information Measures for Spectral Image Color Visualization

We present a new method for the visualization of spectral images, based on a selection of three relevant spectral channels to build a red-green-blue composite. Band selection is achieved by means of information measures at the first, second, and third orders. Irrelevant channels are preliminarily removed by means of a center-surround entropy comparison. A visualization-oriented spectrum segmentation based on the use of color matching functions allows for computational ease and adjustment of the natural rendering. Results from the proposed method are presented and objectively compared to four other dimensionality reduction techniques in terms of naturalness and informative content.

Multispectral imaging using a stereo camera: concept, design and assessment

This paper proposes a one-shot six-channel multispectral color image acquisition system using a stereo camera and a pair of optical filters. The two filters from the best pair, selected from among readily available filters such that they modify the sensitivities of the two cameras in such a way that they produce optimal estimation of spectral reflectance and/or color, are placed in front of the two lenses of the stereo camera. The two images acquired from the stereo camera are then registered for pixel-to-pixel correspondence. The spectral reflectance and/or color at each pixel on the scene are estimated from the corresponding camera outputs in the two images. Both simulations and experiments have shown that the proposed system performs well both spectrally and colorimetrically. Since it acquires the multispectral images in one shot, the proposed system can solve the limitations of slow and complex acquisition process, and costliness of the state of the art multispectral imaging systems, leading to its possible uses in widespread applications.

Improving color correction across camera and illumination changes by contextual sample selection

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 bal- ance control available in commercial cameras is not sufficient to pro- vide reproducible color classification. We address this problem of color constancy on a large image database acquired with varying digi- tal cameras and lighting conditions. A device-independent color repre- sentation 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 applica- tions.

Integration of high-resolution spatial and spectral data acquisition systems to provide complementary datasets for cultural heritage applications

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.