Pierre Gouton

Multispectral Filter Arrays: Recent Advances and Practical Implementation

Thanks to some technical progress in interferencefilter design based on different technologies, we can finally successfully implement the concept of multispectral filter array-based sensors. This article provides the relevant state-of-the-art for multispectral imaging systems and presents the characteristics of the elements of our multispectral sensor as a case study. The spectral characteristics are based on two different spatial arrangements that distribute eight different bandpass filters in the visible and near-infrared area of the spectrum. We demonstrate that the system is viable and evaluate its performance through sensor spectral simulation.

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.

Generalization of Canny---Deriche filter for detection of noisy exponential edge

This paper presents a generalization of the Canny-Deriche filter for ramp edge detection with optimization criteria used by Canny (signal-to-noise ratio, localization, and suppression of false responses). Using techniques similar to those developed by Deriche, we derive a filter which maximizes the product of the first two criteria under the constraint of the last one. The result is an infinite length impulse response filter which leads to a stable third-order recursive implementation. Its performance shows an increase of the signal-to-noise ratio in the case of blurred and noisy images, compared to the results obtained from Deriches filter.

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.

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.

A Video-Based Real-Time Vehicle Counting System Using Adaptive Background Method

This paper presents a video-based solution for real time vehicle detection and counting system, using a surveillance camera mounted on a relatively high place to acquire the traffic video stream.The two main methods applied in this system are: the adaptive background estimation and the Gaussian shadow elimination. The former allows a robust moving detection especially in complex scenes. The latter is based on color space HSV, which is able to deal with different size and intensity shadows. After these two operations, it obtains an image with moving vehicle extracted, and then operation counting is effected by a method called virtual detector.

Karhunen-Loeve transform applied to region- based segmentation of color aerial images

The use of the Karhunen-Loeve transform (KLT) for region- based segmentation of aerial images by color and textural attributes is presented. Our aerial images are shown to be homogeneous color im- ages within the Karhunen-Loeve color representation space, which means they can be represented more easily and the region-based seg- mentation algorithms can be optimized. For texture analysis, the KLT is the basis of the local linear transform (LLT) and allows structural infor- mation about textures to be represented in an optimal and condensed manner. The LLT provides a system of textural analysis in the form of an adapted filter bank. We end the paper by presenting a method for merg- ing texture and color segmentations.

Comparison of bathymetric estimation using different satellite images in coastal sea waters

Bathymetric estimation can be obtained from multispectral satellite images for shallow waters. The method is based on the rotation of a pair of spectral bands. One of the resulting images is depth-dependent. Therefore several pixels corresponding to different depths are required to numerically evaluate the linear relation between the pixel values and the real depth for a training area. The aim of this study is to compare, for one bathymetric estimation method and one mesotrophic site, the results of depth estimation with a large panel of satellite and aerial images: CASI, QUICKBIRD, CHRIS PROBA, ETM, HYPERION and MeRIS. For each image the pair of spectral bands chosen to compute the bathymetry has been optimized. Error on depth estimation has been computed on two regions of the image: the training area and a validation area. This comparison is discussed to identify the influence of image parameters (spectral bands, S/N ratio, spatial resolution, and quantization) on the bathymetric results and to propose the most adapted image parameters for bathymetric estimation. For validation purposes, we compared the results obtained with a CASI image matching the optimized parameters in an oligotrophic site in the Red Sea.

Median filtering in multispectral filter array demosaicking

Inspired by the concept of the colour filter array (CFA), the research community has shown much interest in adapting the idea of CFA to the multispectral domain, producing multispectral filter arrays (MSFAs). In addition to newly devised methods of MSFA demosaicking, there exists a wide spectrum of methods developed for CFA. Among others, some vector based operations can be adapted naturally for multispectral purposes. In this paper, we focused on studying two vector based median filtering methods in the context of MSFA demosaicking. One solves demosaicking problems by means of vector median filters, and the other applies median filtering to the demosaicked image in spherical space as a subsequent refinement process to reduce artefacts introduced by demosaicking. To evaluate the performance of these measures, a tool kit was constructed with the capability of mosaicking, demosaicking and quality assessment. The experimental results demonstrated that the vector median filtering performed less well for natural images except black and white images, however the refinement step reduced the reproduction error numerically in most cases. This proved the feasibility of extending CFA demosaicking into MSFA domain.