KEYWORDS: Raman spectroscopy, Digital micromirror devices, Sensors, Detector arrays, Optical filters, Chemical species, Tablets, Micromirrors, Light scattering, In vivo imaging
We develop a high-speed compressive Raman imaging technology using a programmable binary spectral filter and a single channel detector to perform fast Raman imaging for detection and concentration estimation of know species over millimeters field of view. The technology enables Raman imaging with a pixel dwell time as short as few hundreds of microseconds. We report fast Raman imaging of pharmaceutical tablets and micro-plastics. We also present a novel fast line scan compressive Raman imaging scheme using the 2D digital micro-mirror device (DMD) to encode both space and frequency.
Confocal fluorescence microscopy is a privileged tool for life imaging, but can generate phototoxicity due to the prolonged sample illumination. When cells are organized along sheets lying on 2D surfaces curved in a 3D volume (e.g. epithelial cells), we propose a new approach allowing to automatically estimate the surface on which these cells are distributed from a small number of acquisitions (typically 0.1% of the voxels). This allows to concentrate thereafter the illumination around the surface of interest and thus to scan only a small portion (typically between 1% and 5%) of the volume containing the sample.
We describe an active polarimetric imager with laser illumination at 1.5 µm that can generate any illumination and analysis polarization state on the Poincar sphere. Thanks to its full polarization agility and to image analysis of the scene with an ultrafast active-contour based segmentation algorithm, it can perform adaptive polarimetric contrast optimization. We demonstrate the capacity of this imager to detect manufactured objects in different types of environments for such applications as decamouflage and hazardous object detection. We compare two imaging modes having different number of polarimetric degrees of freedom and underline the characteristics that a polarimetric imager aimed at this type of applications should possess.
Single-cell dry mass measurement is used in biology to follow cell cycle, to address effects of drugs, or to investigate cell metabolism. Quantitative phase imaging technique with quadriwave lateral shearing interferometry (QWLSI) allows measuring cell dry mass. The technique is very simple to set up, as it is integrated in a camera-like instrument. It simply plugs onto a standard microscope and uses a white light illumination source. Its working principle is first explained, from image acquisition to automated segmentation algorithm and dry mass quantification. Metrology of the whole process, including its sensitivity, repeatability, reliability, sources of error, over different kinds of samples and under different experimental conditions, is developed. We show that there is no influence of magnification or spatial light coherence on dry mass measurement; effect of defocus is more critical but can be calibrated. As a consequence, QWLSI is a well-suited technique for fast, simple, and reliable cell dry mass study, especially for live cells.
In the case of textured images and more particularly of directional textures, a new parametric technique is proposed to estimate the orientation field of textures. It consists of segmenting the image into regions with homogeneous orientations, and estimating the orientation inside each of these regions. This allows us to maximize the size of the samples used to estimate the orientation without being corrupted by the presence of boundaries between regions. For that purpose, the local-hence noisy-orientations of the texture are first estimated using small filters (3×3 pixels). The segmentation of the obtained orientation field image then relies on a generalization of a minimum description length based segmentation technique, to the case of π-periodic circular data modeled with Von Mises probability density functions. This leads to a fast segmentation algorithm without tuning parameters in the optimized criterion. The accuracy of the orientations estimated with the proposed method is then compared with other approaches on synthetic images and an application to the processing of real images is finally addressed.
This paper deals with textured images and more particularly with directional textures. We propose a new parametric technique to estimate the orientation field of textures. It consists in partitioning the image into regions with homogeneous orientations, and then to estimate the orientation inside each of these regions, which allows us to maximize the size of the samples used to estimate the orientation without being corrupted by the presence of frontiers between regions. Once estimated the local - hence noisy - orientations of the texture using small filters (3×3 pixels), image partitioning is based on the minimization of the stochastic complexity (Minimum Description Length principle) of the orientation field. The orientation fluctuations are modeled with Von Mises probability density functions, leading to a fast and unsupervised partitioning algorithm. The accuracy of the orientations estimated with the proposed method is then compared with other approaches on synthetic images. An application to the processing of real images is finally addressed.
We describe and analyse a generalization of a parametric segmentation technique adapted to Gamma distributed SAR images to a simple non parametric noise model. The partition is obtained by minimizing the stochastic complexity of a quantized version on Q levels of the SAR image and lead to a criterion without parameters to be tuned by the user. We analyse the reliability of the proposed approach on synthetic images. The quality of the obtained partition will be studied for different possible strategies. In particular, one will discuss the reliability of the proposed optimization procedure. Finally, we will precisely study the performance of the proposed approach in comparison with the statistical parametric technique adapted to Gamma noise. These studies will be led by analyzing the number of misclassified pixels, the standard Hausdorff distance and the number of estimated regions.
Determining the shape of one or several objects in an image is a fundamental task for many imaging systems. We propose here a general review of new techniques based on the information theory principles, which allows one to determine segmentation techniques without parameter to be tuned by the user. These techniques are quite general since they include, polygonal and spline parametric shape descriptions, level set models of contour and homogeneous partition of images. This approach can take into account the physical nature of the grey level fluctuations and is thus adapted to different new imaging systems. Furthermore, it can lead to fast algorithms (from a few hundred of ms to a few seconds depending on the complexity of the task to perform on 256 x 256 pixel images).
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