Size and shape estimation of 3-D convex objects from their 2-D projections: application to crystallization processes.

The aim of this paper is to present a new projective stereological method that enables to estimate the size and the shape of a three-dimensional convex object from measurements only made on its two-dimensional projections. To do so, some geometrical and morphometrical measurements on the projected shadows of the three-dimensional convex object are done and the value of the three-dimensional convex object size and shape parameters are retrieved using the maximum likelihood estimation method. The proposed method is then applied to estimate three-dimensional particle size distributions during crystallization processes.

Improving focus measurements using logarithmic image processing.

The logarithmic image processing (LIP) model is a mathematical framework which provides algebraic and functional operations for the processing of intensity images valued in a bounded range. The LIP model has been proved to be physically consistent, most notably with some image formation models and several laws and characteristics of human brightness perception. This paper addresses the image focus measurement problem using the LIP model. The three most classical image focus measurements: the sum-modified-Laplacian, the tenengrad and the variance, which aim at estimating the degree of focus of an acquired image by emphasizing and quantifying its sharpness information, are considered and reinterpreted using the LIP framework. These reinterpretations notably make attempts at evaluating degrees of focus in terms of human brightness (sensation) from physical light stimuli. Their potential is illustrated and validated on shape-from-focus issues on both simulated data and real acquisitions in digital optical microscopy. The concept of shape-from-focus involves recovering the shape of an observed thick sample by locally maximizing a focus measurement throughout a sequence of differently focused images. Finally, it is shown that the LIP-based focus measurements clearly outperform their respective classical ones.

LIP-model-based three-dimensional reconstruction and visualization of HIV-infected entire cells.

This paper presents a global solution from acquisition to visualization for the three-dimensional reconstruction of cell sections. Original techniques are proposed for the correct handling of geometrical section distortions, and a new interpretation based on the logarithmic image processing (LIP) model is applied in order to create normalized grey-level sections where these are missing. Finally, a new method for generating a mesh of triangles to describe the envelope of the reconstructed cell is proposed, as well as a visualization mixing image synthesis and grey-level information. The product allows the user to explore the reconstructed cellular block in any desired direction, by showing user-defined grey-level sections inside the block mixed to a synthetic view of the cell envelope.

Contrast definition and contour detection for logarithmic images.

Logarithmic images, such as images obtained by transmitted light or those produced by the human visual system, differ from linear images. Their processing and analysis require consequently specific laws and structures. The latter have been developed in the concept of a logarithmic image processing (LIP) model (Jourlin & Pinoli, 1987, 1988; Pinoli, 1987a). This model permits the introduction of a well-justified contrast definition: from a physical point of view, it is closely linked with logarithmic images and from a mathematical point of view, it is set up in an algebraic structure. The applications presented at the end of this paper concern image preprocessing and segmentation. In particular, in the case of microscopic images, the proposed method of segmentation gives good results with transmitted light (thin foils in biology or transmitted electronic microscopy). However, images obtained by reflected light microscopy are not within the scope of this model.