Active skeleton for bacteria modeling

The investigation of spatio-temporal dynamics of bacterial cells and their molecular components requires automated image analysis tools to track cell shape properties and molecular component locations inside the cells. In the study of bacteria aging, the molecular components of interest are protein aggregates accumulated near bacteria boundaries. This particular location makes very ambiguous the correspondence between aggregates and cells, since computing accurately bacteria boundaries in phase-contrast time-lapse imaging is a challenging task. This paper proposes an active skeleton formulation for bacteria modeling which provides several advantages: an easy computation of shape properties (perimeter, length, thickness, orientation), an improved boundary accuracy in noisy images, and a natural bacteria-centered coordinate system that permits the intrinsic location of molecular components inside the cell. Starting from an initial skeleton estimate, the medial axis of the bacterium is obtained by minimizing an energy function which incorporates bacteria shape constraints. Experimental results on biological images and comparative evaluation of the performances validate the proposed approach for modeling cigar-shaped bacteria like Escherichia coli. The Image-J plugin of the proposed method can be found online at http://fluobactracker.inrialpes.fr.Comment: Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualizationto appear i

Unsupervised Learning of Complex Articulated Kinematic Structures combining Motion and Skeleton Information

In this paper we present a novel framework for unsupervised kinematic structure learning of complex articulated objects from a single-view image sequence. In contrast to prior motion information based methods, which estimate relatively simple articulations, our method can generate arbitrarily complex kinematic structures with skeletal topology by a successive iterative merge process. The iterative merge process is guided by a skeleton distance function which is generated from a novel object boundary generation method from sparse points. Our main contributions can be summarised as follows: (i) Unsupervised complex articulated kinematic structure learning by combining motion and skeleton information. (ii) Iterative fine-to-coarse merging strategy for adaptive motion segmentation and structure smoothing. (iii) Skeleton estimation from sparse feature points. (iv) A new highly articulated object dataset containing multi-stage complexity with ground truth. Our experiments show that the proposed method out-performs state-of-the-art methods both quantitatively and qualitatively

Path Planning for Mobile Robot Navigation using Voronoi Diagram and Fast Marching

For navigation in complex environments, a robot need s to reach a compromise between the need for having efficient and optimized trajectories and t he need for reacting to unexpected events. This paper presents a new sensor-based Path Planner w hich results in a fast local or global motion planning able to incorporate the new obstacle information. In the first step the safest areas in the environment are extracted by means of a Vorono i Diagram. In the second step the Fast Marching Method is applied to the Voronoi extracted a reas in order to obtain the path. The method combines map-based and sensor-based planning o perations to provide a reliable motion plan, while it operates at the sensor frequency. The m ain characteristics are speed and reliability, since the map dimensions are reduced to an almost uni dimensional map and this map represents the safest areas in the environment for moving the robot. In addition, the Voronoi Diagram can be calculated in open areas, and with all kind of shaped obstacles, which allows to apply the proposed planning method in complex environments wher e other methods of planning based on Voronoi do not work.This work has been supported by the CAM Project S2009/DPI-1559/ROBOCITY2030 I

Spline-based medial axis transform representation of binary images

Medial axes are well-known descriptors used for representing, manipulating, and compressing binary images. In this paper, we present a full pipeline for computing a stable and accurate piece-wise B-spline representation of Medial Axis Transforms (MATs) of binary images. A comprehensive evaluation on a benchmark shows that our method, called Spline-based Medial Axis Transform (SMAT), achieves very high compression ratios while keeping quality high. Compared with the regular MAT representation, the SMAT yields a much higher compression ratio at the cost of a slightly lower image quality. We illustrate our approach on a multi-scale SMAT representation, generating super-resolution images, and free-form binary image deformation