Boundary Shape Recognition Using Accumulated Length and Angle Information

http://www.springer.com/lncsInternational audienceIn this paper we present a method to recognize shapes by analyzing a polygonal approximation of their boundaries. The method is independent of the used approximation method since its recognition strategy does not rely on the number of segments composing the shape. Length and turning angle information are extracted from the chain of segments. The comparison method is invariant to scale, translation and some occlusions of the extracted contour. A simple pre-processing method, also based on arc-length features, is presented to be used as a coarse fitting method to determine angle rotation and as a first filter to eliminate non pertinent candidates

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

Segmentation and classification of individual tree crowns

By segmentation and classification of individual tree crowns in high spatial resolution aerial images, information about the forest can be automatically extracted. Segmentation is about finding the individual tree crowns and giving each of them a unique label. Classification, on the other hand, is about recognising the species of the tree. The information of each individual tree in the forest increases the knowledge about the forest which can be useful for managements, biodiversity assessment, etc. Different algorithms for segmenting individual tree crowns are presented and also compared to each other in order to find their strengths and weaknesses. All segmentation algorithms developed in this thesis focus on preserving the shape of the tree crown. Regions, representing the segmented tree crowns, grow according to certain rules from seed points. One method starts from many regions for each tree crown and searches for the region that fits the tree crown best. The other methods start from a set of seed points, representing the locations of the tree crowns, to create the regions. The segmentation result varies from 73 to 95 % correctly segmented visual tree crowns depending on the type of forest and the method. The former value is for a naturally generated mixed forest and the latter for a non-mixed forest. The classification method presented uses shape information of the segments and colour information of the corresponding tree crown in order to decide the species. The classification method classifies 77 % of the visual trees correctly in a naturally generated mixed forest, but on a forest stand level the classification is over 90 %

The image ray transform for structural feature detection

The use of analogies to physical phenomena is an exciting paradigm in computer vision that allows unorthodox approaches to feature extraction, creating new techniques with unique properties. A technique known as the "image ray transform" has been developed based upon an analogy to the propagation of light as rays. The transform analogises an image to a set of glass blocks with refractive index linked to pixel properties and then casts a large number of rays through the image. The course of these rays is accumulated into an output image. The technique can successfully extract tubular and circular features and we show successful circle detection, ear biometrics and retinal vessel extraction. The transform has also been extended through the use of multiple rays arranged as a beam to increase robustness to noise, and we show quantitative results for fully automatic ear recognition, achieving 95.2% rank one recognition across 63 subjects

From on-line sketching to 2D and 3D geometry: A fuzzy knowledge based system

The paper describes the development of a fuzzy knowledge based prototype system for conceptual design. This real time system is designed to infer user’s sketching intentions, to segment sketched input and generate corresponding geometric primitives: straight lines, circles, arcs, ellipses, elliptical arcs, and B-spline curves. Topology information (connectivity, unitary constraints and pairwise constraints) is received dynamically from 2D sketched input and primitives. From the 2D topology information, a more accurate 2D geometry can be built up by applying a 2D geometric constraint solver. Subsequently, 3D geometry can be received feature by feature incrementally. Each feature can be recognised by inference knowledge in terms of matching its 2D primitive configurations and connection relationships. The system accepts not only sketched input, working as an automatic design tools, but also accepts user’s interactive input of both 2D primitives and special positional 3D primitives. This makes it easy and friendly to use. The system has been tested with a number of sketched inputs of 2D and 3D geometry

Turning function and shape recognition

The technique of turning function is a powerful method for measuring similarity between two dimensional shapes. The method works well when the boundary of the shape does not contain noise edges. We propose an algorithm for smoothing noise edges by decomposing the boundary into monotone components and smoothing the noise edges in each component. We also present an implementation of the proposed smoothing algorithm

Designing bio-mimetic variational porosity for tissue scaffolds

Reconstructing or repairing the damaged or diseased tissues with porous scaffolds to restore the mechanical, biological and chemical functions is one of the major tissue engineering strategies. Development of Solid Free Form (SFF) techniques and improvement in biomaterial properties by synergy have provided the leverage to fabricate controlled and interconnected porous scaffold structures. But homogeneous scaffolds with regular porosity do not provide all the biological and mechanical requirements of an ideal tissue scaffold. Thus achieving controllable, continuous, interconnected gradient porosity with reproducible and fabricatable design is critical for successful regeneration of the replaced tissue. In this research, a novel scaffold modeling approach has been proposed to achieve bio-mimetic tissue scaffolds. Firstly, the optimum filament deposition angle has been determined based on the internal heterogeneous regions and their locations. Then an area-weight based approach has been applied to generate the spatial porosity function to determine the filament deposition location for the desired bio-mimetic porosity. The proposed methodology has been implemented using computer simulation. A micro-nozzle biomaterial deposition system driven by NC motion control has been used to fabricate a sample designed structure