Stable superhydrophobic surfaces induced by dual-scale topography on SU-8

SU-8 is a photosensitive resist widely used for the fabrication of MEMS and lab-on-a-chip devices, as well as of model structures for testing wetting theories. In this work, superhydrophobic surfaces are fabricated on SU-8 by combining micro- and nano-sized structures formed by means of lithography and plasma etching, respectively. It is found that nanotexturing of the micropatterned SU-8 surfaces is essential in enhancing surface hydrophobicity and rendering the surfaces water repellent (i.e. minimizing contact angle hysteresis). The proposed method will be shown to be of paramount importance for the fabrication of mechanically stable and robust superhydrophobic SU-8 surfaces with low aspect ratio microstructuring.     

Delving deeper into the whorl of flower segmentation

We describe an algorithm for automatically segmenting flowers in colour photographs. This is a challenging problem because of the sheer variety of flower classes, the variability within a class and within a particular flower, and the variability of the imaging conditions – lighting, pose, foreshortening, etc.     

Surface Reconstruction from Unstructured 3D Data

Building 3 0 models from unstructured data is a problem that arises increasingly as new 30 scanning technology is able to produce large and complex databases of full 3 0 information. Huge efforts put into segmenting entire sets of 20 images demand robust tools that are then able to reconstruct any arbitrary 30 surface segmented from the images. In this paper we propose an algorithmic methodology that automatically produces a surface from a set of points in ?³ about which we have no topological knowledge. Our method uses a spatial decomposition and a surface tracking algorithm to produce a rough approximation S' of the unknown manifold S. The produced surface S' serves as a robust initialisation for a physically based modeling technique that yields the fine details of S and so improves the quality of the reconstruction.     

Mesh Simplification

Mesh simplification is an important stage after surface reconstruction since the models produced can contain a large number of polygons making them difficult to manipulate. In this paper we present a mesh simplification algorithm to reduce the number of vertices in a dense mesh of triangles. The algorithm is based on edge operations that are performed in the inside of independent clusters distributed over the entire mesh. The clusters are well-characterized regions that can successfully accept simplification operations. The simplification operations produce only local transformations on the mesh. This region-based, distributed approach permits to easily track and control the changes in the triangulation and avoids the appearance of particular cases that would require a special handling. The algorithm uses two user-specified parameters to guide the operations. These parameters allow various simplification strategies that are illustrated on several dense triangulations.     

Classification of anatomical structures in MR brain images using fuzzy parameters

We present an algorithm that automatically segments and classifies the brain structures in a set of magnetic resonance (MR) brain images using expert information contained in a small subset of the image set. The algorithm is intended to do the segmentation and classification tasks mimicking the way a human expert would reason. The algorithm uses a knowledge base taken from a small subset of semiautomatically classified images that is combined with a set of fuzzy indexes that capture the experience and expectation a human expert uses during recognition tasks. The fuzzy indexes are tissue specific and spatial specific, in order to consider the biological variations in the tissues and the acquisition inhomogeneities through the image set. The brain structures are segmented and classified one at a time. For each brain structure the algorithm needs one semiautomatically classified image and makes one pass through the image set. The algorithm uses low-level image processing techniques on a pixel basis for the segmentations, then validates or corrects the segmentations, and makes the final classification decision using higher level criteria measured by the set of fuzzy indexes. We use single-echo MR images because of their high volumetric resolution; but even though we are working with only one image per brain slice, we have multiple sources of information on each pixel: absolute and relative positions in the image, gray level value, statistics of the pixel and its three-dimensional neighborhood and relation to its counterpart pixels in adjacent images. We have validated our algorithm for ease of use and precision both with clinical experts and with measurable error indexes over a Brainweb simulated MR set.