Snakes based tracking and texture analysis of microscopic images

Comunicación presentada en el VII Symposium Nacional de Reconocimiento de Formas y Análisis de Imágenes, SNRFAI, Barcelona, abril 1997.

Synthesis of denim waste-based adsorbents and their application in water defluoridation

This study evaluates the application of denim fiber scraps as a precursor for the synthesis of adsorbents for water treatment via pyrolysis and their application in water defluoridation. The best pyrolysis conditions for the synthesis of this novel adsorbent have been identified and a metal doping route with different salts of Al3 +, La3 + and Fe3 + was proposed to improve its fluoride adsorption behavior. Different spectroscopic and microscopic techniques (i.e., FTIR, XPS, XRF, SEM) were used to characterize the precursor and adsorbents, and to analyze the surface interactions involved in the fluoride removal mechanism. Experimental results showed that these adsorbents were effective for fluoride adsorption showing uptakes up to 4.25 mg/g. The Si-O–metal–F interactions appear to be highly relevant for the fluoride removal. This study highlights the potential of denim textile waste as a raw material for the production of added-value products, thus minimizing their associated disposal cost. It also shows the performance of denim textile waste as a precursor of adsorbents for addressing relevant environmental concerns such as fluoride pollution.

μ-MAR: Multiplane 3D Marker based Registration for depth-sensing cameras

Many applications including object reconstruction, robot guidance, and. scene mapping require the registration of multiple views from a scene to generate a complete geometric and appearance model of it. In real situations, transformations between views are unknown and it is necessary to apply expert inference to estimate them. In the last few years, the emergence of low-cost depth-sensing cameras has strengthened the research on this topic, motivating a plethora of new applications. Although they have enough resolution and accuracy for many applications, some situations may not be solved with general state-of-the-art registration methods due to the signal-to-noise ratio (SNR) and the resolution of the data provided. The problem of working with low SNR data, in general terms, may appear in any 3D system, then it is necessary to propose novel solutions in this aspect. In this paper, we propose a method, μ-MAR, able to both coarse and fine register sets of 3D points provided by low-cost depth-sensing cameras, despite it is not restricted to these sensors, into a common coordinate system. The method is able to overcome the noisy data problem by means of using a model-based solution of multiplane registration. Specifically, it iteratively registers 3D markers composed by multiple planes extracted from points of multiple views of the scene. As the markers and the object of interest are static in the scenario, the transformations obtained for the markers are applied to the object in order to reconstruct it. Experiments have been performed using synthetic and real data. The synthetic data allows a qualitative and quantitative evaluation by means of visual inspection and Hausdorff distance respectively. The real data experiments show the performance of the proposal using data acquired by a Primesense Carmine RGB-D sensor. The method has been compared to several state-of-the-art methods. The results show the good performance of the μ-MAR to register objects with high accuracy in presence of noisy data outperforming the existing methods.