Coatings libraries achieved through a combinatorial chemistry approach, which may generate tens to hundreds of formulations, can be deposited in an array of 12 patches, each approximately 9 cm², on 10 × 20 cm primed aluminum panels. However, existing methods to quantify algal biomass on coatings are unsuitable for this type of array format. This paper describes an algorithm modelled on a probability distribution that quantifies the area of surface covered by a green alga from digital images. The method allows coatings with potential fouling-release properties to be down-selected for further evaluation. The use of the algorithm is illustrated by a set of eight siloxane-polyurethane coatings made using organofunctional poly(dimethylsiloxane) (PDMS) and poly(ϵ-caprolactone)-PDMS-poly(ϵ-caprolactone) (PCL-PDMS-PCL) triblock copolymers along with four PDMS standards which were deposited on one panel. Six replicate panels were seeded with Ulva zoospores which grew into sporelings (small plants) that completely covered the surface. The ease of removal of the Ulva sporeling biofilms was determined by automated water jetting at six different impact pressures. The coverage of the biofilm on the twelve individual formulations after jet washing was quantified from the green colour of digital images. The data are discussed in relation to the composition of the coatings.

In this paper, we present a solution to the novel problem of recognizing primitive actor-object interactions from videos. Here, we introduce the concept of actor-object states. Our method is based on the observation that at the moment of physical contact, both the motion and the appearance of actors are constrained by the target object. We propose a probabilistic framework that automatically learns models in such constrained states. We use joint probability distributions to represent both actor and object appearances as well as their intrinsic spatio-temporal configurations. Finally, we demonstrate the applicability of our approach on series of human-object interaction classification experiments.

In this paper, a method that combines image analysis techniques, such as segmentation and registration, is proposed for an advanced and progressive evaluation of thermograms. The method is applied for the prevention of muscle injury in high-performance athletes, in collaboration with a Brazilian professional soccer club. The goal is to produce information on spatio-temporal variations of thermograms favoring the investigation of the athletes’ conditions along the competition. The proposed method improves on current practice by providing a means for automatically detecting adaptive body-shaped regions of interest, instead of the manual selection of simple shapes. Specifically, our approach combines the optimization features in Otsu’s method with a correction factor and post-processing techniques, enhancing thermal-image segmentation when compared to other methods. Additional contributions resulting from the combination of the segmentation and registration steps of our approach are the progressive analyses of thermograms in a unique spatial coordinate system and the accurate extraction of measurements and isotherms.