Linking the von Kries model to Wien’s law for the estimation of an illuminant invariant image

Modeling the color variation due to an illuminant change is an important task for many computer vision applications, like color constancy, object recognition, shadow removal and image restoration. The von Kries diagonal model and Wien’s law are widely assumed by many algorithms for solving these problems. In this work we combine these two hypotheses and we show that under Wien’s law, the parameters of the von Kries model are related to each other through the color temperatures of the illuminants and the spectral sensitivities of the acquisition device. Based on this result, we provide a method for estimating some camera cues that are used to compute the illuminant invariant intrinsic image proposed by Finlayson and others. This is obtained by projecting the log-chromaticities of an input color image onto a vector depending only on the spectral cues of the acquisition device.     

Computer-aided detection of lung nodules based on decision fusion techniques

We adopted decision fusion techniques to develop a computer-aided detection (CAD) system for automatic detection of pulmonary nodules in low-dose CT images. Two distinct phases, aimed, respectively, at detecting volumes of interests (VOIs) within the CT scan, and at classifying VOIs into nodules and non-nodules, were considered. Three algorithms, namely thresholding, region growing and robust fuzzy clustering, were used as VOI detectors. For the classification phase, we built multi-classifier systems, which aggregate the decisions of three statistical classifiers, a neural network and a decision tree. Finally, the receiver operating characteristic convex hull method was used to build the final classifier, which results to be the aggregation of the best local behaviors of both classifiers and combiners. All the CAD modules were tested on CT scans analyzed by two expert radiologists. In the experiments, we achieved a sensitivity of 92.5% against a specificity of 83.5%.     

Nexus 2012: Relationships Between Architecture and Mathematics

The ninth edition of the biennial conference series “Nexus: Relationships Between Architecture and Mathematics” took place at the Politecnico di Milan in June 2012. There are many connections between architecture and mathematics: mathematic principles may be used as a basis for an architectural design, or as a tool for analyzing an existing monument; architecture may be a concrete expression of mathematical ideas, becoming, in a sense, “visual mathematics”. The purpose of the Nexus conference series is to bring together all those working with ideas related to both architecture and mathematics, and to allow researchers to exchange ideas first-hand. Papers presented at the conference are published in the Nexus Network Journal, providing a permanent archive of studies in architecture and mathematics, and in 2012 a special volume of papers from the Ph.D. Day and the poster session was published.     

Impact of cranberry juice and proanthocyanidins on the ability of <Emphasis Type="Italic">Escherichia coli</Emphasis> to form biofilms

The effects of cranberry juice cocktail (CJC) and proanthocyanidins (PACs) on biofilm formation were investigated. Escherichia coli strain HB101pDC1 and nonfimbriated strain HB101 were grown in 10 wt% CJC or 120 μg/mL PACs for 12 consecutive cultures. Biofilm formation was investigated by incubating bacteria in 96-well polyvinyl chloride (PVC) plates and studying the optical density of the solution using the crystal violet method. We suspect that biofilm formation occurred due to non-specific interactions between the bacteria and the polymer. Both P-fimbriated E. coli HB101pDC1 and the non-fimbriated strain HB101 formed biofilms. E. coli strain HB101pDC1 formed a thicker and more mature biofilm. Cranberry juice inhibited biofilm formation after the first culture; however, for bacteria grown in PACs, a decrease in biofilm formation was observed with increasing number of cultures. The inhibitory effect was reversible. These results demonstrate that CJC is more effective than isolated PACs at preventing biofilm formation, possibly suggesting that other cranberry compounds also play a role in anti-biofilm activity.     

Allocation and scheduling of Conditional Task Graphs

We propose an original, complete and efficient approach to the allocation and scheduling of Conditional Task Graphs (CTGs). In CTGs, nodes represent activities, some of them are branches and are labeled with a condition, arcs rooted in branch nodes are labeled with condition outcomes and a corresponding probability. A task is executed at run time if the condition outcomes that label the arcs in the path to the task hold at schedule execution time; this can be captured off-line by adopting a stochastic model. Tasks need for their execution either unary or cumulative resources and some tasks can be executed on alternative resources. The solution to the problem is a single assignment of a resource and of a start time to each task so that the allocation and schedule is feasible in each scenario and the expected value of a given objective function is optimized. For this problem we need to extend traditional constraint-based scheduling techniques in two directions: (i) compute the probability of sets of scenarios in polynomial time, in order to get the expected value of the objective function; (ii) define conditional constraints that ensure feasibility in all scenarios. We show the application of this framework on problems with objective functions depending either on the allocation of resources to tasks or on the scheduling part. Also, we present the conditional extension to the timetable global constraint. Experimental results show the effectiveness of the approach on a set of benchmarks taken from the field of embedded system design. Comparing our solver with a scenario based solver proposed in the literature, we show the advantages of our approach both in terms of execution time and solution quality.