Generic object classification for autonomous robots

Un dels principals problemes de la interacció dels robots autònoms és el coneixement de l'escena. El reconeixement és fonamental per a solucionar aquest problema i permetre als robots interactuar en un escenari no controlat. En aquest document presentem una aplicació pràctica de la captura d'objectes, de la normalització i de la classificació de senyals triangulars i circulars. El sistema s'introdueix en el robot Aibo de Sony per a millorar-ne la interacció. La metodologia presentada s'ha comprobat en simulacions i problemes de categorització reals, com ara la classificació de senyals de trànsit, amb resultats molt prometedors.

A classification, up to hyperbolicity, of groups given by 2 generators and one relator of length 8

"Vegeu el resum a l'inici del document del fitxer adjunt."

Urban image classification with semisupervised multiscale cluster kernels

This paper presents a semisupervised support vector machine (SVM) that integrates the information of both labeled and unlabeled pixels efficiently. Method's performance is illustrated in the relevant problem of very high resolution image classification of urban areas. The SVM is trained with the linear combination of two kernels: a base kernel working only with labeled examples is deformed by a likelihood kernel encoding similarities between labeled and unlabeled examples. Results obtained on very high resolution (VHR) multispectral and hyperspectral images show the relevance of the method in the context of urban image classification. Also, its simplicity and the few parameters involved make the method versatile and workable by unexperienced users.

How reliable is the monitoring of permanent vegetation plots? A test with multiple observers

Questions: A multiple plot design was developed for permanent vegetation plots. How reliable are the different methods used in this design and which changes can we measure? Location: Alpine meadows (2430 m a.s.l.) in the Swiss Alps. Methods: Four inventories were obtained from 40 m(2) plots: four subplots (0.4 m(2)) with a list of species, two 10m transects with the point method (50 points on each), one subplot (4 m2) with a list of species and visual cover estimates as a percentage and the complete plot (40 m(2)) with a list of species and visual estimates in classes. This design was tested by five to seven experienced botanists in three plots. Results: Whatever the sampling size, only 45-63% of the species were seen by all the observers. However, the majority of the overlooked species had cover < 0.1%. Pairs of observers overlooked 10-20% less species than single observers. The point method was the best method for cover estimate, but it took much longer than visual cover estimates, and 100 points allowed for the monitoring of only a very limited number of species. The visual estimate as a percentage was more precise than classes. Working in pairs did not improve the estimates, but one botanist repeating the survey is more reliable than a succession of different observers. Conclusion: Lists of species are insufficient for monitoring. It is necessary to add cover estimates to allow for subsequent interpretations in spite of the overlooked species. The choice of the method depends on the available resources: the point method is time consuming but gives precise data for a limited number of species, while visual estimates are quick but allow for recording only large changes in cover. Constant pairs of observers improve the reliability of the records.

Automatic Mallampati Classification Using Active Appearance Models

Difficult tracheal intubation assessment is an important research topic in anesthesia as failed intubations are important causes of mortality in anesthetic practice. The modified Mallampati score is widely used, alone or in conjunction with other criteria, to predict the difficulty of intubation. This work presents an automatic method to assess the modified Mallampati score from an image of a patient with the mouth wide open. For this purpose we propose an active appearance models (AAM) based method and use linear support vector machines (SVM) to select a subset of relevant features obtained using the AAM. This feature selection step proves to be essential as it improves drastically the performance of classification, which is obtained using SVM with RBF kernel and majority voting. We test our method on images of 100 patients undergoing elective surgery and achieve 97.9% accuracy in the leave-one-out crossvalidation test and provide a key element to an automatic difficult intubation assessment system.

Optimal Universal and Categorical Benefits with Classification Errors and Imperfect Enforcement

We determine he optimal combination of a universal benefit, B, and categorical benefit, C, for an economy in which individuals differ in both their ability to work - modelled as an exogenous zero quantity constraint on labour supply - and, conditional on being able to work, their productivity at work. C is targeted at those unable to work, and is conditioned in two dimensions: ex-ante an individual must be unable to work and be awarded the benefit, whilst ex-post a recipient must not subsequently work. However, the ex-ante conditionality may be imperfectly enforced due to Type I (false rejection) and Type II (false award) classification errors, whilst, in addition, the ex-post conditionality may be imperfectly enforced. If there are no classification errors - and thus no enforcement issues - it is always optimal to set C>0, whilst B=0 only if the benefit budget is sufficiently small. However, when classification errors occur, B=0 only if there are no Type I errors and the benefit budget is sufficiently small, while the conditions under which C>0 depend on the enforcement of the ex-post conditionality. We consider two discrete alternatives. Under No Enforcement C>0 only if the test administering C has some discriminatory power. In addition, social welfare is decreasing in the propensity to make each type error. However, under Full Enforcement C>0 for all levels of discriminatory power. Furthermore, whilst social welfare is decreasing in the propensity to make Type I errors, there are certain conditions under which it is increasing in the propensity to make Type II errors. This implies that there may be conditions under which it would be welfare enhancing to lower the chosen eligibility threshold - support the suggestion by Goodin (1985) to "err on the side of kindness".

Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.

Introduction: As part of the MicroArray Quality Control (MAQC)-II project, this analysis examines how the choice of univariate feature-selection methods and classification algorithms may influence the performance of genomic predictors under varying degrees of prediction difficulty represented by three clinically relevant endpoints. Methods: We used gene-expression data from 230 breast cancers (grouped into training and independent validation sets), and we examined 40 predictors (five univariate feature-selection methods combined with eight different classifiers) for each of the three endpoints. Their classification performance was estimated on the training set by using two different resampling methods and compared with the accuracy observed in the independent validation set. Results: A ranking of the three classification problems was obtained, and the performance of 120 models was estimated and assessed on an independent validation set. The bootstrapping estimates were closer to the validation performance than were the cross-validation estimates. The required sample size for each endpoint was estimated, and both gene-level and pathway-level analyses were performed on the obtained models. Conclusions: We showed that genomic predictor accuracy is determined largely by an interplay between sample size and classification difficulty. Variations on univariate feature-selection methods and choice of classification algorithm have only a modest impact on predictor performance, and several statistically equally good predictors can be developed for any given classification problem.

Aspects of classification of Hemiptera hemocytes from six triatomine species

The objective of this work was to characterize, and compare different morphological types of hemocytes of Rhodnius prolixus, Rhodnius, Rhodnius neglectus, Triatoma infestans, Panstrongylus megistus, and Dipetalogaster maximus. This information provides the basis for studying the cellular immune systems of these insects. Seven morphological hemocyte types wereidentified by phase-contrast microscopy: prohemocytes, plasmatocytes, granular cells, cytocytes, oenocytoids, adipohemocytes and giant cells. All seven types of hemocytes are not present in every species. For example, adipohemocytes and oenocytoids were not observed in P. megistus and P. infestans, and giant cells were rarely found in any of the species studied. The hemocytes of rhodnius and Dipetalogaster are more similar to each other than those from Triatoma and Panstrongylus which in turn closely resemble each other. Emphasis is placed on methodological problems arising in this work wicah are discussed in detail.

II Workshop on the use of Computed Tomography (CT) in pig carcass classification. Other CT applications: live animals and meat technology, 16-17th April 2009, Monells (Girona), Spain

Es va realitzar el II Workshop en Tomografia Computeritzada (TC) a Monells. El primer dia es va dedicar íntegrament a la utilització del TC en temes de classificació de canals porcines, i el segon dia es va obrir a altres aplicacions del TC, ja sigui en animals vius o en diferents aspectes de qualitat de la carn o els productes carnis. Al workshop hi van assistir 45 persones de 12 països de la UE. The II workshop on the use of Computed Tomography (CT) in pig carcass classification. Other CT applications: live animals and meat technology was held in Monells. The first day it was dedicated to the use of CT in pig carcass classification. The segond day it was open to otehr CT applications, in live animals or in meat and meat products quality. There were 45 assistants of 12 EU countries.

Anti-tumor necrosis factor drug survival in axial spondyloarthritis is independent of the classification criteria.

To compare the impact of meeting specific classification criteria [modified New York (mNY), European Spondyloarthropathy Study Group (ESSG), and Assessment of SpondyloArthritis international Society (ASAS) criteria] on anti-tumor necrosis factor (anti-TNF) drug retention, and to determine predictive factors of better drug survival. All patients fulfilling the ESSG criteria for axial spondyloarthritis (SpA) with available data on the axial ASAS and mNY criteria, and who had received at least one anti-TNF treatment were retrospectively retrieved in a single academic institution in Switzerland. Drug retention was computed using survival analysis (Kaplan-Meier), adjusted for potential confounders. Of the 137 patients classified as having axial SpA using the ESSG criteria, 112 also met the ASAS axial SpA criteria, and 77 fulfilled the mNY criteria. Drug retention rates at 12 and 24 months for the first biologic therapy were not significantly different between the diagnostic groups. Only the small ASAS non-classified axial SpA group (25 patients) showed a nonsignificant trend toward shorter drug survival. Elevated CRP level, but not the presence of bone marrow edema on magnetic resonance imaging (MRI) scans, was associated with significantly better drug retention (OR 7.9, ICR 4-14). In this cohort, anti-TNF drug survival was independent of the classification criteria. Elevated CRP level, but not positive MRI, was associated with better drug retention.

Redescription of Gibsoniela mandube (Woodland, 1935) (Cestoda: Proteocephalidea), a parasite of Ageneiosus brevifilis (Pisces: Siluriformes), and reappraisal of the classification of the Proteocephalideans

The proteocephalid species Gibsoniela mandube (Woodland, 1935) from the siluriform fish Ageneiosus brevifilis from rio Paraná, is redescribed. This species was originally described from the same species of fish from the Amazon. The genus Gibsoniela Rego, 1984 is redefined and confirmed in the Monticeliidae. Comments are made on the relationships of the proteocephalid and monticelliid subfamilies.