Classification of textures seen from different distances and under varying illumination direction

Changes in the angle of illumination incident upon a 3D surface texture can significantly alter its appearance, implying variations in the image texture. These texture variations produce displacements of class members in the feature space, increasing the failure rates of texture classifiers. To avoid this problem, a model-based texture recognition system which classifies textures seen from different distances and under different illumination directions is presented in this paper. The system works on the basis of a surface model obtained by means of 4-source colour photometric stereo, used to generate 2D image textures under different illumination directions. The recognition system combines coocurrence matrices for feature extraction with a Nearest Neighbour classifier. Moreover, the recognition allows one to guess the approximate direction of the illumination used to capture the test image

Potassium distribution in drip irrigation with fertigation for different injection distances in the main line

The purpose of this research was to evaluate the K2O distribution uniformity by surface drip irrigation at Universitat Politecnica de Valencia, Valencia, Spain (39º 29′ N, 0º 23′ W, 20 m). The irrigation was performed by drip lines with not-compensated emitters, spaced 0.3 m. The fertigation was realized using a fertilizer injector pump of electric action with injection of 0.25 h. The experimental design used completely randomized blocks with five treatments and four replications. The treatments consisted of injection in five distances, located at 10; 20; 30; 40; 50 m of the first drip line. Samples were collected in emitters located at the start, at 1/3, at 2/3 and at the end of the drip lines. The nutrient concentration was determined by flame spectrophotometry. The Christiansen's uniformity coefficients (CUC), of distribution (DUC), of statistical (SUC) and of emission (eUC) were estimated. The K2O concentration and distribution decreased linearly with the increase of the injection distance. In all treatments, the CUC, SUC and DUC were described as 'excellent'. The eUC was described as 'recommended' only at smaller injection distances.

Vascular trauma in the Amazon - the challenge of great distances

ABSTRACTObjective:to evaluate the incidence of unfavorable outcomes in vascular trauma patients and their possible correlation to the distance between the city where the injury was sustained and the hospital where the patient received definitive treatment.Methods:descriptive and retrospective study. Data were collected from medical records of patients submitted to surgical procedures for arterial or venous injuries from February 2011 to February 2013 at the only trauma center providing vascular surgery in a vast area of the Amazon region. Trauma date, patient gender and age, mechanism and anatomic topography of injury, surgical management, need for surgical re-intervention, hospitalization period, postoperative complications, mortality and limb amputation rates were analyzed. The incidence of unfavorable outcomes was assessed according to the distance between the city where the vascular injury was sustained and the trauma center.Results: One hundred seventy-three patients with 255 vascular injuries were analyzed; 95.95% were male (p<0.05), mean age of 28.92 years; 47.4% were caused by firearm projectiles (p<0.05); topographic distribution: 45.66% lower limbs (p<0.05), 37.57% upper limbs, 6.94% abdominal, 5.2% thoracic and 4.62% were cervical vascular injuries; 51.42% of patients required hospitalization for seven days or less (p<0.05); limb amputation was necessary in 15.6% and the overall mortality was 6.36%.Conclusion:distances greater than 200Km were associated to longer hospitalization period; distances greater than 300Km were associated to increased limb amputation probability; severe vascular trauma have an increased death probability when patients need to travel more than 200Km for surgical treatment.

Stars and Strings für Jazzensemble u. Streichorchester

Soitinnus: jazzyhtye, jousiorkesteri.

Differential evolution based classification with pool of distances and aggregation operators

The objective of this thesis is to develop and generalize further the differential evolution based data classification method. For many years, evolutionary algorithms have been successfully applied to many classification tasks. Evolution algorithms are population based, stochastic search algorithms that mimic natural selection and genetics. Differential evolution is an evolutionary algorithm that has gained popularity because of its simplicity and good observed performance. In this thesis a differential evolution classifier with pool of distances is proposed, demonstrated and initially evaluated. The differential evolution classifier is a nearest prototype vector based classifier that applies a global optimization algorithm, differential evolution, to determine the optimal values for all free parameters of the classifier model during the training phase of the classifier. The differential evolution classifier applies the individually optimized distance measure for each new data set to be classified is generalized to cover a pool of distances. Instead of optimizing a single distance measure for the given data set, the selection of the optimal distance measure from a predefined pool of alternative measures is attempted systematically and automatically. Furthermore, instead of only selecting the optimal distance measure from a set of alternatives, an attempt is made to optimize the values of the possible control parameters related with the selected distance measure. Specifically, a pool of alternative distance measures is first created and then the differential evolution algorithm is applied to select the optimal distance measure that yields the highest classification accuracy with the current data. After determining the optimal distance measures for the given data set together with their optimal parameters, all determined distance measures are aggregated to form a single total distance measure. The total distance measure is applied to the final classification decisions. The actual classification process is still based on the nearest prototype vector principle; a sample belongs to the class represented by the nearest prototype vector when measured with the optimized total distance measure. During the training process the differential evolution algorithm determines the optimal class vectors, selects optimal distance metrics, and determines the optimal values for the free parameters of each selected distance measure. The results obtained with the above method confirm that the choice of distance measure is one of the most crucial factors for obtaining higher classification accuracy. The results also demonstrate that it is possible to build a classifier that is able to select the optimal distance measure for the given data set automatically and systematically. After finding optimal distance measures together with optimal parameters from the particular distance measure results are then aggregated to form a total distance, which will be used to form the deviation between the class vectors and samples and thus classify the samples. This thesis also discusses two types of aggregation operators, namely, ordered weighted averaging (OWA) based multi-distances and generalized ordered weighted averaging (GOWA). These aggregation operators were applied in this work to the aggregation of the normalized distance values. The results demonstrate that a proper combination of aggregation operator and weight generation scheme play an important role in obtaining good classification accuracy. The main outcomes of the work are the six new generalized versions of previous method called differential evolution classifier. All these DE classifier demonstrated good results in the classification tasks.

Miss [Arthur] Hamilton [née Violet Antonia Fairlie-Cuninghame] (cliché C.N. Londres) [nageuse de longues distances] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 59246

[Mrs Arthur] Hamilton [nageuse de longues distances] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 60185

Tunis's topographical and pictorial guide to Niagara : containing, also, a description of the route through Canada, and the great northern route, from Niagara Falls to Montreal, Boston, and Saratoga Springs. Also, full and accurate tables of distances on all railroads running to and from Niagara Falls. --

Cover title: Tunis's guide to Niagara and traveller's companion, illustrated.

Les distances entre les attributs internes du visage humain

La zeitgesit contemporaine sur la reconnaissance des visages suggère que le processus de reconnaissance reposerait essentiellement sur le traitement des distances entre les attributs internes du visage. Il est toutefois surprenant de noter que cette hypothèse n’a jamais été évaluée directement dans la littérature. Pour ce faire, 515 photographies de visages ont été annotées afin d’évaluer l’information véhiculée par de telles distances. Les résultats obtenus suggèrent que les études précédentes ayant utilisé des modifications de ces distances ont présenté 4 fois plus d’informations que les distances inter-attributs du monde réel. De plus, il semblerait que les observateurs humains utilisent difficilement les distances inter-attributs issues de visages réels pour reconnaître leurs semblables à plusieurs distances de visionnement (pourcentage correct maximal de 65%). Qui plus est, la performance des observateurs est presque parfaitement restaurée lorsque l’information des distances inter-attributs n’est pas utilisable mais que les observateurs peuvent utiliser les autres sources d’information de visages réels. Nous concluons que des indices faciaux autre que les distances inter-attributs tel que la forme des attributs et les propriétés de la peau véhiculent l’information utilisée par le système visuel pour opérer la reconnaissance des visages.