Research on consumer perceptions towards dairy animal welfare is lacking in Canada. Though consumers are aware of animal welfare issues in other animal industries, the dairy industry remains largely ignored. This thesis employed a mail survey in order to discover the opinions of Canadians towards dairy animal welfare. Respondents answered questions about the importance of animal welfare and its relative importance in comparison to low milk prices, the current state of dairy animal welfare in Canada and how it compared to the United States and other livestock sectors, practices consumers believed to be important for proper dairy animal welfare, as well as what they would pay for animal welfare friendly milk. The results suggested Canadians believed animal welfare was important and that the Canadian dairy animal was currently acceptable. Consumers believed outdoor access and banning growth hormones were important. Finally, a majority indicated they would purchase animal welfare friendly milk.

Yes / We examine whether the inflation rates of the countries that pursueinflation targeting policies have converged as opposed to the expe-rience of the OECD non-inflation targeters. Using a methodologyintroduced by Pesaran (2007a), we examine the stationarity prop-erties of the inflation differentials. This approach has the advantageof avoiding setting arbitrarily a specific country as the benchmarkeconomy. Our results indicate that the inflation rates converge irre-spective of the monetary policy framework.

In this study, a new approach for the matching and reconstruction of line features from multispectral stereo aerial images is presented. The advantages of the existing multispectral information in aerial images are fully taken into account all over the steps of pre-processing and edge detection. To accurately describe the straight line segments, a principal component analysis technique is adapted. The initial correspondences between the stereo images are generated using a new pair-wise stereo matching approach which involves a total of seven relational constraints. The final line to line correspondences between the stereo images are established in a precise matching stage in which the final line matches are assigned by means of three novel measures and a final similarity voting scheme. Once the line matches are established, the stereo reconstruction of those matches is performed by an innovative reconstruction approach that manipulates the redundancy inherent in line pair-relations. By this way, the reconstruction of the stereo matches that are observed in a nearly-parallel geometry with the epipolar lines can also be performed accurately. The proposed approach is tested over two different urban test sites with various built-up characteristics, and as a result, very successful and promising stereo line matching and reconstruction performances are reached. Besides, the comparison of the results of the proposed approach with the results of one of the state-of-the-art stereo matching approaches proves the superiority and the potential of proposed approach.

Plankton imaging systems such as SIPPER produce a large quantity of data in the form of plankton images from a variety of classes. A system known as PICES was developed to quickly extract, classify and manage the millions of images produced from a single one-week research cruise. A new fast technique for parameter tuning and feature selection for Support Vector Machines using Wrappers was created. This technique allows for faster feature selection, while at the same time maintaining and sometimes improving classification accuracy. It also gives the user greater flexibility in the management of class contents in existing training libraries. Support vector machines are binary classifiers that can implement multi-class classifiers by creating a classifier for each possible combination of classes or for each class using a one class versus all strategy. Feature selection searches for a single set of features to be used by each of the binary classifiers. This ignores the fact that features that may be good discriminators for two particular classes might not do well for other class combinations. As a result, the feature selection process may not include these features in the common set to be used by all support vector machines. It is shown through experimentation that by selecting features for each binary class combination, overall classification accuracy can be improved and the time required for training a multi-class support vector machine can be reduced. Another benefit of this approach is that significantly less time is required for feature selection when additional classes are added to the training data. This is because the features selected for the existing class combinations are still valid, so that feature selection only needs to be run for the new combination added. This work resulted in a system called PICES, a GUI based user friendly system, which aids in the classification management of over 55 million images of plankton split amongst 180 classes. PICES embodies an improved means of performing Wrapper based feature selection that creates classifiers that train faster and are just as accurate and sometimes more accurate, while reducing the feature selection time.

There are plenty of solutions for the task of multi-class recognition. Unfortunately, these solutions are not always unanimous. Most of them are based on empirical experiments while statistical data features consideration is often omitted. That’s why questions like when and which method should be used, what the reliability of any chosen method is for solving a multi-class recognition task arise. In this dissertation two-stage multi-class decision methods are analyzed. Pair-wise classifiers able to better exploit statistical data features are used in the first stage of such methods. In the second stage a particular fusion rule of the first stage results is used to fuse the first stage results in order to produce the final classification decision. Complexity issues of pair-wise classifiers, training data size and precision of method quality estimation are pointed out in the research. The precision of algorithm highly depends on the data and the number of experiments performed (data permutation, division into training and testing data). It is shown that the declared superiority of some known algorithms is not reliable due to low precision of estimation. A detailed comparison of well known multi-class classification methods is performed and a new pair-wise classifier fusion method based on similar method used in multi-class classifier fusion is presented. The recommendations for multi-class classification task designer are provided. Methods which allow reducing classification... [to full text] / Daugelio klasių atpažinimo uždaviniams spręsti yra sukurta aibė sprendimų ir ne visada vieningų rekomendacijų. Dauguma jų paremta empiriniais bandymais, retai atsižvelgiama į statistines duomenų savybes. Dėl to sprendžiant daugelio klasių klasifikavimo uždavinį kyla klausimų, kurį metodą ir kada geriausia naudoti, koks vieno ar kito metodo patikimumas. Disertacijoje nagrinėjami dviejų pakopų sprendimo priėmimo metodai, kai pirmame etape sudaromi klasifikatoriai poroms (angl. pair-wise), sugebantys geriau išnaudoti klasių tarpusavio statistines savybes, o kitame etape yra atliekamas klasifikatorių poroms rezultatų apjungimas. Tyrime ypatingas dėmesys yra skiriamas klasifikatorių poroms sudėtingumui, mokymo duomenų kiekiui bei algoritmų kokybės įvertinimo tikslumui. Tikslumas labai priklauso nuo duomenų bei atliktų eksperimentų kiekio (duomenų permaišymo klasėse, juos skirstant į mokymo ir testavimo). Parodyta, jog dėl žemo įvertinimo tikslumo kai kurių publikuotų algoritmų deklaruojamas pranašumas prieš žinomus algoritmus nėra patikimas. Darbe atliktas detalus žinomų metodų palyginimas bei pristatytas naujai sukurtas klasifikatorių poroms apjungimo algoritmas, kuris yra paremtas analogišku algoritmu daugelio klasių klasifikatorių rezultatų apjungimui. Pateiktos bendros rekomendacijos, kaip projektuotojui elgtis daugelio klasių atveju. Pasiūlyti metodai, leidžiantys sumažinti klasifikavimo klaidą atliekant klasifikatorių poroms apjungimo koregavimą, kad algoritmas nebūtų... [toliau žr. visą tekstą]

Daugelio klasių atpažinimo uždaviniams spręsti yra sukurta aibė sprendimų ir ne visada vieningų rekomendacijų. Dauguma jų paremta empiriniais bandymais, retai atsižvelgiama į statistines duomenų savybes. Dėl to sprendžiant daugelio klasių klasifikavimo uždavinį kyla klausimų, kurį metodą ir kada geriausia naudoti, koks vieno ar kito metodo patikimumas. Disertacijoje nagrinėjami dviejų pakopų sprendimo priėmimo metodai, kai pirmame etape sudaromi klasifikatoriai poroms (angl. pair-wise), sugebantys geriau išnaudoti klasių tarpusavio statistines savybes, o kitame etape yra atliekamas klasifikatorių poroms rezultatų apjungimas. Tyrime ypatingas dėmesys yra skiriamas klasifikatorių poroms sudėtingumui, mokymo duomenų kiekiui bei algoritmų kokybės įvertinimo tikslumui. Tikslumas labai priklauso nuo duomenų bei atliktų eksperimentų kiekio (duomenų permaišymo klasėse, juos skirstant į mokymo ir testavimo). Parodyta, jog dėl žemo įvertinimo tikslumo kai kurių publikuotų algoritmų deklaruojamas pranašumas prieš žinomus algoritmus nėra patikimas. Darbe atliktas detalus žinomų metodų palyginimas bei pristatytas naujai sukurtas klasifikatorių poroms apjungimo algoritmas, kuris yra paremtas analogišku algoritmu daugelio klasių klasifikatorių rezultatų apjungimui. Pateiktos bendros rekomendacijos, kaip projektuotojui elgtis daugelio klasių atveju. Pasiūlyti metodai, leidžiantys sumažinti klasifikavimo klaidą atliekant klasifikatorių poroms apjungimo koregavimą, kad algoritmas nebūtų... [toliau žr. visą tekstą] / There are plenty of solutions for the task of multi-class recognition. Unfortunately, these solutions are not always unanimous. Most of them are based on empirical experiments while statistical data features consideration is often omitted. That’s why questions like when and which method should be used, what the reliability of any chosen method is for solving a multi-class recognition task arise. In this dissertation two-stage multi-class decision methods are analyzed. Pair-wise classifiers able to better exploit statistical data features are used in the first stage of such methods. In the second stage a particular fusion rule of the first stage results is used to fuse the first stage results in order to produce the final classification decision. Complexity issues of pair-wise classifiers, training data size and precision of method quality estimation are pointed out in the research. The precision of algorithm highly depends on the data and the number of experiments performed (data permutation, division into training and testing data). It is shown that the declared superiority of some known algorithms is not reliable due to low precision of estimation. A detailed comparison of well known multi-class classification methods is performed and a new pair-wise classifier fusion method based on similar method used in multi-class classifier fusion is presented. The recommendations for multi-class classification task designer are provided. Methods which allow reducing classification... [to full text]

L'aide à la décision multicritères regroupe des méthodes permettant de choisir la meilleure solution en fonction des différents critères et compte tenu des préférences des experts. Toutefois, ces préférences sont parfois exprimées de manière imparfaite. La théorie des fonctions de croyance modélise de manière souple les connaissances et fournit des outils mathématiques pour gérer les différents types d'imperfection. Ainsi dans cette thèse, nous nous intéressons à la prise de décision multicritères dans un cadre incertain en étendant la méthode d’Analyse Hiérarchique des Procédés (AHP) à la théorie des fonctions de croyance. Après avoir présenté les fondements théoriques de la méthode AHP, nous avons proposé une approche qui permet de réduire le nombre de comparaisons par paires en jugeant des sous-ensembles de critères et d’alternatives. En outre, nous avons examiné la dépendance entre les critères et les alternatives. Dans ce cas, l'incertitude au niveau des évaluations est donnée sous forme de masses conditionnelles. Une autre partie de nos travaux répond aux critiques concernant la procédure de comparaison. Pour cela, nous avons proposé deux approches. La première technique d’élicitation des jugements de l’expert est fondée sur des distributions de masses, alors que la seconde s'appuie sur des relations de préférence. Dans ce cadre, nous avons introduit un modèle qui permet de générer des distributions de masse quantitatives à partir des relations de préférence. Ainsi, nous avons développé une méthode multicritères qui permet d'imiter le raisonnement humain. Cette méthode produit des résultats meilleurs et plus robustes que les approches de la littérature. / Multi-criteria decision making is the study of identifying and choosing alternatives to find the best solution based on different criteria and considering the decision makers’ expectations. However, the expert assessments are sometimes expressed imperfectly. Belief function theory can then provide more flexible and reliable tools to manage different types of imperfection. Thus, in this thesis, we are interested in multi-criteria decision making in an uncertain framework by extending the Analytic Hierarchy Process (AHP) method to the belief function framework. After presenting the theoretical foundations of the AHP method, we proposed an approach that reduces the number of pair-wise comparisons by judging subsets of criteria and alternatives. In addition, we examined the dependence between the criteria and alternatives. In this case, the uncertainty is given in terms of conditional mass distributions. Another part of the work provides critical concerning the pair-wise comparison process. For this purpose, we proposed two approaches. The first expert judgment elicitation method is based on mass distributions, while the second one is based on preference relations. In this context, we have introduced a model that is able to generate quantitative mass distributions from preference relations. Thus, we have developed a multi-criteria decision making method that imitates human reasoning. This method gives better and more robust results than existing approaches.

<p>  </p> <p>Dicamba (3,6-dichloro-2-methoxybenzoic acid) and 2,4-D (2,4-dichlorophenoxyacetic acid) are two widely used herbicides for broadleaf weed control in soybeans. However, off-target application of dicamba and 2,4-D can cause severe damage to sensitive vegetation and crops. Early detection and assessment of off-target damage caused by these herbicides are necessary to help plant diagnostic labs and state regulatory agencies collect more information of the on-site conditions so to develop solutions to resolve the issue in the future. In 2021, the study was conducted to detect damage to soybean leaves caused by dicamba and 2,4-D by using LeafSpec, an accurate handheld hyperspectral leaf scanner. . High resolution single leaf hyperspectral images of 180 soybean plants in the greenhouse exposed to nine different herbicide treatments were taken 1, 7, 14, 21 and 28 days after herbicide spraying. Pairwise PLS-DA models based on spectral features were able to distinguish leaf damage caused by two different modes of action herbicides, specifically dicamba and 2,4-D, as early as 2 hours after herbicide spraying. In the spatial distribution analysis, texture and morphological features were selected for separating the dosages of herbicide treatments. Compared to the mean spectrum method, new models built upon the spectrum, texture, and morphological features, improved the overall accuracy to over 70% for all evaluation dates. The combined features are able to classify the correct dosage of the right herbicide as early as 7 days after herbicide sprays. Overall, this work has demonstrated the potential of using spectral and spatial features of LeafSpec hyperspectral images for early and accurate detection of dicamba and 2,4-D damage in soybean plants.</p> <p>   </p>