Abstract The current study evaluated the effects of training on the haptic classification of culturally universal facial expressions of emotion as depicted in simple 2D raised-line drawings. Blindfolded sighted (N = 60) and blind (N = 4) participants participated in Experiments 1 and 3, respectively. A small vision control study (N = 12) was also conducted (Experiment 2) to compare haptic versus visual learning patterns. A hybrid learning paradigm consisting of pre/post- and old/new-training procedures was used to address the nature of the underlying learning process in terms of token-specific learning and/or generalization. During the Pre-Training phase, participants were tested on their ability to classify facial expressions of emotion using the set with which they would be subsequently trained. During the Post-Training phase, they were tested with the training set (Old) intermixed with a completely novel set (New). For sighted observers, visual classification was more accurate than haptic classification; in addition, two of the three adventitiously blind individuals tended to be at least as accurate as the sighted haptic group. All three groups showed similar learning patterns across the learning stages of the experiment: accuracy improved substantially with training; however, while classification accuracy for the Old set remained high during the Post-Training test stage, learning effects for novel (New) drawings were reduced, if present at all. These results imply that learning by the sighted was largely token-specific for both haptic and visual classification. Additional results from a limited number of blind subjects tentatively suggest that the accuracy with which facial expressions of emotion are classified is not impaired when visual loss occurs later in life. / Thesis (Master, Neuroscience Studies) -- Queen's University, 2009-10-23 12:04:41.133

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Drawing sketches is certainly one of the most important but at the same time elaborate parts of archaeological work. Currently, 3D scanning technology is affording a number of new applications, and only one of them is using virtual copies instead of the originals as the basis for documentation. Our major contribution are methods for automatically generating stylized images from 3D models. These are not only intuitive and easy to read but also more objective and accurate than traditional drawings. Besides some other useful tools we show several examples from our daily work proving that the system accelerates the whole documentation process considerably.

Drawing sketches is certainly one of the most important but at the same time elaborate parts of archaeological work. Currently, 3D scanning technology is affording a number of new applications, and only one of them is using virtual copies instead of the originals as the basis for documentation. Our major contribution are methods for automatically generating stylized images from 3D models. These are not only intuitive and easy to read but also more objective and accurate than traditional drawings. Besides some other useful tools we show several examples from our daily work proving that the system accelerates the whole documentation process considerably.

Les caractéristiques locales sont essentielles dans de nombreux domaines de l’analyse d’images comme la détection et la reconnaissance d’objets, la recherche d’images, etc. Ces dernières années, plusieurs détecteurs dits locaux ont été proposés pour extraire de telles caractéristiques. Ces détecteurs locaux fonctionnent généralement bien pour certaines applications, mais pas pour toutes. Prenons, par exemple, une application de recherche dans une large base d’images. Dans ce cas, un détecteur à base de caractéristiques binaires pourrait être préféré à un autre exploitant des valeurs réelles. En effet, la précision des résultats de recherche pourrait être moins bonne tout en restant raisonnable, mais probablement avec un temps de réponse beaucoup plus court. En général, les détecteurs locaux sont utilisés en combinaison avec une méthode d’indexation. En effet, une méthode d’indexation devient nécessaire dans le cas où les ensembles de points traités sont composés de milliards de points, où chaque point est représenté par un vecteur de caractéristiques de grande dimension. / Local features are of central importance to deal with many different problems in image analysis and understanding including image registration, object detection and recognition, image retrieval, etc. Over the years, many local detectors have been presented to detect such features. Such a local detector usually works well for some particular applications but not all. Taking an application of image retrieval in large database as an example, an efficient method for detecting binary features should be preferred to other real-valued feature detection methods. The reason is easily seen: it is expected to have a reasonable precision of retrieval results but the time response must be as fast as possible. Generally, local features are used in combination with an indexing scheme. This is highly needed for the case where the dataset is composed of billions of data points, each of which is in a high-dimensional feature vector space.