Semantic Interpretation of an Artificial Neural Network

Recent advances in machine learning theory have opened the door for applications to many difficult problem domains. One area that has achieved great success for stock market analysis/prediction is artificial neural networks. However, knowledge embedded in the neural network is not easily translated into symbolic form. Recent research, exploring the viability of merging artificial neural networks with traditional rule-based expert systems, has achieved limited success. In particular, extracting production (IF.. THEN) rules from a trained neural net based on connection weights provides a valid set of rules only when neuron outputs are close to 0 or 1 (e.g. the output sigmoid function is saturated). This thesis presents two new ways to interpret neural network knowledge. The first, called Knowledge Math, extends the use of connection weights, generating rules for general (i.e. non-binary) input and output values. The second method, based on decision boundaries, utilizes the inherent border between output classification regions to draw symbolic interpretation. The Decision Boundary method generates more complex symbolic rules than Knowledge Math, but provides valid feature relationships in the uncertain regions around the midpoints of the neuron output functions. The main result is a complementary relationship between Knowledge Math and Decision Boundaries, as well as subsymbolic and symbolic knowledge representations for a general multi-layer perceptron

Propagating the dimensional uncertainty in ellipse fitting. Application to the automatic detection of elliptic shapes in images

The conic fitting from image points is a very old topic in estimation and pattern recognition. This problem gave rise to a lot of studies and arouses interests still today. Systematically, these works have been based on the algebraic representation of the conic to establish the optimization criteria. Less studied, the polar representation of the ellipse is costlier because it needs the optimization of the parametrization. Yet, we propose in this paper some new ideas about this question. First, we show that the estimation of the parameters and the parametrization separated permit to make the problem easier leading to a direct inversion and the search of the roots of a four degree polynomial respectively. We also show that the parametrization carries the dimensional characteristics of the ellipse and when it is correctly disrupted in the minimization process, we constraint the ellipse search space. This new result gives an estimate without dimensional bias in a noised and incomplete context. A confidence envelope is then estimated to direct the search for continuations of the ellipse. At last, we propose a hierarchical grouping and fitting stage following with a fuzzy decision step to detect automatically the elliptic shapes in the images.L'ajustement d'une ellipse sur des données 2D est un très vieux sujet en estimation et en RDF, qui a donné lieu à de nombreuses études [2,6,10,15,16,21] et en suscite encore aujourd'hui [12,17,19,24]. D'une façon systématique, ces travaux se sont appuyés sur la représentation algébrique de la conique pour établir leur critère de minimisation. Un peu moins étudiée [5], la représentation polaire de l'ellipse constitue une alternative plus coûteuse car elle nécessite l'optimisation de sa paramétrisation. D'une représentation nécessitant au plus 5 paramètres à une autre définie par 5 + N (N étant le nombre de données), le choix semble évident. Cependant, nous proposons dans cet article de nouvelles idées sur la question. Tout d'abord, nous montrons que l'estimation séparée des paramètres et de la paramétrisation de l'ellipse permet de simplifier le problème en aboutissant respectivement à une inversion directe pour les premiers et à la recherche des racines d'un polynôme du 4ième ordre pour la seconde. Nous montrons également que la paramétrisation est « porteuse » de l'information dimensionnelle de l'ellipse et qu'en la « perturbant » correctement dans le processus de minimisation il est possible de forcer la solution à rester dans un espace paramétrique préétabli. Ce résultat nouveau permet de fournir une solution sans biais dimensionnel même dans un contexte fortement bruité et incomplet. Une enveloppe de confiance est ensuite estimée assurant à la fois un encadrement plus large de la solution et le rôle de filtre pour la recherche des segments voisins candidats potentiels pour affiner l'estimation. Enfin, nous proposons une stratégie de regroupement/ajustement suivie d'une phase de décision floue constituant ainsi un schéma robuste de détection de formes elliptiques dans les images

Extraction of buildings from high-resolution satellite data and airborne LIDAR

Automatic building extraction is a difficult object recognition problem due to a high complexity of the scene content and the object representation. There is a dilemma to select appropriate building models to be reconstructed; the models have to be generic in order to represent a variety of building shape, whereas they also have to be specific to differentiate buildings from other objects in the scene. Therefore, a scientific challenge of building extraction lies in constructing a framework for modelling building objects with appropriate balance between generic and specific models. This thesis investigates a synergy of IKONOS satellite imagery and airborne LIDAR data, which have recently emerged as powerful remote sensing tools, and aims to develop an automatic system, which delineates building outlines with more complex shape, but by less use of geometric constraints. The method described in this thesis is a two step procedure: building detection and building description. A method of automatic building detection that can separate individual buildings from surrounding features is presented. The process is realized in a hierarchical strategy, where terrain, trees, and building objects are sequentially detected. Major research efforts are made on the development of a LIDAR filtering technique, which automatically detects terrain surfaces from a cloud of 3D laser points. The thesis also proposes a method of building description to automatically reconstruct building boundaries. A building object is generally represented as a mosaic of convex polygons. The first stage is to generate polygonal cues by a recursive intersection of both datadriven and model-driven linear features extracted from IKONOS imagery and LIDAR data. The second stage is to collect relevant polygons comprising the building object and to merge them for reconstructing the building outlines. The developed LIDAR filter was tested in a range of different landforms, and showed good results to meet most of the requirements of DTM generation and building detection. Also, the implemented building extraction system was able to successfully reconstruct the building outlines, and the accuracy of the building extraction is good enough for mapping purposes.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Extraction of buildings from high-resolution satellite data and airborne Lidar

Automatic building extraction is a difficult object recognition problem due to a high complexity of the scene content and the object representation. There is a dilemma to select appropriate building models to be reconstructed; the models have to be generic in order to represent a variety of building shape, whereas they also have to be specific to differentiate buildings from other objects in the scene. Therefore, a scientific challenge of building extraction lies in constructing a framework for modelling building objects with appropriate balance between generic and specific models. This thesis investigates a synergy of IKONOS satellite imagery and airborne LIDAR data, which have recently emerged as powerful remote sensing tools, and aims to develop an automatic system, which delineates building outlines with more complex shape, but by less use of geometric constraints. The method described in this thesis is a two step procedure: building detection and building description. A method of automatic building detection that can separate individual buildings from surrounding features is presented. The process is realized in a hierarchical strategy, where terrain, trees, and building objects are sequentially detected. Major research efforts are made on the development of a LIDAR filtering technique, which automatically detects terrain surfaces from a cloud of 3D laser points. The thesis also proposes a method of building description to automatically reconstruct building boundaries. A building object is generally represented as a mosaic of convex polygons. The first stage is to generate polygonal cues by a recursive intersection of both datadriven and model-driven linear features extracted from IKONOS imagery and LIDAR data. The second stage is to collect relevant polygons comprising the building object and to merge them for reconstructing the building outlines. The developed LIDAR filter was tested in a range of different landforms, and showed good results to meet most of the requirements of DTM generation and building detection. Also, the implemented building extraction system was able to successfully reconstruct the building outlines, and the accuracy of the building extraction is good enough for mapping purposes