Representation Learning With Convolutional Neural Networks

Deep learning methods have achieved great success in the areas of Computer Vision and Natural Language Processing. Recently, the rapidly developing field of deep learning is concerned with questions surrounding how we can learn meaningful and effective representations of data. This is because the performance of machine learning approaches is heavily dependent on the choice and quality of data representation, and different kinds of representation entangle and hide the different explanatory factors of variation behind the data. In this dissertation, we focus on representation learning with deep neural networks for different data formats including text, 3D polygon shapes, and brain fiber tracts. First, we propose a topic-based word representation learning approach for text classification. The proposed approach takes global semantic relationship between words over the whole corpus into consideration and encodes the relationships into distributed vector representations with continuous Skip-gram model. The learned representations which capture a large number of precise syntactic and semantic word relationships are taken as input of Convolution Neural Networks for classification. Our experimental results show the effectiveness of the proposed method on indexing of biomedical articles, behavior code annotation of clinical text fragments, and classification of news groups. Second, we present a 3D polygon shape representation learning framework for shape segmentation. We propose Directionally Convolutional Network (DCN) that extends convolution operations from images to the polygon mesh surface with rotation-invariant property. Based on the proposed DCN, we learn effective shape representations from raw geometric features and then classify each face of a given polygon into predefined semantic parts. Through extensive experiments, we demonstrate that our framework outperforms the current state-of-the-arts. Third, we propose to learn effective and meaningful representations for brain fiber tracts using deep learning frameworks. We handle the highly unbalanced dataset by introducing asymmetrical loss function for easily classified samples and hard classified ones. The training loss avoids to be dominated by the easy samples and the training step is more efficient. In addition, we learn more effective and meaningful representations by introducing deeper network and metric learning approaches. Furthermore, we propose to improve the interpretability of our framework by inducing attention mechanism. Our experimental results show that our proposed framework outperforms current golden standard significantly on the real-world dataset

A common ontology for multi-dimensional shapes

In recent years, digital shapes have become more and more widespread and have been made available in a plethora of online repositories. A systematic and formal approach for capturing and representing shape-related information is needed to facilitate its reuse and enable the demonstration of useful cross-domain usage scenarios. In this paper we present an ontology for digital shapes, called the Common Shape Ontology (CSO). We discuss the rationale, the requirements and the scope of this ontology, we present in detail its structure and describe the most relevant choices related to its development. Finally, we show how the CSO conceptualization is used in domain-specific application scenarios

FROM DOCUMENTATION IMAGES TO RESTAURATION SUPPORT TOOLS: A PATHFOLLOWING THE NEPTUNE FOUNTAIN IN BOLOGNA DESIGN PROCESS

The sixteenth-century Fountain of Neptune is one of Bologna's most renowned landmarks. During the recent restoration activities of the monumental sculpture group, consisting in precious marbles and highly refined bronzes with water jets, a photographic campaign has been carried out exclusively for documentation purposes of the current state of preservation of the complex. Nevertheless, the highquality imagery was used for a different use, namely to create a 3D digital model accurate in shape and color by means of automated photogrammetric techniques and a robust customized pipeline. This 3D model was used as basic tool to support many and different activities of the restoration site. The paper describes the 3D model construction technique used and the most important applications in which it was used as support tool for restoration: (i) reliable documentation of the actual state; (ii) surface cleaning analysis; (iii) new water system and jets; (iv) new lighting design simulation; (v) support for preliminary analysis and projectual studies related to hardly accessible areas; (vi) structural analysis; (vii) base for filling gaps or missing elements through 3D printing; (viii) high-quality visualization and rendering and (ix) support for data modelling and semantic-based diagrams