The Iray Light Transport Simulation and Rendering System

While ray tracing has become increasingly common and path tracing is well understood by now, a major challenge lies in crafting an easy-to-use and efficient system implementing these technologies. Following a purely physically-based paradigm while still allowing for artistic workflows, the Iray light transport simulation and rendering system allows for rendering complex scenes by the push of a button and thus makes accurate light transport simulation widely available. In this document we discuss the challenges and implementation choices that follow from our primary design decisions, demonstrating that such a rendering system can be made a practical, scalable, and efficient real-world application that has been adopted by various companies across many fields and is in use by many industry professionals today

Voronoi-Based Compact Image Descriptors: Efficient Region-of-Interest Retrieval With VLAD and Deep-Learning-Based Descriptors

We investigate the problem of image retrieval based on visual queries when the latter comprise arbitrary regionsof- interest (ROI) rather than entire images. Our proposal is a compact image descriptor that combines the state-of-the-art in content-based descriptor extraction with a multi-level, Voronoibased spatial partitioning of each dataset image. The proposed multi-level Voronoi-based encoding uses a spatial hierarchical K-means over interest-point locations, and computes a contentbased descriptor over each cell. In order to reduce the matching complexity with minimal or no sacrifice in retrieval performance: (i) we utilize the tree structure of the spatial hierarchical Kmeans to perform a top-to-bottom pruning for local similarity maxima; (ii) we propose a new image similarity score that combines relevant information from all partition levels into a single measure for similarity; (iii) we combine our proposal with a novel and efficient approach for optimal bit allocation within quantized descriptor representations. By deriving both a Voronoi-based VLAD descriptor (termed as Fast-VVLAD) and a Voronoi-based deep convolutional neural network (CNN) descriptor (termed as Fast-VDCNN), we demonstrate that our Voronoi-based framework is agnostic to the descriptor basis, and can easily be slotted into existing frameworks. Via a range of ROI queries in two standard datasets, it is shown that the Voronoibased descriptors achieve comparable or higher mean Average Precision against conventional grid-based spatial search, while offering more than two-fold reduction in complexity. Finally, beyond ROI queries, we show that Voronoi partitioning improves the geometric invariance of compact CNN descriptors, thereby resulting in competitive performance to the current state-of-theart on whole image retrieval

Novel neural approaches to data topology analysis and telemedicine

1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen676. INGEGNERIA ELETTRICAnoopenRandazzo, Vincenz

Machine Learning Algorithms for Robotic Navigation and Perception and Embedded Implementation Techniques

L'abstract è presente nell'allegato / the abstract is in the attachmen

Large-scale image retrieval using similarity preserving binary codes

Image retrieval is a fundamental problem in computer vision, and has many applications. When the dataset size gets very large, retrieving images in Internet image collections becomes very challenging. The challenges come from storage, computation speed, and similarity representation. My thesis addresses learning compact similarity preserving binary codes, which represent each image by a short binary string, for fast retrieval in large image databases. I will first present an approach called Iterative Quantization to convert high-dimensional vectors to compact binary codes, which works by learning a rotation to minimize the quantization error of mapping data to the vertices of a binary Hamming cube. This approach achieves state-of-the-art accuracy for preserving neighbors in the original feature space, as well as state-of-the-art semantic precision. Second, I will extend this approach to two different scenarios in large-scale recognition and retrieval problems. The first extension is aimed at high-dimensional histogram data, such as bag-of-words features or text documents. Such vectors are typically sparse and nonnegative. I develop an algorithm that explores the special structure of such data by mapping feature vectors to binary vertices in the positive orthant, which gives improved performance. The second extension is for Fisher Vectors, which are dense descriptors having tens of thousands to millions of dimensions. I develop a novel method for converting such descriptors to compact similarity-preserving binary codes that exploits their natural matrix structure to reduce their dimensionality using compact bilinear projections instead of a single large projection matrix. This method achieves retrieval and classification accuracy comparable to that of the original descriptors and to the state-of-the-art Product Quantization approach while having orders of magnitude faster code generation time and smaller memory footprint. Finally, I present two applications of using Internet images and tags/labels to learn binary codes with label supervision, and show improved retrieval accuracy on several large Internet image datasets. First, I will present an application that performs cross-modal retrieval in the Hamming space. Then I will present an application on using supervised binary classeme representations for large-scale image retrieval.Doctor of Philosoph