Walt Disney Animation Studios has transitioned to path-traced global illumination as part of a progression of brute-force physically based rendering in the name of artist efficiency. To achieve this without compromising our geometric or shading complexity, we built our Hyperion renderer based on a novel architecture that extracts traversal and shading coherence from large, sorted ray batches. In this article, we describe our architecture and discuss our design decisions. We also explain how we are able to provide artistic control in a physically based renderer, and we demonstrate through case studies how we have benefited from having a proprietary renderer that can evolve with production needs.

The ability to discern malignant from benign tissue in excised human breast specimens in Breast Conservation Surgery (BCS) was evaluated using single frequency terahertz radiation. Terahertz (THz) images of the specimens in reflection mode were obtained by employing a gas laser source and mechanical scanning. The images were correlated with optical histological micrographs of the same specimens, and a mean discrimination of 73% was found for five out of six samples using Receiver Operating Characteristic (ROC) analysis. The system design and characterization is discussed in detail. The initial results are encouraging but further development of the technology and clinical evaluation is needed to evaluate its feasibility in the clinical environment.

We describe polynomial--time algorithms that produce approximate solutions with guaranteed accuracy for a class of QP problems that are used in the design of support vector machine classifiers. These algorithms employ a two--stage process where the first stage produces an approximate solution to a dual QP problem and the second stage maps this approximate dual solution to an approximate primal solution. For the second stage we describe an O(n log n) algorithm that maps an approximate dual solution with accuracy (2(2Km)1/2+8(λ)1/2)-2 λ εp2 to an approximate primal solution with accuracy εp where n is the number of data samples, Kn is the maximum kernel value over the data and λ > 0 is the SVM regularization parameter. For the first stage we present new results for decomposition algorithms and describe new decomposition algorithms with guaranteed accuracy and run time. In particular, for τ-rate certifying decomposition algorithms we establish the optimality of τ = 1/(n-1). In addition we extend the recent τ = 1/(n-1) algorithm of Simon (2004) to form two new composite algorithms that also achieve the τ = 1/(n-1) iteration bound of List and Simon (2005), but yield faster run times in practice. We also exploit the τ-rate certifying property of these algorithms to produce new stopping rules that are computationally efficient and that guarantee a specified accuracy for the approximate dual solution. Furthermore, for the dual QP problem corresponding to the standard classification problem we describe operational conditions for which the Simon and composite algorithms possess an upper bound of O(n) on the number of iterations. For this same problem we also describe general conditions for which a matching lower bound exists for any decomposition algorithm that uses working sets of size 2. For the Simon and composite algorithms we also establish an O(n2) bound on the overall run time for the first stage. Combining the first and second stages gives an overall run time of O(n2(ck + 1)) where ck is an upper bound on the computation to perform a kernel evaluation. Pseudocode is presented for a complete algorithm that inputs an accuracy εp and produces an approximate solution that satisfies this accuracy in low order polynomial time. Experiments are included to illustrate the new stopping rules and to compare the Simon and composite decomposition algorithms.

We describe an automated script identification system for typeset document images. Templates for each script are created by clustering textual symbols from a training set. Symbols from new images are compared to the templates to find the best script. Our current system processes thirteen scripts with minimal preprocessing and high accuracy.

We describe a system that automatically identifies the script used in documents stored electronically in image form. The system can learn to distinguish any number of scripts. It develops a set of representative symbols (templates) for each script by clustering textual symbols from a set of training documents and representing each cluster by its centroid. "Textual symbols" include discrete characters in scripts such as Cyrillic, as well as adjoined characters, character fragments, and whole words in connected scripts such as Arabic. To identify a new document's script, the system compares a subset of symbols from the document to each script's templates, screening out rare or unreliable templates, and choosing the script whose templates provide the best match. Our current system, trained on thirteen scripts, correctly identifies all test documents except those printed in fonts that differ markedly from fonts in the training set.