The aceToolbox: low-level audiovisual feature extraction for retrieval and classification

In this paper we present an overview of a software platform that has been developed within the aceMedia project, termed the aceToolbox, that provides global and local lowlevel feature extraction from audio-visual content. The toolbox is based on the MPEG-7 eXperimental Model (XM), with extensions to provide descriptor extraction from arbitrarily shaped image segments, thereby supporting local descriptors reflecting real image content. We describe the architecture of the toolbox as well as providing an overview of the descriptors supported to date. We also briefly describe the segmentation algorithm provided. We then demonstrate the usefulness of the toolbox in the context of two different content processing scenarios: similarity-based retrieval in large collections and scene-level classification of still images

Consistent Density Scanning and Information Extraction From Point Clouds of Building Interiors

Over the last decade, 3D range scanning systems have improved considerably enabling the designers to capture large and complex domains such as building interiors. The captured point cloud is processed to extract specific Building Information Models, where the main research challenge is to simultaneously handle huge and cohesive point clouds representing multiple objects, occluded features and vast geometric diversity. These domain characteristics increase the data complexities and thus make it difficult to extract accurate information models from the captured point clouds. The research work presented in this thesis improves the information extraction pipeline with the development of novel algorithms for consistent density scanning and information extraction automation for building interiors. A restricted density-based, scan planning methodology computes the number of scans to cover large linear domains while ensuring desired data density and reducing rigorous post-processing of data sets. The research work further develops effective algorithms to transform the captured data into information models in terms of domain features (layouts), meaningful data clusters (segmented data) and specific shape attributes (occluded boundaries) having better practical utility. Initially, a direct point-based simplification and layout extraction algorithm is presented that can handle the cohesive point clouds by adaptive simplification and an accurate layout extraction approach without generating an intermediate model. Further, three information extraction algorithms are presented that transforms point clouds into meaningful clusters. The novelty of these algorithms lies in the fact that they work directly on point clouds by exploiting their inherent characteristic. First a rapid data clustering algorithm is presented to quickly identify objects in the scanned scene using a robust hue, saturation and value (H S V) color model for better scene understanding. A hierarchical clustering algorithm is developed to handle the vast geometric diversity ranging from planar walls to complex freeform objects. The shape adaptive parameters help to segment planar as well as complex interiors whereas combining color and geometry based segmentation criterion improves clustering reliability and identifies unique clusters from geometrically similar regions. Finally, a progressive scan line based, side-ratio constraint algorithm is presented to identify occluded boundary data points by investigating their spatial discontinuity

A Multi-Code Analysis Toolkit for Astrophysical Simulation Data

The analysis of complex multiphysics astrophysical simulations presents a unique and rapidly growing set of challenges: reproducibility, parallelization, and vast increases in data size and complexity chief among them. In order to meet these challenges, and in order to open up new avenues for collaboration between users of multiple simulation platforms, we present yt (available at http://yt.enzotools.org/), an open source, community-developed astrophysical analysis and visualization toolkit. Analysis and visualization with yt are oriented around physically relevant quantities rather than quantities native to astrophysical simulation codes. While originally designed for handling Enzo's structure adaptive mesh refinement (AMR) data, yt has been extended to work with several different simulation methods and simulation codes including Orion, RAMSES, and FLASH. We report on its methods for reading, handling, and visualizing data, including projections, multivariate volume rendering, multi-dimensional histograms, halo finding, light cone generation and topologically-connected isocontour identification. Furthermore, we discuss the underlying algorithms yt uses for processing and visualizing data, and its mechanisms for parallelization of analysis tasks.Comment: 18 pages, 6 figures, emulateapj format. Resubmitted to Astrophysical Journal Supplement Series with revisions from referee. yt can be found at http://yt.enzotools.org

A reconfigurable real-time morphological system for augmented vision

There is a significant number of visually impaired individuals who suffer sensitivity loss to high spatial frequencies, for whom current optical devices are limited in degree of visual aid and practical application. Digital image and video processing offers a variety of effective visual enhancement methods that can be utilised to obtain a practical augmented vision head-mounted display device. The high spatial frequencies of an image can be extracted by edge detection techniques and overlaid on top of the original image to improve visual perception among the visually impaired. Augmented visual aid devices require highly user-customisable algorithm designs for subjective configuration per task, where current digital image processing visual aids offer very little user-configurable options. This paper presents a highly user-reconfigurable morphological edge enhancement system on field-programmable gate array, where the morphological, internal and external edge gradients can be selected from the presented architecture with specified edge thickness and magnitude. In addition, the morphology architecture supports reconfigurable shape structuring elements and configurable morphological operations. The proposed morphology-based visual enhancement system introduces a high degree of user flexibility in addition to meeting real-time constraints capable of obtaining 93 fps for high-definition image resolution

Image-based window detection: an overview

Automated segmentation of buildings’ façade and detection of its elements is of high relevance in various fields of research as it, e. g., reduces the effort of 3 D reconstructing existing buildings and even entire cities or may be used for navigation and localization tasks. In recent years, several approaches were made concerning this issue. These can be mainly classified by their input data which are either images or 3 D point clouds. This paper provides a survey of image-based approaches. Particularly, this paper focuses on window detection and therefore groups related papers into the three major detection strategies. We juxtapose grammar based methods, pattern recognition and machine learning and contrast them referring to their generality of application. As we found out machine learning approaches seem most promising for window detection on generic façades and thus we will pursue these in future work

Laser range data based semantic labeling of places

Extending metric space representations of an environment with other high level information, such as semantic and topological representations enable a robotic device to efficiently operate in complex environments. This paper proposes a methodology for a robot to classify indoor environments into semantic categories. Classification task, using data collected from a laser range finder, is achieved by a machine learning approach based on the logistic regression algorithm. The classification is followed by a probabilistic temporal update of the semantic labels of places. The innovation here is that the new algorithm is able to classify parts of a single laser scan into different semantic labels rather than the conventional approach of gross categorization of locations based on the whole laser scan. We demonstrate the effectiveness of the algorithm using a data set available in the public domain. ©2010 IEEE