Part Description and Segmentation Using Contour, Surface and Volumetric Primitives

The problem of part definition, description, and decomposition is central to the shape recognition systems. The Ultimate goal of segmenting range images into meaningful parts and objects has proved to be very difficult to realize, mainly due to the isolation of the segmentation problem from the issue of representation. We propose a paradigm for part description and segmentation by integration of contour, surface, and volumetric primitives. Unlike previous approaches, we have used geometric properties derived from both boundary-based (surface contours and occluding contours), and primitive-based (quadric patches and superquadric models) representations to define and recover part-whole relationships, without a priori knowledge about the objects or object domain. The object shape is described at three levels of complexity, each contributing to the overall shape. Our approach can be summarized as answering the following question : Given that we have all three different modules for extracting volume, surface and boundary properties, how should they be invoked, evaluated and integrated? Volume and boundary fitting, and surface description are performed in parallel to incorporate the best of the coarse to fine and fine to coarse segmentation strategy. The process involves feedback between the segmentor (the Control Module) and individual shape description modules. The control module evaluates the intermediate descriptions and formulates hypotheses about parts. Hypotheses are further tested by the segmentor and the descriptors. The descriptions thus obtained are independent of position, orientation, scale, domain and domain properties, and are based purely on geometric considerations. They are extremely useful for the high level domain dependent symbolic reasoning processes, which need not deal with tremendous amount of data, but only with a rich description of data in terms of primitives recovered at various levels of complexity

Searchable Sky Coverage of Astronomical Observations: Footprints and Exposures

Sky coverage is one of the most important pieces of information about astronomical observations. We discuss possible representations, and present algorithms to create and manipulate shapes consisting of generalized spherical polygons with arbitrary complexity and size on the celestial sphere. This shape specification integrates well with our Hierarchical Triangular Mesh indexing toolbox, whose performance and capabilities are enhanced by the advanced features presented here. Our portable implementation of the relevant spherical geometry routines comes with wrapper functions for database queries, which are currently being used within several scientific catalog archives including the Sloan Digital Sky Survey, the Galaxy Evolution Explorer and the Hubble Legacy Archive projects as well as the Footprint Service of the Virtual Observatory.Comment: 11 pages, 7 figures, submitted to PAS

Environmental modeling and recognition for an autonomous land vehicle

An architecture for object modeling and recognition for an autonomous land vehicle is presented. Examples of objects of interest include terrain features, fields, roads, horizon features, trees, etc. The architecture is organized around a set of data bases for generic object models and perceptual structures, temporary memory for the instantiation of object and relational hypotheses, and a long term memory for storing stable hypotheses that are affixed to the terrain representation. Multiple inference processes operate over these databases. Researchers describe these particular components: the perceptual structure database, the grouping processes that operate over this, schemas, and the long term terrain database. A processing example that matches predictions from the long term terrain model to imagery, extracts significant perceptual structures for consideration as potential landmarks, and extracts a relational structure to update the long term terrain database is given

Historical Grassland Turboveg Database Project. 2067 Relevés recorded by Dr Austin O’ Sullivan 1962 – 1982

User Guide and CD of Database are availableEnd of project reportThe more common grassland types occupy about 70% of the Irish landscape (O’Sullivan, 1982), but information on these vegetation types is rare. Generally, Irish grasslands are distinguished based on the intensity of their management (improved or semi-natural grasslands), and the drainage conditions and acidity of the soil (dry or wet, calcareous or acidic grassland types) (Fossitt, 2000). However, little is known about their floristic composition and the changes in floristic composition over time. The current knowledge on grassland vegetation is mostly based on a survey of Irish grasslands by Dr. Austin O’Sullivan completed in the 1960’s and 1970’s (O’Sullivan, 1982). In this survey O’Sullivan identified Irish grassland types in accordance with the classification of continental European grasslands based on the principles of the School of Phytosociology. O’Sullivan distinguished five main grassland types introducing agricultural criteria as well as floristic criteria into grassland classification (O’Sullivan, 1982). In 1978, O’Sullivan made an attempt at mapping Ireland’s vegetation types including the five grassland types distinguished in his later publication as well as two types of peatland vegetation (Figures 1 and 2). This map was completed using 1960’s soils maps (National Soil Survey, Teagasc, Johnstown Castle) and a subsample of the dataset on the composition of Irish grasslands. Phytosociological classification of vegetation is based on the full floristic composition of the vegetation as determined by assessing the abundance and spatial structure of the plant species in a given area. The actual area of the survey (or relevé) is determined according to strict criteria, which include how representative the sample area is for the wider vegetation (i.e. how many of the species found in the wider area are also present in the survey area).National Parks and Wildlife Service of the Department of the Environment, Heritage and Local Government, Dublin, Ireland

Research on Symbolic Inference in Computational Vision

This paper provides an overview of ongoing research in the GRASP laboratory which focuses on the general problem of symbolic inference in computational vision. In this report we describe a conceptual framework for this research, and describe our current research programs in the component areas which support this work

A robust and fast method for 6DoF motion estimation from generalized 3D data

Nowadays, there is an increasing number of robotic applications that need to act in real three-dimensional (3D) scenarios. In this paper we present a new mobile robotics orientated 3D registration method that improves previous Iterative Closest Points based solutions both in speed and accuracy. As an initial step, we perform a low cost computational method to obtain descriptions for 3D scenes planar surfaces. Then, from these descriptions we apply a force system in order to compute accurately and efficiently a six degrees of freedom egomotion. We describe the basis of our approach and demonstrate its validity with several experiments using different kinds of 3D sensors and different 3D real environments.This work has been supported by project DPI2009-07144 from Ministerio de Educación y Ciencia (Spain) and GRE10-35 from Universidad de Alicante (Spain)

Reconstruction of Solar Subsurfaces by Local Helioseismology

Local helioseismology has opened new frontiers in our quest for understanding of the internal dynamics and dynamo on the Sun. Local helioseismology reconstructs subsurface structures and flows by extracting coherent signals of acoustic waves traveling through the interior and carrying information about subsurface perturbations and flows, from stochastic oscillations observed on the surface. The initial analysis of the subsurface flow maps reconstructed from the 5 years of SDO/HMI data by time-distance helioseismology reveals the great potential for studying and understanding of the dynamics of the quiet Sun and active regions, and the evolution with the solar cycle. In particular, our results show that the emergence and evolution of active regions are accompanied by multi-scale flow patterns, and that the meridional flows display the North-South asymmetry closely correlating with the magnetic activity. The latitudinal variations of the meridional circulation speed, which are probably related to the large-scale converging flows, are mostly confined in shallow subsurface layers. Therefore, these variations do not necessarily affect the magnetic flux transport. The North-South asymmetry is also pronounced in the variations of the differential rotation ("torsional oscillations"). The calculations of a proxy of the subsurface kinetic helicity density show that the helicity does not vary during the solar cycle, and that supergranulation is a likely source of the near-surface helicity.Comment: 17 pages, 10 figures, in "Cartography of the Sun and the Stars", Editors: Rozelot, Jean-Pierre, Neiner, Corali

A perception and manipulation system for collecting rock samples

An important part of a planetary exploration mission is to collect and analyze surface samples. As part of the Carnegie Mellon University Ambler Project, researchers are investigating techniques for collecting samples using a robot arm and a range sensor. The aim of this work is to make the sample collection operation fully autonomous. Described here are the components of the experimental system, including a perception module that extracts objects of interest from range images and produces models of their shapes, and a manipulation module that enables the system to pick up the objects identified by the perception module. The system was tested on a small testbed using natural terrain

Range Image Segmentation for 3-D Object Recognition

Three dimensional scene analysis in an unconstrained and uncontrolled environment is the ultimate goal of computer vision. Explicit depth information about the scene is of tremendous help in segmentation and recognition of objects. Range image interpretation with a view of obtaining low-level features to guide mid-level and high-level segmentation and recognition processes is described. No assumptions about the scene are made and algorithms are applicable to any general single viewpoint range image. Low-level features like step edges and surface characteristics are extracted from the images and segmentation is performed based on individual features as well as combination of features. A high level recognition process based on superquadric fitting is described to demonstrate the usefulness of initial segmentation based on edges. A classification algorithm based on surface curvatures is used to obtain initial segmentation of the scene. Objects segmented using edge information are then classified using surface curvatures. Various applications of surface curvatures in mid and high level recognition processes are discussed. These include surface reconstruction, segmentation into convex patches and detection of smooth edges. Algorithms are run on real range images and results are discussed in detail