4,158 research outputs found
Dynamics of Attention in Depth: Evidence from Mutli-Element Tracking
The allocation of attention in depth is examined using a multi-element tracking paradigm. Observers are required to track a predefined subset of from two to eight elements in displays containing up to sixteen identical moving elements. We first show that depth cues, such as binocular disparity and occlusion through T-junctions, improve performance in a multi-element tracking task in the case where element boundaries are allowed to intersect in the depiction of motion in a single fronto-parallel plane. We also show that the allocation of attention across two perceptually distinguishable planar surfaces either fronto-parallel or receding at a slanting angle and defined by coplanar elements, is easier than allocation of attention within a single surface. The same result was not found when attention was required to be deployed across items of two color populations rather than of a single color. Our results suggest that, when surface information does not suffice to distinguish between targets and distractors that are embedded in these surfaces, division of attention across two surfaces aids in tracking moving targets.National Science Foundation (IRI-94-01659); Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657
Probabilistic framework for image understanding applications using Bayesian Networks
Machine learning algorithms have been successfully utilized in various systems/devices. They have the ability to improve the usability/quality of such systems in terms of intelligent user interface, fast performance, and more importantly, high accuracy. In this research, machine learning techniques are used in the field of image understanding, which is a common research area between image analysis and computer vision, to involve higher processing level of a target image to make sense of the scene captured in it. A general probabilistic framework for image understanding where topics associated with (i) collection of images to generate a comprehensive and valid database, (ii) generation of an unbiased ground-truth for the aforesaid database, (iii) selection of classification features and elimination of the redundant ones, and (iv) usage of such information to test a new sample set, are discussed. Two research projects have been developed as examples of the general image understanding framework; identification of region(s) of interest, and image segmentation evaluation. These techniques, in addition to others, are combined in an object-oriented rendering system for printing applications. The discussion included in this doctoral dissertation explores the means for developing such a system from an image understanding/ processing aspect. It is worth noticing that this work does not aim to develop a printing system. It is only proposed to add some essential features for current printing pipelines to achieve better visual quality while printing images/photos. Hence, we assume that image regions have been successfully extracted from the printed document. These images are used as input to the proposed object-oriented rendering algorithm where methodologies for color image segmentation, region-of-interest identification and semantic features extraction are employed. Probabilistic approaches based on Bayesian statistics have been utilized to develop the proposed image understanding techniques
Turbulence Measurements of Rectangular Nozzles with Bevel
This paper covers particle image velocimetry measurements of a family of rectangular nozzles with aspect ratios 2, 4, and 8, in the high subsonic flow regime. Far-field acoustic results, presented previously, showed that increasing aspect ratios increased the high frequency noise, especially directed in the polar plane containing the minor axis of the nozzle. The measurements presented here have important implications in the modeling of turbulent sources for acoustic analogy theories. While the nonaxisymmetric mean flow from the rectangular nozzles can be studied reliably using computational solutions, the nonaxisymmetry of the turbulent fluctuations, particularly at the level of velocity components, cannot; only measurements such as these can determine the impact of nozzle geometry on acoustic source anisotropy. Additional nozzles were constructed that extended the wide lip on one side of these nozzles to form beveled nozzles. The paper first documents the velocity fields, mean and variance, from the round, rectangular, and beveled rectangular nozzles at high subsonic speeds. A second section introduces measures of the isotropy of the turbulence, such as component ratios and lengthscales, first by showing them for a round jet and then for the rectangular nozzles. From these measures the source models of acoustic analogy codes can be judged or modified to account for these anisotropies
Motion analysis report
Human motion analysis is the task of converting actual human movements into computer readable data. Such movement information may be obtained though active or passive sensing methods. Active methods include physical measuring devices such as goniometers on joints of the body, force plates, and manually operated sensors such as a Cybex dynamometer. Passive sensing de-couples the position measuring device from actual human contact. Passive sensors include Selspot scanning systems (since there is no mechanical connection between the subject's attached LEDs and the infrared sensing cameras), sonic (spark-based) three-dimensional digitizers, Polhemus six-dimensional tracking systems, and image processing systems based on multiple views and photogrammetric calculations
Efficient Match Pair Retrieval for Large-scale UAV Images via Graph Indexed Global Descriptor
SfM (Structure from Motion) has been extensively used for UAV (Unmanned
Aerial Vehicle) image orientation. Its efficiency is directly influenced by
feature matching. Although image retrieval has been extensively used for match
pair selection, high computational costs are consumed due to a large number of
local features and the large size of the used codebook. Thus, this paper
proposes an efficient match pair retrieval method and implements an integrated
workflow for parallel SfM reconstruction. First, an individual codebook is
trained online by considering the redundancy of UAV images and local features,
which avoids the ambiguity of training codebooks from other datasets. Second,
local features of each image are aggregated into a single high-dimension global
descriptor through the VLAD (Vector of Locally Aggregated Descriptors)
aggregation by using the trained codebook, which remarkably reduces the number
of features and the burden of nearest neighbor searching in image indexing.
Third, the global descriptors are indexed via the HNSW (Hierarchical Navigable
Small World) based graph structure for the nearest neighbor searching. Match
pairs are then retrieved by using an adaptive threshold selection strategy and
utilized to create a view graph for divide-and-conquer based parallel SfM
reconstruction. Finally, the performance of the proposed solution has been
verified using three large-scale UAV datasets. The test results demonstrate
that the proposed solution accelerates match pair retrieval with a speedup
ratio ranging from 36 to 108 and improves the efficiency of SfM reconstruction
with competitive accuracy in both relative and absolute orientation
Experimental investigation of the vortical activity in the close wake of a simplified military transport aircraft
This paper focuses on the experimental characterization of the vortex structures that develop in the aft fuselage region and in the wake of a simplified geometry of a military transport aircraft. It comes within the framework of the military applications of airflow influence on airdrop operations. This work relies on particle image velocimetry measurements combined with a vortex-tracking approach. Complex vortex dynamics is revealed, in terms of vortex positions, intensities, sizes, shapes and fluctuation levels, for both closed and opened cargo-door and ramp airdrop configurations
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