14,397 research outputs found
Automatic Face Recognition System Based on Local Fourier-Bessel Features
We present an automatic face verification system inspired by known properties
of biological systems. In the proposed algorithm the whole image is converted
from the spatial to polar frequency domain by a Fourier-Bessel Transform (FBT).
Using the whole image is compared to the case where only face image regions
(local analysis) are considered. The resulting representations are embedded in
a dissimilarity space, where each image is represented by its distance to all
the other images, and a Pseudo-Fisher discriminator is built. Verification test
results on the FERET database showed that the local-based algorithm outperforms
the global-FBT version. The local-FBT algorithm performed as state-of-the-art
methods under different testing conditions, indicating that the proposed system
is highly robust for expression, age, and illumination variations. We also
evaluated the performance of the proposed system under strong occlusion
conditions and found that it is highly robust for up to 50% of face occlusion.
Finally, we automated completely the verification system by implementing face
and eye detection algorithms. Under this condition, the local approach was only
slightly superior to the global approach.Comment: 2005, Brazilian Symposium on Computer Graphics and Image Processing,
18 (SIBGRAPI
The role of artificial intelligence, knowledge and wisdom in automatic image understanding
In the paper, the roles of intelligence, knowledge, learning and wisdom are discussed in the context of image content understanding. The known model of automatic image understanding is extended by the role of learning. References to example implementations are also given
Space exploration: The interstellar goal and Titan demonstration
Automated interstellar space exploration is reviewed. The Titan demonstration mission is discussed. Remote sensing and automated modeling are considered. Nuclear electric propulsion, main orbiting spacecraft, lander/rover, subsatellites, atmospheric probes, powered air vehicles, and a surface science network comprise mission component concepts. Machine, intelligence in space exploration is discussed
Object detection and tracking using a parts-based approach
One of the main goals of artificial intelligence is to allow computers to understand the world around them. As humans we extract a large amount of knowledge about the world from our visual perception, and the field of computer vision is determined to give computers access to this same wealth of knowledge. One of the fundamental steps in understanding the world is finding specific objects within our field of view, and the related task of following these objects as they move.
In this thesis the Implicit Shape Model algorithm, a local feature-based object detection algorithm, is implemented and used to develop an appearance model and object tracking algorithm based on it. This algorithm is very robust to intraclass variation, and can successfully track objects when both occlusion and non-stationary backgrounds are present. The usefulness of the proposed appearance model is analyzed, and results of the algorithm on real video sequences are presented. Several enhancements to the method are also proposed, and performance in terms of recall and precision is analyzed
The Agile Alert System For Gamma-Ray Transients
In recent years, a new generation of space missions offered great
opportunities of discovery in high-energy astrophysics. In this article we
focus on the scientific operations of the Gamma-Ray Imaging Detector (GRID)
onboard the AGILE space mission. The AGILE-GRID, sensitive in the energy range
of 30 MeV-30 GeV, has detected many gamma-ray transients of galactic and
extragalactic origins. This work presents the AGILE innovative approach to fast
gamma-ray transient detection, which is a challenging task and a crucial part
of the AGILE scientific program. The goals are to describe: (1) the AGILE
Gamma-Ray Alert System, (2) a new algorithm for blind search identification of
transients within a short processing time, (3) the AGILE procedure for
gamma-ray transient alert management, and (4) the likelihood of ratio tests
that are necessary to evaluate the post-trial statistical significance of the
results. Special algorithms and an optimized sequence of tasks are necessary to
reach our goal. Data are automatically analyzed at every orbital downlink by an
alert pipeline operating on different timescales. As proper flux thresholds are
exceeded, alerts are automatically generated and sent as SMS messages to
cellular telephones, e-mails, and push notifications of an application for
smartphones and tablets. These alerts are crosschecked with the results of two
pipelines, and a manual analysis is performed. Being a small scientific-class
mission, AGILE is characterized by optimization of both scientific analysis and
ground-segment resources. The system is capable of generating alerts within two
to three hours of a data downlink, an unprecedented reaction time in gamma-ray
astrophysics.Comment: 34 pages, 9 figures, 5 table
Crowdsourcing in Computer Vision
Computer vision systems require large amounts of manually annotated data to
properly learn challenging visual concepts. Crowdsourcing platforms offer an
inexpensive method to capture human knowledge and understanding, for a vast
number of visual perception tasks. In this survey, we describe the types of
annotations computer vision researchers have collected using crowdsourcing, and
how they have ensured that this data is of high quality while annotation effort
is minimized. We begin by discussing data collection on both classic (e.g.,
object recognition) and recent (e.g., visual story-telling) vision tasks. We
then summarize key design decisions for creating effective data collection
interfaces and workflows, and present strategies for intelligently selecting
the most important data instances to annotate. Finally, we conclude with some
thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in
Computer Graphics and Vision, 201
Terrestrial applications: An intelligent Earth-sensing information system
For Abstract see A82-2214
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