3,557 research outputs found
Kansas environmental and resource study: A Great Plains model
There are no author-identified significant results in this report
Adaptive pattern recognition by using a predictive model in construction of similarity sets
Adaptive pattern recognition by using predictive model in construction of similarity set
A Hybrid Search Algorithm for the Whitehead Minimization Problem
The Whitehead Minimization problem is a problem of finding elements of the
minimal length in the automorphic orbit of a given element of a free group. The
classical algorithm of Whitehead that solves the problem depends exponentially
on the group rank. Moreover, it can be easily shown that exponential blowout
occurs when a word of minimal length has been reached and, therefore, is
inevitable except for some trivial cases.
In this paper we introduce a deterministic Hybrid search algorithm and its
stochastic variation for solving the Whitehead minimization problem. Both
algorithms use search heuristics that allow one to find a length-reducing
automorphism in polynomial time on most inputs and significantly improve the
reduction procedure. The stochastic version of the algorithm employs a
probabilistic system that decides in polynomial time whether or not a word is
minimal. The stochastic algorithm is very robust. It has never happened that a
non-minimal element has been claimed to be minimal
Documentation of procedures for textural/spatial pattern recognition techniques
A C-130 aircraft was flown over the Sam Houston National Forest on March 21, 1973 at 10,000 feet altitude to collect multispectral scanner (MSS) data. Existing textural and spatial automatic processing techniques were used to classify the MSS imagery into specified timber categories. Several classification experiments were performed on this data using features selected from the spectral bands and a textural transform band. The results indicate that (1) spatial post-processing a classified image can cut the classification error to 1/2 or 1/3 of its initial value, (2) spatial post-processing the classified image using combined spectral and textural features produces a resulting image with less error than post-processing a classified image using only spectral features and (3) classification without spatial post processing using the combined spectral textural features tends to produce about the same error rate as a classification without spatial post processing using only spectral features
Combined spectral and spatial processing of ERTS imagery data
A procedure for extracting a set of textural features for ERTS-1 MSS data is presented. The textural features were combined with a set of spectral features and were used to develop a classification algorithm for identifying the land use categories of blocks of digital MSS data. The classification algorithm was derived from a training set of 314 blocks and tested on a set of 310 blocks. The overall accuracy of the classifier was found to be 83.5% on seven land use categories
Vision-model-based Real-time Localization of Unmanned Aerial Vehicle for Autonomous Structure Inspection under GPS-denied Environment
UAVs have been widely used in visual inspections of buildings, bridges and
other structures. In either outdoor autonomous or semi-autonomous flights
missions strong GPS signal is vital for UAV to locate its own positions.
However, strong GPS signal is not always available, and it can degrade or fully
loss underneath large structures or close to power lines, which can cause
serious control issues or even UAV crashes. Such limitations highly restricted
the applications of UAV as a routine inspection tool in various domains. In
this paper a vision-model-based real-time self-positioning method is proposed
to support autonomous aerial inspection without the need of GPS support.
Compared to other localization methods that requires additional onboard
sensors, the proposed method uses a single camera to continuously estimate the
inflight poses of UAV. Each step of the proposed method is discussed in detail,
and its performance is tested through an indoor test case.Comment: 8 pages, 5 figures, submitted to i3ce 201
A comprehensive data processing plan for crop calendar MSS signature development from satellite imagery
There are no author-identified significant results in this report
Heuristics for The Whitehead Minimization Problem
In this paper we discuss several heuristic strategies which allow one to
solve the Whitehead's minimization problem much faster (on most inputs) than
the classical Whitehead algorithm. The mere fact that these strategies work in
practice leads to several interesting mathematical conjectures. In particular,
we conjecture that the length of most non-minimal elements in a free group can
be reduced by a Nielsen automorphism which can be identified by inspecting the
structure of the corresponding Whitehead Graph
Texture-based crowd detection and localisation
This paper presents a crowd detection system based on texture analysis. The state-of-the-art techniques based on co-occurrence matrix have been revisited and a novel set of features proposed. These features provide a richer description of the co-occurrence matrix, and can be exploited to obtain stronger classification results, especially when smaller portions of the image are considered. This is extremely useful for crowd localisation: acquired images are divided into smaller regions in order to perform a classification on each one. A thorough evaluation of the proposed system on a real world data set is also presented: this validates the improvements in reliability of the crowd detection and localisation
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