124,318 research outputs found
Literature Survey On Stereo Vision Disparity Map Algorithms
This paper presents a literature survey on existing disparity map algorithms. It focuses on four main stages of processing as proposed by Scharstein and Szeliski in a taxonomy and evaluation of dense two-frame stereo correspondence algorithms performed in 2002. To assist future researchers in developing their own stereo matching algorithms, a summary of the existing algorithms developed for every stage of processing is also provided. The survey also notes the implementation of previous software-based and hardware-based algorithms. Generally, the main processing module for a software-based implementation uses only a central processing unit. By contrast, a hardware-based implementation requires one or more additional processors for its processing module, such as graphical processing unit or a field programmable gate array. This literature survey also presents a method of qualitative measurement that is widely used by researchers in the area of stereo vision disparity mappings
Faster Approximate String Matching for Short Patterns
We study the classical approximate string matching problem, that is, given
strings and and an error threshold , find all ending positions of
substrings of whose edit distance to is at most . Let and
have lengths and , respectively. On a standard unit-cost word RAM with
word size we present an algorithm using time When is
short, namely, or this
improves the previously best known time bounds for the problem. The result is
achieved using a novel implementation of the Landau-Vishkin algorithm based on
tabulation and word-level parallelism.Comment: To appear in Theory of Computing System
Star-galaxy separation strategies for WISE-2MASS all-sky infrared galaxy catalogs
We combine photometric information of the WISE and 2MASS all-sky infrared
databases, and demonstrate how to produce clean and complete galaxy catalogs
for future analyses. Adding 2MASS colors to WISE photometry improves
star-galaxy separation efficiency substantially at the expense of loosing a
small fraction of the galaxies. We find that 93% of the WISE objects within
W1<15.2 mag have a 2MASS match, and that a class of supervised machine learning
algorithms, Support Vector Machines (SVM), are efficient classifiers of objects
in our multicolor data set. We constructed a training set from the SDSS
PhotoObj table with known star-galaxy separation, and determined redshift
distribution of our sample from the GAMA spectroscopic survey. Varying the
combination of photometric parameters input into our algorithm we show that W1
- J is a simple and effective star-galaxy separator, capable of producing
results comparable to the multi-dimensional SVM classification. We present a
detailed description of our star-galaxy separation methods, and characterize
the robustness of our tools in terms of contamination, completeness, and
accuracy. We explore systematics of the full sky WISE-2MASS galaxy map, such as
contamination from Moon glow. We show that the homogeneity of the full sky
galaxy map is improved by an additional J<16.5 mag flux limit. The all-sky
galaxy catalog we present in this paper covers 21,200 sq. degrees with dusty
regions masked out, and has an estimated stellar contamination of 1.2% and
completeness of 70.1% among 2.4 million galaxies with .
WISE-2MASS galaxy maps with well controlled stellar contamination will be
useful for spatial statistical analyses, including cross correlations with
other cosmological random fields, such as the Cosmic Microwave Background. The
same techniques also yield a statistically controlled sample of stars as well.Comment: 10 pages, 11 figures. Accepted for publication in MNRA
Scan matching by cross-correlation and differential evolution
Scan matching is an important task, solved in the context of many high-level problems including pose estimation, indoor localization, simultaneous localization and mapping and others. Methods that are accurate and adaptive and at the same time computationally efficient are required to enable location-based services in autonomous mobile devices. Such devices usually have a wide range of high-resolution sensors but only a limited processing power and constrained energy supply. This work introduces a novel high-level scan matching strategy that uses a combination of two advanced algorithms recently used in this field: cross-correlation and differential evolution. The cross-correlation between two laser range scans is used as an efficient measure of scan alignment and the differential evolution algorithm is used to search for the parameters of a transformation that aligns the scans. The proposed method was experimentally validated and showed good ability to match laser range scans taken shortly after each other and an excellent ability to match laser range scans taken with longer time intervals between them.Web of Science88art. no. 85
Edge Potential Functions (EPF) and Genetic Algorithms (GA) for Edge-Based Matching of Visual Objects
Edges are known to be a semantically rich representation of the contents of a digital image. Nevertheless, their use in practical applications is sometimes limited by computation and complexity constraints. In this paper, a new approach is presented that addresses the problem of matching visual objects in digital images by combining the concept of Edge Potential Functions (EPF) with a powerful matching tool based on Genetic Algorithms (GA). EPFs can be easily calculated starting from an edge map and provide a kind of attractive pattern for a matching contour, which is conveniently exploited by GAs. Several tests were performed in the framework of different image matching applications. The results achieved clearly outline the potential of the proposed method as compared to state of the art methodologies. (c) 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works
- …