30,478 research outputs found
The Data Big Bang and the Expanding Digital Universe: High-Dimensional, Complex and Massive Data Sets in an Inflationary Epoch
Recent and forthcoming advances in instrumentation, and giant new surveys,
are creating astronomical data sets that are not amenable to the methods of
analysis familiar to astronomers. Traditional methods are often inadequate not
merely because of the size in bytes of the data sets, but also because of the
complexity of modern data sets. Mathematical limitations of familiar algorithms
and techniques in dealing with such data sets create a critical need for new
paradigms for the representation, analysis and scientific visualization (as
opposed to illustrative visualization) of heterogeneous, multiresolution data
across application domains. Some of the problems presented by the new data sets
have been addressed by other disciplines such as applied mathematics,
statistics and machine learning and have been utilized by other sciences such
as space-based geosciences. Unfortunately, valuable results pertaining to these
problems are mostly to be found only in publications outside of astronomy. Here
we offer brief overviews of a number of concepts, techniques and developments,
some "old" and some new. These are generally unknown to most of the
astronomical community, but are vital to the analysis and visualization of
complex datasets and images. In order for astronomers to take advantage of the
richness and complexity of the new era of data, and to be able to identify,
adopt, and apply new solutions, the astronomical community needs a certain
degree of awareness and understanding of the new concepts. One of the goals of
this paper is to help bridge the gap between applied mathematics, artificial
intelligence and computer science on the one side and astronomy on the other.Comment: 24 pages, 8 Figures, 1 Table. Accepted for publication: "Advances in
Astronomy, special issue "Robotic Astronomy
Unsupervised Learning of Edges
Data-driven approaches for edge detection have proven effective and achieve
top results on modern benchmarks. However, all current data-driven edge
detectors require manual supervision for training in the form of hand-labeled
region segments or object boundaries. Specifically, human annotators mark
semantically meaningful edges which are subsequently used for training. Is this
form of strong, high-level supervision actually necessary to learn to
accurately detect edges? In this work we present a simple yet effective
approach for training edge detectors without human supervision. To this end we
utilize motion, and more specifically, the only input to our method is noisy
semi-dense matches between frames. We begin with only a rudimentary knowledge
of edges (in the form of image gradients), and alternate between improving
motion estimation and edge detection in turn. Using a large corpus of video
data, we show that edge detectors trained using our unsupervised scheme
approach the performance of the same methods trained with full supervision
(within 3-5%). Finally, we show that when using a deep network for the edge
detector, our approach provides a novel pre-training scheme for object
detection.Comment: Camera ready version for CVPR 201
Object Proposals for Text Extraction in the Wild
Object Proposals is a recent computer vision technique receiving increasing
interest from the research community. Its main objective is to generate a
relatively small set of bounding box proposals that are most likely to contain
objects of interest. The use of Object Proposals techniques in the scene text
understanding field is innovative. Motivated by the success of powerful while
expensive techniques to recognize words in a holistic way, Object Proposals
techniques emerge as an alternative to the traditional text detectors.
In this paper we study to what extent the existing generic Object Proposals
methods may be useful for scene text understanding. Also, we propose a new
Object Proposals algorithm that is specifically designed for text and compare
it with other generic methods in the state of the art. Experiments show that
our proposal is superior in its ability of producing good quality word
proposals in an efficient way. The source code of our method is made publicly
available.Comment: 13th International Conference on Document Analysis and Recognition
(ICDAR 2015
Gray Image extraction using Fuzzy Logic
Fuzzy systems concern fundamental methodology to represent and process
uncertainty and imprecision in the linguistic information. The fuzzy systems
that use fuzzy rules to represent the domain knowledge of the problem are known
as Fuzzy Rule Base Systems (FRBS). On the other hand image segmentation and
subsequent extraction from a noise-affected background, with the help of
various soft computing methods, are relatively new and quite popular due to
various reasons. These methods include various Artificial Neural Network (ANN)
models (primarily supervised in nature), Genetic Algorithm (GA) based
techniques, intensity histogram based methods etc. providing an extraction
solution working in unsupervised mode happens to be even more interesting
problem. Literature suggests that effort in this respect appears to be quite
rudimentary. In the present article, we propose a fuzzy rule guided novel
technique that is functional devoid of any external intervention during
execution. Experimental results suggest that this approach is an efficient one
in comparison to different other techniques extensively addressed in
literature. In order to justify the supremacy of performance of our proposed
technique in respect of its competitors, we take recourse to effective metrics
like Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal to Noise
Ratio (PSNR).Comment: 8 pages, 5 figures, Fuzzy Rule Base, Image Extraction, Fuzzy
Inference System (FIS), Membership Functions, Membership values,Image coding
and Processing, Soft Computing, Computer Vision Accepted and published in
IEEE. arXiv admin note: text overlap with arXiv:1206.363
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