323,363 research outputs found
Volumetric Super-Resolution of Multispectral Data
Most multispectral remote sensors (e.g. QuickBird, IKONOS, and Landsat 7
ETM+) provide low-spatial high-spectral resolution multispectral (MS) or
high-spatial low-spectral resolution panchromatic (PAN) images, separately. In
order to reconstruct a high-spatial/high-spectral resolution multispectral
image volume, either the information in MS and PAN images are fused (i.e.
pansharpening) or super-resolution reconstruction (SRR) is used with only MS
images captured on different dates. Existing methods do not utilize temporal
information of MS and high spatial resolution of PAN images together to improve
the resolution. In this paper, we propose a multiframe SRR algorithm using
pansharpened MS images, taking advantage of both temporal and spatial
information available in multispectral imagery, in order to exceed spatial
resolution of given PAN images. We first apply pansharpening to a set of
multispectral images and their corresponding PAN images captured on different
dates. Then, we use the pansharpened multispectral images as input to the
proposed wavelet-based multiframe SRR method to yield full volumetric SRR. The
proposed SRR method is obtained by deriving the subband relations between
multitemporal MS volumes. We demonstrate the results on Landsat 7 ETM+ images
comparing our method to conventional techniques.Comment: arXiv admin note: text overlap with arXiv:1705.0125
Offline Arabic Handwriting Recognition Using Artificial Neural Network
The ambition of a character recognition system is to transform a text
document typed on paper into a digital format that can be manipulated by word
processor software Unlike other languages, Arabic has unique features, while
other language doesn't have, from this language these are seven or eight
language such as ordo, jewie and Persian writing, Arabic has twenty eight
letters, each of which can be linked in three different ways or separated
depending on the case. The difficulty of the Arabic handwriting recognition is
that, the accuracy of the character recognition which affects on the accuracy
of the word recognition, in additional there is also two or three from for each
character, the suggested solution by using artificial neural network can solve
the problem and overcome the difficulty of Arabic handwriting recognition.Comment: Submitted to Journal of Computer Science and Engineering, see
http://sites.google.com/site/jcseuk/volume-1-issue-1-may-201
A Study of Sindhi Related and Arabic Script Adapted languages Recognition
A large number of publications are available for the Optical Character
Recognition (OCR). Significant researches, as well as articles are present for
the Latin, Chinese and Japanese scripts. Arabic script is also one of mature
script from OCR perspective. The adaptive languages which share Arabic script
or its extended characters; still lacking the OCRs for their language. In this
paper we present the efforts of researchers on Arabic and its related and
adapted languages. This survey is organized in different sections, in which
introduction is followed by properties of Sindhi Language. OCR process
techniques and methods used by various researchers are presented. The last
section is dedicated for future work and conclusion is also discussed.Comment: 11 pages, 8 Figures, Sindh Univ. Res. Jour. (Sci. Ser.
Advances in Human Action Recognition: A Survey
Human action recognition has been an important topic in computer vision due
to its many applications such as video surveillance, human machine interaction
and video retrieval. One core problem behind these applications is
automatically recognizing low-level actions and high-level activities of
interest. The former is usually the basis for the latter. This survey gives an
overview of the most recent advances in human action recognition during the
past several years, following a well-formed taxonomy proposed by a previous
survey. From this state-of-the-art survey, researchers can view a panorama of
progress in this area for future research
A New Approach in Persian Handwritten Letters Recognition Using Error Correcting Output Coding
Classification Ensemble, which uses the weighed polling of outputs, is the
art of combining a set of basic classifiers for generating high-performance,
robust and more stable results. This study aims to improve the results of
identifying the Persian handwritten letters using Error Correcting Output
Coding (ECOC) ensemble method. Furthermore, the feature selection is used to
reduce the costs of errors in our proposed method. ECOC is a method for
decomposing a multi-way classification problem into many binary classification
tasks; and then combining the results of the subtasks into a hypothesized
solution to the original problem. Firstly, the image features are extracted by
Principal Components Analysis (PCA). After that, ECOC is used for
identification the Persian handwritten letters which it uses Support Vector
Machine (SVM) as the base classifier. The empirical results of applying this
ensemble method using 10 real-world data sets of Persian handwritten letters
indicate that this method has better results in identifying the Persian
handwritten letters than other ensemble methods and also single
classifications. Moreover, by testing a number of different features, this
paper found that we can reduce the additional cost in feature selection stage
by using this method.Comment: Journal of Advances in Computer Researc
Fonts design-shapes processing of text information structures in process of non-invasive data acquisition
Computer fonts can be one of solutions supporting a protection of information
against electromagnetic penetration. This solution is called -Soft TEMPEST-.
However, not every font has features which counteract the process of
electromagnetic infiltration. The distinctive features of characters of font
determine it. This article presents two sets of new computer fonts. These fonts
are fully usable in everyday work. Simultaneously they make it impossible to
obtain information using the non-invasive method. Names of these fonts are
directly related to the shapes of the characters. Each character of these fonts
is built only with vertical and horizontal lines. The differences between them
consist in the different widths of the vertical lines. The Symmetrical Safe
font is built from vertical lines with the same widths. The Asymmetrical Safe
font is built from vertical lines with two different widths of lines. However,
the appropriate proportions of the widths of the lines and clearances of each
character of the safe font have to be met.Comment: 13 pages, 14 figure
Appearance Descriptors for Person Re-identification: a Comprehensive Review
In video-surveillance, person re-identification is the task of recognising
whether an individual has already been observed over a network of cameras.
Typically, this is achieved by exploiting the clothing appearance, as classical
biometric traits like the face are impractical in real-world video surveillance
scenarios. Clothing appearance is represented by means of low-level
\textit{local} and/or \textit{global} features of the image, usually extracted
according to some part-based body model to treat different body parts (e.g.
torso and legs) independently. This paper provides a comprehensive review of
current approaches to build appearance descriptors for person
re-identification. The most relevant techniques are described in detail, and
categorised according to the body models and features used. The aim of this
work is to provide a structured body of knowledge and a starting point for
researchers willing to conduct novel investigations on this challenging topic
Multistage Hybrid Arabic/Indian Numeral OCR System
The use of OCR in postal services is not yet universal and there are still
many countries that process mail sorting manually. Automated Arabic/Indian
numeral Optical Character Recognition (OCR) systems for Postal services are
being used in some countries, but still there are errors during the mail
sorting process, thus causing a reduction in efficiency. The need to
investigate fast and efficient recognition algorithms/systems is important so
as to correctly read the postal codes from mail addresses and to eliminate any
errors during the mail sorting stage. The objective of this study is to
recognize printed numerical postal codes from mail addresses. The proposed
system is a multistage hybrid system which consists of three different feature
extraction methods, i.e., binary, zoning, and fuzzy features, and three
different classifiers, i.e., Hamming Nets, Euclidean Distance, and Fuzzy Neural
Network Classifiers. The proposed system, systematically compares the
performance of each of these methods, and ensures that the numerals are
recognized correctly. Comprehensive results provide a very high recognition
rate, outperforming the other known developed methods in literature.Comment: IEEE Publication format, International Journal of Computer Science
and Information Security, IJCSIS, Vol. 8 No. 1, April 2010, USA. ISSN 1947
5500, http://sites.google.com/site/ijcsis
Manifold Regularized Slow Feature Analysis for Dynamic Texture Recognition
Dynamic textures exist in various forms, e.g., fire, smoke, and traffic jams,
but recognizing dynamic texture is challenging due to the complex temporal
variations. In this paper, we present a novel approach stemmed from slow
feature analysis (SFA) for dynamic texture recognition. SFA extracts slowly
varying features from fast varying signals. Fortunately, SFA is capable to
leach invariant representations from dynamic textures. However, complex
temporal variations require high-level semantic representations to fully
achieve temporal slowness, and thus it is impractical to learn a high-level
representation from dynamic textures directly by SFA. In order to learn a
robust low-level feature to resolve the complexity of dynamic textures, we
propose manifold regularized SFA (MR-SFA) by exploring the neighbor
relationship of the initial state of each temporal transition and retaining the
locality of their variations. Therefore, the learned features are not only
slowly varying, but also partly predictable. MR-SFA for dynamic texture
recognition is proposed in the following steps: 1) learning feature extraction
functions as convolution filters by MR-SFA, 2) extracting local features by
convolution and pooling, and 3) employing Fisher vectors to form a video-level
representation for classification. Experimental results on dynamic texture and
dynamic scene recognition datasets validate the effectiveness of the proposed
approach.Comment: 12 page
A Hybrid NN/HMM Modeling Technique for Online Arabic Handwriting Recognition
In this work we propose a hybrid NN/HMM model for online Arabic handwriting
recognition. The proposed system is based on Hidden Markov Models (HMMs) and
Multi Layer Perceptron Neural Networks (MLPNNs). The input signal is segmented
to continuous strokes called segments based on the Beta-Elliptical strategy by
inspecting the extremum points of the curvilinear velocity profile. A neural
network trained with segment level contextual information is used to extract
class character probabilities. The output of this network is decoded by HMMs to
provide character level recognition. In evaluations on the ADAB database, we
achieved 96.4% character recognition accuracy that is statistically
significantly important in comparison with character recognition accuracies
obtained from state-of-the-art online Arabic systems.
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