The Journal of Pattern Recognition Research (JPRR) provides an international forum for the electronic publication of high-quality research and industrial experience articles in all areas of pattern recognition, machine learning, and artificial intelligence. JPRR is committed to rigorous yet rapid reviewing. Final versions are published electronically
(ISSN 1558-884X) immediately upon acceptance.
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Published Online: Boosting Shape Classifiers Accuracy by Considering the Inverse Shape
JPRR Article #727
Many techniques exist for describing shapes. These techniques almost exclusively consider the contour or the inside of the shape; the major problem for describing the outside of a shape, or inverse shape, being that it has an infinite extension. In this paper, we show how to adapt two shape descriptors , one region based, the Cover By Rectangles, and one transform based, the Zernike moments, to be applicable to the inverse shape. We analyze their properties, and show how to deal with the infinite extension of the inverse shape. Then, we apply these descriptors to shape classification and compare representations that use the shape, its inverse, or both. Our experiments establish that, for shape classification, a representation integrating the inverse shape often outperforms a representation restricted to the shape. This opens the path for better techniques that could use, as a rule of thumb, both the representations of a shape and its inverse for the purpose of classification.
Qiaotian Li joins JPRR as an Associate Editor
Dr. LiDr. Qiaotian  Li Motorola Mobility
Qiaotian LI earned his Ph.D degree in Electrical Engineering and Computer Science. His PhD research focused on computational algorithm, machine learning, and close-range photogrammetry. Currently, He is a Principal Imaging Architect and Image Scientist at Motorola Mobility LLC, conducting and developing commercialized camera system. He consistently contributes to the Intellectual Property development process in Motorola Mobility LLC and also provides state-of-art engineering solution to worldwide mobile customers. His research interests are image processing and analysis, pattern recognition, image metrology/photogrammetry, image enhancement, color science, camera system HW/SW, numerical method, optimization, machine learning, etc. He continuously supports R&D activities of both international academic community and industrial field.
Published Online: Application of Approximate Equality for Reduction of Feature Vector Dimension
JPRR Article #639
Reduction of feature vector dimension is a problem of selecting the most informative features from an information system. Using rough set theory (RST) we can reduce the feature vector dimension when all the attribute values are crisp or discrete. For any information system or decision system, if attributes contain real-valued data, RST can not be applied directly. Fuzzy-rough set techniques may be applied on this kind of system to reduce the dimension. But, Fuzzy-rough set uses the concept of fuzzy-equivalence relation, which is not suitable to model approximate equality. In this paper we propose a new alternative method to reduce the dimension of feature vectors of a decision system where the attribute values may be discrete or real or even mixed in nature. To model approximate equality we first consider the intuitive relationship between distance measure and equality. Subsequently we fuzzify the distance measures to establish the degree of equality (or closeness) among feature vectors (objects or points). Finally we use the concept of α-cut to obtain equivalence relation based on which dimension of feature vectors can be reduced. We also compare the performance of the present method to reduce the feature vector dimension with those of principle component analysis, Kernel Principal Component Analysis and independent component analysis. In most of the cases the present method performs same or even better than the other methods.
Published Online: A Brief Review of Human Identification Using Eye Movement
JPRR Article #705
The vulnerability of conventional biometrics to spoof has caused considerable concern especially in those fields that require high reliable user identification. This heightened concern leads to great interest in assessing the probability and efficiency of using eye movements in identification systems. By applying eye movements as biometrics a new approach has been taken into human identification including all the crucial attributes of previous traditional identification that may offer certain notable advantages. The most obvious is the inherent difficulty in forging them. The purpose of this article is to review examples of researches utilizing eye movements in human identification. These studies can be divided into two groups: the first group utilizes eye movement bioelectrical signals in identification purpose and another one uses eye movement tracking in human identification.
JPRR selected for the Emerging Sources Citation Index (ESCI)
ESCI

We are pleased to announce that the Journal of Pattern Recognition Research (JPRR) has been selected for coverage in Thomson Reuters products and services. Beginning with content published in 2015, this publication will be indexed and abstracted in Emerging Sources Citation Index (ESCI).

JPRR's content in ESCI is under consideration by Thomson Reuters for inclusion in products such as the Science Citation Index Expanded™ (SCIE), the Social Sciences Citation Index® (SSCI), and the Arts & Humanities Citation Index® (AHCI).

Read the journal's most cited open-access articles online! Submit your next article to JPRR!

Vol 11, No 1 (2016)
Applications
An Efficient Skew Estimation Technique for Scanned Documents: An Application of Piece-Wise Painting Algorithm 1-14
In this paper, an effcient skew estimation technique based on iterative employment of the Piece-wise Painting Algorithm (PPA) on document images is presented.
JPRR Vol 11, No 1 (2016); doi:10.13176/11.635
A Brief Review of Human Identification Using Eye Movement 15-25
The vulnerability of conventional biometrics to spoof has caused considerable concern especially in those fields that require high reliable user identification.
JPRR Vol 11, No 1 (2016); doi:10.13176/11.705
Application of Approximate Equality for Reduction of Feature Vector Dimension 26-40
Reduction of feature vector dimension is a problem of selecting the most informative features from an information system. Using rough set theory we can reduce the feature vector dimension when all the attribute values are crisp or discrete.
JPRR Vol 11, No 1 (2016); doi:10.13176/11.639
Pattern Recognition Theory
Boosting Shape Classifiers Accuracy by Considering the Inverse Shape 41-54
Many techniques exist for describing shapes. These techniques almost exclusively consider the contour or the inside of the shape; the major problem for describing the outside of a shape, or inverse shape, being that it has an infinite extension.
JPRR Vol 11, No 1 (2016); doi:10.13176/11.727
Vol 10, No 1 (2015)
Applications
IR Contrast Enhancement Through Log-Power Histogram Modification 1-23
A simple power-logarithm histogram modification operator is proposed to enhance IR image contrast. The algorithm combines a logarithm operator that smoothes the input image histogram while retaining the relative ordering of the original bins, with a power operator that restores the smoothed histogram to an approximation of the original input histogram.
JPRR Vol 10, No 1 (2015); doi:10.13176/11.617
Dynamic Hand Gesture Recognition for Sign Words and Novel Sentence Interpretation Algorithm for Indian Sign Language Using Microsoft Kinect Sensor 24-38
Indian sign language interpretation is an important task to facilitate communication among Indian deaf community and other people.
JPRR Vol 10, No 1 (2015); doi:10.13176/11.626
Fuzzy C-Means With Local Membership Based Weighted Pixel Distance and KL Divergence for Image Segmentation 53-60
This paper presents a new technique for incorporating local membership information into the standard fuzzy C-means (FCM) clustering algorithm.
JPRR Vol 10, No 1 (2015); doi:10.13176/11.605
Multi Cameras Based Indoors Human Action Recognition Using Fuzzy Rules 61-74
In this paper, the recognition of human actions in an indoor work environment using multi cameras is proposed. HOG features learned using AdaBoost and optimized by background differencing are used to detect people, while the overlapping camera views are merged using perspective transformation.
JPRR Vol 10, No 1 (2015); doi:10.13176/11.651
Pattern Recognition Theory
Best Model Classification 39-52
This is about frames of reference for analyzing data and how the frames can be parameterized by measurements of the data. The topic is discussed in terms of a classification method that chooses among alternative frames of reference.
JPRR Vol 10, No 1 (2015); doi:10.13176/11.634