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.
Article In Press: 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 (PCA), Kernel Principal Component Analysis (KPCA) and independent component analysis (ICA). In most of the cases the present method performs same or even better than the other methods.
Article In Press: 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.
Published Online: An Efficient Skew Estimation Technique for Scanned Documents: An Application of Piece-Wise Painting Algorithm
JPRR Article #635
An efficient skew estimation technique based on Piece-wise Painting Algorithm (PPA) for scanned documents is presented. In PPA, the input image is decomposed into vertical stripes and the gray value of each pixel in each row of a stripe is modified to the average gray value of all pixels present in that row of the stripe. The resultant gray-scale image is then converted into two-tone image which is called painted image. In this research work, we, at first, employ the PPA on the document image horizontally and vertically. Applying the PPA on both directions, two painted images (horizontally and vertically) are obtained. Next, based on statistical analysis some regions from horizontally or vertically painted images are selected. Top (left), middle (middle) and bottom (right) points of horizontal or vertical selected regions are identified in 6 separate lists. Utilizing linear regression of the selected points, a few fit lines are drawn. A voting approach based on statistical mode of angles obtained from fit lines is also proposed to find the best-fit line amongst all the lines and the skew angle of the document image is estimated from the slope of the best-fit line. The proposed technique was tested extensively on three different datasets containing various categories of documents. Comparisons of the results are made with recently published methods when the same datasets are used. Experimental results showed that the proposed technique achieved more accurate results than the state-of-the-art methods.
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Vol 11, No 1 (2016)
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
Vol 10, No 1 (2015)
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