• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.4
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• pp.370-380
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• 2002

This paper proposes an ATR (Automatic Target Recognition) algorithm for identifying non-occluded and occluded military vehicles in natural FLIR (Forward Looking InfraRed) images. After segmenting a target, a radial function is defined from the target boundary to extract global shape features. Also, to extract local shape features of upper region of a target, a distance function is defined from boundary points and a line between two extreme points. From two functions and target contour, four global and four local shape features are proposed. They are much more invariant to translation, rotation and scale transform than traditional feature sets. In the experiments, we show that the proposed feature set is superior to the traditional feature sets with respect to the similarity-transform invariance and recognition performance.

• Journal of Advanced Navigation Technology
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• v.16 no.2
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• pp.394-400
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• 2012

SURF is a robust local invariant feature descriptor and has been used in many applications such as object recognition. Even though this algorithm has similar matching accuracy compared to the SIFT, which is another popular feature extraction algorithm, it has advantage in matching time. However, these descriptors do not consider relative location information of extracted interesting points to guarantee rotation invariance. Also, since they use gray image of original color image, they do not use the color information of images, either. In this paper, we propose a method for improving matching performance of SURF descriptor using the color and relative location information of interest points. The location information is built from the angles between the line connecting the centers of interest points and the orientation line constructed for the center of each interest points. For the color information, color histogram is constructed for the region of each interest point. We show the performance of our scheme through experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4467-4486
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• 2018

Trying to deal with the problem of low robustness of Copy-Move Forgery Detection (CMFD) under various transformation and degradation attacks, a novel CMFD method is proposed in this paper. The main advantages of proposed work include: (1) Discrete Analytical Fourier-Mellin Transform (DAFMT) and Locality Sensitive Hashing (LSH) are combined to extract the block features and detect the potential copy-move pairs; (2) The Euclidian distance is incorporated in the pixel variance to filter out the false potential copy-move pairs in the post-verification step. In addition to extracting the effective features of an image block, the DAMFT has the properties of rotation and scale invariance. Unlike the traditional lexicographic sorting method, LSH is robust to the degradations of Gaussian noise and JEPG compression. Because most of the false copy-move pairs locate closely to each other in the spatial domain or are in the homogeneous regions, the Euclidian distance and pixel variance are employed in the post-verification step. After evaluating the proposed method by the precision-recall-$F_1$ model quantitatively based on the Image Manipulation Dataset (IMD) and Copy-Move Hard Dataset (CMHD), our method outperforms Emam et al.'s and Li et al.'s works in the recall and $F_1$ aspects.

• Communications for Statistical Applications and Methods
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• v.30 no.6
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• pp.631-652
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• 2023

In the two-condition within-subject mediation design, pairs of variables such as mediator and outcome are observed under two treatment conditions. The main objective of the design is to investigate the indirect effects of the condition difference (sum) on the outcome difference (sum) through the mediator difference (sum) for comparison of two treatment conditions. The natural condition variables mean the original variables, while the rotated condition variables mean the difference and the sum of two natural variables. The outcome difference (sum) is expressed as a linear model regressed on two natural (rotated) mediators as a parallel two-mediator design in two condition approaches: the natural condition approach uses regressors as the natural condition variables, while the rotated condition approach uses regressors as the rotated condition variables. In each condition approach, the total indirect effect on the outcome difference (sum) can be expressed as the sum of two individual indirect effects: within- and cross-condition indirect effects. The total indirect effects on the outcome difference (sum) for both condition approaches are the same. The invariance of the total indirect effect makes it possible to analyze the nature of two pairs of individual indirect effects induced from the natural conditions and the rotated conditions. The two-condition within-subject design is extended to the addition of a between-subject moderator. Probing of the conditional indirect effects given the moderator values is implemented by plotting the bootstrap confidence intervals of indirect effects against the moderator values. The expected indirect effect with respect to the moderator is derived to provide the overall effect of moderator on the indirect effect. The model coefficients are estimated by the structural equation modeling approach and their statistical significance is tested using the bias-corrected bootstrap confidence intervals. All procedures are evaluated using function lavaan() of package {lavaan} in R.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.187-195
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• 2013

Most of former studies for car license plate detection restrict the image acquisition environment. The aim of this research is to diminish the restrictions by proposing a new method of using SIFT and neural network. SIFT can be used in diverse situations with less restriction because it provides size- and rotation-invariance and large discriminating power. SIFT extracted from the license plate image is divided into the internal(inside class) and the external(outside class) ones and the classifier is trained using them. In the proposed method, by just putting the various types of license plates, the trained neural network classifier can process all of the types. Although the classification performance is not high, the inside class appears densely over the plate region and sparsely over the non-plate regions. These characteristics create a local feature map, from which we can identify the location with the global maximum value as a candidate of license plate region. We collected image database with much less restriction than the conventional researches. The experiment and evaluation were done using this database. In terms of classification accuracy of SIFT keypoints, the correct recognition rate was 97.1%. The precision rate was 62.0% and recall rate was 50.2%. In terms of license plate detection rate, the correct recognition rate was 98.6%.