• Proceedings of the KIEE Conference
• /
• 2004.11c
• /
• pp.165-168
• /
• 2004

This paper propose a target tracker using phase correlation. The tracker consist of a pre-processing module, a translation estimation module based on phase correlation, a fine motion estimation module applied when confidence rate could not fulfill a threshold value and a reference image update module. The fine motion estimation module measure the shift, rotation and scale of input image compared to reference using Fourier-Mellin transform. Proposed tracker was tested its accuracy and robustness using some real indoor and outdoor image sequences.

• International Journal of Precision Engineering and Manufacturing
• /
• v.9 no.4
• /
• pp.45-50
• /
• 2008

The Fourier-Mellin transform is the theoretical basis for the translation, rotation, and scale invariance of an image. However, its implementation requires a log-polar map of the original image, which requires logarithmic sampling of a radial variable in that image. This means that the mapping process is accompanied by considerable loss of data. To solve this problem, we propose a dual log-polar map that uses both a forward image map and a reverse image map simultaneously. Data loss due to the forward map sub-sampling can be offset by the reverse map. This is the first step in creating an invertible log-polar map. Experimental results have demonstrated the effectiveness of the proposed scheme.

• Journal of KIISE:Computer Systems and Theory
• /
• v.28 no.12
• /
• pp.653-659
• /
• 2001

In this paper, we propose an efficient algorithm for constructing cylindrical panoramic image. At first, we describe a fast image alignment algorithm, which matches image strips located on equal distance for image centers. And then, we explain how to estimate accurately the effective focal length of camera by a bisection method. Although there is a limitation in that the image should be taken by a camera with pure panning motion, the proposed simple and fast algorithm is applicable to practical application.

• Journal of Korea Multimedia Society
• /
• v.18 no.12
• /
• pp.1453-1461
• /
• 2015

Since digital images and videos are rapidly increasing in the internet with the spread of mobile computers and smartphones, research on image retrieval has gained tremendous momentum. Color, shape, and texture are major features used in image retrieval. Especially, color information has been widely used in image retrieval, because it is robust in translation, rotation, and a small change of camera view. This paper proposes a new method for histogram refinement based on local color difference. Firstly, the proposed method converts a RGB color image into a HSV color image. Secondly, it reduces the size of color space from 256³ to 32. It classifies pixels in the 32-color image into three groups according to the color difference between a central pixel and its neighbors in a 3x3 local region. Finally, it makes a color difference vector(CDV) representing three refined color histograms, then image retrieval is performed by the CDV matching. The experimental results using public image database show that the proposed method has higher retrieval accuracy than other conventional ones. They also show that the proposed method can be effectively applied to search low resolution images such as thumbnail images.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.21 no.5
• /
• pp.171-182
• /
• 2022

The safe use of a structure requires it to be maintained in an undamaged state. Thus, a typical factor that determines the safety of a structure is a crack in it. In addition, cracks are caused by various reasons, damage the structure in various ways, and exist in different shapes. Making matters worse, if these cracks are unattended, the risk of structural failure increases and proceeds to a catastrophe. Hence, recently, methods of checking structural damage using deep learning and computer vision technology have been introduced. These methods usually have the premise that there should be a large amount of training image data. However, the amount of training image data is always insufficient. Particularly, this insufficiency negatively affects the performance of deep learning crack detection algorithms. Hence, in this study, a method of augmenting crack image data based on the image translation technique was developed. In particular, this method obtained the crack image data for training a deep learning neural network model by transforming a specific case of a asphalt crack image into a concrete crack image or vice versa . Eventually, this method expected that a robust crack detection algorithm could be developed by increasing the diversity of its training data.

• Proceedings of the IEEK Conference
• /
• 2008.06a
• /
• pp.999-1000
• /
• 2008

Image Registration is used many works in image processing widely. But It is difficult to find the accuracy informations such as translation, rotation, and scaling between images. This paper proposes an algorithm that Fourier based image registration using the pyramid edge detection and line fitting. It can be estimated the informations by each sub-pixels. The proposed algorithm can be used for image registrations which high efficiency is required such as GIS, or MRI, CT, image mosaicing, weather forecasting, etc.

• Journal of KIISE:Software and Applications
• /
• v.27 no.1
• /
• pp.62-73
• /
• 2000

In this paper, we propose a criteria which can be applied to classify conventional color feature based Content Based Image Retrieval (CBIR) methods with its application areas, and a new image retrieval method which can represent sufficient spatial information in the image and is powerful in invariant searching to translation, rotation and enlargement transform. As the conventional color feature based CBIR methods can not sufficiently include the spatial information in the image, in general, they have drawbacks, which are weak to the translation or rotation, enlargement transform. To solve it, they have represented the spatial information by partitioning the image. Retrieval efficiency, however, is decreased rapidly as increasing the number of the feature vectors. We classify conventional methods to ones using 1st order relations and ones using 2nd order relations as their color object relation, and propose a new method using 3rd order relation of color objects which is good for the translation, rotation and enlargement transform. It makes quantized 24 buckets and selects 3 high scored histogram buckets and calculates 3 mean positions of pixels in 3 buckets and 3 angles. Then, it uses them as feature vectors of a given image. Experiments show that the proposed method is especially good at enlarged images and effective for its small calculation.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.48 no.4
• /
• pp.67-75
• /
• 2011

This paper presents a scale and rotation-invariant gait recognition method using R-transform, which is computed by projecting squared coefficients of Radon transform. Since R-transform is invariant to translation, rotation, and scaling, it particularly suitable for extracting object poses without camera calibration. Coefficients of R-transform are used to compute correlation, and the maximum correlation value determines the similarity between two gait images. The proposed method requires neither camera calibration nor geometric compensation, and as a result, it makes robust gait recognition possible without additional compensation for translation, rotation, and scaling.

• Korean Journal of Remote Sensing
• /
• v.30 no.5
• /
• pp.547-557
• /
• 2014

This paper presents a method for designing circular template used in similarity measurement for image registration. Circular template has translation and rotation invariant property, which results in correct matching of control points for image registration under the condition of translation and rotation between reference and sensed images. Circular template consisting of the pixels located on the multiple circumferences of the circles whose radii vary from zero to a certain distance, is converted to two-dimensional Discrete Polar Coordinate Matrix (DPCM), whose elements are the pixels of the circular template. For sensed image, the same type of circular template and DPCM are created by rotating the circular template repeatedly by a certain degree in the range between 0 and 360 degrees and then similarity is calculated using mutual information of the two DPCMs. The best match is determined when the mutual information for each rotation angle at each pixel in search area is maximum. The proposed algorithm was tested using KOMPSAT-2 images acquired at two different times and the results indicate high accurate matching performance under image rotation.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.19 no.4
• /
• pp.44-50
• /
• 2011

This paper presents the algorithm for attitude determination of an aircraft using binary image. An image feature vector, which is invariant to translation, scale and rotation, is constructed to capture the functional relations between the feature vector and the corresponding aircraft attitude. An iterated least squares method is suggested for estimating the attitude of given aircraft using the constructed feature vector library. Simulation results show that the proposed algorithm yields good estimates of aircraft attitude in most viewing range, although a relatively large error occurs in some limited viewing direction.