• Journal of KIISE:Computing Practices and Letters
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• v.14 no.3
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• pp.291-295
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• 2008

In this paper, we propose an input image reduction method to solve the problems of Radon transform which is a line structure analysis tool to correct a rotated object through a vision system. First we extract an object image removed background from the input image. Then we also select a reduced object image as a final input mage of Radon transform from the object image by considering slope. Finally we extract a rotated angle by using Radon transform with the final input image and correct the rotated object with the angle. In experimental results, we could improve the process time of about 64%, reduce the memory space of about 18% and make progress the line detection rate of about 18%.

• Journal of Platform Technology
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• v.8 no.1
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• pp.16-23
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• 2020

For protecting copyright for trademark, convolutional neural network can be used to confirm genuine of trademark image. For this, repeated training one trademark image degrades the performance of machine learning because of overfitting problem. Therefore, this type of machine learning application generates training data in various way. But if genuine trademark image is rotated, this image is classified as not genuine trademark. In this paper, we propose the way for extending training data to confirm genuine of trademark image which is rotated. Our proposed way generates rotated image from genuine trademark image as training data. To show effectiveness of our proposed way, we use CNN machine learning model, and evaluate the accuracy with test image. From evaluation result, our way can be used to generate training data for machine learning application which confirms genuine of rotated trademark image.

• Journal of Korea Multimedia Society
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• v.18 no.1
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• pp.17-24
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• 2015

In this paper, a novel scheme for correcting rotated region in medical images using SIFT(Scale Invariant Feature Transform) algorithm is presented. Using the feature extraction function of SIFT, the rotation angle of rotated object in medical images is calculated as follows. First, keypoints of both reference and rotated medical images are extracted by SIFT. Second, the matching process is performed to the keypoints located at the predetermined ROI(Region Of Interest) at which objects are not cropped or added by rotating the image. Finally, degrees of matched keypoints are calculated and the rotation angle of the rotated object is determined by averaging the difference of the degrees. The simulation results show that the proposed scheme has excellent performance for correcting the rotated region in medical images.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.405-408
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• 2010

This paper presents a new feature extraction technique, correlation coefficient and Manhattan distance (MD) based method for recognition of rotated object in an image. This paper also represented a new concept of intensity invariant. We extracted global features of an image and converts a large size image into a one-dimensional vector called circular feature vector's (CFVs). An especial advantage of the proposed technique is that the extracted features are same even if original image is rotated with rotation angles 1 to 360 or rotated. The proposed technique is based on fuzzy sets and finally we have recognized the object by using histogram matching, correlation coefficient and manhattan distance of the objects. The proposed approach is very easy in implementation and it has implemented in Matlab7 on Windows XP. The experimental results have demonstrated that the proposed approach performs successfully on a variety of small as well as large scale rotated images.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.67-73
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• 2011

The image retrieval generally shows the same or similar images to a query image as a result. In the case of rotated image, however, its performance tends to be debased significantly. We propose a method to ensure a reliable image retrieval of rotated images as follows; First, to obtain feature points of query/DB images by Harris Corner Detector; and then, utilizing the feature points, to find the object's axis and query/DB images into rotation invariant images with Principal Components Analysis algorithm. We have experimented with 6,000 natural images which are 256 pixels in diameter. They are 1,000 Wang's images and their rotated images by $30^{\circ}$, $45^{\circ}$, $90^{\circ}$, $135^{\circ}$ and $180^{\circ}$. The simulation results show that the proposed method retrieves rotated images more effectively than the conventional method.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.20 no.2
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• pp.219-225
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• 1990

Panoramic radiography is convenient in clinic and visualizes those areas which other technique do not give. But the technique has limitation of image distortion which results from the relationship of the ramus to the focal trough and from the direction of the central ray. This study is, using 7 dry skulls, to determine the effect of rotation of patient's head on reducing those distortion and determine the magnification ratio of images of mandibular condyle in rotated patient head position. The obtained results were as follows: 1. Generally, in panoramic radiography the anterolateral portion of the mandibular condyle was best to be visualized. 2. There are no significant difference between the image readability of anteromedial portion and that of anterocentral portion of the mandibular condyle. 3. Anterolateral portion of the mandibular condyle was better visualized in rotated head position by 20 degree or horizontal condylar inclination than in conventional position or in rotated head position by 10 degree. 4. The magnification ratio of the anteroposterior diameter in the image of mandibular condyle was least in the rotated head position by horizontal inclination of the mandibular condyle and was largest by 20 degree.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.31-33
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• 2009

Face detection using AdaBoost algorithm is capable of processing images rapidly while having high detection rates. It seemed to be the fastest and the most robust and it is still today. Many improvements or extensions of this method have been proposed. However, previous approaches only deal with upright faces. They suffer from limited discriminant capability for rotated faces as these methods apply the same features for both upright and rotated faces. To solve this problem, it is necessary that we rotate input images or make independently trained detectors. However, this can be slow and can require a lot of training data, since each classifier requires the computation of many different image features. This paper proposes a robust algorithm for finding rotated faces within an image. It reduces the computational and sample complexity, by finding common features that can be shared across the classes. And it will be able to apply with multi-class object detection.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.6
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• pp.800-807
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• 2016

We propose a fast algorithm using Extreme Contour Point (ECP) to detect the angle of rotated images, is implemented by rotation feature of one covered frame image that can be applied to correct the rotated images like in image processing for real time applications, while CORDIC is inefficient to calculate various points like high definition image since it is only possible to detect rotated angle between one point and the other point. The two advantages of this algorithm, namely compatibility to images in preprocessing by using Sobel edge process for pattern recognition. While the other one is its simplicity for rotated angle detection with cyclic shift of two $1{\times}n$ matrix set without complexity in calculation compared with CORDIC algorithm. In ECP, the edge features of the sample image of gray scale were determined using the Sobel Edge Process. Then, it was subjected to binary code conversion of 0 or 1 with circular boundary to constitute the rotation in invariant conditions. The results were extracted to extreme points of the binary image. Its components expressed not just only the features of angle ${\theta}$ but also the square of radius $r^2$ from the origin of the image. The detected angle of this algorithm is limited only to an angle below 10 degrees but it is appropriate for real time application because it can process a 200 degree with an assumption 20 frames per second. ECP algorithm has an O ($n^2$) in Big O notation that improves the execution time about 7 times the performance if CORDIC algorithm is used.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.10
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• pp.1554-1560
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• 1989

This paper suggests the algorihtm which extracts the classification parameter relative to the only feature of document patterns even though they are rotated or scaled, and also classifies them. With the complex logarithmic conformal mapping, the sample of the document pattern image makes the pattern image of the complex logarithmic plane. Because the power spectrum of this plane is invariant to the rotation, and scale of the pattern image, it is used as the characteristics parameter of the patten image. By using the coherence function, this method analyzes the standard and input power spectrum. additionally, it classifies the input pattern image. Even though input image is rotated, our algorithm can classify it without reference to the rotation, and this is possible when the scale is in the range of 0.5-1.5.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.746-751
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• 2003

A method of measuring the length of defects on the wall of the spent nuclear fuel pool using the image processing and a laser slit beam is proposed. Since the defect monitoring camera is suspended by a crane and hinged to the crane hook, the camera viewing direction can not be adjusted to the orientation that is exactly perpendicular to the wall. Thus, the image taken by the camera, which is horizontally rotated along the axis of the camera supporting beam, is distorted and thus, the precise length can not be measured. In this paper, by using the LASER slit beam generator, the horizontally rotated angle of the camera is estimated. Once the angle is obtained, the distorted image can be easily reconstructed to the image normal to the wall. The estimation algorithm adopts a 3-dimensional coordinate transformation of the image plane where both the laser slit beam and the original image of the defects exist. The estimation equation is obtained by using the information of the beam projected on the wall and the parameters of this equation are experimentally obtained. With this algorithm, the original image of the defect taken at arbitrary rotated angle can be reconstructed to an image normal to the wall. From the result of a series of experiments, the accuracy of the defect is measured within 0.6 and 1.3 % error bound of real defect size in the air and underwater, respectively under 30 degree of the inclined angle of the laser slit beam generator. Also, the error increases as the inclined angle increases upto 60 degree. Over this angle, the defect length can not be measured since the defect image disappears. The proposed algorithm enables the accurate measurement of the defect length only by using a single camera and a laser slit beam.