• Journal of Biomedical Engineering Research
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• v.24 no.4
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• pp.247-257
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• 2003

This paper proposes a three-dimensional (3D) segmentation algorithm for extracting a diagnostic object from ultrasound images by using a LoG operator In the proposed algorithm, 2D cutting planes are first obtained by the equiangular revolution of a cross sectional Plane on a reference axis for a 3D volume data. In each 2D ultrasound image. a region of interest (ROI) box that is included tightly in a diagnostic object of interest is set. Inside the ROI box, a LoG operator, where the value of $\sigma$ is adaptively selected by the distance between reference points and the variance of the 2D image, extracts edges in the 2D image. In Post processing. regions of the edge image are found out by region filling, small regions in the region filled image are removed. and the contour image of the object is obtained by morphological opening finally. a 3D volume of the diagnostic object is rendered from the set of contour images obtained by post-processing. Experimental results for a tumor and gall bladder volume data show that the proposed method yields on average two times reduction in error rate over Krivanek's method when the results obtained manually are used as a reference data.

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

• 한국정보디스플레이학회:학술대회논문집
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• 2009.10a
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• pp.1363-1366
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• 2009

A new recognition method is used to find the three-dimensional target object on integral imaging. For finding the location of a target image, amount of reconstructed reference image is needed. This method is giving accurate information of target image by correlated among reconstructed target images and reference images.

• Ecology and Resilient Infrastructure
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• v.8 no.2
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• pp.88-96
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• 2021

In surface image velocimetry, a wide area of a river is photographed at an angle to measure its velocity, inevitably causing image distortion. Although a distorted image can be corrected into an orthogonal image by using 2D projective coordinate transformation and considering reference points on the same plane as the water surface, this method is limited by the uncertainty of changes in the water level in the event of a flood. Therefore, in this study, we developed a tilt image correction technique that corrects distortions in oblique images without resetting the reference points while coping with changes in the water level using the geometric relationship between the coordinates of the reference points set at a high position the camera, and the vertical distance between the water surface and the camera. Furthermore, we developed a distortion correction method to verify the corrected image, wherein we conducted a full-scale river experiment to verify the reference point transformation equation and measure the surface velocity. Based on the verification results, the proposed tilt image correction method was found to be over 97% accurate, whereas the experiment result of the surface velocity differed by approximately 4% as compared to the results calculated using the proposed method, thereby indicating high accuracy. Application of the proposed method to an image-based fixed automatic discharge measurement system can improve the accuracy of discharge measurement in the event of a flood when the water level changes rapidly.

• Journal of Sensor Science and Technology
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• v.27 no.6
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• pp.357-361
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• 2018

This paper presents an averaging current adjustment technique for reducing the pixel resistance variation in a bolometer-type uncooled infrared image sensor. Each unit pixel was composed of an active pixel, a reference pixel for the averaging current adjustment technique, and a calibration circuit. The reference pixel was integrated with a polysilicon resistor using a standard complementary metal-oxide-semiconductor (CMOS) process, and the active pixel was applied from outside of the chip. The averaging current adjustment technique was designed by using the reference pixel. The entire circuit was implemented on a chip that was composed of a reference pixel array for the averaging current adjustment technique, a calibration circuit, and readout circuits. The proposed reference pixel array for the averaging current adjustment technique, calibration circuit, and readout circuit were designed and fabricated by a $0.35-{\mu}m$ standard CMOS process.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.1
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• pp.18-23
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• 2013

In this paper we propose an image enhancement technique based on histogram specification method over local overlapping regions referred as Local Histogram Specification. First, both reference and original images are splitted into local regions that each overlaps half of its adjacent regions and general histogram specification method is used between corresponding local regions of reference and original image. However it produces noticeable boundary effects. Linear weighted image blending method is used to reduce this effect in order to make seamless image and we also proposed new technique dealing with over-enhanced contrast areas. We satisfied with our experimental results that showed better enhancement accuracy and less noise amplifications compared to other well-known image enhancement methods. We conclude that the proposed method is well suited for motion detection systems as a responsible part to overcome sudden illumination changes.

• The KIPS Transactions:PartB
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• v.10B no.1
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• pp.73-80
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• 2003

In this paper we propose an algorithm which matches the corresponding feature points between the reference image and the search image. We use Harris's corner detector to find the feature points in both image. For each feature point in the reference image, we can extract the candidate matching points as feature points in the starch image which the normalized correlation coefficient goes greater than a threshold. Finally we determine a corresponding feature points among candidate points by using dynamic programming. In experiments we show results that match feature points in synthetic image and real image.

• Journal of Korea Multimedia Society
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• v.25 no.9
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• pp.1291-1306
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• 2022

The dual image-based reversible data hiding scheme embeds secret data into two images to increase the embedding capacity of secret data. The dual image-based reversible data hiding scheme can transmit a lot of secret data. Therefore, various schemes have been proposed until recently. In 2021, Chen and Hong proposed a dual image-based reversible data hiding scheme that embeds a large amount of secret data using a reference matrix, secret data, and bit values. However, in this paper, more secret data can be embedded than Chen and Hong's scheme. To achieve this goal, the proposed scheme generates polynomials and shared values using secret sharing scheme, and embeds secret data using reference matrix and septenary number, and random value. Experimental results show that the proposed scheme can transmit more secret data to the receiver while maintaining the image quality similar to other dual image-based reversible data hiding schemes.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1131-1142
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• 2018

The autonomous vehicles are being developed and operated widely because of the advantages of reducing the traffic accident and saving time and cost for driving. The vehicle localization is an essential component for autonomous vehicle operation. In this paper, localization algorithm based on sensor fusion is developed for cost-effective localization using in-vehicle sensors, GNSS, an image sensor and reference images that made in advance. Information of the reference images can overcome the limitation of the low positioning accuracy that occurs when only the sensor information is used. And it also can acquire estimated result of stable position even if the car is located in the satellite signal blockage area. The particle filter is used for sensor fusion that can reflect various probability density distributions of individual sensors. For evaluating the performance of the algorithm, a data acquisition system was built and the driving data and the reference image data were acquired. Finally, we can verify that the vehicle positioning can be performed with an accuracy of about 0.7 m when the route image and the reference image information are integrated with the route path having a relatively large error by the satellite sensor.

• International Journal of Contents
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• v.11 no.2
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• pp.20-30
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• 2015

We propose a new method that specifies objective image quality factors by evaluating an image quality measurement model using random images. In other words, No-Reference variables are used to evaluate the quality of an original image without using any reference for comparison. 1000 portrait images were collected from a web gallery with votes constituting over 30 recommendation values. The bottom-up data collecting process was used to calculate the following image quality factors: total range, average, standard deviation, normalized distribution, z-score, preference percentage. A final grade is awarded out of 100 points, and this method ranks and grades the final estimated image quality preference in terms of total image quality factors. The results of the proposed image quality evaluation model consist of the specific dynamic range, skin tone R, G, B, L, A, B, and RSC contrast. We can present the total for the expected preference points as the average of the objective image qualities. Our proposed image quality evaluation model can measure the preferences for an actual image using a statistical analysis. The results indicate that this is a practical image quality measurement model that can extract a subject's preferred image quality.