• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.665-672
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• 2012

Amid rapidly increasing imagery inputs and their volume in a remote sensing imagery database, Content-Based Image Retrieval (CBIR) is an effective tool to search for an image feature or image content of interest a user wants to retrieve. It seeks to capture salient features from a 'query' image, and then to locate other instances of image region having similar features elsewhere in the image database. For a CBIR approach that uses texture as a primary feature primitive, designing a texture descriptor to better represent image contents is a key to improve CBIR results. For this purpose, an extended feature vector combining the Gabor filter and co-occurrence histogram method is suggested and evaluated for quantitywise and qualitywise retrieval performance criterion. For the better CBIR performance, assessing similarity between high dimensional feature vectors is also a challenging issue. Therefore a number of distance metrics (i.e. L1 and L2 norm) is tried to measure closeness between two feature vectors, and its impact on retrieval result is analyzed. In this paper, experimental results are presented with several CBIR samples. The current results show that 1) the overall retrieval quantity and quality is improved by combining two types of feature vectors, 2) some feature is better retrieved by a specific feature vector, and 3) retrieval result quality (i.e. ranking of retrieved image tiles) is sensitive to an adopted similarity metric when the extended feature vector is employed.

• Journal of Digital Contents Society
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• v.7 no.1
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• pp.9-14
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• 2006

In this paper, we propose the algorithm that automatically measures the height of the object to move on the base plane by using the geometric information. To extract a moving object from images, we use the difference image and morphology operation. The top and bottom point of an object are extracted by the histogram vertical projection in the extracted region. The two points, top and bottom, are used for measuring the height. Given the vanishing line of the ground plane, the vertical vanishing point, and at least one reference height in the scene; then the height of any point from the ground may be computed by specifying the image of the point and the image of the vertical intersection with the ground plane at that point. Through a confidence valuation of the height to be measured, we confirmed similar actual height and result in the simulation experiment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.639-640
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• 2013

In this study, a new method that finds an area where interesting objects are placed to generate DIBR-based intermediate images with higher quality. This method complements the existing object segmentation algorithm called Grabcut by finding the bounding box automatically, whereas the existing algorithm requires a user to select the region specifically. Then, the histogram of the depth map information is then used to separate the background and the frontal objects after applying the GrabCut algorithm. By using the new method, it is found that it produces better result than the existing algorithm. This paper describes the new method and future research.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.3
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• pp.229-235
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• 2011

To make a robust object tracking and identifying system for an intelligent robot and/or home system, heterogeneous sensor fusion between visible ray system and infrared ray system is proposed. The proposed system separates the object by combining the ROI (Region of Interest) estimated from two different images based on a heterogeneous sensor that consolidates the ordinary CCD camera and the IR (Infrared) camera. Human's body and face are detected in both images by using different algorithms, such as histogram, optical-flow, skin-color model and Haar model. Also the pose of human body is estimated from the result of body detection in IR image by using PCA algorithm along with AdaBoost algorithm. Then, the results from each detection algorithm are fused to extract the best detection result. To verify the heterogeneous sensor fusion system, few experiments were done in various environments. From the experimental results, the system seems to have good tracking and identification performance regardless of the environmental changes. The application area of the proposed system is not limited to robot or home system but the surveillance system and military system.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.8
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• pp.160-171
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• 1994

The wide dynamic range and severely attenuated contrast in mediastinal area appearing in typical chest radiographs have often caused difficulties in effective visualization and diagnosis of lung diseases. This paper proposes a new adaptive image enhancement technique which potentially solves this problem and there by improves observer performance through image processing. In the proposed method image processing is applied to the chest radiograph with different processing parameters for the lung field and mediastinum adaptively since there are much differences in anatomical and imaging properties between these two regions. To achieve this the chest radiograph is divided into the lung and mediastinum by gray level thresholding using the cumulative histogram and the dynamic range compression and local contrast enhancement are carried out selectively in the mediastinal region. Thereafter a gray scale transformation is performed considering the JND(just noticeable difference) characteristic for effective image displa. The processed images showed apparenty improved contrast in mediastinum and maintained moderate brightness in the lung field. No artifact could be observed. In the visibility evaluation experiment with 5 radiologists the processed images with better visibility was observed for the 5 important anatomical structures in the thorax.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.3
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• pp.321-326
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• 2010

A method that can decide the existence and the severeness of flaws in ceramic materials through the use of non-destructive testing by image processing techniques, is proposed in this paper. The edges of the acquired image are first extracted using Sobel mask and the regions of the image are clustered using another mask after that. Histogram stretching is applied to each of the regions to enhance the image region-wise and objects are extracted by an edge following algorithm. Morphological information is incorporated to remove noise and detect flawed regions. The proposed method can detect flaws in the acquired images and the experimental results also supports that.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.1
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• pp.28-37
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• 2001

An image retrieval method combining shape information of 2-dimension color histograms with color information of HSI color histograms is proposed in this paper. In addition, the proposed method can find location information of image through the comparison of similarity among subimages. The suggested retrieval method applies the location information to shape and color information and can retrieve region information which is hard to distinguish in the binary image. Some simulation results show that it works very well in the behalf of precision/recall graph compare with conventional method which uses color histogram. Especially, the proposed method brought well effects such as rotations and transitions of the objects in an image was found.

• Korean Journal of Remote Sensing
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• v.26 no.3
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• pp.325-333
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• 2010

Image registration involves overlapping images of an identical region and assigning the data into one coordinate system. Image registration has proved important in remote sensing, enabling registered satellite imagery to be used in various applications such as image fusion, change detection and the generation of digital maps. The image descriptor, which extracts matching points from each image, is necessary for automatic registration of remotely sensed data. Using contrast enhancement algorithms such as histogram equalization and image stretching, the normalized data are applied to the image descriptor. Drawing on the different spectral characteristics of high resolution satellite imagery based on sensor type and acquisition date, the applied normalization method can be used to change the results of matching interest point descriptors. In this paper, the matching points by scale invariant feature transformation (SIFT) are extracted using various contrast enhancement algorithms and injection of Gaussian noise. The results of the extracted matching points are compared with the number of correct matching points and matching rates for each point.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.447-450
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• 2011

This paper shows the application of P-N Learning [4] method in the soccer ball detection and improvement for increasing the speed of processing. In the P-N learning, the learning process is guided by positive (P) and negative (N) constraints which restrict the labeling of the unlabeled data, identify examples that have been classified in contradiction with structural constraints and augment the training set with the corrected samples in an iterative process. But for the long-view in the soccer game, P-N learning will produce so many ferns that more time is spent than other methods. We propose that color histogram of each frame is constructed to delete the unnecessary details in order to decreasing the number of feature points. We use the mask to eliminate the gallery region and Line Hough Transform to remove the line and adjust the P-N learning's parameters to optimize accurate and speed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.359-360
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• 2021

The pixel-based processing of an image refers to a process of converting a value of one pixel only depending on the value of the current pixel, regardless of the value of another pixel. Pixel-based processing is used as the most basic operation in many fields such as image conversion, image enhancement, and image synthesis. There are processing methods such as arithmetic operation, histogram smoothing, and contrast stretching. In this paper, in order to clearly distinguish the tidal flat region from the tidal flat image of the west coast taken with a drone, we seek a method to find an efficient outline using pixel-based processing in the boundary detection part of the pre-processing process.