• KIPS Transactions on Software and Data Engineering
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• v.2 no.9
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• pp.643-650
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• 2013

In this paper, we propose a novel method for enhancing the detection speed and rate by reducing the computation in Hough Circle Transform on real-time iris detection of smartphone camera image. First of all, we find a face and eyes from input image to detect iris and normalize the iris region into fixed size to prevent variation of size for iris region according to distance from camera lens. Moreover, we carry out histogram equalization to get regular image in bright and dark illumination from smartphone and calculate minimal iris range that contains iris with the distance between corner of the left eye and corner of the right eye on the image. Subsequently, we can minimize the computation of iris detection by applying Hough Circle Transform on the range including the iris only. The experiment is carried out in two case with bright and dark illumination. Our proposed method represents that detection speed is 40% faster and detection rate is 14% better than existing methods.

• The Journal of the Korea Contents Association
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• v.2 no.3
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• pp.1-15
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• 2002

A noel automated brain region extraction method in single channel MR images for visualization and analysis of a human brain is presented. The method generates a volume of brain masks by automatic thresholding using a dual curve fitting technique and by 3D morphological operations. The dual curve fitting can reduce an error in clue fitting to the histogram of MR images. The 3D morphological operations, including erosion, labeling of connected-components, max-feature operation, and dilation, are applied to the cubic volume of masks reconstructed from the thresholded Drain masks. This method can automatically extract a brain region in any displayed type of sequences, including extreme slices, of SPGR, T1-, T2-, and PD-weighted MR image data sets which are not required to contain the entire brain. In the experiments, the algorithm was applied to 20 sets of MR images and showed over 0.97 of similarity index in comparison with manual drawing.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.4
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• pp.345-354
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• 2014

In this paper, we propose a real-time algorithm for estimating the relative position and velocity of a person with respect to a robot using a Kinect sensor and an extended Kalman filter (EKF). Additionally, we propose an algorithm for controlling the robot in the proximity of a person in a variety of modes. The algorithm detects the head and shoulder regions of the person using a histogram of oriented gradients (HOG) and a support vector machine (SVM). The EKF algorithm estimates the relative positions and velocities of the person with respect to the robot using data acquired by a Kinect sensor. We tested the various modes of proximity movement for a human in indoor situations. The accuracy of the algorithm was verified using a motion capture system.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.6
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• pp.559-568
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• 2016

For the economic management of photovoltaic power plants, it is necessary to regularly monitor the panels within the plants to detect malfunctions. Thermal infrared image cameras are generally used for monitoring, since malfunctioning panels emit higher temperatures compared to those that are functioning. Recently, technologies that observe photovoltaic arrays by mounting thermal infrared cameras on UAVs (Unmanned Aerial Vehicle) are being developed for the efficient monitoring of large-scale photovoltaic power plants. However, the technologies developed until now have had the shortcomings of having to analyze the images manually to detect malfunctioning panels, which is time-consuming. In this paper, we propose an automatic photovoltaic panel area extraction algorithm for thermal infrared images acquired via a UAV. In the thermal infrared images, panel boundaries are presented as obvious linear features, and the panels are regularly arranged. Therefore, we exaggerate the linear features with a vertical and horizontal filtering algorithm, and apply a modified hierarchical histogram clustering method to extract candidates of panel boundaries. Among the candidates, initial panel areas are extracted by exclusion editing with the results of the photovoltaic array area detection. In this step, thresholding and image morphological algorithms are applied. Finally, panel areas are refined with the geometry of the surrounding panels. The accuracy of the results is evaluated quantitatively by manually digitized data, and a mean completeness of 95.0%, a mean correctness of 96.9%, and mean quality of 92.1 percent are obtained with the proposed algorithm.

• Journal of Wetlands Research
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• v.13 no.2
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• pp.219-229
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• 2011

In this study, Correlation analysis for relationship between storm characteristics and flood damage cost was carried out using histogram analysis. The spatial range of flood damage cost data was the whole country and 16 provinces in the Korea, and period range was 16 years, from 1994 to 2009. According to the results of this analysis, most of total flood damage cost was highest in small; middle scale rainfall events of high frequency. Based on the results of comparison among 16 provinces, the ability to prevent flood demage of metropolitan was better than that of provinces. And the storm characteristics of causing maximum flood damage cost was different from each provinces. Therefore, the construction size in flood defence measures has to be determined when flood defence measures considering the results of this study as an useful guideline.

• Journal of Biomedical Engineering Research
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• v.27 no.3
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• pp.131-141
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• 2006

A visual decision by clinical experts like physical therapists is a best way to detect onset and offset time of muscle activation. The current computer-based algorithms are being researched toward similar results of clinical experts. The new algorithm in this paper has an ability to extract a trend from noisy input data. Kalman smoother is used to recognize the trend to be revealed from disorderly signals. Histogram of smoothed signals by Kalman smoother has a clear boundary to separate muscle contractions from relaxations. To verify that the Kalman smoother algorithm is reliable way to detect onset and offset time of muscle contractions, the algorithm of Robert P. Di Fabio (published in 1987) is compared with Kalman smoother. For 31 templates of subjects, an average and a standard deviation are compared. The average of errors between Di Fabio's algorithm and experts is 109 milliseconds in onset detection and 142 milliseconds in offset detection. But the average between Kalman smoother and experts is 90 and 137 milliseconds in each case. Moreover, the standard deviations of errors are 133 (onset) and 210 (offset) milliseconds in Di Fabio's one, but 48 (onset) and 55 (offset) milliseconds in Kalman smoother. As a result, the Kalman smoother is much closer to determinations of clinical experts and more reliable than Di Fabio's one.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.6
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• pp.825-830
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• 2010

Generally, the copy protection mark and 2D bar-code techniques are widely used for forgery protection in printed public documents. But, it is hard to discriminate truth from the copy documents by using exisiting methods, because of that existing 2D-barcode is separated from the copy protection mark and it can be only recognized by specified optical barcord scanner. Therefor, in this paper, we proposed the forgery protection tehchnique for discriminating truth from the copy document by using watermark inserted 2D-barcord, which can be accurately distinguished not only by naked eye, but also by scanner. The copy protection mark consists of deformed patterns that are caused by the lowpass filter characteristic of digital I/O device. From these, we verified the performance of the proposed techniques by applying the histogram analysis based on the original, copy, and scanned copy image of the printed documents. Also, we suggested 2D-barcord confirmation system which can be accessed through the online server by using certification key data which is detected by web-camera, cell phone camera.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.7
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• pp.201-208
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• 2014

The performance of a landmine detection system depends on consistent extractions of the features of landmines. Since landmines have diverse sizes, it is critical to select an appropriate window size to represent the landmine region consistently. Conventional detection systems are incapable of extracting consistent landmine features because they employ fixed window sizes. This paper proposes a window size selection method according to the size of a landmine. The proposed method selects an appropriate window size based on the type of a landmine estimated from the response signal of the system. Data on various types of soils and landmines were generated from a simulation program to evaluate the performance of the proposed method. The results verified that the proposed method, which employs an adaptive window size, yields a better landmine detection rate than the conventional methods, which employ fixed window sizes.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.1
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• pp.67-74
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• 2002

A study on image searching and management techniques is actively developed by user requirements for multimedia information that are existing as images, audios, texts data from various information processing devices. If we can automatically detect and segment changing scenes from real-time image sequences, we can improve an effectiveness of image searching systems. In this paper, we propose cut detection techniques based on image color distribution and we evaluated its performance on various real-time image sequences. Results of experiments show that the proposed method are robust on various image patterns than color histogram method using statistical informations of images. Also, these methods can be used for cut detection on real-time image sequences.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.7
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• pp.1292-1302
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• 2009

In this paper, we proposed Single Tone Detection Algorithm for prevent decoloration and color distortion of single tone image in auto white balance of mobile phone camera. Conventional auto white balance which accomplish very well using complex color images, but there are some problems if input images are single tone image or included large part of single tone. If input images are singlet one, or included large part of single tone, which cause decoloration or distorted color in output images. In this paper, we proposed single tone detection algorithm using color histogram data for solve decoloration or distorted color problems. If this algorithm is applied to auto white balance, it will be improvement in auto white balance.