• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.6
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• pp.485-492
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• 2019

The observation system has been developed to investigate the rip currents at Haeundae beach using X-band marine radar. X-band radar system can observe shape, size, and velocity of rip currents, which is difficult to obtain through field observation by conventional device. Algorithms which automatically detect locations, shapes, and magnitudes of rip currents were developed using time averaged X-band radar sea clutter images. X-band sea clutter images are transformed through 3D FFT into 2D wave number spectrum and frequency spectrum. Rip current velocities were estimated using differences in wave-number spectra and wave frequency spectra due to Doppler shift. The algorithm was verified by drift experiments. At Haeundae beach, the radar system exactly located the rip currents and found to be sustained for 1-2 days at fixed locations.

• Journal of Korea Multimedia Society
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• v.12 no.3
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• pp.383-396
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• 2009

Bone age assessment has been widely used in pediatrics to identify endocrine problems of children. Since the number of trained doctors is far less than the demands, there has been numerous requests for automatic estimation of bone age. Therefore, in this paper, we propose an automatic bone age assessment method that utilizes pattern classification techniques. The proposed method consists of three modules; a finger segmentation module, a normalized shape model generation module and a bone age estimation module. The finger segmentation module segments fingers and epiphyseal regions by means of various image processing algorithms. The shape model abstraction module employ ASM to improves the accuracy of feature extraction for bone age estimation. In addition, SVM is used for estimation of bone age. Features for the estimation include the length of bone and the ratios of bone length. We evaluated the performance of the proposed method through statistical analysis by comparing the bone age assessment results by clinical experts and the proposed automatic method. Through the experimental results, the mean error of the assessment was 0.679 year, which was better than the average error acceptable in clinical practice.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.427-430
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• 2007

Many victims and property damages are caused in fires every year. In this paper, flame and smoke detection algorithm by using image processing technique is proposed to early alarm fires. The first decision of proposed algorithms is to check candidate of flame region with its unique color distribution distinguished from artificial lights. If it is not a flame region then we can check to candidate of smoke region by measuring difference of brightness and chroma at present frame. If we just check flame and smoke with only simple brightness and hue, we will occasionally get false alarms. Therefore we also use motion information about candidate of flame and smoke regions. Finally, to determine the flame after motion detection, activity information is used. And in order to determine the smoke, edges detection method is adopted. As a result of simulation with real CCTV video signal, it is shown that the proposed algorithm is useful for early fire recognition.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.7
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• pp.63-70
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• 2006

This paper describes research of using area of artificial mark on vision based head tracker system. A head tracker system consists of the translational and rotational motions which are detected by web camera. Results of the motion are taken from image processing and neural network. Because of the characteristics of cockpit, the specific color on the helmet is tracked for translational motion. And rotational motion is tracked via neural network. Ratio of two different colored area on the helmet is used as input of network. Neural network algorithms used, such as back-propagation and RBFN (Radial Basis Function Network). Both back-propagation using a characteristic of feedback and RBFN using a characteristic of statistics have a good performances for the tracking of nonlinear system such as a head motion. Finally, this paper analyzes and compares with tracking performance.

• Journal of the Korea Computer Industry Society
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• v.8 no.2
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• pp.103-106
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• 2007

In order to construct a Video Database, shot change detection should be made first. But, because these processes are not automated perfectly, we need a lot of time and efforts now. And, there are many shot change detection algorithms, which can't always insure the perfect result because of the editing effects such as cut, wipe, and dissolves used in film production. Therefore, in order to receive the exact shot change, It needs the verification and correction by manual processing at any cost. Spatiotemporal slice is a simple image condensing method for the content changes of video. The editing effects are expressed on the Spatiotemporal slice in the visually noticed form of vertical line, diagonal line, curved line and gradual color changes, etc. Accordingly the parts doubted as a shot change can be easily detected by the change of the Spatiotemporal slice without replaying the video. The system proposed in this study makes it possible to delete the false detected key frames, and create the undetected key frames on the Spatiotemporal slice.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.3955-3971
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• 2020

Due to the significant increase in the use of autonomous car technology, it is essential to integrate this technology with high-precision digital map data containing more precise and accurate roadway information, as compared to existing conventional map resources, to ensure the safety of self-driving operations. While existing map technologies may assist vehicles in identifying their locations via Global Positioning System, it is however difficult to update the environmental changes of roadways in these maps. Roadway vision algorithms can be useful for building autonomous vehicles that can avoid accidents and detect real-time location changes. We incorporate a hybrid architectural design that combines unsupervised classification of vision data with supervised joint fusion classification to achieve a better noise-resistant algorithm. We identify, via a deep learning approach, an intelligent hybrid fusion algorithm for fusing multimodal vision feature data for roadway classifications and characterize its improvement in accuracy over unsupervised identifications using image processing and supervised vision classifiers. We analyzed over 93,000 vision frame data collected from a test vehicle in real roadways. The performance indicators of the proposed hybrid fusion algorithm are successfully evaluated for the generation of roadway digital maps for autonomous vehicles, with a recall of 0.94, precision of 0.96, and accuracy of 0.92.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.1
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• pp.131-139
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• 2009

The existing vessel monitoring system using the ground surveillance radar has a difficulty in monitoring ships continuously due to the limited range of detecting ships. For resolving this problem, we carry out a research on ship detection which is to be the core technology of vessel monitoring system for ocean monitoring using SAR imagery. There are two different methods of detecting ships in SAR imagery: detection of the ship target itself and detection of the ship wake. In this paper, we mainly focus on algorithms which detect the ship itself, and also present the accuracy test after extracting positional and directional figures of the ships. After rectifying input SAR imagery using polynomial transformation, we use Wiener filter to remove speckle noises. A labeling technique and morphological filtering in conjunction with Otsu's method are used to automatically detect the ships based on the image processing domain. For ground truth data, information from a radar system is used, which allows assessing the accuracy of the proposed method. The results show that the proposed method has the high potential in automatically detecting the ships and its positional/directional figures in a fast way.

• Journal of Broadcast Engineering
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• v.21 no.2
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• pp.180-191
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• 2016

When up-scaling algorithms for visible light images are applied to infrared (IR) images, they rarely work because IR images are usually blurred. In order to solve such a problem, this paper proposes an up-scaling algorithm for IR images. We employ adaptive dynamic range encoding (ADRC) as a simple classifier based on the observation that IR images have weak details. Also, since human visual systems are more sensitive to edges, our algorithm focuses on edges. Then, we add pre-processing in learning phase. As a result, we can improve visibility of IR images without increasing computational cost. Comparing with Anchored neighborhood regression (A+), the proposed algorithm provides better results. In terms of just noticeable blur, the proposed algorithm shows higher values by 0.0201 than the A+, respectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.467-472
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• 2002

In this paper, we propose a Fuzzy Cellular Neural Network(FCNN) that is based on a-Least Trimmed Square Hausdorff distance(a-LTSHD) which applies Hausdorff distance(HD) to the FCNN structure in order to remove the impulse noise of images effectively and also improve the speed of operation. FCNN incorporates Fuzzy set theory to Cellular Neural Network(CNN) structure and HD is used as a scale which computes the distance between set or two pixels in binary images without confrontation of the feature object. This method has been widely used with the adjustment of the object. For performance evaluation, our proposed method is analyzed in comparison with the conventional FCNN, with the Opening-Closing(OC) method, and the LTSHD based FCNN by using Mean Square Error(MSE) and Signal to Noise Ratio(SNR). As a result, the performance of our proposed network structure is found to be superior to the other algorithms in the removal of impulse noise.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.7
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• pp.62-70
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• 1999

To achieve precision positioning, working area is required within $5mm{\times}5mm$ and positioning error is allowed within minimum ${\pm}4{\mu}m$. As a general three-layered table takes working range from several centimeters and a few tens of centimeters, it has disadvantages compared with precision positioning table, such as larger working range and rough accuracy. In this paper we design and implement a parallel $XY{\theta}$ table with three linear actuators, where one is on the horizontal direction and the others on the vertical direction on behalf of a degree of $XY{\theta}$ freedom. Finally, the experimental results of precision positioning is showed by using new image processing algorithms with two CCD cameras.