• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.4
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• pp.35-40
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• 2010

Skyline extraction in mountainous images which has been used for navigation of vehicles or micro unmanned air vehicles is very hard to implement because of the complexity of skyline shapes, occlusions by environments, dfficulties to detect precise edges and noises in an image. In spite of these difficulties, skyline extraction is avery important theme that can be applied to the various fields of unmanned vehicles applications. In this paper, we developed a robust skyline extraction algorithm using two-scale canny edge images, topological information and location of the skyline in an image. Two-scale canny edge images are composed of High Scale Canny edge image that satisfies good localization criterion and Low Scale Canny edge image that satisfies good detection criterion. By applying each image to the proper steps of the algorithm, we could obtain good performance to extract skyline in images under complex environments. The performance of the proposed algorithm is proved by experimental results using various images and compared with an existing method.

• The KIPS Transactions:PartA
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• v.16A no.1
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• pp.17-26
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• 2009

Large-scale wireless sensor network are expected to play an increasingly important role for the data collection in harmful area. However, the physical fragility of sensor node makes reliable routing in harmful area a challenging problem. Since several sensor nodes in harmful area could be damaged all at once, the network should have the availability to recover routing from node failures in large area. Many routing protocols take accounts of failure recovery of single node but it is very hard these protocols to recover routing from large scale failures. In this paper, we propose a routing protocol, which we refer to as LSFA, to recover network fast from failures in large area. LSFA detects the failure by counting the packet loss from parent node and in case of failure detection LSFAdecreases the routing interval to notify the failure to the neighbor nodes. Our experimental results indicate clearly that LSFA could recover large area failures fast with less packets than previous protocols.

• Proceedings of the KIEE Conference
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• 2004.07c
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• pp.1748-1750
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• 2004

This paper deals with the detection method of the Quench phenomenon for superconducting magnet using the Acoustic Emission (AE) sensor. AE sensor is the elements, which is used to change the Acoustic signal to the voltage value. This signal may be used to detect whether the superconducting magnet has been at the Quench state or not. Recently, the development of the Quench detection technique, which is the using voltage and current signals, fiber-optic sensor, and so on, for the superconducting applications is widely studying. This method for the Quench detection of the superconducting magnet is also studying at some kinds of institute in Japan and the united state. Because of the large-scale superconducting magnet like International Thermonuclear Experimental Reactor(ITER) is charged a lot of energy, when the Quench phenomenon is being at the superconducting magnet it is happen to the problem of the protection for the applications. In this paper, we concluded that the Quench detection was possible when the mechanical stress by means of the local heat is generated at the part of inside superconducting magnets.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.11
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• pp.1074-1081
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• 2007

This paper presents a method for real-time face detection and tracking which combined Adaboost and Camshift algorithm. Adaboost algorithm is a method which selects an important feature called weak classifier among many possible image features by tuning weight of each feature from learning candidates. Even though excellent performance extracting the object, computing time of the algorithm is very high with window size of multi-scale to search image region. So direct application of the method is not easy for real-time tasks such as multi-task OS, robot, and mobile environment. But CAMshift method is an improvement of Mean-shift algorithm for the video streaming environment and track the interesting object at high speed based on hue value of the target region. The detection efficiency of the method is not good for environment of dynamic illumination. We propose a combined method of Adaboost and CAMshift to improve the computing speed with good face detection performance. The method was proved for real image sequences including single and more faces.

• Journal of Information Processing Systems
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• v.12 no.2
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• pp.183-195
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• 2016

This paper presents a complete method for vehicle detection and tracking in a fixed setting based on computer vision. Vehicle detection is performed based on Scale Invariant Feature Transform (SIFT) feature matching. With SIFT feature detection and matching, the geometrical relations between the two images is estimated. Then, the previous image is aligned with the current image so that moving vehicles can be detected by analyzing the difference image of the two aligned images. Vehicle tracking is also performed based on SIFT feature matching. For the decreasing of time consumption and maintaining higher tracking accuracy, the detected candidate vehicle in the current image is matched with the vehicle sample in the tracking sample set, which contains all of the detected vehicles in previous images. Most remarkably, the management of vehicle entries and exits is realized based on SIFT feature matching with an efficient update mechanism of the tracking sample set. This entire method is proposed for highway traffic environment where there are no non-automotive vehicles or pedestrians, as these would interfere with the results.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1406-1416
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• 2017

In complex and large-scale industries, properly designed fault detection and identification (FDI) systems considerably improve safety, reliability and availability of target processes. In thermal power plants (TPPs), generating units operate under very dangerous conditions; system failures can cause severe loss of life and property. In this paper, we propose a bagged auto-associative kernel regression (AAKR)-based FDI approach for steam boilers in TPPs. AAKR estimates new query vectors by online local modeling, and is suitable for TPPs operating under various load levels. By combining the bagging method, more stable and reliable estimations can be achieved, since the effects of random fluctuations decrease because of ensemble averaging. To validate performance, the proposed method and comparison methods (i.e., a clustering-based method and principal component analysis) are applied to failure data due to water wall tube leakage gathered from a 250 MW coal-fired TPP. Experimental results show that the proposed method fulfills reasonable false alarm rates and, at the same time, achieves better fault detection performance than the comparison methods. After performing fault detection, contribution analysis is carried out to identify fault variables; this helps operators to confirm the types of faults and efficiently take preventive actions.

• Ocean Systems Engineering
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• v.6 no.3
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• pp.217-231
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• 2016

This paper presents a new approach for underwater image analysis using the bi-dimensional empirical mode decomposition (BEMD) technique and the phase congruency information. The BEMD algorithm, fully unsupervised, it is mainly applied to texture extraction and image filtering, which are widely recognized as a difficult and challenging machine vision problem. The phase information is the very stability feature of image. Recent developments in analysis methods on the phase congruency information have received large attention by the image researchers. In this paper, the proposed method is called the EP model that inherits the advantages of the first two algorithms, so this model is suitable for processing underwater image. Moreover, the receiver operating characteristic (ROC) curve is presented in this paper to solve the problem that the threshold is greatly affected by personal experience when underwater image edge detection is performed using the EP model. The EP images are computed using combinations of the Canny detector parameters, and the binaryzation image results are generated accordingly. The ideal EP edge feature extractive maps are estimated using correspondence threshold which is optimized by ROC analysis. The experimental results show that the proposed algorithm is able to avoid the operation error caused by manual setting of the detection threshold, and to adaptively set the image feature detection threshold. The proposed method has been proved to be accuracy and effectiveness by the underwater image processing examples.

• The Journal of Advanced Prosthodontics
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• v.9 no.6
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• pp.432-438
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• 2017

PURPOSE. The purpose of this study was to evaluate the in vitro validity of quantitative light-induced fluorescence-digital (QLF-D) and laser fluorescence (DIAGNOdent) for assessing proximal caries in extracted premolars, using digital radiography as reference method. MATERIALS AND METHODS. A total of 102 extracted premolars with similar lengths and shapes were used. A single operator conducted all the examinations using three different detection methods (bitewing radiography, QLF-D, and DIAGNOdent). The bitewing x-ray scale, QLF-D fluorescence loss (${\Delta}F$), and DIAGNOdent peak readings were compared and statistically analyzed. RESULTS. Each method showed an excellent reliability. The correlation coefficient between bitewing radiography and QLF-D, DIAGNOdent were -0.644 and 0.448, respectively, while the value between QLF-D and DIAGNOdent was -0.382. The kappa statistics for bitewing radiography and QLF-D had a higher diagnosis consensus than those for bitewing radiography and DIAGNOdent. The QLF-D was moderately to highly accurate (AUC = 0.753 - 0.908), while DIAGNOdent was moderately to less accurate (AUC = 0.622 - 0.784). All detection methods showed statistically significant correlation and high correlation between the bitewing radiography and QLF-D. CONCLUSION. QLF-D was found to be a valid and reliable alternative diagnostic method to digital bitewing radiography for in vitro detection of proximal caries.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2141-2155
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• 2020

In order to meet the requirements of background change, illumination variation, moving shadow interference and high accuracy in object detection of moving camera, and strive for real-time and high efficiency, this paper presents an object detection algorithm based on sparse approximation recursion and sparse coding migration in subspace. First, low-rank sparse decomposition is used to reduce the dimension of the data. Combining with dictionary sparse representation, the computational model is established by the recursive formula of sparse approximation with the video sequences taken as subspace sets. And the moving object is calculated by the background difference method, which effectively reduces the computational complexity and running time. According to the idea of sparse coding migration, the above operations are carried out in the down-sampling space to further reduce the requirements of computational complexity and memory storage, and this will be adapt to multi-scale target objects and overcome the impact of large anomaly areas. Finally, experiments are carried out on VDAO datasets containing 59 sets of videos. The experimental results show that the algorithm can detect moving object effectively in the moving camera with uniform speed, not only in terms of low computational complexity but also in terms of low storage requirements, so that our proposed algorithm is suitable for detection systems with high real-time requirements.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.25 no.1
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• pp.76-83
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• 2012

In a real-time indoor place recognition system using image features detection, specific markers included in input image should be detected exactly and quickly. However because the same markers in image are shown up differently depending to movement, direction and angle of camera, it is required a method to solve such problems. This paper proposes a technique to extract the features of object without regard to change of the object scale. To support real-time operation, it adopts SURF(Speeded up Robust Features) which enables fast feature detection. Another feature of this system is the user mark designation which makes possible for user to designate marks from input image for location detection in advance. Unlike to use hardware marks, the feature above has an advantage that the designated marks can be used without any manipulation to recognize location in input image.