• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.248-250
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• 2003

An automatic approach and strategy for extracting building information from aerial images using combined image analysis and interpretation techniques is described in this paper. A dense DSM is obtained by stereo image matching. Multi-band classification, DSM, texture segmentation and Normalised Difference Vegetation Index (NDVI) are used to reveal building interest areas. Then, based on the derived approximate building areas, a shape modelling algorithm based on the level set formulation of curve and surface motion has been used to precisely delineate the building boundaries. Data fusion, based on the Dempster-Shafer technique, is used to interpret simultaneously knowledge from several data sources of the same region, to find the intersection of propositions on extracted information derived from several datasets, together with their associated probabilities. A number of test areas, which include buildings with different sizes, shape and roof colour have been investigated. The tests are encouraging and demonstrate that the system is effective for building extraction, and the determination of more accurate elevations of the terrain surface.

• Journal of Mechanical Science and Technology
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• v.18 no.7
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• pp.1236-1245
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• 2004

In this study, an optical imaging method was developed for the measurements of the sizes and velocities of droplets in sprays. Double-exposure single-frame spray images were captured by the imaging system. An image processing program was developed for the measurements of the sizes and positions of individual particles including separation of the overlapped particles and particle tracking and pairing at two time instants. To recognize and separate overlapping particles, the morphological method based on watershed segmentation as well as separation using the perimeter and convex hull of image was used consecutively. Better results in separation were obtained by utilization of both methods especially for the multiple or heavily-overlapped particles. The match probability method was adopted for particle tracking and pairing after identifying the positions of individual particles and it produced good matching results even for large particles like droplets in sprays. Therefore, the developed optical imaging method could provide a reliable way of analyzing the motion and size distribution of droplets produced by various sprays and atomization devices.

• Journal of Advanced Information Technology and Convergence
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• v.9 no.2
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• pp.65-74
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• 2019

In this paper, we describe a new swing analysis system. We design this system to provide various information about golf swings and to help to correct wrong swings. We visualize three-dimensional skeletal information obtained from Kinect through various views. Golfers can see their swing behavior through these views. This system can calculate the similarity between the two trajectories obtained from Kinect to determine the similarity of swing trajectories of different golfers. Input trajectories are resampled to have equal spacing and are performed scaling and translation for accurate trajectory comparison. We have verified the usefulness of the proposed system through various analyzes.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.113-121
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• 2012

In this paper, we proposed fast and efficient algorithm for detecting near-duplication based on content based retrieval in large scale video database. For handling large amounts of video easily, we split the video into small segment using scene change detection. In case of video services and copyright related business models, it is need to technology that detect near-duplicates, that longer matched video than to search video containing short part or a frame of original. To detect near-duplicate video, we proposed motion distribution and frame descriptor in a video segment. The motion distribution descriptor is constructed by obtaining motion vector from macro blocks during the video decoding process. When matching between descriptors, we use the motion distribution descriptor as filtering to improving matching speed. However, motion distribution has low discriminability. To improve discrimination, we decide to identification using frame descriptor extracted from selected representative frames within a scene segmentation. The proposed algorithm shows high success rate and low false alarm rate. In addition, the matching speed of this descriptor is very fast, we confirm this algorithm can be useful to practical application.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.3
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• pp.8-15
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• 2007

Recently, it is important to process multimedia data efficiently. Especially, in case of retrieval of multimedia information, technique of user interface and retrieval technique are necessary. This paper proposes a new technique which detects cuts effectively in compressed image information by MPEG. A cut is a turning point of scenes. The cut-detection is the basic work and the first-step for video indexing and retrieval. Existing methods have a weak point that they detect wrong cuts according to change of a screen such as fast motion of an object, movement of a camera and a flash. Because they compare between previous frame and present frame. The proposed technique detects shots at first using DC(Direct Current) coefficient of DCT(Discrete Cosine Transform). The database is composed of these detected shots. Features are extracted by HMMD color model and edge histogram descriptor(EHD) among the MPEG-7 visual descriptors. And detections are performed in sequence by the proposed matching technique. Through this experiments, an improved video segmentation system is implemented that it performs more quickly and precisely than existing techniques have.

• Journal of the Korea Society of Computer and Information
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• v.18 no.5
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• pp.35-42
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• 2013

Shot boundary detection is an essential step for efficient browsing, sorting, and classification of video data. Robust shot detection method should overcome the disturbances caused by pixel brightness and object movement between frames. In this paper, two shot boundary detection methods are presented to address these problem by using segmentation, object movement, and pixel brightness. The first method is based on the histogram that reflects object movements and the morphological dilation operation that considers pixel brightness. The second method uses the pixel brightness information of segmented and whole blocks between frames. Experiments on digitized video data of National Archive of Korea show that the proposed methods outperforms the existing pixel-based and histogram-based methods.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1445-1456
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• 2018

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.9
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• pp.2091-2098
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• 2010

This paper proposes a hologram data compression scheme that uses the existing image/video compression techniques, in which the existing techniques are modified appropriately to fit to the characteristics of hologram. In this paper we use CGH as the hologram data. The proposed scheme uses the generation characteristics of a CGH to consist of a pre-processing, spatial segmentation of a CGH, frequency-transformation with 2D-DCT (2-dimensional discrete cosine transform), and motion estimation and residual image generation in the frequency-domain. It uses H.264/AVC, the lossless compressor BinHex, and a linear quantizer that we have made. From the experiments the proposed scheme showed the image quality of about 25.4 dB at the compression ratio of 10:1 and about 16.5dB at 90:1 compression ratio.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.12B
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• pp.2349-2358
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• 1999

Object-based stereoscopic video coding scheme is presented in this paper. In conventional BMA based stereoscopic video coding for low bit rate transmission, image prediction errors such as block artifacts and mosquito phenomena are occurred. In order to reduce these errors, object based coding scheme is adopted. The proposed scheme consists of preprocessing, object extraction, and object update procedures. The preprocessing procedure extracts non-object regions having low reliability for motion and disparity estimation. This procedure prohibits extracting inaccurate objects. For the better prediction of left channel image, the disparity information is added to the object extraction. And the proposed algorithm can reduce the accumulated error through the object update procedure that detects newly emerging objects, merges objects that have the same object-disparity and object motion, and splits object which has large image prediction error. The experimental results show that the proposed algorithms improve the quality of the prediction without block artifacts and mosquito phenomena.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.4C
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• pp.401-410
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• 2007

In this paper, we propose water quality monitoring system using vision camera and multiple objects tracking method. The proposed system analyzes object individually using vision camera unlike monitoring system using sensor method. The system using vision camera consists of individual object segmentation part and objects tracking part based on interrelation between successive frames. For real-time processing, we make background image using non-parametric estimation and extract objects using background image. If we use non-parametric estimation, objects extraction method can reduce large amount of computation complexity, as well as extract objects more effectively. Multiple objects tracking method predicts next motion using moving direction, velocity and acceleration of individual object then carries out tracking based on the predicted motion. And we apply exception handling algorithms to improve tracking performance. From experiment results under various conditions, it shows that the proposed system can be available for real-time water quality monitoring system since it has very short processing time and correct multiple objects tracking.