• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11B
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• pp.1296-1302
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• 2009

We propose an efficient coding method of digital holograms using MCTF and standard compression tools for video. The hologram is generated by a computer-generated hologram (CGH) algorithm with both an object image and its depth information. The proposed coding consists of localization by segmenting a hologram, frequency transform using $64\times64$ segment size, 2-D discrete cosine transform DCT for extracting redundancy, motion compensated temporal filtering (MCTF), segment scanning the segmented hologram to form a video sequence, and video coding, which uses H.264/AVC. The proposed algorithm illustrates that it has better properties for reconstruction, 10% higher compression rate than previous research in case of object.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.175-178
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• 2007

According to recently presented QoS architecture by 3GPP, a traffic conditioner may be deployed to provide conformance of the negotiated QoS. A real-time frame-layer rate control method which can be applied to the traffic conditioner is proposed. The proposed rate control method uses a non-iterative optimization method for low computational complexity, and performs bit allocation at the frame level to minimize the average distortion over an entire sequence as well as variations in distortion between frames. The proposed algorithm does not produce time delay from encoding, and is suitable for real-time low-complexity video encoder.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1825-1839
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• 2015

The final draft of the latest video coding standard, High Efficiency Video Coding (HEVC), was approved in January 2013. The coding efficiency of HEVC surpasses its predecessor, H.264/MPEG-4 Advanced Video Coding (AVC), by using only half of the bitrate to encode the same sequence with similar quality. However, the complexity of HEVC is sharply increased compared to H.264/AVC. In this paper, a method is proposed to decrease the complexity of intra coding in HEVC. Early pruning and an early splitting strategy are applied to the quadtree structure of coding tree units (CTU) and residual quadtree (RQT). According to our experiment, when our method is applied to sequences from Class A to Class E, the coding time is decreased by 44% at the cost of a 1.08% Bjontegaard delta rate (BD-rate) increase on average.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.1 no.1
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• pp.10-16
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• 2002

In this paper, a novel algorithm for segmentation of moving multiple vehicles in video sequences using logic operations is proposed. For the case of multiple vehicles in a scene, the proposed algorithm begins with a robust double-edge image derived from the difference between two successive frames using exclusive OR operation. After extracting only the edges of moving multiple vehicles using Laplacian filter, AND operation and dilation operation, the image is segmented into moving multiple vehicle image. The features of moving vehicles can be directly extracted from the segmented images. The proposed algorithm has no the two preprocessing steps, so it can reduce noises which are norm at in preprocessing of the original images. The algorithm is more simplified using logic operations. The proposed algorithm is evaluated on an outdoor video sequence with moving multiple vehicles in 90,000 frames of 30fps by a low-end video camera and produces promising results.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.3
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• pp.166-171
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• 2009

In recent years, video detection systems have been implemented in various infrastructures such as airport, public transportation, power generation system, water dam and so on. Recognizing moving objects in video sequence is an important problem in computer vision, with applications in several fields, such as video surveillance and target tracking. Segmentation and tracking of multiple vehicles in crowded situations is made difficult by inter-object occlusion. In the system described in this paper, the mean shift algorithm is firstly used to filter and segment a color vehicle image in order to get candidate regions. These candidate regions are then analyzed and classified in order to decide whether a candidate region contains a license plate or not. And then some characters in the license plate is recognized by using the fuzzy ARTMAP neural network, which is a relatively new architecture of the neural network family and has the capability to learn incrementally unlike the conventional BP network. We finally design a license plate recognition system using the mean shift algorithm and fuzzy ARTMAP neural network and show its performance via some computer simulations.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.5
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• pp.120-125
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• 2009

For effective noise removal prior to video processing, noise power or noise variance of an input video sequence needs to be found exactly, but it is actually a very difficult process. This paper presents an accurate noise variance estimation algorithm based on motion compensation between two adjacent noisy pictures. Firstly, motion estimation is performed for each block in a picture, and the residue variance of the best motion-compensated block is calculated. Then, a noise variance estimate of the picture is obtained by adaptively averaging and properly scaling the variances close to the best variance. The simulation results show that the proposed noise estimation algorithm is very accurate and stable irrespective of noise level.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.2C
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• pp.41-48
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• 2005

In this paper, we present a low complexity modified Gaussian model based pre-processing filter to improve the performance of H.264 compressed video. Video sequence captured by general imaging system represents the degraded version due to the additive noise which decreases coding efficiency and results in unpleasant coding artifacts due to higher frequency components. By incorporating local statistics and quantization parameter into filtering process, the spurious noise is significantly attenuated and coding efficiency is improved for given quantization step size. In addition, in order to reduce the complexity of the pre-processing filter, the simplified local statistics and quantization parameter are introduced. The simulation results show the capability of the proposed algorithm.

• Journal of Chest Surgery
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• v.54 no.4
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• pp.239-245
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• 2021

Video-assisted thoracoscopic surgery (VATS) has been established as the surgical approach of choice for lobectomy in patients with early-stage non-small cell lung cancer (NSCLC). Patients with clinical stage I NSCLC with no lymph node metastasis are considered candidates for VATS lobectomy. To rule out the presence of metastasis to lymph nodes or distant organs, patients should undergo meticulous clinical staging. Assessing patients' functional status is required to ensure that there are no medical contraindications, such as impaired pulmonary function or cardiac comorbidities. Although various combinations of the number, size, and location of ports are available, finding the best method of port placement for each surgeon is fundamental to maximize the efficiency of the surgical procedure. When conducting VATS lobectomy, it is always necessary to comply with the following oncological principles: (1) the vessels and bronchus of the target lobe should be individually divided, (2) systematic lymph node dissection is mandatory, and (3) touching the lymph node itself and rupturing the capsule of the lymph node should be minimized. Most surgeons conduct the procedure in the following sequence: (1) dissection along the hilar structure, (2) fissure division, (3) perivascular and peribronchial dissection, (4) individual division of the vessels and bronchus, (5) specimen retrieval, and (6) mediastinal lymph node dissection. Surgeons should obtain experience in enhancing the exposure of the dissection target and facilitating dissection. This review article provides the basic principles of the surgical techniques and practical maneuvers for performing VATS lobectomy easily, safely, and efficiently.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.574-578
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• 2009

Real-time object detection for distinguishing a moving object of interests from the background image in still image or video image sequence is an essential step to a correct object tracking and recognition. Moving cast shadow can be misclassified as part of objects or moving objects because the shadow region is included in the moving object region after object segmentation. For this reason, an algorithm for shadow removal plays an important role in the results of accurate moving object detection and tracking systems. To handle with the problems, an accurate algorithm based on the features of moving object and shadow in color space is presented in this paper. Experimental results show that the proposed algorithm is effective to detect a moving object and to remove shadow in test video sequences.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3991-4007
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• 2022

In the public healthcare, a computational system that can automatically and efficiently detect and classify falls from a video sequence has significant potential. With the advancement of deep learning, which can extract temporal and spatial information, has become more widespread. However, traditional 3D CNNs that usually adopt shallow networks cannot obtain higher recognition accuracy than deeper networks. Additionally, some experiences of neural network show that the problem of gradient explosions occurs with increasing the network layers. As a result, an enhanced three-dimensional ResNet-based method for fall detection (3D-ERes-FD) is proposed to directly extract spatio-temporal features to address these issues. In our method, a 50-layer 3D residual network is used to deepen the network for improving fall recognition accuracy. Furthermore, enhanced residual units with four convolutional layers are developed to efficiently reduce the number of parameters and increase the depth of the network. According to the experimental results, the proposed method outperformed several state-of-the-art methods.