• The Journal of Korean Institute of Communications and Information Sciences
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• v.37C no.12
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• pp.1271-1280
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• 2012

In the recent parking assistant systems, finding out the distance to the object behind a car is often performed by the range sensors such as ultrasonic sensors, radars. However, the installation of additional sensors on the used vehicle could be difficult and require extra cost. On the other hand, the motion stereo technique that extracts distance information using only an image sensor was also proposed. However, In the stereo rectification step, the motion stereo requires good features and exacts matching result. In this paper, we propose a fast algorithm that extracts the accurate distance information for the parallel parking situation using the consecutive images that is acquired by a rear-view camera. The proposed algorithm uses the quadrangle transform of the image, the horizontal line integral projection, and the blocking-based correlation measurement. In the experiment with the magna parallel test sequence, the result shows that the line-accurate distance measurement with the image sequence from the rear-view camera is possible.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.29-37
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• 2024

In this paper, we propose an efficient image processing system to detect and track the movement of specific objects such as patients. The proposed system extracts the outline area of an object from a binarized difference image by applying a thinning algorithm that enables more precise detection compared to previous algorithms and is advantageous for mixed-mode design. The binarization and thinning steps, which require a lot of computation, are designed based on RTL (Register Transfer Level) and replaced with optimized hardware blocks through logic circuit synthesis. The designed binarization and thinning block was synthesized into a logic circuit using the standard 180n CMOS library and its operation was verified through simulation. To compare software-based performance, performance analysis of binary and thinning operations was also performed by applying sample images with 640 × 360 resolution in a 32-bit FPGA embedded system environment. As a result of verification, it was confirmed that the mixed-mode design can improve the processing speed by 93.8% in the binary and thinning stages compared to the previous software-only processing speed. The proposed mixed-mode system for object recognition is expected to be able to efficiently monitor patient movements even in an edge computing environment where artificial intelligence networks are not applied.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.11
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• pp.41-47
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• 2016

High efficiency video coding (HEVC) employs a coding tree unit (CTU) to improve the coding efficiency. A CTU consists of coding units (CU), prediction units (PU), and transform units (TU). All possible block partitions should be performed on each depth level to obtain the best combination of CUs, PUs, and TUs. To reduce the complexity of block partitioning process, this paper proposes the PU mode skip algorithm with region of interest (RoI) selection using motion vector. In addition, this paper presents the CU depth level skip algorithm using the co-located block information in the previously encoded frames. First, the RoI selection algorithm distinguishes between dynamic CTUs and static CTUs and then, asymmetric motion partitioning (AMP) blocks are skipped in the static CTUs. Second, the depth level skip algorithm predicts the most probable target depth level from average depth in one CTU. The experimental results show that the proposed fast CU decision algorithm can reduce the total encoding time up to 44.8% compared to the HEVC test model (HM) 14.0 reference software encoder. Moreover, the proposed algorithm shows only 2.5% Bjontegaard delta bit rate (BDBR) loss.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.114-125
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• 2002

In this paper, we are proposing a hierarchical segmentation method that first segments the video data into units of shots by detecting cut and dissolve, and then decides types of camera operations or object movements in each shot. In our previous work[1], each picture group is divided into one of the three detailed categories, Shot(in case of scene change), Move(in case of camera operation or object movement) and Static(in case of almost no change between images), by analysing DC(Direct Current) component of I(Intra) frame. In this process, we have designed two-stage hierarchical neural network with inputs of various multiple features combined. Then, the system detects the accurate shot position, types of camera operations or object movements by searching P(Predicted), B(Bi-directional) frames of the current picture group selectively and hierarchically. Also, the statistical distributions of macro block types in P or B frames are used for the accurate detection of cut position, and another neural network with inputs of macro block types and motion vectors method can reduce the processing time by using only DC coefficients of I frames without decoding and by searching P, B frames selectively and hierarchically. The proposed method classified the picture groups in the accuracy of 93.9-100.0% and the cuts in the accuracy of 96.1-100.0% with three different together is used to detect dissolve, types of camera operations and object movements. The proposed types of video data. Also, it classified the types of camera movements or object movements in the accuracy of 90.13% and 89.28% with two different types of video data.

• Journal of the Korea Society of Computer and Information
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• v.19 no.7
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• pp.141-149
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• 2014

This paper describes a method to insert and detect watermark or fingerprint to/from videos in low-computing powered system as Android platforms. Fingerprint, which is a kind of watermark, has features such as patterns that contain information. The inserting frame numbers in video-stream and the positions in a picture were chosen from the encrypted user ID to insert the watermarks. The used encrypt algorithm is the HIGHT algorithm which was developed for low-computing powered systems by KISA(Korean Internet & Security Agency). Subtracting an inferred picture from the previous picture was used to extract a candidate feature. Median filtering was used to get rid of noise and stabilize the candidate feature. New algorithm that reduces calculating steps of the median filtering was developed and applied for low-specification systems. The stabilized features were accumulated over 150 times and calculated by correlation coefficient method to recognize the patterns. We examined 22 videos and successfully detected the patterns from 21 videos. The correlation coefficient r values that we examined through this study exceeded over 0.79 more than the threshold (0.7).

• Journal of Broadcast Engineering
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• v.7 no.1
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• pp.45-54
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• 2002

This paper proposes a video compression algorithm using characteristics of wavelet coefficients. The proposed algorithm can provide lowed bit rate and faster running time while guaranteeing the reconstructed image qualify by the human virtual system. In this approach, each video sequence is decomposed into a pyramid structure of subimages with various resolution to use multiresolution capability of discrete wavelet transform. Then similarities between two neighboring frames are obtained from a low-frequency subband which Includes an important information of an image and motion informations are extracted from the similarity criteria. Four legion selection filters are designed according to the similarity criteria and compression processes are carried out by encoding the coefficients In preservation legions and replacement regions of high-frequency subbands. Region selection filters classify the high-frequency subbands Into preservation regions and replacement regions based on the similarity criteria and the coefficients In replacement regions are replaced by that of a reference frame or reduced to zero according to block-based similarities between a reference frame and successive frames. Encoding is carried out by quantizing and arithmetic encoding the wavelet coefficients in preservation regions and replacement regions separately. A reference frame is updated at the bottom point If the curve of similarity rates looks like concave pattern. Simulation results show that the proposed algorithm provides high compression ratio with proper Image quality. It also outperforms the previous Milton's algorithm in an Image quality, compression ratio and running time, leading to compression ratio less than 0.2bpp. PSNR of 32 dB and running tome of 10ms for a standard video image of size 352${\times}$240 pixels.