• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.101-108
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• 2024

AI-OCR (Artificial Intelligence Optical Character Recognition) combines OCR technology with Artificial Intelligence to overcome limitations that required human intervention. To enhance the performance of AI-OCR, training on diverse data sets is essential. However, the recognition rate declines when image colors have similar brightness levels. To solve this issue, this study employs Homomorphic filtering as a preprocessing step to clearly differentiate color levels, thereby increasing text recognition rates. While Homomorphic filtering is ideal for text extraction because of its ability to adjust the high and low frequency components of an image separately using a gamma value, it has the downside of requiring manual adjustments to the gamma value. This research proposes a range for gamma threshold values based on tests involving image contrast, brightness, and entropy. Experimental results using the proposed range of gamma values in Homomorphic filtering suggest a high likelihood for effective AI-OCR performance.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.6
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• pp.58-66
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• 1997

This paper proposes a multiresolution surface parameter estimation method for range images. Based on robust estimation of surface parameters, it approximates a patch to a planar surface in the locally adaptive window. Selection of resolution is made pixelwise by comparing a locally computed homogeneity measure with th eglobal threshold determined by te distribution of the approximation error. The proposed multiresolution surface parameter estimation method is applied to range image reconstruction. Computer simulation results with noisy rnag eimages contaminated by additive gaussian noise and impulse noise show that the proposed multiresolution reconstruction method well preserves step and roof edges compared with the conventional methods. Also the segmentation method based on the estimated surface parameters is shown to be robust to noise.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.1121-1139
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• 2015

In texture-plus-depth format, depth map compression is an important task. Different from normal texture images, depth maps have less texture information, while contain many homogeneous regions separated by sharp edges. This feature will be employed to form an efficient depth map coding scheme in this paper. Firstly, the histogram of the depth map will be analyzed to find an appropriate threshold that segments the depth map into the foreground and background regions, allowing the edge between these two kinds of regions to be obtained. Secondly, the two regions will be encoded through rate distortion optimization with a shape adaptive wavelet transform, while the edges are lossless encoded with JBIG2. Finally, a depth-updating algorithm based on the threshold and the depth range is applied to enhance the quality of the decoded depth maps. Experimental results demonstrate the effective performance on both the depth map quality and the synthesized view quality.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.8
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• pp.17-22
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• 2016

For the self-adaptive equalization of higher-order QAM systems, this paper proposes a new soft decision-directed (SDD) algorithm that opens the eye patterns quickly as well as significantly reducing the error level in the steady-state when it is applied to the initial equalization stage with completely closed eye patterns. The proposed method for M-QAM application minimized the computational complexity of the existing SDD by the symbol estimated based on the two symbols closest to the observation, and greatly simplified the soft decision independently of the QAM order. Furthermore, in the symbol estimating it increased the reliability of the estimates by applying the superior properties of the sigmoid function and avoiding the erroneous estimation of the threshold function. In addition, the initialization performance was improved when an error is generated to update the equalizer, weighting the symbol decision by the threshold function to the error, resulting in an extension of the range of error fluctuations. As a result, the proposed method improves remarkably the computational complexity and the properties of initialization and convergence of the traditional SDD. Through simulations for 64-QAM and 256-QAM under multipath channel conditions with additive noise, the usefulness of the proposed methods was confirmed by comparing the performance of the proposed 2-SDD and two forms of weighted 2-SDD with CMA.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.106-116
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• 2024

Ensuring robust 3D object detection is a core challenge for autonomous driving systems operating in urban environments. To tackle this issue, various 3D representation, including point cloud, voxels, and pillars, have been widely adopted, making use of LiDAR, Camera, and Radar sensors. These representations improved 3D object detection performance, but real-world urban scenarios with unexpected situations can still lead to numerous false positives, posing a challenge for robust 3D models. This paper presents a post-processing algorithm that dynamically adjusts object detection thresholds based on the distance from the ego-vehicle. While conventional perception algorithms typically employ a single threshold in post-processing, 3D models perform well in detecting nearby objects but may exhibit suboptimal performance for distant ones. The proposed algorithm tackles this issue by employing adaptive thresholds based on the distance from the ego-vehicle, minimizing false negatives and reducing false positives in the 3D model. The results show performance enhancements in the 3D model across a range of scenarios, encompassing not only typical urban road conditions but also scenarios involving adverse weather conditions.

• Korean Journal of Radiology
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• v.22 no.5
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• pp.688-698
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• 2021

Objective: To compare the lumen parameters measured by the location-adaptive threshold method (LATM), in which the inter- and intra-scan attenuation variabilities of coronary computed tomographic angiography (CCTA) were corrected, and the scan-adaptive threshold method (SATM), in which only the inter-scan variability was corrected, with the reference standard measurement by intravascular ultrasonography (IVUS). Materials and Methods: The Hounsfield unit (HU) values of whole voxels and the centerline in each of the cross-sections of the 22 target coronary artery segments were obtained from 15 patients between March 2009 and June 2010, in addition to the corresponding voxel size. Lumen volume was calculated mathematically as the voxel volume multiplied by the number of voxels with HU within a given range, defined as the lumen for each method, and compared with the IVUS-derived reference standard. Subgroup analysis of the lumen area was performed to investigate the effect of lumen size on the studied methods. Bland-Altman plots were used to evaluate the agreement between the measurements. Results: Lumen volumes measured by SATM was significantly smaller than that measured by IVUS (mean difference, 14.6 mm³; 95% confidence interval [CI], 4.9-24.3 mm³); the lumen volumes measured by LATM and IVUS were not significantly different (mean difference, -0.7 mm³; 95% CI, -9.1-7.7 mm³). The lumen area measured by SATM was significantly smaller than that measured by LATM in the smaller lumen area group (mean of difference, 1.07 mm²; 95% CI, 0.89-1.25 mm²) but not in the larger lumen area group (mean of difference, -0.07 mm²; 95% CI, -0.22-0.08 mm²). In the smaller lumen group, the mean difference was lower in the Bland-Altman plot of IVUS and LATM (0.46 mm²; 95% CI, 0.27-0.65 mm²) than in that of IVUS and SATM (1.53 mm²; 95% CI, 1.27-1.79 mm²). Conclusion: SATM underestimated the lumen parameters for computed lumen segmentation in CCTA, and this may be overcome by using LATM.

• The Journal of Korea Robotics Society
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• v.7 no.2
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• pp.142-149
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• 2012

The camera has limitations of poor visibility in underwater environment due to the limited light source and medium noise of the environment. However, its usefulness in close range has been proved in many studies, especially for navigation. Thus, in this paper, vision-based object detection and tracking techniques using artificial objects for underwater robots have been studied. We employed template matching and mean shift algorithms for the object detection and tracking methods. Also, we propose the weighted correlation coefficient of adaptive threshold -based and color-region-aided approaches to enhance the object detection performance in various illumination conditions. The color information is incorporated into the template matched area and the features of the template are used to robustly calculate correlation coefficients. And the objects are recognized using multi-template matching approach. Finally, the water basin experiments have been conducted to demonstrate the performance of the proposed techniques using an underwater robot platform yShark made by KORDI.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.11
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• pp.866-875
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• 1991

In this paper, multi pulse excited-linear predictive coding (MPE-LPC), where the correlation eliminated residual signal is modeled by a few pules, is shown to be effective for the compression of electrocardiogram (ECG) data, and a more efficient scheme for a faithful reconstruction of ECG is proposed. The reconstruction charateristic of QRS's and P.T waves is improved using the adaptive pulse allocation (APA), and the compression ratio (CR) can be changed by controlling the mumber of modeling pulses. The performance of the proposed method was evaluated using 10 normal and 10 abnormal ECG data. The proposed method had a better performance than the variable threshold amplitude zone time epoch coding (AZTEC) algorithm and the scan-along polygonal approximation (SAPA) algorithm with the same CR. With the CR in kthe range of 8:1 to 14:1, we could compress ECG data efficiently.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.411-412
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• 2006

In this paper, we proposed a fast algorithm for inter prediction included the most complexity in H.264/AVC. It decide search range according to direction of predicted motion vector, and then perform adaptive candidate spiral search. Simultaneously, it perform motion estimation of variable loop with threshold for variable block size. Conclusively, it is implemented in JM FME with high complexity applying to rate-distortion optimization. Experimental results show that significant complexity reduction is achieved while the degradation in video quality is negligible.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.11C
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• pp.1077-1084
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• 2006

This paper proposed a fast algorithm to reduce the amount of calculation for inter prediction which takes a great deal of the operational time in H.264/AVC. This algorithm decides a search range according to the direction of predicted motion vector, and then performs an adaptive spiral search for the candidates with JM(Joint Model) FME(Fast Motion Estimation) which employs the rate-distortion optimization(RDO) method. Simultaneously, it decides a threshold cost value for each of the variable block sizes and performs the motion estimation for the variable search ranges with the threshold. These activities reduce the great amount of the complexity in inter prediction encoding. Experimental results by applying the proposed method .to various video sequences showed that the process time was decreased up to 80% comparing to the previous prediction methods. The degradation of video quality was only from 0.05dB to 0.19dB and the compression ratio decreased as small as 0.58% in average. Therefore, we are sure that the proposed method is an efficient method for the fast inter prediction.