• The KIPS Transactions:PartB
• /
• v.18B no.4
• /
• pp.221-230
• /
• 2011

Digital comic images are one of popular contents on the Internet. Usually, they are scanned from comic books by digital scanners. Without post-processing, they may have different sizes, skews and margins other than contents at the boundary. To normalize the size of their contents without the skews and margins is an important step in comic image analysis and application such as content-based comic image retrieval system. In this paper, we propose a method to detect a box frame in comic images by extracting of line segments using the gradient Radon transform. The box frame in comic images is the maximum rectangle which consists of contents without margins. We use the detected box frame to normalize the size of comic images and to make them no skew. In addition, the proposed method is implemented by OpenCL to speed up the detection of the line segments. Experimental results show that our proposed method effectively detects the box frame in comic images.

• Journal of Korea Society of Industrial Information Systems
• /
• v.9 no.1
• /
• pp.92-102
• /
• 2004

Performance assessment was introduced to improve self-directed learning and method of assessment for differenced learning when the seventh educational curriculum was enforced. Written examinations often fail to properly assess students higher thinking abilities ad problem solving abilities. Performance assessment addresses this drawback and also allows normalization of class and school quality. However, performance assessment also has drawbacks that could lead to faulty assessment due to lack of fairness, reliability and validity of grading, ambiguity of grading standard etc. This study proposes a fuzzy performance assessment system to address the drawbacks of the conventional performance assessment. This paper presents in objective and reliable performance assesment method through fuzzy reasoning, design of fuzzy membership function. We define a fuzzy rule analyzing factor that influences in each sacred ground of performance assessment and accounts for the principle subject The proposed performance assessment method divides into three categories, namely, formation estimation subject estimation and design of membership function. Performance assessment result that is worked through fuzzy performance assessment system can reduce the burden of appraisal's fault and provide. We fair and reliable assessment results through grading that have correct standard mid consistency to students.

• Journal of KIISE:Databases
• /
• v.30 no.4
• /
• pp.362-371
• /
• 2003

In this paper, we propose an optimal interest point extraction method to support shape-base image classification and indexing for image database by applying a dynamic threshold that reflects the characteristics of the shape contour. The threshold is determined dynamically by comparing the contour length ratio of the original shape and the approximated polygon while the algorithm is running. Because our algorithm considers the characteristics of the shape contour, it can minimize the number of interest points. For n points of the contour, the proposed algorithm has O(nlogn) computational cost on an average to extract the number of m optimal interest points. Experiments were performed on the 70 synthetic shapes of 7 different contour types and 1100 fish shapes. It shows the average optimization ratio up to 0.92 and has 14% improvement, compared to the fixed threshold method. The shape features extracted from our proposed method can be used for shape-based image classification, indexing, and similarity search via normalization.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.7
• /
• pp.1243-1248
• /
• 2008

As development in technology of bioinformatics recently mates it possible to operate micro-level experiments, we can observe the expression pattern of total genome through on chip and analyze the interactions of thousands of genes at the same time. In this thesis, we used CDNA microarrays of 3840 genes obtained from neuronal differentiation experiment of cortical stem cells on white mouse with cancer. It analyzed and compared performance of each of the experiment result using existing DT, NB, SVM and multi-perceptron neural network classifier combined the similar scale combination method after constructing class classification model by extracting significant gene list with a similar scale combination method proposed in this paper through normalization. Result classifying in Multi-Perceptron neural network classifier for selected 200 genes using combination of PC(Pearson correlation coefficient) and ED(Euclidean distance coefficient) represented the accuracy of 98.84%, which show that it improve classification performance than case to experiment using other classifier.

• Journal of the Korea Convergence Society
• /
• v.13 no.3
• /
• pp.33-41
• /
• 2022

In this article, we proposed to predict natural gas (NG) leakage levels through feature selection based on a factor analysis (FA) of the integrating the Korean Meteorological Agency data and natural gas leakage data for considering complex factors. The paper has been divided into three modules. First, we filled missing data based on the linear interpolation method on the integrated data set, and selected essential features using FA with OrdinalEncoder (OE)-based normalization. The dataset is labeled by K-means clustering. The final module uses four algorithms, K-nearest neighbors (KNN), decision tree (DT), random forest (RF), Naive Bayes (NB), to predict gas leakage levels. The proposed method is evaluated by the accuracy, area under the ROC curve (AUC), and mean standard error (MSE). The test results indicate that the OrdinalEncoder-Factor analysis (OE-F)-based classification method has improved successfully. Moreover, OE-F-based KNN (OE-F-KNN) showed the best performance by giving 95.20% accuracy, an AUC of 96.13%, and an MSE of 0.031.

• Journal of the Korea Computer Graphics Society
• /
• v.29 no.1
• /
• pp.1-11
• /
• 2023

Deep-learning-based image segmentation is one of the most widely employed lane detection approaches, and it requires a post-process for extracting the key points on the lanes. A general approach for key-point extraction is using a fixed threshold defined by a user. However, finding the best threshold is a manual process requiring much effort, and the best one can differ depending on the target data set (or an image). We propose a novel key-point extraction algorithm that automatically adapts to the target image without any manual threshold setting. In our adaptive key-point extraction algorithm, we propose a line-level normalization method to distinguish the lane region from the background clearly. Then, we extract a representative key point for each lane at a line (row of an image) using a kernel density estimation. To check the benefits of our approach, we applied our method to two lane-detection data sets, including TuSimple and CULane. As a result, our method achieved up to 1.80%p and 17.27% better results than using a fixed threshold in the perspectives of accuracy and distance error between the ground truth key-point and the predicted point.

• Current Optics and Photonics
• /
• v.7 no.4
• /
• pp.362-377
• /
• 2023

Optical information processing technology is characterized by high speed and parallelism, and the light features short wavelength and large information capacity; At the same time, it has various attributes including amplitude, phase, wavelength and polarization, and is a carrier of multi-dimensional information. Therefore, optical encryption is of great significance in the field of information security transmission, and is widely used in the field of image encryption. For multi-image encryption, this paper proposes a multi-image encryption algorithm based on a modified Gerchberg-Saxton algorithm (MGSA) in the Fresnel-transform domain and computational ghost imaging. First, MGSA is used to realize "one code, one key"; Second, phase function superposition and normalization are used to reduce the amount of ciphertext transmission; Finally, computational ghost imaging is used to improve the security of the whole encryption system. This method can encrypt multiple images simultaneously with high efficiency, simple calculation, safety and reliability, and less data transmission. The encryption effect of the method is evaluated by using correlation coefficient and structural similarity, and the effectiveness and security of the method are verified by simulation experiments.

• Archives of Craniofacial Surgery
• /
• v.25 no.1
• /
• pp.17-21
• /
• 2024

Background: The pain caused by local anesthetic injection can lead to patient anxiety prior to surgery, potentially necessitating sedation or general anesthesia during the excision procedure. In this study, we aim to compare the pain relief efficacy and safety of using a digital automatic anesthetic injector for local anesthesia. Methods: Thirty-three patients undergoing excision of a benign soft tissue tumor under local anesthesia were prospectively enrolled from September 2021 to February 2022. A single-blind, randomized controlled study was conducted. Patients were divided into two groups by randomization: the experimental group with digital automatic anesthetic injector method (I-JECT group) and the control group with conventional injection method. Before surgery, the Amsterdam preoperative anxiety information scale was used to measure the patients' anxiety. After local anesthetic was administered, the Numeric Pain Rating Scale was used to measure the pain. The amount of anesthetic used was divided by the surface area of the lesion was recorded. Results: Seventeen were assigned to the conventional group and 16 to the I-JECT group. The mean Numeric Pain Rating Scale was 1.75 in the I-JECT group and 3.82 in conventional group. The injection pain was lower in the I-JECT group (p< 0.01). The mean Amsterdam preoperative anxiety information scale was 11.00 in the I-JECT group and 9.65 in conventional group. Patient's anxiety did not correlate to injection pain regardless of the method of injection (p= 0.47). The amount of local anesthetic used per 1 cm² of tumor surface area was 0.74 mL/cm² in the I-JECT group and 2.31 mL/cm² in the conventional group. The normalization amount of local anesthetic was less in the I-JECT group (p< 0.01). There was no difference in the incidence of complications. Conclusion: The use of a digital automatic anesthetic injector has shown to reduce pain and the amount of local anesthetics without complication.

• Journal of Advanced Navigation Technology
• /
• v.5 no.1
• /
• pp.1-10
• /
• 2001

Speech has a good characteristics of which car drivers busy to concern with miscellaneous operation can make use in convenient handling and manipulating of devices. By utilizing this, this works proposes a speaker verification method for protecting cars from being stolen and identifying a person trying to access critical on-line services. In this, continuant phonemes recognition which uses language information of speech and MLP(mult-layer perceptron) which has some advantages against previous stochastic methods are adopted. The recognition method, though, involves huge computation amount for learning, so it is somewhat difficult to adopt this in speaker verification application in which speakers should enroll themselves at real time. To relieve this problem, this works presents a solution that introduces speaker cohort models from speaker verification score normalization technique established before, dividing background speakers into small cohorts in advance. As a result, this enables computation burden to be reduced through classifying the enrolling speaker into one of those cohorts and going through enrollment for only that cohort.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.53 no.11
• /
• pp.82-88
• /
• 2016

In this paper, we propose an intelligibility enhancement algorithm for multimedia contents using spectral shaping. The dialogue signals is essential to understand the plot of audio-visual media contents such as movie and TV. However, the non-dialogue components as like sound effects and background music often degrade the dialogue clarity. To overcome this problem, this paper tries to improves the dialogue clarity of audio soundtracks which contain important cues for the visual scenes. In the proposed method, the dialogue components are first detected by soft masker based on speech presence probability (SPP) which is widely used in speech enhancement field. Then, extracted dialogue signals are applied to the spectral shaping method. It reallocate the spectral-temporal energy of speech to enhanced the intelligibility. The total energy is maintained as unchanged via a loudness normalization process to prevent saturation. The algorithm was evaluated using the modeled and real movie soundtracks and it was shown that the proposed algorithm enhances the dialogue clarity while preserving the total audio power.