• Journal of Korea Multimedia Society
• /
• v.25 no.4
• /
• pp.568-574
• /
• 2022

In this paper, direction normalization and CNN deep learning are used to develop a more reliable license plate recognition system. The existing license plate recognition system consists of three main modules: license plate detection module, character segmentation module, and character recognition module. The proposed system minimizes recognition error by adding a direction normalization module when a detected license plate is inclined. Experimental results show the superiority of the proposed method in comparison to the previous system.

• Kyungpook Mathematical Journal
• /
• v.55 no.4
• /
• pp.1053-1067
• /
• 2015

A two-direction multiscaling function ${\phi}$ satisfies a recursion relation that uses scaled and translated versions of both itself and its reverse. This offers a more general and flexible setting than standard one-direction wavelet theory. In this paper, we investigate how to find and normalize point values and derivative values of two-direction multiscaling and multiwavelet functions. Determination of point values is based on the eigenvalue approach. Normalization is based on normalizing conditions for the continuous moments of ${\phi}$. Examples for illustrating the general theory are given.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.51 no.6
• /
• pp.267-271
• /
• 2002

The Hotelling transform is based on statistical properties of an image. The principal uses of this transform are in data compression. The basic concept of the Hotelling transform is that the choice of basis vectors pointing the direction of maximum variance of the data. This property can be used for rotation normalization. Many objects of interest in pattern recognition applications can be easily standardized by performing a rotation normalization that aligns the coordinate axes with the axes of maximum variance of the pixels in the object. However, this transform can not be used to rotation normalization of color images directly. In this paper, we propose a new method for rotation normalization of color images based on the Hotelling transform. The Hotelling transform is performed to calculate basis vectors of each channel. Then the summation of vectors of all channels are processed. Rotation normalization is performed using the result of summation of vectors. Experimental results showed the proposed method can be used for rotation normalization of color images effectively.

• The Journal of the Korea Contents Association
• /
• v.5 no.4
• /
• pp.94-102
• /
• 2005

In this paper, three axial accelerometer was used to develop a small sensor module, which was attached to human body to calculate the acceleration in gravity direction by human motion, when it was positioned in any direction. To measure its wearer's walking or running motion using the sensor module, the acquired sensor data was pre-processed to enable its quantitative analysis. The acquired digital data was transformed to orthogonal coordinate value in three dimension and calculated to be single scalar acceleration data in gravity direction and normalized to be physical unit value. The normalized sensor data was used to detect walking pattern and calculate their step counts. Developed algorithm was implemented in the form of PDA application. The accuracy of the developed sensor to detect step count was about 97% in laboratory experiment.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.30B no.6
• /
• pp.76-85
• /
• 1993

In this paper, we propose a stroke matching method for the off-line recognition of handprinted Hangul. In this method, the preprocessing steps such as position normalization, contour tracing and thinning are carried out first. Then, after extracting features such as the firection component distribution of contour, the direction component distribution of skeleton, and the distribution of structural feature points, strokes are extracted and matched based on the midpont distribution of the direction and the length of each stroke. In order to reduce the recognition time, a preliminary classification based on the direction component distribution features of the contour is performed. In order to domonstrate the performance of the proposed method, experiments with 520 most frequently used Hangul were performed, and 90.7% of correct recognition rate and 0.46second of recognition time per one character has been obtained. This results reveal that the proposed method can absorb effectively the noise in input character and the variations of stroke slant.

• Journal of the Korean Society of Radiology
• /
• v.13 no.1
• /
• pp.111-118
• /
• 2019

Chest digital tomosynthesis has become a practical imaging modality because it can solve the problem of anatomy overlapping in conventional chest radiography. However, because of both limited scan angle and finite-size detector, a portion of chest cannot be represented in some or all of the projection. These bring a discontinuity in intensity across the field of view boundaries in the reconstructed slices, which we refer to as the truncation artifacts. The purpose of this study was to reduce truncation artifacts using a weighted normalization approach and to investigate the performance of this approach for our prototype chest digital tomosynthesis system. The system source-to-image distance was 1100 mm, and the center of rotation of X-ray source was located on 100 mm above the detector surface. After obtaining 41 projection views with ${\pm}20^{\circ}$ degrees, tomosynthesis slices were reconstructed with the filtered back projection algorithm. For quantitative evaluation, peak signal to noise ratio and structure similarity index values were evaluated after reconstructing reference image using simulation, and mean value of specific direction values was evaluated using real data. Simulation results showed that the peak signal to noise ratio and structure similarity index was improved respectively. In the case of the experimental results showed that the effect of artifact in the mean value of specific direction of the reconstructed image was reduced. In conclusion, the weighted normalization method improves the quality of image by reducing truncation artifacts. These results suggested that weighted normalization method could improve the image quality of chest digital tomosynthesis.

• Journal of the Optical Society of Korea
• /
• v.18 no.4
• /
• pp.317-329
• /
• 2014

In vision measurement systems based on structured light, the key point of detection precision is to determine accurately the central position of the projected laser line in the image. The purpose of this research is to extract laser line centers based on a decision function generated to distinguish the real centers from candidate points with a high recognition rate. First, preprocessing of an image adopting a difference image method is conducted to realize image segmentation of the laser line. Second, the feature points in an integral pixel level are selected as the initiating light line centers by the eigenvalues of the Hessian matrix. Third, according to the light intensity distribution of a laser line obeying a Gaussian distribution in transverse section and a constant distribution in longitudinal section, a normalized model of Hessian matrix eigenvalues for the candidate centers of the laser line is presented to balance reasonably the two eigenvalues that indicate the variation tendencies of the second-order partial derivatives of the Gaussian function and constant function, respectively. The proposed model integrates a Gaussian recognition function and a sinusoidal recognition function. The Gaussian recognition function estimates the characteristic that one eigenvalue approaches zero, and enhances the sensitivity of the decision function to that characteristic, which corresponds to the longitudinal direction of the laser line. The sinusoidal recognition function evaluates the feature that the other eigenvalue is negative with a large absolute value, making the decision function more sensitive to that feature, which is related to the transverse direction of the laser line. In the proposed model the decision function is weighted for higher values to the real centers synthetically, considering the properties in the longitudinal and transverse directions of the laser line. Moreover, this method provides a decision value from 0 to 1 for arbitrary candidate centers, which yields a normalized measure for different laser lines in different images. The normalized results of pixels close to 1 are determined to be the real centers by progressive scanning of the image columns. Finally, the zero point of a second-order Taylor expansion in the eigenvector's direction is employed to refine further the extraction results of the central points at the subpixel level. The experimental results show that the method based on this normalization model accurately extracts the coordinates of laser line centers and obtains a higher recognition rate in two group experiments.

• International conference on construction engineering and project management
• /
• 2025.11a
• /
• pp.540-547
• /
• 2025

Struck-by incidents are a leading cause of fatalities on construction sites, especially involving collisions with heavy equipment during reversing. However, gathering real-world data on these hazards is challenging due to ethical and physical limitations, as it hampers data-driven safety development. To overcome this, this study introduces a bounding box-based framework that generates realistic risk scenarios using a text-to-image method with the Stable Diffusion model. The framework encodes important risk factors, such as worker postures (head-down or kneeling) and equipment orientation (front or rear), through specially designed prompts. Equipment direction, vital for identifying back-over incidents, is automatically classified using a ResNet18-based direction recognition model. Additionally, size and distance normalization techniques based on bounding boxes are used to extract features for predicting risk levels. Results show the direction classifier achieves 87.3% accuracy in differentiating front- and rear-facing machinery. The combined normalized distance and size data support consistent risk grading based on posture and direction. This research explores the technical feasibility and potential of synthetic image-based models, with future work planned to validate generalization to real-world video data and expand scenarios.

• Proceedings of the KIEE Conference
• /
• 1992.07a
• /
• pp.462-464
• /
• 1992

In this paper, an extraction of depth intonation in pattern using neural network is presented. All the 3D images represent the depth information in grey pixels. This pixels which have analog values translated digital values. Because of the noise and distortion in pattern, we use the normalization in learning and recalling the patterns. Our method has eight direction vectors and slopes for pattern. Also, we use potential to obtain the mean slope and direction vectors of given 3D patches. The higher level of deduction finding the global depth information is also carried out by using neural network.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.10
• /
• pp.2489-2496
• /
• 2014

The existing ship repair tool management system based on hand writing has many problems such as frequent loss of tool and overdue. To solve this problem, same systems have adopted the bar-code system. However, the systems can't cope with a problem to substitute spurious tool for genuine one on bar-code damage. Therefore, additional validation steps are necessary in order to manage expensive ship repair tool. In this paper, we propose an image comparison method for ship repair tool management. To be more concrete, we propose a normalization method and determination conditions for image comparison to use characteristics of mobile device. The normalization method makes use of the characteristics of mobile device that provides functions of real time recording, overlapping and cropping images. The proposed method applies three conditions(sum of inner angles, size of angle, position of corner coordinates) into the comparison module. The implemented system shows good performance on change direction, lighting, size and etc. The accuracy is more than 95%.