• KIPS Transactions on Software and Data Engineering
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• v.3 no.9
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• pp.361-368
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• 2014

The LPR(License plate recognition) system has been developed to efficient control for complex traffic environment and currently be used in many places. However, because of light, noise, background changes, environmental changes, damaged plate, it only works limited environment, so it is difficult to use in real-time. This paper presents a heuristic segmentation algorithm for robust to noise and illumination changes and introduce a real-time license plate recognition system using it. In first step, We detect the plate utilized Haar-like feature and Adaboost. This method is possible to rapid detection used integral image and cascade structure. Second step, we determine the type of license plate with adaptive histogram equalization, bilateral filtering for denoise and segment accurate character based on adaptive threshold, pixel projection and associated with the prior knowledge. The last step is character recognition that used histogram of oriented gradients (HOG) and multi-layer perceptron(MLP) for number recognition and support vector machine(SVM) for number and Korean character classifier respectively. The experimental results show license plate detection rate of 94.29%, license plate false alarm rate of 2.94%. In character segmentation method, character hit rate is 97.23% and character false alarm rate is 1.37%. And in character recognition, the average character recognition rate is 98.38%. Total average running time in our proposed method is 140ms. It is possible to be real-time system with efficiency and robustness.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.1
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• pp.59-68
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• 1999

Recently, research and developmental direction of communication system is concurrent adopting voice data and face image in speaking to provide more higher recognition rate then in the case of only voice data. Therefore, we present a method of lipreading in speech image sequence by using the 3-D facial shape model. The method use a feature information of the face image such as the opening-level of lip, the movement of jaw, and the projection height of lip. At first, we adjust the 3-D face model to speeching face image sequence. Then, to get a feature information we compute variance quantity from adjusted 3-D shape model of image sequence and use the variance quality of the adjusted 3-D model as recognition parameters. We use the intensity inclination values which obtaining from the variance in 3-D feature points as the separation of recognition units from the sequential image. After then, we use discrete HMM algorithm at recognition process, depending on multiple observation sequence which considers the variance of 3-D feature point fully. As a result of recognition experiment with the 8 Korean vowels and 2 Korean consonants, we have about 80% of recognition rate for the plosives and vowels. We propose that usability with visual distinguishing factor that using feature vector because as a result of recognition experiment for recognition parameter with the 10 korean vowels, obtaining high recognition rate.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.508-518
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• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.

• The korean journal of orthodontics
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• v.30 no.5 s.82
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• pp.657-668
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• 2000

The purpose of this study was to evaluate the amount and interrelationship of the soft tissue of nose and maxillary changes and to identify the nasal morphologic features that indicate susceptibility to nasal deflection in such a manner that they would be useful in presurgical prediction of nasal changes after maxillary advancement surgery in skeletal Class III malocclusion. The sample consisted of 25 adult patients (13 males and 12 females) who had severe anteroposterior skeletal discrepancy. The patients had received presurgical orthodontic treatment. They underwent a Le Fort I advancement osteotomy, rigid internal fixation, alar cinch suture and V-Y advancement lip closure. The presurgical and postsurgical lateral cephalograms and lateral and frontal facial photographs were evaluated. The computerized statistical analysis was carried out. Soft tissue of nose change to h point change ratios were calculated by regression equations. The results were as follows 1. The correlation of maxillary hard tissue horizontal changes and nasal soft tissue vortical changes were high and the ${\beta}_0$ for soft tissue to ADV were 0.228 at ANt, 0.257 at SNt. 2. The correlation of maxillary hard tissue and nasal soft tissue horizontal changes were high and the ${\beta}_0$ for soft tissue to ADV were 0.484 at ANt, 0.431 at SNt, 0.806 at Sn. 3. The correlation of maxillary hard tissue horizontal changes and width changes of ala of nose were high and the ${\beta}_0$ lot alar base width ratio to ADV were 0.002. 4. The DRI, Prominence of nose, Pre-Op CA is not a quantitative measure that can be used clinically to improve the predictability of vertical and horizontal nasal tip deflection. In this study, increases in nasal tip projection and anterosuperior rotation occur when there is an anterior vector of maxillary movement. These nasal changes were Quantitatively correlated to magnitude of maxillary(A point) movement.