• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1147-1155
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• 2005

This paper deals with Iris recognition as one of biometric techniques which is applied to identify a person using his/her behavior or congenital characteristics. The Iris of a human eye has a texture that is unique and time invariant for each individual. First, we obtain the feature vector from the 2D Iris pattern having a property of size invariant and using the fuzzy LDA which is further through four types of 2D Gabor wavelet. At the recognition process, we compute the similarity measure based on the correlation values. Here, since we use four different matching values obtained from four different directional Gabor wavelet and select the maximum value, it is possible to minimize the recognition error rate. To show the usefulness of the proposed algorithm, we applied it to a biometric database consisting of 300 Iris Patterns extracted from 50 subjects and finally got more higher than $90\%$ recognition rate.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.716-721
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• 2003

This paper deals with the iris recognition as one of biometric techniques which are applied to identify a person using his/her behavior or congenital characteristics. The iris of a human eye has a texture that is unique and time invariant for each individual. First, we obtain the feature vector from the 2D iris pattern having a property of size invariant and divide it into 24 sectors which are further through three types of 2D Gabor filters. At the recognition process, we compute the similarity measure based on the correlation values. Here, since we use three different matching values obtained from three different directional Gabor filters and select the maximum value among them, it is possible to minimize the recognition error rate. To show the usefulness of the proposed algorithm, we applied it to a biometric database consisting of 50 iris patterns extracted from 10 subjects and finally get more higher than 90% recognition rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.12
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• pp.2836-2842
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• 2010

An NPC grouping method is proposed for various 3D games depending on their characteristics. Immovable objects tend to have particular orientation features in their Gabor filtering results whereas the movable objects controlled by AI appearing as a human or an animal do not. First of all, We analyzed directional and frequency domain features in the NPC object and configured them as 24 Gabor filter banks. Then, 24-dimensional feature vectors according to the scale and direction of the filter are calculated. Each extracted vector represents the energy of a certain direction. This energy indicates the particular direction strength of the object texture. Thus, using this property, NPCs could be grouped as artificial objects and natural objects effectively and it draws the game more speed and strategic actions as a result.

• Journal of Service Research and Studies
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• v.5 no.2
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• pp.103-110
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• 2015

Recently, Classification of characters in a 3D FPS game has emerged as a very significant issue. In this study, We propose the game character Classification method using Image Region Segmentation of the extracting meaningful object in a simple operation. In this method, first used a non-linear RGB color model and octree color quantization scheme. The input image represented a less than 20 quantized color and uses a small number of meaningful color histogram. And then, the image divided into small blocks, calculate the degree of similarity between the color histogram intersection and adjacent block in block units. Because, except for the block boundary according to the texture and to extract only the boundaries of the object block. Set a region by these boundary blocks as a game object and can be used for FPS game play. Through experiment, we obtain accuracy of more than 80% for Classification method using each feature. Thus, using this property, characters could be classified effectively and it draws the game more speed and strategic actions as a result.

• Archives of Plastic Surgery
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• v.38 no.4
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• pp.451-457
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• 2011

Purpose: As a central feature of the face, the nose has considerable significance in appearance and expression. Reconstruction of full thickness defects of the nasal ala has always been a challenge because of the 3-dimensional structure. For reconstruction of post burn defects of ala, skin graft, local or pedicled flap and composite graft are optionally available. We have reconstructed the ala defects using adiposocutaneous graft and observed the outcome. Methods: From March 2003 to December 2010, 19 cases in 11 patients with scar contracture and defect on ala portion were performed operation using adiposocutaneous graft. As a donor site, we used the inguinal crease and posterior auricular area and the donor site was primarily closed. We made incision through the superior rim of ala and released fully. A graft is applied to recipient site with larger size than recipient volume. Results: The mean age of the patient was 38.6 years (16~51), males are seven patients and females are four patients. The operation was performed bilaterally in 5 patients and unilaterally in 6 patients. Composite grafts were harvested from inguinal area in 13 cases and posterior auricular area in 6 cases. In one case, we did 4 times of operation to get enough volume. All the grafts were well taken. The mean size of the graft was 3.63 $cm^2$. Conclusion: For reconstruction of post burn defects of ala, it's not easy to use local flap or pedicled flap because of hardness and fibrosis of surrounding tissue. So, we choose adiposocutaneous graft for ala deformity reconstruction, got satisfactory outcome in color matching and texture.

• The KIPS Transactions:PartB
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• v.17B no.3
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• pp.239-248
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• 2010

Leukocyte in blood smear image provides significant information to doctors for diagnosis of patient health status. Therefore, it is necessary step to separate leukocyte from blood smear image among various blood cells for early disease prediction. In this paper, we present a saliency map and stepwise region merging based leukocyte segmentation method. Since leukocyte region has salient color and texture, we create a saliency map using these feature map. Saliency map is used for sub-image separation. Then, clustering is performed on each sub-image using mean-shift. After mean-shift is applied, stepwise region-merging is applied to particle clusters to obtain final leukocyte nucleus. The experimental results show that our system can indeed improve segmentation performance compared to previous researches with average accuracy rate of 71%.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.3
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• pp.205-208
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• 2013

Generally, binary pattern transforms have been used in the field of the face recognition and facial expression, since they are robust to illumination. Thus, this paper proposes an illumination-robust face recognition system combining an MLDP, which improves the texture component of the LDP, and a 2D-PCA algorithm. Unlike that binary pattern transforms such as LBP and LDP were used to extract histogram features, the proposed method directly uses the MLDP image for feature extraction by 2D-PCA. The performance evaluation of proposed method was carried out using various algorithms such as PCA, 2D-PCA and Gabor wavelets-based LBP on Yale B and CMU-PIE databases which were constructed under varying lighting condition. From the experimental results, we confirmed that the proposed method showed the best recognition accuracy.

• Animal Bioscience
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• v.34 no.11
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• pp.1849-1858
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• 2021

Objective: Tenderness is a very complex feature, and the process of its formation is very complicated and not fully understood. Its diversification is one of the most important problems of beef production, as a result beef aging is widely used to improve tenderness as it is believed to provide a homogeneous product to consumers. While few studies have evaluated the muscle structure properties in relation to tenderness from early post-mortem, there little to no information available on how the muscle nanostructure of beef carcasses changes during post-mortem ageing to determine the appropriate aging time for acceptable tenderness. Methods: Muscle nanostructure (myofibril diameter [MYD], myofibril spacing [MYS], muscle fibre diameter [MFD], muscle fibre spacing [MFS], and sarcomere length [SL]), meat tenderness and cooking loss [CL]) were measured on 20 A2 longissimus muscles of Bonsmara, Beefmaster, Hereford, and Simbra at 45_mins, 1, 3, and 7 days post-slaughter. Muscle nanostructure was measured using a scanning electron microscope, while tenderness was measured using Warner Bratzler shear force. Results: At 45 minutes post-slaughter, breed affected MYD and MYS only, while at 24_hrs it also affected MFD and MFS. On day 3 breed effected MFS and SL, while on day 7 breed effected tenderness only. As the muscles matured, both MYD and MYS decreased while CL increased, and the muscles became tender. There was no uniformity on muscle texture features (surface structure, fibre separation, muscle contraction, and relaxation) throughout the ageing period. Conclusion: Meat tenderness can be directly linked to breed related myofibril structure changes during aging in particular the MYD, spacing between myofibrils and their interaction; while the MFD, spacing between muscle fibres, SL, and CL explain the non-uniformity in beef tenderness.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.288-296
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• 2021

Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1816-1825
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• 2022

In this paper, we propose a new approach for tracking each human in a surveillance camera network with various resolution cameras. When tracking human on multiple non-overlapping cameras, the traditional appearance features are easily affected by various camera viewing conditions. To overcome this limitation, the proposed system utilizes facial information along with appearance information. In general, human images captured by the surveillance camera are often low resolution, so it is necessary to be able to extract useful features even from low-resolution faces to facilitate tracking. In the proposed tracking scheme, texture-based face descriptor is exploited to extract features from detected face after face frontalization. In addition, when the size of the face captured by the surveillance camera is very small, a super-resolution technique that enlarges the face is also exploited. The experimental results on the public benchmark Dana36 dataset show promising performance of the proposed algorithm.