• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.6
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• pp.88-100
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• 1998

In this paper, an algorithm for a real-time automatic fingerprint identification system is proposed. The fingerprint feature volume is extracted by considering distinct and local characteristics(such as intensity and image quality difference etc.) in fingerprint images, which makes the algorithm properly adaptive to various image acquisitionj methods. Also the matching technique is designed to be invariant on rotation, scaling and translation (RST) changes while being capable of real-time processing. And the classification of fingerprints is performed based on the ridge flow and the relations among singular points such as cores and deltas. The developed fingerprint identification algorithm has been applied to various sets of fingerprint images such as one from NIST(National Institute of Standards and Technology, USA), a pressed fingerprint database constructed according to Korean population distributions in sex, ages and jobs, and a set of rolled-than-scanned fingerprint images. The overall performance of the algorithm has been analyzed and evaluated to the false rejection ratio of 0.07% while holding the false acceptance ratio of 0%.

• Journal of KIISE:Software and Applications
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• v.34 no.1
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• pp.70-78
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• 2007

The performance of face recognition is greatly affected by the illumination effect because intra-person variation under different lighting conditions can be much bigger than the inter-person variation. In this paper, we propose an illumination robust face recognition by separating identity factor and illumination factor using the symmetric bilinear models. The translation procedure in the bilinear model requires a repetitive computation of matrix inverse operation to reach the identity and illumination factors. Sometimes, this computation may result in a nonconvergent case when the observation has an noisy information. To alleviate this situation, we suggest a ridge regressive bilinear model that combines the ridge regression into the bilinear model. This combination provides some advantages: it makes the bilinear model more stable by shrinking the range of identity and illumination factors appropriately, and it improves the recognition performance by reducing the insignificant factors effectively. Experiment results show that the ridge regressive bilinear model outperforms significantly other existing methods such as the eigenface, quotient image, and the bilinear model in terms of the recognition rate under a variety of illuminations.

• Investigative Magnetic Resonance Imaging
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• v.20 no.2
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• pp.95-104
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• 2016

Purpose: To evaluate the knee joint after double-bundle anterior cruciate ligament (ACL) reconstruction with three-dimensional (3D) isotropic magnetic resonance (MR) image, and to directly compare the ACL graft findings on 3D MR with the clinical results. Materials and Methods: From January 2009 to December 2014, we retrospectively reviewed MRIs of 39 patients who had reconstructed ACL with double bundle technique. The subjects were examined using 3D isotropic proton-density sequence and routine two-dimensional (2D) sequence on 3.0T scanner. The MR images were qualitatively evaluated for the intraarticular curvature, graft tear, bony impingement, intraosseous tunnel cyst, and synovitis of anteromedial and posterolateral bundles (AMB, PLB). In addition anterior tibial translation, PCL angle, PCL ratio were quantitatively measured. KT arthrometric values were reviewed for anterior tibial translation as positive or negative. The second look arthroscopy results including tear and laxity were reviewed. Results: Significant correlations were found between an AMB tear on 3D-isotropic proton density MR images and arthroscopic proven AMB tear or laxity (P < 0.05). Also, a significant correlation was observed between increased PCL ratio on 3D isotropic MRI and the arthroscopic findings such as tear, laxities of grafts (P < 0.05). KT arthrometric results were found to be significantly correlated with AMB tears (P < 0.05) and tibial tunnel cysts (P < 0.05). Conclusion: An AMB tear on 3D-isotropic MRI was correlated with arthroscopic results qualitatively and quantitatively. 3D isotropic MRI findings can aid the evaluation of ACL grafts after double bundle reconstruction.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.2C
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• pp.16-24
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• 2005

In this paper, we proposed a face tracking algorithm for a viewpoint adaptive multi-view synthesis system. The original scene captured by a depth camera contains a texture image and 8 bit gray-scale depth map. From this original image, multi-view images can be synthesized which correspond to viewer's position by using geometrical transformation such as a rotation and a translation. The proposed face tracking technique gives a motion parallax cue by different viewpoints and view angles. In the proposed algorithm, tracking of viewer's dominant face initially established from camera by using statistical characteristics of face colors and deformable templates is done. As a result, we can provide motion parallax cue by detecting viewer's dominant face area and tracking it even under a heterogeneous background and can successfully display the synthesized sequences.

• Journal of Korea Game Society
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• v.7 no.4
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• pp.63-70
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• 2007

Since the fisheye lens has a wide field of view, it can capture the scene and illumination from all directions from far less number of omnidirectional images. Due to these advantages of the omnidirectional camera, it is widely used in surveillance and reconstruction of 3D structure of the scene In this paper, we present a new self-calibration algorithm of omnidirectional camera from uncalibrated images by considering the inlier distribution. First, one parametric non-linear projection model of omnidirectional camera is estimated with the known rotation and translation parameters. After deriving projection model, we can compute an essential matrix of the camera with unknown motions, and then determine the camera information: rotation and translations. The standard deviations are used as a quantitative measure to select a proper inlier set. The experimental results showed that we can achieve a precise estimation of the omnidirectional camera model and extrinsic parameters including rotation and translation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.12
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• pp.2586-2594
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• 2009

A digital watermarking technique used as a protection and certifying mechanism of copyrighted creations including music, still images, and videos in terms of finding any loss in data, reproduction and pursuit. This study suggests using a selected geometric invariant point through the whole processing procedure of an image and inserting and extracting based on the invariant point so that it will be robust in a geometric transformation attack. The introduced algorithm here is based on a watershed splitting method in order to make medical images strong against RST(Rotation Scale, Translation) transformation and other processing. It also helps to maintain the watermark in images that are compressed and stored for a period of time. This algorithm also proved that is has robustness against not only JPEG compression attack, but also RST attack and filtering attack.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.10 no.4
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• pp.78-84
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• 1996

In this study, 3-D object recognition and restoration independent of the object translation for automotive kind recognition in parking administration system using an ultrasonic sensor array, neural networks and invariant moments are presented. Using invariant moment vectors of the acquired data 16$\times$8 pixels, 3-D objects could be classified by SCL (Simple Competitive Learning) neural networks. Modified SCL neural networks using the 16$\times$8 low resolution image was used for object restoration of 32$\times$32 high resolution image. Invariant moment vectors kept constant independent of the object translation. The recognition rates for the training and the testing data were 98[%] and 95[%], respectively. The experimental results have shown that ultrasonic sensor array with the neural networks could be applied for the detection of the automobiles and classification of the automotive kind.

• Korean Journal of Applied Biomechanics
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• v.31 no.3
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• pp.155-161
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• 2021

Objective: There is a lack of studies using the 3D-2D image registration techniques on the mechanism of a shoulder injury for ice hockey players. This study aimed to analyze in vivo 3D glenohumeral joint arthrokinematics in collegiate ice hockey athletes and compare shoulder scaption with or without a hockey stick using the 3D-2D image registration technique. Method: We recruited 12 male elite ice hockey players (age, 19.88 ± 0.65 years). For arthrokinematic analysis of the common shoulder abduction movements of the injury pathogenesis of ice hockey players, participants abducted their dominant arm along the scapular plane and then grabbed a stick using the same motion under C-arm fluoroscopy with 16 frames per second. Computed tomography (CT) scans of the shoulder complex were obtained with a 0.6-mm slice pitch. Data from the humerus translation distances, scapula upward rotation, anterior-posterior tilt, internal to external rotation angles, and scapulohumeral rhythm (SHR) ratio on glenohumeral (GH) joint kinematics were outputted using a MATLAB customized code. Results: The humeral translation in the stick hand compared to the bare hand moved more anterior and more superior until the abduction angle reached 40°. When the GH joint in the stick hand was at the maximal abduction of the scapula, the scapula was externally rotated 2~5° relative to 0°. The SHR ratio relative to the abduction along the scapular plane at 40° indicated a statistically significant difference between the two groups (p < 0.05). Conclusion: With arm loading with the stick, the humeral and scapular kinematics showed a significant correlation in the initial section of the SHR. Although these correlations might be difficult in clinical settings, ice hockey athletes can lead to the movement difference of the scapulohumeral joints with inherent instability.

• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.17-22
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• 2021

It is difficult for the hearing impaired to use the counseling service without sign language interpretation. Due to the shortage of sign language interpreters, it takes a lot of time to connect to sign language interpreters, or there are many cases where the connection is not available. Therefore, in this paper, we propose a system that captures sign language as an image using OpenCV and CNN (Convolutional Neural Network), recognizes sign language motion, and converts the meaning of sign language into textual data and provides it to users. The counselor can conduct counseling by reading the stored sign language translation counseling contents. Consultation is possible without a professional sign language interpreter, reducing the burden of waiting for a sign language interpreter. If the proposed system is applied to counseling services for the hearing impaired, it is expected to improve the effectiveness of counseling and promote academic research on counseling for the hearing impaired in the future.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.101-112
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• 2024

Telugu language is considered as fourth most used language in India especially in the regions of Andhra Pradesh, Telangana, Karnataka etc. In international recognized countries also, Telugu is widely growing spoken language. This language comprises of different dependent and independent vowels, consonants and digits. In this aspect, the enhancement of Telugu Handwritten Character Recognition (HCR) has not been propagated. HCR is a neural network technique of converting a documented image to edited text one which can be used for many other applications. This reduces time and effort without starting over from the beginning every time. In this work, a Unicode based Handwritten Character Recognition(U-HCR) is developed for translating the handwritten Telugu characters into English language. With the use of Centre of Gravity (CG) in our model we can easily divide a compound character into individual character with the help of Unicode values. For training this model, we have used both online and offline Telugu character datasets. To extract the features in the scanned image we used convolutional neural network along with Machine Learning classifiers like Random Forest and Support Vector Machine. Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMS-P) and Adaptative Moment Estimation (ADAM)optimizers are used in this work to enhance the performance of U-HCR and to reduce the loss function value. This loss value reduction can be possible with optimizers by using CNN. In both online and offline datasets, proposed model showed promising results by maintaining the accuracies with 90.28% for SGD, 96.97% for RMS-P and 93.57% for ADAM respectively.