• Journal of KIISE:Software and Applications
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• v.35 no.12
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• pp.780-788
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• 2008

Modeling hand poses and tracking its movement are one of the challenging problems in computer vision. There are two typical approaches for the reconstruction of hand poses in 3D, depending on the number of cameras from which images are captured. One is to capture images from multiple cameras or a stereo camera. The other is to capture images from a single camera. The former approach is relatively limited, because of the environmental constraints for setting up multiple cameras. In this paper we propose a method of reconstructing 3D hand poses from a 2D input image sequence captured from a single camera by means of Belief Propagation in a graphical model and recognizing a finger clicking motion using a hidden Markov model. We define a graphical model with hidden nodes representing joints of a hand, and observable nodes with the features extracted from a 2D input image sequence. To track hand poses in 3D, we use a Belief Propagation algorithm, which provides a robust and unified framework for inference in a graphical model. From the estimated 3D hand pose we extract the information for each finger's motion, which is then fed into a hidden Markov model. To recognize natural finger actions, we consider the movements of all the fingers to recognize a single finger's action. We applied the proposed method to a virtual keypad system and the result showed a high recognition rate of 94.66% with 300 test data.

• Journal of Korean Ophthalmic Optics Society
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• v.14 no.3
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• pp.43-49
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• 2009

Purpose: The purpose of this study was to obtain the fundamental data of dynamic visual acuity and dynamic stereoacuity. Methods: The subjects were 20 athletes (high school baseball player) and 40 nonathletes (20 male, 20 female). We assessed static visual acuity, dynamic visual acuity, static stereoacuity and dynamic stereoacuity using rotating mirror projection system and computer program. Results: Three groups had similar static visual acuity and static stereoacuity. On the other hand, the dynamic visual acuity and dynamic stereoacuity showed statistically significant differency. The mean dynamic visual acuity for athletes was 174.80${\pm}$28.70 deg/sec, 137.10${\pm}$16.54 deg/sec for male nonathletes and 111.59${\pm}$15.40 deg/sec for female nonathletes. The mean dynamic stereoacuity for athlets was 234.55${\pm}$19.64, 249.05${\pm}$8.86 for male nonathletes and 247.10${\pm}$14.89 for female nonathletes. The group of athletes had better dynamic visual acuity and dynamic stereoacuity. Conclusions: If the result of this study apply to sports, it will be very useful to improve sports performance.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.51-58
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• 2022

Drivable area detection, one of the main functions of advanced driver assistance systems, means detecting an area where a vehicle can safely drive. The drivable area detection is closely related to the safety of the driver and it requires high accuracy with real-time operation. To satisfy these conditions, V-disparity-based method is widely used to detect a drivable area by calculating the road disparity value in each row of an image. However, the V-disparity-based method can falsely detect a non-road area as a road when the disparity value is not accurate or the disparity value of the object is equal to the disparity value of the road. In a road environment including vegetation, such as a highway and a country road, the vegetation area may be falsely detected as the drivable area because the disparity characteristics of the vegetation are similar to those of the road. Therefore, this paper proposes a drivable area detection method and hardware architecture with a high accuracy in road environments including vegetation areas by reducing the number of false detections caused by V-disparity characteristic. When 289 images provided by KITTI road dataset are used to evaluate the road detection performance of the proposed method, it shows an accuracy of 90.12% and a recall of 97.96%. In addition, when the proposed hardware architecture is implemented on the FPGA platform, it uses 8925 slice registers and 7066 slice LUTs.