• Journal of Digital Convergence
• /
• v.16 no.5
• /
• pp.435-443
• /
• 2018

This study explores the effects of different Focus-on-Form Instruction (FFI) on improving learners' grammatical knowledge development and observes how the learners apply the knowledge to their output. A total of 112 college students were placed into three groups: 35 input-enhanced group students, who received visually enhanced input reading materials, 41 output-enhanced group students, performing dictogloss tasks, and 46 control group students given traditional grammar instruction. All the participant students took pre/post grammatical tests and completed pre/post writing tasks, which aimed to look into how the target grammatical structures were used in writing. The research findings indicated that both input-enhanced and output-enhanced FFI were effective on learners' language form learning and made contribution to their writing. Based on the findings, this study suggests that the elaborate design of combination of both FFI can maximize learners' language form learning.

• English Language & Literature Teaching
• /
• v.9 no.spc
• /
• pp.99-112
• /
• 2003

This study investigated how different types of formal instruction affect the second language looming of English grammatical structure among Korean high-school students. The linguistic focus of the study was English present perfect, which often creates learning problems for Korean learners of English. Subjects were divided into a control group and an experimental group (Enhanced group). The input the subjects in the experimental group received was manipulated by visually enhancing (with highlighting of the target structures in a reading text). Learners' awareness of the rules throughout the treatment period, as well as accuracy of target structures was measured. Results indicated that subjects in the Enhanced group showed higher performance than the control group. Further, awareness of rules that learners developed over the treatment period did not provide any advantage in learning.

• IEIE Transactions on Smart Processing and Computing
• /
• v.3 no.3
• /
• pp.110-117
• /
• 2014

This paper reports a depth-adaptive sharpness adjustment algorithm for stereoscopic perception improvement, and presents its field-programmable gate array (FPGA) implementation results. The first step of the proposed algorithm was to estimate the depth information of an input stereo video on a block basis. Second, the objects in the input video were segmented according to their depths. Third, the sharpness of the foreground objects was enhanced and that of the background was maintained or weakened. This paper proposes a new sharpness enhancement algorithm to suppress visually annoying artifacts, such as jagging and halos. The simulation results show that the proposed algorithm can improve stereoscopic perception without intentional depth adjustments. In addition, the hardware architecture of the proposed algorithm was designed and implemented on a general-purpose FPGA board. Real-time processing for full high-definition stereo videos was accomplished using 30,278 look-up tables, 24,553 registers, and 1,794,297 bits of memory at an operating frequency of 200MHz.

• Korean Journal of Cognitive Science
• /
• v.25 no.1
• /
• pp.51-71
• /
• 2014

This paper presents an image enhancement method in real road traffic scenes. The images captured by the camera on the car cannot keep the color constancy as illumination or weather changes. In the real environment, these problems are more worse at back light conditions and at night that make more difficult to the applications of the vision-based intelligent vehicles. Using the existing image enhancement methods without considering the position and intensity of the light source and their geometric relations the image quality can even be deteriorated. Thus, this paper presents a fast and effective method for image enhancement resembling human cognitive system which consists of 1) image preprocessing, 2) color-contrast evaluation, 3) alpha blending of over/under estimated image and preprocessed image. An input image is first preprocessed by gamma correction, and then enhanced by an Automatic Color Enhancement(ACE) method. Finally, the preprocessed image and the ACE image are blended to improve image visibility. The proposed method shows drastically enhanced results visually, and improves the performance in traffic sign detection of the vision based intelligent vehicle applications.