Kuk‐Jin Yoon
KAIST 기계공학과 · 컴퓨터과학
Kuk-Jin Yoon 교수의 연구실은 스마트 센서 기반의 환경 모니터링 및 비전 기술을 핵심으로 하는 융합 연구를 수행하고 있습니다. 특히 반도체 금속 산화물(SMO) 기반의 고감도·저전력 가스 센서와 전자코 nose 시스템을 개발하여 공기질 모니터링 및 실시간 가스 감지의 정밀도를 향상시키는 데 주력하고 있습니다. 또한 단일 이미지에서 반사광 성분을 고속으로 분리하는 스테레오 비전 기술을 통해 환경 인식의 정확성을 높이는 연구도 진행 중입니다. 이는 스마트 시티, 산업 안전, 환경 보호 등 실생활 응용에 기여할 수 있는 기술 기반 연구입니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
We present a new window-based method for correspondence search using varying support-weights. We adjust the support-weights of the pixels in a given support window based on color similarity and geometric proximity to reduce the image ambiguity. Our method outperforms other local methods on standard stereo benchmarks.
Semiconductor metal oxide (SMO) gas sensors are attracting great attention as next-generation environmental monitoring sensors. However, there are limitations to the actual application of SMO gas sensors due to their low selectivity. Although the electronic nose (E-nose) systems based on a sensor array are regarded as a solution for the selectivity issue, poor accuracy caused by the nonuniformity of the fabricated gas sensors and difficulty of real-time gas detection have yet to be resolved. In
As interests in air quality monitoring related to environmental pollution and industrial safety increase, demands for gas sensors are rapidly increasing. Among various gas sensor types, the semiconductor metal oxide (SMO)-type sensor has advantages of high sensitivity, low cost, mass production, and small size but suffers from poor selectivity. To solve this problem, electronic nose (e-nose) systems using a gas sensor array and pattern recognition are widely used. However, as the number of senso
In this paper, we propose a fast method for separating reflection components using a single color image. We first propose a specular-free two-band image that is a specularity-invariant color image representation. Reflection components separation is achieved by comparing local ratios at each pixel and making those ratios equal in an iterative framework. The proposed method is very fast and shows reasonable results for textured indoor/outdoor images.
The point ambiguity owing to the ambiguous local appearances of image points is the one of the main causes making the stereo problem difficult. Under the point ambiguity, local similarity measures are easy to be ambiguous and this results in false matches in ambiguous regions. In this paper, we present the new similarity measure to resolve the point ambiguity problem based on the idea that the distinctiveness, not the interest, is the appropriate criterion for the feature selection under the poi
