컨텐츠 시작
학술대회/행사
초록검색
| 제출번호(No.) | 0172 |
|---|---|
| 분류(Section) | Contributed Talk |
| 분과(Session) | (AM) Applied Mathematics(including AI, Data Science) (AM) |
| 발표시간(Time) | 20th-C-10:00 -- 10:20 |
| 영문제목 (Title(Eng.)) |
Wavelet filters and coefficients: Theory and applications |
| 저자(Author(s)) |
Youngmi Hur1 Yonsei University / KIAS1 |
| 초록본문(Abstract) | In this presentation, we explore the realm of wavelet filters and their associated coefficients. Wavelet filters play a pivotal role in generating wavelet coefficients, which can be challenging to construct, particularly when aiming for specific attributes. These coefficients are crucial for delineating fine details from the broader strokes in data. Interestingly, the Laplacian pyramid algorithm can derive wavelet coefficients without the need of constructing wavelet filters. Consequently, this method offers a novel avenue for embedding wavelet properties into various learning processes. |
| 분류기호 (MSC number(s)) |
42C40, 65D15, 94A08 |
| 키워드(Keyword(s)) | Wavelet filters, wavelet coefficients, Laplacian pyramid algorithms, image processing |
| 강연 형태 (Language of Session (Talk)) |
Korean |