So Sasatani
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
So Sasatani has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Masaya Itoh
Research & Development Group, Hitachi Ltd., Hitachi 3191292, Japan
1 shared publication - Ming Fang
School of Artificial Intelligence, Changchun University of Science and Technology, Changchun 130022, China
1 shared publication - Shun’ichi Kaneko
Graduate School of Information Science and Technology, Hokkaido University, Sapporo 060-0814, Japan
1 shared publication - Ying Tian
Graduate School of Information Science and Technology, Hokkaido University, Sapporo 060-0814, Japan
1 shared publication
Latest Publications
- Open Access
- 18 October 2021
- Published by MDPI AG in Information
- Vol. 12 (10) , 428
Robust and Precise Matching Algorithm Combining Absent Color Indexing and Correlation Filter
This paper presents a novel method that absorbs the strong discriminative ability from absent color indexing (ABC) to enhance sensitivity and combines it with a correlation filter (CF) for obtaining a higher precision; this method is named ABC-CF. First, by separating the original color histogram, apparent and absent colors are introduced. Subsequently, an automatic threshold acquisition is proposed using a mean color histogram. Next, a histogram intersection is selected to calculate the similarity. Finally, CF follows them to solve the drift caused by ABC during the matching process. The novel approach proposed in this paper realizes robustness in distortion of target images and higher margins in fundamental matching problems, and then achieves more precise matching in positions. The effectiveness of the proposed approach can be evaluated in the comparative experiments with other representative methods by use of the open data.
- 18 October 2021
- Published by MDPI AG in Information
- Vol. 12 (10) , 428
Open AccessRobust and Precise Matching Algorithm Combining Absent Color Indexing and Correlation Filter
This paper presents a novel method that absorbs the strong discriminative ability from absent color indexing (ABC) to enhance sensitivity and combines it with a correlation filter (CF) for obtaining a higher precision; this method is named ABC-CF. First, by separating the original color histogram, apparent and absent colors are introduced. Subsequently, an automatic threshold acquisition is proposed using a mean color histogram. Next, a histogram intersection is selected to calculate the similarity. Finally, CF follows them to solve the drift caused by ABC during the matching process. The novel approach proposed in this paper realizes robustness in distortion of target images and higher margins in fundamental matching problems, and then achieves more precise matching in positions. The effectiveness of the proposed approach can be evaluated in the comparative experiments with other representative methods by use of the open data.