التفاصيل البيبلوغرافية
| العنوان: |
Dynamic spectral residual superpixels |
| المؤلفون: |
Carola-Bibiane Schönlieb, Xiaosheng Zhuang, Jianchao Zhang, Angelica I. Aviles-Rivero, Daniel Heydecker, Raymond H. Chan |
| المصدر: |
Pattern Recognition |
| سنة النشر: |
2021 |
| مصطلحات موضوعية: |
FOS: Computer and information sciences, Computer science, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Context (language use), 02 engineering and technology, Residual, 01 natural sciences, Measure (mathematics), Image (mathematics), Artificial Intelligence, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Segmentation, 010306 general physics, Cluster analysis, business.industry, k-means clustering, Pattern recognition, Computer Science::Computer Vision and Pattern Recognition, Signal Processing, Metric (mathematics), 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Artificial intelligence, business, Software |
| الوصف: |
We consider the problem of segmenting an image into superpixels in the context of k -means clustering, in which we wish to decompose an image into local, homogeneous regions corresponding to the underlying objects. Our novel approach builds upon the widely used Simple Linear Iterative Clustering (SLIC), and incorporate a measure of objects’ structure based on the spectral residual of an image. Based on this combination, we propose a modified initialisation scheme and search metric, which keeps fine-details. This combination leads to better adherence to object boundaries, while preventing unnecessary segmentation of large, uniform areas, and remaining computationally tractable in comparison to other methods. We demonstrate through numerical and visual experiments that our approach outperforms the state-of-the-art techniques. |
| تدمد: |
0031-3203 |
| DOI: |
10.1016/j.patcog.2020.107705 |
| URL الوصول: |
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c530e4f8b2f9adda001dfdd40103b4c |
| Rights: |
OPEN |
| رقم الانضمام: |
edsair.doi.dedup.....3c530e4f8b2f9adda001dfdd40103b4c |
| قاعدة البيانات: |
OpenAIRE |