Object segmentation framework based on dictionary‐group and sparse shape representation
| Autor: | Jincao Yao, Roland Hu, Huimin Yu |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Segmentation-based object categorization
Computer science business.industry 020208 electrical & electronic engineering Fuzzy set ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Representation (systemics) Scale-space segmentation Pattern recognition 02 engineering and technology Image segmentation Object (computer science) Fuzzy logic 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Computer vision Artificial intelligence Electrical and Electronic Engineering business |
| Zdroj: | Electronics Letters. 53:584-586 |
| ISSN: | 1350-911X 0013-5194 |
| DOI: | 10.1049/el.2016.3744 |
| Popis: | Given an input object whose shape is partly similar to some of the samples in the training set, a dictionary-group-based sparse model is introduced that can use the local information of those less similar shape neighbours to represent the object and guide the segmentation. The model follows from a new sparse energy function that combines a series of sparse local constraints with the fuzzy log-polar decomposition-based shape elements. Finally, a unified framework is built to connect the high-level shape representation with the low-level image segmentation. The model on the public datasets is tested, and the experimental results show the superior shape segmentation capabilities of the proposed model. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text