## Abstract The success of contentβbased image retrieval (CBIR) relies critically on the ability to find effective image features to represent the database images. The shape of an object is a fundamental image feature and belongs to one of the most important image features used in CBIR. In this art
Ore image segmentation by learning image and shape features
β Scribed by Dipti Prasad Mukherjee; Yury Potapovich; Ilya Levner; Hong Zhang
- Publisher
- Elsevier Science
- Year
- 2009
- Tongue
- English
- Weight
- 689 KB
- Volume
- 30
- Category
- Article
- ISSN
- 0167-8655
No coin nor oath required. For personal study only.
β¦ Synopsis
We present an image segmentation system specifically targeted for oil sand ore size estimation. The system learns spectral and shape characteristics of training images of oil sand ore samples for image quality enhancement followed by segmentation of ore image shapes. The proposed segmentation has achieved superior accuracy over the current state of the art systems.
π SIMILAR VOLUMES
are typically generated manually by human beings, they We present a two-pass image retrieval system in which re-provide compact, important [8], though sometimes biased trieval techniques for text and image documents are combined and incomplete, descriptions of the visual content. Such in a novel app
Robots require a form of visual attention to perform a wide range of tasks effectively. Existing approaches specify in advance the image features and attention control scheme required for a given robot to perform a specific task. However, to cope with different tasks in a dynamic environment, a robo