摘要
Describing image features in a concise and perceivable manner is essential to focus on candidate solutions for classification purpose. In addition to image recognition with geometric modeling and frequency domain transformation, this paper presents a novel 2D on-chip feature extraction named semantics-based vague image representation (SVIR) to reduce the semantic gap of content-based image retrieval. The development of SVIR aims at successively deconstructing object silhouette into intelligible features by pixel scans and then evolves and combines piecewise features into another pattern in a linguistic form. In addition to semantic annotations, SVIR is free of complicated calculations so that on-chip designs of SVIR can attain real-time processing performance without making use of a high-speed clock. The effectiveness of SVIR algorithm was demonstrated with timing sequences and real-life operations based on a field-programmable-gate-array (FPGA) development platform. With low hardware resource consumption on a single FPGA chip, the design of SVIR can be used on portable machine vision for ambient intelligence in the future.
原文 | English |
---|---|
頁(從 - 到) | 249-264 |
頁數 | 16 |
期刊 | Journal of Real-Time Image Processing |
卷 | 15 |
發行號 | 2 |
DOIs | |
出版狀態 | Published - 2018 8月 1 |
All Science Journal Classification (ASJC) codes
- 資訊系統