On-chip real-time feature extraction using semantic annotations for object recognition

Ying Hao Yu, Tsu Tian Lee, Pei Yin Chen, Ngaiming Kwok

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)249-264
Number of pages16
JournalJournal of Real-Time Image Processing
Issue number2
Publication statusPublished - 2018 Aug 1

All Science Journal Classification (ASJC) codes

  • Information Systems

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