Paper Info
Title
On-chip real-time feature extraction using semantic annotations for object recognition
Abstract
Abstract 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.
Year
DOI
Venue
2018
10.1007/s11554-014-0474-2
Journal of Real-time Image Processing
Keywords
Field
DocType
Semantics-based vague image representation (SVIR),Bipolar image encoding,Vertical evolution,Lateral combination
Computer vision,Pattern recognition,Machine vision,Silhouette,Computer science,Ambient intelligence,Feature (computer vision),Semantic gap,Image retrieval,Feature extraction,Artificial intelligence,Cognitive neuroscience of visual object recognition
Journal
Volume
Issue
ISSN
15
2
1861-8219
Citations 
PageRank 
References 
4
0.43
18
Authors
4
Name
Order
Citations
PageRank
Ying-Hao Yu1302.84
Tsu-Tian Lee21635148.07
Pei-Yin Chen331438.47
Ngaiming Kwok450.80