TY - GEN
T1 - Biologically-inspired video enhancement method for robust shape recognition
AU - Poursoltan, Saman
AU - Brinkworth, Russell
AU - Sorell, Matthew
PY - 2013
Y1 - 2013
N2 - The way image sequences are encoded by technological systems, that is video, is fundamentally tied to the way in which the human eye and brain interpret images and motion. This includes such aspects as resolution, colour, dynamic range, frame rates and spatial and temporal compression techniques. On the contrary, object identification algorithms are commonly based on single image analysis, such as the extraction of a single video frame from a sequence. This mismatch of, in particular, temporal processing paradigms means that most object analysis algorithms are not well suited to the data with which they are presented. In order to bridge this gap we investigate the temporal preconditioning of video data through a biologically-inspired vision model, based on multi-stage processing analogous to the vision systems of insects. In doing so, we argue that such an approach can lead to improved object identification through the enhancement of object perimeters and the amelioration of lighting and compression artefacts such as shadows and blockiness.
AB - The way image sequences are encoded by technological systems, that is video, is fundamentally tied to the way in which the human eye and brain interpret images and motion. This includes such aspects as resolution, colour, dynamic range, frame rates and spatial and temporal compression techniques. On the contrary, object identification algorithms are commonly based on single image analysis, such as the extraction of a single video frame from a sequence. This mismatch of, in particular, temporal processing paradigms means that most object analysis algorithms are not well suited to the data with which they are presented. In order to bridge this gap we investigate the temporal preconditioning of video data through a biologically-inspired vision model, based on multi-stage processing analogous to the vision systems of insects. In doing so, we argue that such an approach can lead to improved object identification through the enhancement of object perimeters and the amelioration of lighting and compression artefacts such as shadows and blockiness.
KW - Human Vision System
KW - Shape Recognition
KW - video enhancement
KW - wavelet transform
UR - http://www.scopus.com/inward/record.url?scp=84876030279&partnerID=8YFLogxK
U2 - 10.1109/ICCSPA.2013.6487252
DO - 10.1109/ICCSPA.2013.6487252
M3 - Conference contribution
AN - SCOPUS:84876030279
SN - 9781467328203
SN - 1467328200
T3 - 2013 1st International Conference on Communications, Signal Processing and Their Applications, ICCSPA 2013
BT - 2013 1st International Conference on Communications, Signal Processing and Their Applications, ICCSPA 2013
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2013 1st International Conference on Communications, Signal Processing and Their Applications, ICCSPA 2013
Y2 - 12 February 2013 through 14 February 2013
ER -