381 research outputs found
Graph Signal Processing: Overview, Challenges and Applications
Research in Graph Signal Processing (GSP) aims to develop tools for
processing data defined on irregular graph domains. In this paper we first
provide an overview of core ideas in GSP and their connection to conventional
digital signal processing. We then summarize recent developments in developing
basic GSP tools, including methods for sampling, filtering or graph learning.
Next, we review progress in several application areas using GSP, including
processing and analysis of sensor network data, biological data, and
applications to image processing and machine learning. We finish by providing a
brief historical perspective to highlight how concepts recently developed in
GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE
A METHOD FOR DENOISING IMAGE CONTOURS
The edge detection techniques have to compromise between sensitivity and noise. In order for the main contours to be uninterrupted, the level of sensitivity has to be raised, which however has the negative effect of producing a multitude of insignificant contours (noise). This article proposes a method of removing this noise, which acts directly on the binary representation of the image contours
Face recognition by cortical multi-scale line and edge representations
Empirical studies concerning face recognition suggest that
faces may be stored in memory by a few canonical representations. Models
of visual perception are based on image representations in cortical
area V1 and beyond, which contain many cell layers for feature extraction.
Simple, complex and end-stopped cells provide input for line, edge
and keypoint detection. Detected events provide a rich, multi-scale object
representation, and this representation can be stored in memory in
order to identify objects. In this paper, the above context is applied to
face recognition. The multi-scale line/edge representation is explored in
conjunction with keypoint-based saliency maps for Focus-of-Attention.
Recognition rates of up to 96% were achieved by combining frontal and
3/4 views, and recognition was quite robust against partial occlusions
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