Name
Title | ||
|---|---|---|
Fast detection of small infrared objects in maritime scenes using local minimum patterns |
Abstract | ||
|---|---|---|
This paper describes a novel approach for fast detecting small maritime objects in infrared (IR) images. It is based on the local minimum patterns (LMP), which are theoretically the approximations of some stationary wavelet transforms (SWT). Using LMP to estimate the background with a single image, we obtain an object-aware saliency map by background subtraction. Regions of potential objects are then segmented by an adaptive threshold based on the histogram of the saliency map. We finally propose a fast clustering algorithm for localizing objects from segmented regions. Extensive experiments on challenging data sets show a competitive performance. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICIP.2011.6116483 | ICIP |
Keywords | Field | DocType |
stationary wavelet transforms approximation,infrared images,pattern clustering,adaptive threshold,marine engineering,object-aware saliency map,clustering algorithm,approximation theory,wavelet transforms,maritime object detection,background estimation,infrared imaging,adaptive signal processing,image segmentation,object localization,maritime scenes,infrared object detection,histogram,infrared surveillance,local minimum pattern,wavelet,object detection,background subtraction,potential object segmentation,clustering algorithms,infrared,stationary wavelet transform,adaptive thresholding,estimation,noise,noise measurement,real time systems | Background subtraction,Object detection,Histogram,Computer vision,Pattern recognition,Computer science,Image segmentation,Artificial intelligence,Adaptive filter,Cluster analysis,Wavelet transform,Wavelet | Conference |
Volume | Issue | ISSN |
null | null | 1522-4880 E-ISBN : 978-1-4577-1302-6 |
ISBN | Citations | PageRank |
978-1-4577-1302-6 | 3 | 0.48 |
References | Authors | |
3 | 4 | |