Unstructured Scene Object Localization Algorithm Based on Sparse Overcomplete Representation

Lu, Peng; Tang, Yuhe; Chen, Eryan; Shi, Huige; Zhang, Shanshan

doi:10.1007/978-3-642-31588-6_79

Cited by 1 publication

(1 citation statement)

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“…In recent years, due to the development of the theory of sparse representation, many target detection algorithms based on sparse representation have been also proposed by researchers. For instance, [7] proposed a computing model based on visual sparse overcomplete representation method, which achieves the object detection in unstructured scenes. In this method, firstly, a sparse overcomplete computing model was established based on energy model and score matching method.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid method for robust infrared target detection

Wang¹,

Xu²,

Zhang³

et al. 2017

KSII TIIS

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Effect and robust detection of targets in infrared images has crucial meaning for many applications, such as infrared guidance, early warning, and video surveillance. However, it is not an easy task due to the special characteristics of the infrared images, in which the background clutters are severe and the targets are weak. The recent literature demonstrates that sparse representation can help handle the detection problem, however, the detection performance should be improved. To this end, in this text, a hybrid method based on local sparse representation and contrast is proposed, which can effectively and robustly detect the infrared targets. First, a residual image is calculated based on local sparse representation for the original image, in which the target can be effectively highlighted. Then, a local contrast based method is adopted to compute the target prediction image, in which the background clutters can be highly suppressed. Subsequently, the residual image and the target prediction image are combined together adaptively so as to accurately and robustly locate the targets. Based on a set of comprehensive experiments, our algorithm has demonstrated better performance than other existing alternatives.

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Section: Introductionmentioning

confidence: 99%