Semi-supervised learning based edge-preserving background estimation for small target detection

Bai, Kun; Wang, Yuehuan; Song, Qiong; Liu, Dang

doi:10.1016/j.infrared.2015.07.002

Cited by 3 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, the effectiveness and practicality of the proposed method are demonstrated by comparing with some of the methods mentioned above. BF, 10 TDLMS, 14 and Bai and Wang's method 16 are applied to small target images against different complex and noisy backgrounds to do the comparison. As shown in Figure 5, the other four typical infrared images (image 5 to image 8) are added in this experiments.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…15 Bai and Wang proposed a novel semi-supervised learning-based edge-preserving background estimation. 16 Background estimation ability of this method is improved to some extent; nevertheless, the model not suitable for practical application owing to its parameters tuning result usually cannot catch up with the vagaries of practical images and heavy computation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Small target detection using edge-preserving background estimation based on maximum patch similarity

Wang

Yue

et al. 2017

International Journal of Advanced Robotic Systems

Self Cite

View full text Add to dashboard Cite

Infrared small target detection is widely applied in lots of practical applications, but due to the complicated edges in practical scenarios, most existing detection algorithms usually lead to many false alarms and cannot detect the target accurately. Addressing this problem, a novel edge-preserving background estimation method based on maximum patch similarity was proposed in this article. At first, we will propose an improved local adaptive contrast measure to suppress the pixel-size electronic noises. Then, maximum patch similarity with minimum improved local adaptive contrast measure can be utilized to preserve the edge in the estimated background. Finally, we can obtain target image by filtering the background image from original image and use adaptive threshold segmentation to detect the small target in our target image. It is shown from experiments that our proposed method has better detection results in diverse infrared images, improving signal-to-clutter ratio gain and background suppression factor of the images significantly and efficiently.

show abstract

Section: Experiments and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%