Analysis of new top-hat transformation and the application for infrared dim small target detection

Bai, Xiangzhi; Zhou, Fugen

doi:10.1016/j.patcog.2009.12.023

Cited by 478 publications

(232 citation statements)

References 19 publications

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“…When considering the effects of shadows, the result of binarization is affected which influences the reconstruction. In our pre-processing step, we transform the image from the RGB space to the HSV space [38] and apply a top-hat transformation [39] to reduce the influence of shadow. This is based on the notion that an image of one zebra crossing should have a similar tone.…”

Section: Zebra Crossing With Shadowmentioning

confidence: 99%

Extraction and Reconstruction of Zebra Crossings from High Resolution Aerial Images

Sun

Zhang

Gao

et al. 2016

IJGI

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Abstract:In this paper, an automatic approach for zebra crossing extraction and reconstruction from high-resolution aerial images is proposed. In the extraction procedure, zebra crossings are extracted by the JointBoost classifier based on GLCM (Gray Level Co-occurrence Matrix) features and 2D Gabor Features. In the reconstruction procedure, a geometric parameter model based on spatial repeatability relationships is globally fitted to reconstruct the geometric shape of zebra crossings. Additionally, a group of representative experiments is conducted to test the proposed method under interfered conditions, such as zebra crossings covered by pedestrians, shadows and color fading. Furthermore, the performance of the proposed extraction method is compared with the template matching method. Finally, the results show the validation of our proposed method, both in the extraction and reconstruction of zebra crossings.

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Section: Zebra Crossing With Shadowmentioning

confidence: 99%

Extraction and Reconstruction of Zebra Crossings from High Resolution Aerial Images

Sun

Zhang

Gao

et al. 2016

IJGI

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“…Background image obtained by the opening operation is subtracted from the original image, the acquired vascular tree have got image enhancement, the process is known as top-hat operation. People frequently use the top-hat operation [7,8] for image processing, the expression of which shows as follows： 00 (4) …”

Section: Morphological Top-hat Operationsmentioning

confidence: 99%

“…Rarely vessel enhancement methods use the method for the extraction of blood vessels. Compared with the image enhanced methods based on diffusion, top-hat enhancement method [3,4] can not only effectively suppress the local background, but also make the vascular structure become clearer. In this paper, a method of image de-noising and enhancement based on the improved multi-scale Hessian matrix that integrate the multi-scale Hessian matrix with morphological top-hat method for coronary angiography images is proposed.…”

Section: Introductionmentioning

confidence: 99%

Image Enhancement based Improved Multi-scale Hessian Matrix for Coronary Angiography

Chen¹,

Yin²,

Xiao³

et al. 2015

IJCA

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The coronary angiography image is easy to be affected by many factors, such as vascular thickness varied huge, complex background noise, uneven illumination intensity and so on. The coronary angiography image is more difficult to deal with compared with other similar medical images. By using Hessian matrix multi-scale vascular detection method, the vicinity of blood vessels will yield a lot of background noise, and the small tiny blood vessels become blurred or even lost, which seriously affect the experiment results. In this paper, an improved multi-scale Hessian matrix is presented, combined with morphological top-hat operation for the detection of coronary angiography. The experimental results demonstrate the effectiveness of the proposed method.

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“…If there are cluttered backgrounds, most of the clutter will have outputs in the result image. In Bai's work [17], an improved top-hat transformation is proposed for infrared dim small target detection. We follow that with the proper selection of structuring elements based on the vehicle's properties.…”

Section: Vehicle Candidate Location Extractionmentioning

confidence: 99%

Moving Vehicle Information Extraction from Single-Pass WorldView-2 Imagery Based on ERGAS-SNS Analysis

Gao

et al. 2014

Remote Sensing

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Due to the fact that WorldView-2 (WV2) has a small time lag while acquiring images from panchromatic (PAN) and two multispectral (MS1 and MS2) sensors, a moving vehicle is located at different positions in three image bands. Consequently, such displacement can be utilized to identify moving vehicles, and vehicle information, such as speed and direction can be estimated. In this paper, we focus on moving vehicle detection according to the displacement information and present a novel processing chain. The vehicle locations are extracted by an improved morphological detector based on the vehicle's shape properties. To make better use of the time lag between MS1 and MS2, a band selection process is performed by both visual inspection and quantitative analysis. Moreover, three spectral-neighbor band pairs, which have a major contribution to vehicle identification, are selected. In addition, we improve the spatial and spectral analysis method by incorporating local ERGAS index analysis (ERGAS-SNS) to identify moving vehicles. The experimental results on WV2 images showed that the correctness, completeness and quality rates of the proposed method were about 94%, 91% and 86%, respectively. Thus, the proposed method has good performance for moving vehicle detection and information extraction.

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Analysis of new top-hat transformation and the application for infrared dim small target detection

Cited by 478 publications

References 19 publications

Extraction and Reconstruction of Zebra Crossings from High Resolution Aerial Images

Extraction and Reconstruction of Zebra Crossings from High Resolution Aerial Images

Image Enhancement based Improved Multi-scale Hessian Matrix for Coronary Angiography

Moving Vehicle Information Extraction from Single-Pass WorldView-2 Imagery Based on ERGAS-SNS Analysis

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