The Experts below are selected from a list of 222873 Experts worldwide ranked by ideXlab platform

Yantao Wei - One of the best experts on this subject based on the ideXlab platform.

  • infrared moving point target detection based on spatial temporal Local Contrast filter
    Infrared Physics & Technology, 2016
    Co-Authors: Lizhen Deng, Hu Zhu, Chao Tao, Yantao Wei
    Abstract:

    Abstract Infrared moving point target detection is a challenging task. In this paper, we define a novel spatial Local Contrast (SLC) and a novel temporal Local Contrast (TLC) to enhance the target’s Contrast. Based on the defined spatial Local Contrast and temporal Local Contrast, we propose a simple but powerful spatial–temporal Local Contrast filter (STLCF) to detect moving point target from infrared image sequences. In order to verify the performance of spatial–temporal Local Contrast filter on detecting moving point target, different detection methods are used to detect the target from several infrared image sequences for comparison. The experimental results show that the proposed spatial–temporal Local Contrast filter has great superiority in moving point target detection.

  • Infrared moving point target detection based on spatial–temporal Local Contrast filter
    Infrared Physics & Technology, 2016
    Co-Authors: Lizhen Deng, Hu Zhu, Chao Tao, Yantao Wei
    Abstract:

    Abstract Infrared moving point target detection is a challenging task. In this paper, we define a novel spatial Local Contrast (SLC) and a novel temporal Local Contrast (TLC) to enhance the target’s Contrast. Based on the defined spatial Local Contrast and temporal Local Contrast, we propose a simple but powerful spatial–temporal Local Contrast filter (STLCF) to detect moving point target from infrared image sequences. In order to verify the performance of spatial–temporal Local Contrast filter on detecting moving point target, different detection methods are used to detect the target from several infrared image sequences for comparison. The experimental results show that the proposed spatial–temporal Local Contrast filter has great superiority in moving point target detection.

  • a Local Contrast method for small infrared target detection
    IEEE Transactions on Geoscience and Remote Sensing, 2014
    Co-Authors: C Philip L Chen, Yantao Wei, Tian Xia, Yuan Yan Tang
    Abstract:

    Robust small target detection of low signal-to-noise ratio (SNR) is very important in infrared search and track applications for self-defense or attacks. Consequently, an effective small target detection algorithm inspired by the Contrast mechanism of human vision system and derived kernel model is presented in this paper. At the first stage, the Local Contrast map of the input image is obtained using the proposed Local Contrast measure which measures the dissimilarity between the current location and its neighborhoods. In this way, target signal enhancement and background clutter suppression are achieved simultaneously. At the second stage, an adaptive threshold is adopted to segment the target. The experiments on two sequences have validated the detection capability of the proposed target detection method. Experimental evaluation results show that our method is simple and effective with respect to detection accuracy. In particular, the proposed method can improve the SNR of the image significantly.

Jinhui Han - One of the best experts on this subject based on the ideXlab platform.

  • Infrared Small Target Detection Based on the Weighted Strengthened Local Contrast Measure
    IEEE Geoscience and Remote Sensing Letters, 2020
    Co-Authors: Jinhui Han, Qian Zhao, Honghui Zhang, Saed Moradi, Iman Faramarzi, Xiaojian Zhang
    Abstract:

    In this letter, a weighted strengthened Local Contrast measure (WSLCM) algorithm for infrared (IR) small target detection is proposed, it consists of two modules, the strengthened Local Contrast measure (SLCM), and the weighting function. In the SLCM calculation, the ideas of matched filter and background estimation are adopted to enhance true target and suppress complex background, then both ratio and difference operations are used to calculate the SLCM. In the weighting function definition, three components are considered: the characteristics of the target, the characteristics of the background, and the difference between them. Especially, an improved regional intensity level (IRIL) algorithm is proposed to evaluate the complexity of a cell, thus it can suppress random noises better. Experiments on some real IR images show that the proposed WSLCM can achieve a better detection performance under complex background.

  • A Local Contrast Method for Infrared Small-Target Detection Utilizing a Tri-Layer Window
    IEEE Geoscience and Remote Sensing Letters, 2020
    Co-Authors: Jinhui Han, Honghui Zhang, Saed Moradi, Iman Faramarzi, Chengyin Liu, Qian Zhao
    Abstract:

    Local Contrast has been proved efficient for infrared (IR) small-target detection. However, current algorithms do not enhance true target purposefully before Local Contrast calculation and may easily be disturbed by noises. In this letter, a new detection framework named multiscale tri-layer Local Contrast measure (TLLCM) is proposed. First, a tri-layer filtering window is proposed, and it consists of a core layer, a reserve layer, and a surrounding layer. The idea of a matched filter is adopted, and a Gaussian filtering will be performed on the core layer to enhance true target purposefully according to the target shape. Then, the multiscale TLLCM of the central pixel of the window will be calculated between the enhanced core and the surrounding Local background. Finally, the target can be extracted by an adaptive threshold. Experimental results show that the proposed method can achieve better detection performance than some existing algorithms.

  • A Local Contrast Method Combined With Adaptive Background Estimation for Infrared Small Target Detection
    IEEE Geoscience and Remote Sensing Letters, 2019
    Co-Authors: Jinhui Han, Sibang Liu, Qin Gang, Qian Zhao, Honghui Zhang
    Abstract:

    Local Contrast has been proven as an efficient method for infrared (IR) small target detection, but existing Local Contrast algorithms just directly choosing the neighboring area of a current position as the reference when calculating the Local Contrast of the current position, which may bring an inaccurate result. Meanwhile, existing algorithms are either ratio form or difference form, they cannot effectively enhance true target and suppress all the types of complex backgrounds simultaneously. In this letter, a new Local Contrast scheme that introduces the adaptive background estimation is proposed to provide a more accurate reference, and the multidirectional 2-D least mean square (MDTDLMS) algorithm that is more suitable for small target detection is presented. Then, a new ratio-difference joint Local Contrast measure (RDLCM) is proposed between raw IR image and the MDTDLMS result to enhance true small target and suppress all the types of complex backgrounds simultaneously. Experimental results show that the proposed MDTDLMS-RDLCM algorithm can achieve a good detection performance for different types of backgrounds and targets.

Qian Zhao - One of the best experts on this subject based on the ideXlab platform.

  • Infrared Small Target Detection Based on the Weighted Strengthened Local Contrast Measure
    IEEE Geoscience and Remote Sensing Letters, 2020
    Co-Authors: Jinhui Han, Qian Zhao, Honghui Zhang, Saed Moradi, Iman Faramarzi, Xiaojian Zhang
    Abstract:

    In this letter, a weighted strengthened Local Contrast measure (WSLCM) algorithm for infrared (IR) small target detection is proposed, it consists of two modules, the strengthened Local Contrast measure (SLCM), and the weighting function. In the SLCM calculation, the ideas of matched filter and background estimation are adopted to enhance true target and suppress complex background, then both ratio and difference operations are used to calculate the SLCM. In the weighting function definition, three components are considered: the characteristics of the target, the characteristics of the background, and the difference between them. Especially, an improved regional intensity level (IRIL) algorithm is proposed to evaluate the complexity of a cell, thus it can suppress random noises better. Experiments on some real IR images show that the proposed WSLCM can achieve a better detection performance under complex background.

  • A Local Contrast Method for Infrared Small-Target Detection Utilizing a Tri-Layer Window
    IEEE Geoscience and Remote Sensing Letters, 2020
    Co-Authors: Jinhui Han, Honghui Zhang, Saed Moradi, Iman Faramarzi, Chengyin Liu, Qian Zhao
    Abstract:

    Local Contrast has been proved efficient for infrared (IR) small-target detection. However, current algorithms do not enhance true target purposefully before Local Contrast calculation and may easily be disturbed by noises. In this letter, a new detection framework named multiscale tri-layer Local Contrast measure (TLLCM) is proposed. First, a tri-layer filtering window is proposed, and it consists of a core layer, a reserve layer, and a surrounding layer. The idea of a matched filter is adopted, and a Gaussian filtering will be performed on the core layer to enhance true target purposefully according to the target shape. Then, the multiscale TLLCM of the central pixel of the window will be calculated between the enhanced core and the surrounding Local background. Finally, the target can be extracted by an adaptive threshold. Experimental results show that the proposed method can achieve better detection performance than some existing algorithms.

  • A Local Contrast Method Combined With Adaptive Background Estimation for Infrared Small Target Detection
    IEEE Geoscience and Remote Sensing Letters, 2019
    Co-Authors: Jinhui Han, Sibang Liu, Qin Gang, Qian Zhao, Honghui Zhang
    Abstract:

    Local Contrast has been proven as an efficient method for infrared (IR) small target detection, but existing Local Contrast algorithms just directly choosing the neighboring area of a current position as the reference when calculating the Local Contrast of the current position, which may bring an inaccurate result. Meanwhile, existing algorithms are either ratio form or difference form, they cannot effectively enhance true target and suppress all the types of complex backgrounds simultaneously. In this letter, a new Local Contrast scheme that introduces the adaptive background estimation is proposed to provide a more accurate reference, and the multidirectional 2-D least mean square (MDTDLMS) algorithm that is more suitable for small target detection is presented. Then, a new ratio-difference joint Local Contrast measure (RDLCM) is proposed between raw IR image and the MDTDLMS result to enhance true small target and suppress all the types of complex backgrounds simultaneously. Experimental results show that the proposed MDTDLMS-RDLCM algorithm can achieve a good detection performance for different types of backgrounds and targets.

Honghui Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Infrared Small Target Detection Based on the Weighted Strengthened Local Contrast Measure
    IEEE Geoscience and Remote Sensing Letters, 2020
    Co-Authors: Jinhui Han, Qian Zhao, Honghui Zhang, Saed Moradi, Iman Faramarzi, Xiaojian Zhang
    Abstract:

    In this letter, a weighted strengthened Local Contrast measure (WSLCM) algorithm for infrared (IR) small target detection is proposed, it consists of two modules, the strengthened Local Contrast measure (SLCM), and the weighting function. In the SLCM calculation, the ideas of matched filter and background estimation are adopted to enhance true target and suppress complex background, then both ratio and difference operations are used to calculate the SLCM. In the weighting function definition, three components are considered: the characteristics of the target, the characteristics of the background, and the difference between them. Especially, an improved regional intensity level (IRIL) algorithm is proposed to evaluate the complexity of a cell, thus it can suppress random noises better. Experiments on some real IR images show that the proposed WSLCM can achieve a better detection performance under complex background.

  • A Local Contrast Method for Infrared Small-Target Detection Utilizing a Tri-Layer Window
    IEEE Geoscience and Remote Sensing Letters, 2020
    Co-Authors: Jinhui Han, Honghui Zhang, Saed Moradi, Iman Faramarzi, Chengyin Liu, Qian Zhao
    Abstract:

    Local Contrast has been proved efficient for infrared (IR) small-target detection. However, current algorithms do not enhance true target purposefully before Local Contrast calculation and may easily be disturbed by noises. In this letter, a new detection framework named multiscale tri-layer Local Contrast measure (TLLCM) is proposed. First, a tri-layer filtering window is proposed, and it consists of a core layer, a reserve layer, and a surrounding layer. The idea of a matched filter is adopted, and a Gaussian filtering will be performed on the core layer to enhance true target purposefully according to the target shape. Then, the multiscale TLLCM of the central pixel of the window will be calculated between the enhanced core and the surrounding Local background. Finally, the target can be extracted by an adaptive threshold. Experimental results show that the proposed method can achieve better detection performance than some existing algorithms.

  • A Local Contrast Method Combined With Adaptive Background Estimation for Infrared Small Target Detection
    IEEE Geoscience and Remote Sensing Letters, 2019
    Co-Authors: Jinhui Han, Sibang Liu, Qin Gang, Qian Zhao, Honghui Zhang
    Abstract:

    Local Contrast has been proven as an efficient method for infrared (IR) small target detection, but existing Local Contrast algorithms just directly choosing the neighboring area of a current position as the reference when calculating the Local Contrast of the current position, which may bring an inaccurate result. Meanwhile, existing algorithms are either ratio form or difference form, they cannot effectively enhance true target and suppress all the types of complex backgrounds simultaneously. In this letter, a new Local Contrast scheme that introduces the adaptive background estimation is proposed to provide a more accurate reference, and the multidirectional 2-D least mean square (MDTDLMS) algorithm that is more suitable for small target detection is presented. Then, a new ratio-difference joint Local Contrast measure (RDLCM) is proposed between raw IR image and the MDTDLMS result to enhance true small target and suppress all the types of complex backgrounds simultaneously. Experimental results show that the proposed MDTDLMS-RDLCM algorithm can achieve a good detection performance for different types of backgrounds and targets.

Lei Shao - One of the best experts on this subject based on the ideXlab platform.

  • Tiny and Dim Infrared Target Detection Based on Weighted Local Contrast
    IEEE Geoscience and Remote Sensing Letters, 2018
    Co-Authors: Jie Liu, Zuolong Chen, Lei Shao
    Abstract:

    Robust detection of infrared (IR) tiny and dim targets in a single frame remains a hot and difficult problem in military fields. In this letter, we introduce a method for IR tiny and dim target detection based on a new weighted Local Contrast measure. Our method simultaneously exploits the Local Contrast of target, the consistency of image background, and the imaging characteristics of the background edges. The proposed method is simple to implement and computationally efficient. We compared our algorithm with six state-of-the-art methods on four real-world videos with different targets and backgrounds. Our method outperforms all the compared algorithms on the ground-truth evaluation with both higher detection rate and lower false alarm rate.