Joint contrast optimization and object segmentation in active polarimetric images

Anna, Guillaume; Bertaux, Nicolas; Galland, Frédéric; Goudail, François; Dolfi, Daniel

doi:10.1364/ol.37.003321

Cited by 12 publications

(6 citation statements)

References 9 publications

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“…金属疲劳过程是一个复杂的能量耗散过程，可以通过温度来表征 [1] 。目前基于疲劳红外热像分析，已有文献提出了一些有效的疲劳评估方法以与相应的疲劳参数建立联系 [2][3][4] 。但在实际工况条件下，因为疲劳热像法分析主要关注材料或构件表面的能量耗散，红外图像在生成和传输过程中不可避免地受到由于热像仪的成像特点及环境干扰等原因引起的随机噪声、起伏背景干扰与热扩散效应的影响，使红外热像存在图像模糊、边缘扩散、对比度低等不足而分割困难 [5][6][7] 。偏振成像以目标辐射能量偏振特征作为探测信息，从多个不同偏振方向进行光强成像，获得能够反映目标材料性质、表面粗糙度、温度、辐射率等特征的信息，将目标识别从表面上升到多维空间结构，从而能很好地分辨目标上低反射区域和目标轮廓，将目标从复杂的背景中提取出来，解决了传统遥感所不能解决的问题 [8] 。因此，在热像检测的基础上融入偏振探测技术，将有助于降低测量噪声的影响，提高目标分割的精度、增加热图的区分度与识别率。但要准确分析和理解红外偏振图像，图像分割是关键的一步，其结果将直接影响到红外热像特征的提取和描述以及进一步的金属目标疲劳损伤区域的识别、分类和解读。目前国内外专家针对红外热像提出了各种分割算法，如 Ahmed Kabouri 使用多分辨率小波变换对金属试件的热红外图像进行分割，提取金属试件缺陷区域，估计缺陷区域尺寸 [9] ； Changhang Xu 从原始灰度图像和高通滤波图像中选择超像素的组合特征，采用模糊 C 均值聚类方法对超像素点进行聚类，对红外图像进行分割，实现了针对裂纹的自动检测 [10] ；付冬梅针对中波红外图像的模糊特性，在生物免疫协调网络机制的启发下，提出了一种基于红外图像成像机制和聚类网络统计特性的多层免疫聚类神经网络分割方法，有效地抑制了红外图像的噪声，突显带有较多边缘细节的成像目标 [11] 。但偏振成像针对同一目标会得到多幅不同偏振方位角的原始光强图像，然后通过偏振解析还可以获得 Stokes 参量、偏振度、偏振角、椭偏度等偏振图像，因此上述主要针对单幅图像进行处理的方法在适用于偏振图像时就会受到一定的限制。基于此，国内外学者提出将光强、偏振度、偏振角等图像进行融合再利用上述方法进行图像分割，如虞文俊通过综合红外光强和偏振信息，再利用模糊 C 均值聚类和边缘提取算法进行处理，最后采用加权法融合实现了金属试件的分类 [12] 。 Xuelian YU 利用非下采样轮廓波变换对偏振度和偏振角图像进行融合，然后利用 C 均值聚类算法对融合图像进行分割，具有较好的效果 [13] 。Guillaume Anna 等人则利用主动偏振成像器件的相互作用对获取的原始图像对比度进行优化并基于 snake 方法进行图像分割从而实现了目标的自动探测 [14] 。 Nitya Subramaniam 和 Edwin Hancock [15] (1) [19][20][21]…”

Section: 引言unclassified

Infrared Polarization Thermal Image Segmentation Algorithm Based on Tsallis Entropy

王¹,

王²,

汪³

et al. 2020

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There are some deficiencies in traditional infrared thermal images, such as low contrast and blurred edges, making segmentation of the target region difficult. Infrared polarization thermal imaging can clearly highlight the edges and contours of the observed objects. It has been effectively applied in environmental monitoring, military reconnaissance, industrial nondestructive testing, and other fields. However, there are few studies on infrared polarization thermal image segmentation. In this work, a novel infrared polarization thermal image segmentation algorithm based on Tsallis entropy is proposed. First, the polarization azimuth thermal image is segmented with a Tsallis threshold. Second, the Tsallis index is optimized by minimizing the intersection and union error rate of the initial segmentation thermal image. Subsequently, the polarization azimuth thermal image is segmented by utilizing the exponentially optimized Tsallis threshold, and the quadratic segmentation image is obtained by moving false segmentation through connected domain detection. Finally, taking the intersection area as the seed region and taking the union region as the boundary, the final segmentation thermal image is obtained through the regional growth method. The experimental results show that the algorithm proposed can diminish the false segmentation region. It can improve the evaluation index of the subjective visual effect, interregional contrast, and shape measurement and segment the target accurately compared with the minimum Tsallis cross-entropy, Otsu, and

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Section: 引言unclassified

Infrared Polarization Thermal Image Segmentation Algorithm Based on Tsallis Entropy

王¹,

王²,

汪³

et al. 2020

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“…Fully adaptive scalar imaging [12,14,15] consists of acquiring a single polarimetric image while setting the PSA and PSG so that the contrast between a target of interest and its surrounding background is maximal. If we assume that the target and the background are characterized by Mueller matrices M t and M b , the goal is to determine the PSG state S opt and PSA state T opt that optimize the contrast C between these two regions.…”

Section: A Fully Adaptive Scalar Imagingmentioning

confidence: 99%

“…(3) is not straightforward. To address this issue, a three-step adaptive strategy has been proposed in [14,15]. First, the complete polarimetric properties of the scene are measured by acquiring a Mueller image.…”

Section: A Fully Adaptive Scalar Imagingmentioning

confidence: 99%

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Comparison of different active polarimetric imaging modes for target detection in outdoor environment

Vannier¹,

Goudail²,

Plassart³

et al. 2016

Appl. Opt.

Self Cite

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We address the detection of manufactured objects in different types of environments with active polarimetric imaging. Using an original, fully adaptive imager, we compare several imaging modes having different numbers of polarimetric degrees of freedom. We demonstrate the efficiency of active polarimetric imaging for decamouflage and hazardous object detection, and underline the characteristics that a polarimetric imager aimed at this type of application should possess. We show that in most encountered scenarios the Mueller matrices are nearly diagonal, and sufficient detection performance can be obtained with simple polarimetric imaging systems having reduced degrees of freedom. Moreover, intensity normalization of images is of paramount importance to better reveal polarimetric contrast.

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“…The polarization properties of light reflected from a surface depends on its granularity and therefore can help in distinguishing between materials with different surface properties [1]. Polarization sensitive imaging has been used in various fields that include medical diagnostics [3], industrial quality control [1,4], machine vision [5], remote sensing [6] and imaging through turbid medium (e.g. fog, turbid and colloidal solutions) [7,8].…”

Section: Introductionmentioning

confidence: 99%

Contrast enhancement in polarimetric imaging with correlated noise fluctuations

Panigrahi¹,

Fade²,

Alouini³

2015

AIP Conference Proceedings

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Abstract. We compare the measurement precision of a polarimetric camera to that of a simple intensity camera when imaging a partially polarized light-mark embedded in an intense and partially polarized background. We show that the gain in measurement precision while using a polarimetric camera is maximized when the noise fluctuations on the two polarimetric channels are significantly correlated. Further, we implement a snapshot polarimetric camera for long distance imaging of a highly polarized light source through fog and compare the contrast obtained using various representations of the polarimetric images. We show that the representation that provides the best contrast depends on the visibility conditions and matches well with theoretical predictions.

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Joint contrast optimization and object segmentation in active polarimetric images

Cited by 12 publications

References 9 publications

Infrared Polarization Thermal Image Segmentation Algorithm Based on Tsallis Entropy

Infrared Polarization Thermal Image Segmentation Algorithm Based on Tsallis Entropy

Comparison of different active polarimetric imaging modes for target detection in outdoor environment

Contrast enhancement in polarimetric imaging with correlated noise fluctuations

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