2022
DOI: 10.3390/jimaging8100266
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Obscurant Segmentation in Long Wave Infrared Images Using GLCM Textures

Abstract: The benefits of autonomous image segmentation are readily apparent in many applications and garners interest from stakeholders in many fields. The wide range of benefits encompass applications ranging from medical diagnosis, where the shape of the grouped pixels increases diagnosis accuracy, to autonomous vehicles where the grouping of pixels defines roadways, traffic signs, other vehicles, etc. It even proves beneficial in many phases of machine learning, where the resulting segmentation can be used as inputs… Show more

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Cited by 5 publications
(2 citation statements)
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“…Recently, there have been significant advancements in object detection and segmentation techniques [6,7], which have found widespread applications across various domains. These methodologies have been extensively researched and applied to diverse fields, such as autonomous driving [8], medical imaging [9], and industrial automation [10][11][12]. The development of deep learning algorithms, particularly convolutional neural networks (CNNs) [13], has revolutionized many fields [14][15][16], especially the field of computer vision, enabling more accurate and efficient detection and segmentation of objects within images [17].…”
Section: Introductionmentioning
confidence: 99%
“…Recently, there have been significant advancements in object detection and segmentation techniques [6,7], which have found widespread applications across various domains. These methodologies have been extensively researched and applied to diverse fields, such as autonomous driving [8], medical imaging [9], and industrial automation [10][11][12]. The development of deep learning algorithms, particularly convolutional neural networks (CNNs) [13], has revolutionized many fields [14][15][16], especially the field of computer vision, enabling more accurate and efficient detection and segmentation of objects within images [17].…”
Section: Introductionmentioning
confidence: 99%
“…Unfortunately, there are not many experimental studies in the literature that measures the effectiveness of algorithms on different (nonoverlapping) window sizes. Texture segmentation [4] applications use sliding windows which makes them very slow.…”
Section: Introductionmentioning
confidence: 99%