NLEBS: automatic target detection using a unique nonlinear-enhancement-based system in IR images

Greenberg, Shlomo; Yehezkel, Raanan; Gurevich, Yaniv; Guterman, Hugo

doi:10.1117/1.602511

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…Greenberg et. al [211] used morphological region growing and segmentation pruning after binarization for detecting objects with small false alarm rate.…”

Section: B Object Trackingmentioning

confidence: 99%

Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey

Prasad

Rajan

Rachmawati³

et al. 2017

IEEE Trans. Intell. Transport. Syst.

308

168

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We present a survey on maritime object detection and tracking approaches, which are essential for the development of a navigational system for autonomous ships. The electro-optical (EO) sensor considered here is a video camera that operates in the visible or the infrared spectra, which conventionally complement radar and sonar and have demonstrated effectiveness for situational awareness at sea has demonstrated its effectiveness over the last few years. This paper provides a comprehensive overview of various approaches of video processing for object detection and tracking in the maritime environment. We follow an approachbased taxonomy wherein the advantages and limitations of each approach are compared. The object detection system consists of the following modules: horizon detection, static background subtraction and foreground segmentation. Each of these has been studied extensively in maritime situations and has been shown to be challenging due to the presence of background motion especially due to waves and wakes. The main processes involved in object tracking include video frame registration, dynamic background subtraction, and the object tracking algorithm itself. The challenges for robust tracking arise due to camera motion, dynamic background and low contrast of tracked object, possibly due to environmental degradation. The survey also discusses multisensor approaches and commercial maritime systems that use EO sensors. The survey also highlights methods from computer vision research which hold promise to perform well in maritime EO data processing. Performance of several maritime and computer vision techniques is evaluated on newly proposed Singapore Marine Dataset.• the difficulty in modeling the dynamics of water (including waves, wakes and foams) for background subtraction and detection of foreground objects, • variations in object appearances due to distance and angle of viewing, and • changes in illumination and weather conditions, such as due to clouds, sunshine, rain, glint, etc.

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“…Greenberg et. al [211] used morphological region growing and segmentation pruning after binarization for detecting objects with small false alarm rate.…”

Section: B Object Trackingmentioning

confidence: 99%

Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey

Prasad

Rajan

Rachmawati³

et al. 2017

IEEE Trans. Intell. Transport. Syst.

308

168

View full text Add to dashboard Cite

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“…The main goal of an ATD algorithm is to ascertain the presence of specific objects in an image automatically, without human intervention. Many region of interest (ROI) based ATD applications are presented in the literature [1][2][3][4]. An important stage in such an algorithm is the automatic acquisition of points of interest (POI) in the image [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Fusion of visual salience maps for object acquisition

Greenberg

Bensimon

Podeneshko

et al. 2020

IET Computer Vision

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The paradigm of visual attention has been widely investigated and applied to many computer vision applications. In this study, the authors propose a new saliency‐based visual attention algorithm applied to object acquisition. The proposed algorithm automatically extracts points of visual attention (PVA) in the scene, based on different feature saliency maps. Each saliency map represents a specific feature domain, such as textural, contrast, and statistical‐based features. A feature selection, based on probability of detection and false alarm rate and repeatability criteria, is proposed to choose the most efficient feature combination for saliency map. Motivated by the assumption that the extracted PVA represents the most visually salient regions in the image, they suggest using the visual attention approach for object acquisition. A comparison with other well‐known algorithms for point of interest detection shows that the proposed algorithm performs better. The proposed algorithm was successfully tested on synthetic, charge‐coupled device (CCD), and infrared (IR) images. Evaluation of the algorithm for object acquisition, based on ground truth, is carried out using synthetic images, which contain multiple examples of objects, with various sizes and brightness levels. A high probability of correct detection (greater than 90%) with a low false alarm rate (about 20 false alarms per image) was achieved.

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Research on automatic target detection and recognition based on deep learning

Wang

Liu²,

et al. 2019

Journal of Visual Communication and Image Representation

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NLEBS: automatic target detection using a unique nonlinear-enhancement-based system in IR images

Cited by 4 publications

References 20 publications

Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey

Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey

Fusion of visual salience maps for object acquisition

Research on automatic target detection and recognition based on deep learning

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