Detection and tracking of targets in infrared images using Bayesian techniques

Shaik, Jahangheer; Iftekharuddin, Khan M.

doi:10.1016/j.optlastec.2008.11.007

Cited by 31 publications

(12 citation statements)

References 23 publications

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“…Then, the Receiver Operation Characteristic (ROC) curves of the target regions detected by the LSSVM method [1], the Facet method [2], the GST method [3] and our method were compared for the robustness of the detected target region. The true positive rate is defined as the ratio of the detected pixels' number to the one of the real targets' pixels, and the false-positive rate is denoted as the ratio of the false alarms' number to the total pixels' in the infrared image [5].…”

Section: Experimental Workmentioning

confidence: 99%

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A Small Infrared Target Detection Method Based on Coarse-to-Fine Segmentation and Confidence Analysis

Wang

Tian

et al. 2015

Communications in Computer and Information Science

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In this paper, a small target detection algorithm in infrared image is proposed. First, an infrared image is coarse-to-fine segmented automatically by self-adaptive histogram segmentation. After detecting small abnormal region in segmented image and defining them to candidate targets, the abnormality based confidence of each candidate target is calculated and sorted. Finally, the candidate target with the maximum confidence is weighed to be real one. The experiments demonstrate that the proposed method is efficient and robust.

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Section: Experimental Workmentioning

confidence: 99%

“…It is a hard problem for the variability of the target's appearance due to different atmospheric conditions, background, camera ego-motion and et al [1]. It is a hard problem for the variability of the target's appearance due to different atmospheric conditions, background, camera ego-motion and et al [1].…”

Section: Introductionmentioning

confidence: 99%

A Small Infrared Target Detection Method Based on Coarse-to-Fine Segmentation and Confidence Analysis

Wang

Tian

et al. 2015

Communications in Computer and Information Science

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“…Infrared small target detection is one of the most crucial techniques in automatic target recognition (ATR) [1] and holds the key for automatic target detection (ATD) [2]. Benefiting from its valuable outputs, small target detection is widely applied in military reconnaissance, early warning, terminal guidance, etc.…”

Section: Introductionmentioning

confidence: 99%

Robust method for infrared small-target detection based on Boolean map visual theory

Ming

et al. 2014

Appl. Opt.

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In this paper, we present an infrared small target detection method based on Boolean map visual theory. The scheme is inspired by the phenomenon that small targets can often attract human attention due to two characteristics: brightness and Gaussian-like shape in the local context area. Motivated by this observation, we perform the task under a visual attention framework with Boolean map theory, which reveals that an observer's visual awareness corresponds to one Boolean map via a selected feature at any given instant. Formally, the infrared image is separated into two feature channels, including a color channel with the original gray intensity map and an orientation channel with the orientation texture maps produced by a designed second order directional derivative filter. For each feature map, Boolean maps delineating targets are computed from hierarchical segmentations. Small targets are then extracted from the target enhanced map, which is obtained by fusing the weighted Boolean maps of the two channels. In experiments, a set of real infrared images covering typical backgrounds with sky, sea, and ground clutters are tested to verify the effectiveness of our method. The results demonstrate that it outperforms the state-of-the-art methods with good performance.

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“…They are thus more effective than visible cameras under poor lighting conditions. Current studies have focused on object detection and tracking from infrared images, and infrared sensor-based methods have shown good performance [1][2][3][4]. However, sensors often differ in modalities, considering physical and technological limitations.…”

Section: Introductionmentioning

confidence: 99%

Infrared Target Detection and Location for Visual Surveillance Using Fusion Scheme of Visible and Infrared Images

Zhang

Yuan

Wang

2013

Mathematical Problems in Engineering

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The main goal of image fusion is to combine substantial information from different images of the same scene into a single image that is suitable for human and machine perception or for further image-processing tasks. In this study, a simple and efficient image fusion approach based on the application of the histogram of infrared images is proposed. A fusion scheme to select adaptively weighted coefficients for preserving salient infrared targets from the infrared image and for obtaining most spatial detailed information from the visible image is presented. Moving and static infrared targets in the fused image are labeled with different colors. This technique enhances perception of the image for the human visual system. In view of the modalities of infrared images, low resolution, and low signal-to-noise ratio, an anisotropic diffusion equation model is adopted to remove noise and to effectively preserve edge information before the fusion stage. By using the proposed method, relevant spatial information is preserved and infrared targets are clearly identified in the resulting fused images.

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Detection and tracking of targets in infrared images using Bayesian techniques

Cited by 31 publications

References 23 publications

A Small Infrared Target Detection Method Based on Coarse-to-Fine Segmentation and Confidence Analysis

A Small Infrared Target Detection Method Based on Coarse-to-Fine Segmentation and Confidence Analysis

Robust method for infrared small-target detection based on Boolean map visual theory

Infrared Target Detection and Location for Visual Surveillance Using Fusion Scheme of Visible and Infrared Images

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