&lt;title&gt;Detection of targets in low-resolution FLIR images using two-dimensional directional wavelets&lt;/title&gt;

Murenzi, Romain; Johnson, D. B.; Kaplan, Lance; Namuduri, Kamesh

doi:10.1117/12.323869

Cited by 10 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ye et al [4] proposed a maximal coefficient based wavelet. In addition, directional wavelet [5], morphological wavelet [6], and so on, were all developed and studied by researchers. Mathematical morphology theory is another important bunch in small target detection domain.…”

Section: Introductionmentioning

confidence: 99%

In-frame and inter-frame information based infrared moving small target detection under complex cloud backgrounds

Wan

Cao³

et al. 2016

Infrared Physics & Technology

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

In-frame and inter-frame information based infrared moving small target detection under complex cloud backgrounds

Wan

Cao³

et al. 2016

Infrared Physics & Technology

View full text Add to dashboard Cite

“…Many researchers have developed various target detection algorithms in FLIR images. For example, typical methods are hit-miss transform, morphological wavelet [5], morphology [21], and directional wavelet [22]. To reject clutters in potential targets, S. A. Rizvi et al proposed neural network approach using principal component analysis [18,23], and L. A. Chan et al used eigenspace separation transform [24] in the last few years.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Targets Using Center-Surround Difference and Local Thresholding

Sun

Kwak

2006

JMM

View full text Add to dashboard Cite

This paper proposes a new target detection method in low contrast forward looking infrared (FLIR) images. Automatic detection of small targets in remotely sensed images is a difficult and challenging work. The goal is to find out target locations with low false alarms in a thermal infrared scene of battlefield. The interesting targets are military vehicles such as battle tanks and armored personal carriers in ground-to-ground scenarios. The proposed method consists of three following stages. First, center-surround difference is proposed in order to find salient areas in an input image. Second, local thresholding for a region of interest (ROI) is proposed. The ROI is selected on the basis of a salient region that is the result of first step. Third, the shape of extracted binary images is compared with binary target templates using size and affinity to remove clutters. In the experiments, the proposed method is compared with morphology method using many natural infrared images with high variability. The result demonstrates that our method is superior to the morphological method in terms of receiver operating characteristic (ROC) curve and average computation time.

show abstract

“…A number of transforms have been proposed to extract the essential target features for ATR in various domains. Examples include intensity peaks [2], topographic primal sketches [3], class segmentations [4], and features derived from the continuous wavelet transform [5] . In this work, we investigate the robustness of various pointwise nonlinear transforms for template matching ATR operating on synthetic aperture radar (SAR) imagery.…”

Section: Introductionmentioning

confidence: 99%

“…ATR performance in various template matching domains using one set of templates per a target class with an aspect angular bin size of (a) 5 degrees, (b) 10 degrees, (c) 15 degrees, (d) 30 degrees, and (ATR performance in various template matching domains using multiple sets of templates per a target class with an aspect angular bin size of (a)5 degrees, (b) 10 degrees, (c) 15 degrees, (d) 30 degrees, and (e) 45 degrees. ATR performance of the power transforms at various values of a for O = 5 using 504 templates.…”

mentioning

confidence: 99%

<title>Effect of signal-to-clutter ratio on template-based ATR</title>

et al. 1998

Self Cite

View full text Add to dashboard Cite

In this work, we evaluate the robustness of template matching schemes for automatic target recognition (ATR) against the effects of clutter layover. The results of our experiments indicate the performance of template matching ATR in various image transform domains against the signal to clutter ratio (SCR). The purpose of these transforms is to enhance the target features in a chip while suppressing features representative of background clutter or simple noise. The ATR experiments were performed for synthetic aperture radar (SAR) imagery using target chips in the public domain MSTAR database. The transforms include pointwise nonlinearities such as the logarithm and power operations. The templates are generated using the training portion of the MSTAR database at the nominal SCR. Many different ATR parameterizations are considered for each transform domain where templates are built to represent different ranges of aspect angles in uniform angular bins of 5, 10, 15, 30, and 45 degree increments. The different ATRs were evaluated using the testing portion of the database where synthetic clutter was added to lower the SCR.

show abstract

<title>Detection of targets in low-resolution FLIR images using two-dimensional directional wavelets</title>

Cited by 10 publications

References 7 publications

In-frame and inter-frame information based infrared moving small target detection under complex cloud backgrounds

In-frame and inter-frame information based infrared moving small target detection under complex cloud backgrounds

Automatic Detection of Targets Using Center-Surround Difference and Local Thresholding

<title>Effect of signal-to-clutter ratio on template-based ATR</title>

Contact Info

Product

Resources

About