Comparative study of maximum average correlation height filter variants using ladar imagery

Nevel, Alan J. Van; Mahalanobis, Abhijit

doi:10.1117/1.1531975

Cited by 33 publications

(2 citation statements)

References 14 publications

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“…the ACE and ONV, also need to be balanced to better suit different application scenarios. Thus, based on Refregier's approach on optimal trade-off [36] filters, Mahalanobis et al designed the optimal-tradeoff [37,38] maximum average correlation height (OT-MACH) filter, which minimises the average correlation height criterion, holding the others constant. By adjusting the values of the three non-negative parameters of α,β and γ (0 ≤ α,β,γ ≤ 1), we control the OT-MACH filter's behaviour to match different application requirements.…”

Section: Optical Correlators Categoriesmentioning

confidence: 99%

A Cognitive Digital-Optical Architecture for Object Recognition Applications in Remote Sensing

Kypraios¹

2023

Vision Sensors - Recent Advances

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From coastal landscapes to biodiversity remote sensing can on the one hand capture all the natural heritage elements and on the other hand can help in maintaining protected species. In a typical remote sensing application, a few thousands of super high-resolution images are captured and need to be processed. The next step of the processing involves converting those images to an appropriate format for visual display of the data. Then, the image analyst needs to define the regions of interests (ROIs) in each captured image. Next, ROIs need to be defined for identifying specific objects or extracting the required information. First drawback of this processing cycle is the use of image analysis tools which provide them only with scaling or zooming features. Second, there is no conceptual connection between the image analysis tools and the actual processing cycle. Third, such existing tools do not usually automate any steps in the processing cycle. We combine an optical correlator with a supervised or an unsupervised classifier learning algorithm and show how our proposed novel cognitive architecture is conceptually connected with the image analysis processing cycle. We test the architecture with captured images and describe how it can automate the processing cycle.

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Section: Optical Correlators Categoriesmentioning

confidence: 99%

A Cognitive Digital-Optical Architecture for Object Recognition Applications in Remote Sensing

Kypraios¹

2023

Vision Sensors - Recent Advances

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“…Over the last two decades, several statistical-and artificial intelligence-based detection techniques have been investigated. Mahalanobis et al 9,10 defined and utilized quadratic correlation filters for target detection and discrimination using the Fukunaga-Koontz transform where shift invariance is inherently obtained and no pixel-based feature extraction or preprocessing is required. In addition, neural network-based target recognition algorithms have been developed and applied for SAR images.…”

Section: Introductionmentioning

confidence: 99%

Detection of reappearing targets in forward-looking infrared video sequences

Alam

Bal

2015

Opt. Eng

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Often sensor ego-motion or fast target movement causes the target to temporarily go out of the fieldof-view leading to reappearing target detection problem in target tracking applications. Since the target goes out of the current frame and re-enters at a later frame, the re-entering location and variations in rotation, scale, and other three-dimensional orientations of the target are not known, thus complicating the detection and tracking of reappearing targets. A new training-based target detection algorithm has been developed using tuned basis functions (TBFs). The detection algorithm uses target and background information, extracted from training samples, to detect possible candidate target images. The detected candidate target images are then introduced into the second algorithm, called clutter rejection module, to determine the target re-entering frame and location of the target. The second algorithm has been designed using the spatial domain correlation-based template matching (TM) technique. If the target re-enters the current frame, the target coordinates are detected and tracking algorithm is initiated. The performance of the proposed TBF-TM-based reappearing target detection algorithm has been tested using real-world forward-looking infrared video sequences.Alam and Bal: Detection of reappearing targets in forward-looking infrared video sequences Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 08/19/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

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Phase-Encoded Cascaded Autoencoders based Correlation Filters and Adaptive Thresholding for Illumination Invariant Face Recognition

Banerjee

2024

Lecture Notes in Computer Science

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Comparative study of maximum average correlation height filter variants using ladar imagery

Cited by 33 publications

References 14 publications

A Cognitive Digital-Optical Architecture for Object Recognition Applications in Remote Sensing

A Cognitive Digital-Optical Architecture for Object Recognition Applications in Remote Sensing

Detection of reappearing targets in forward-looking infrared video sequences

Phase-Encoded Cascaded Autoencoders based Correlation Filters and Adaptive Thresholding for Illumination Invariant Face Recognition

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