&lt;title&gt;Detection of moving targets in time-sequenced imagery using statistical background rejection&lt;/title&gt;

Watson, Graham H.; Watson, Sharon K.

doi:10.1117/12.279539

Cited by 9 publications

(5 citation statements)

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“…Since its inception, the concept of SL has evolved [52] and incorporates elements related to the context in which the statistical thinking can be applied [53]. SL plays a crucial role in society [54], and communicating statistical information is now more important than ever [55]. More recently, SL is prompting individuals to recognise the importance that mathematics plays in the world [56].…”

Section: Explainable Algorithmsmentioning

confidence: 99%

Trust in algorithms. An experimental approach

Marmolejo-Ramos

2023

Preprint

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Algorithms have become ubiquitous in our day-to-day activities. Their presence ranges from low-stakes decisions such as what music we should listen to, to high-stakes decisions regarding promotion decisions. Despite the frequency and range with which we interact with algorithms, academic research, as well as public outcries, suggest that humans often express distrust of algorithms and their decisions. Previous research suggests that there may be ways to mitigate this distrust through educational efforts that help explain how algorithms operate. With a sample of 1,841 participants across 19 countries, the present study investigates these notion by examining variations in algorithmic trust across low and high-stakes decisions, and the role of explainability and statistical literacy in shaping reactions. Broadly, results suggest a relationship between how much one understands about algorithms and their propensity to trust. Implicationsof these findings are discussed.

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Section: Explainable Algorithmsmentioning

confidence: 99%

Trust in algorithms. An experimental approach

Marmolejo-Ramos

2023

Preprint

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“…So, the static clutter can be removed by frame difference [32]. Principal component analysis (PCA) for multi-frames can remove temporal noise such as sun-glint [33].…”

Section: Background Information-based Target Detectionmentioning

confidence: 99%

Double Layered-Background Removal Filter for Detecting Small Infrared Targets in Heterogenous Backgrounds

Kim

2010

J Infrared Milli Terahz Waves

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Detecting small targets is essential for mitigating the sea-based Infrared search and track (IRST) problem. It is easy to detect small targets in homogeneous backgrounds such as the sky. When targets are on the border line of heterogeneous backgrounds such as the horizon in the sky and sea surface, solving the problem of detection becomes difficult. This paper presents a novel spatial filtering method, called Double Layered-Background Removal Filter (DL-BRF), for achieving high detection rates and low false alarm rates. DL-BRF consists of a Modified-Mean Subtraction Filter (M-MSF) and a consecutive LocalDirectional Background Removal Filter (L-DBRF). M-MSF enhances the target signal and reduces background noise. L-DBRF removes horizontal structures, which upgrade the signal-to-clutter ratio and background suppression factor. L-DBRF used after M-MSF enhances the synergistic performance of horizontal target detection. Additionally, the adaptive Hysteresis threshold-based scheme is a suitable detection method. We validate the superior performance of the proposed method via three types of evaluation tests, including a real test scenario.

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“…Therefore, a background estimation can be performed using a weighted autocorrelation matrix update using the recursive technique [ 41 ]. Static clutter can also be removed by the frame difference [ 42 ]. An advanced adaptive spatial-temporal filter derived by the multi-parametric approximation of clutter can achieve tremendous gain compared to that of the spatial filtering method [ 43 ].…”

Section: Related Work In Terms Of Clutter Rejectionmentioning

confidence: 99%

Small Infrared Target Detection by Region-Adaptive Clutter Rejection for Sea-Based Infrared Search and Track

Kim

Lee

2014

Sensors

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This paper presents a region-adaptive clutter rejection method for small target detection in sea-based infrared search and track. In the real world, clutter normally generates many false detections that impede the deployment of such detection systems. Incoming targets (missiles, boats, etc.) can be located in the sky, horizon and sea regions, which have different types of clutters, such as clouds, a horizontal line and sea-glint. The characteristics of regional clutter were analyzed after the geometrical analysis-based region segmentation. The false detections caused by cloud clutter were removed by the spatial attribute-based classification. Those by the horizontal line were removed using the heterogeneous background removal filter. False alarms by sun-glint were rejected using the temporal consistency filter, which is the most difficult part. The experimental results of the various cluttered background sequences show that the proposed region adaptive clutter rejection method produces fewer false alarms than that of the mean subtraction filter (MSF) with an acceptable degradation detection rate.

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<title>Detection of moving targets in time-sequenced imagery using statistical background rejection</title>

Cited by 9 publications

References 0 publications

Trust in algorithms. An experimental approach

Trust in algorithms. An experimental approach

Double Layered-Background Removal Filter for Detecting Small Infrared Targets in Heterogenous Backgrounds

Small Infrared Target Detection by Region-Adaptive Clutter Rejection for Sea-Based Infrared Search and Track

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