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Conference on Artificial Intelligence and Machine Learning in Defense Applications IV

作者： Pitts, Christopher Danford, Forest Moore, Emily Marchetto, William Qiu, Henry Ross, Leon Pitts, Todd Sandia Natl Labs 1515 Eubank Blvd SE Albuquerque NM 87185 USA

ISBN: (纸本)9781510655560

Any program tasked with the evaluation and acquisition of algorithms for use in deployed scenarios must have an impartial, repeatable, and auditable means of benchmarking both candidate and fielded algorithms. Success in this endeavor requires a body of representative sensor data, data labels indicating the proper algorithmic response to the data as adjudicated by subject matter experts, a means of executing algorithms under review against the data, and the ability to automatically score and report algorithm performance. Each of these capabilities should be constructed in support of program and mission goals. By curating and maintaining data, labels, tests, and scoring methodology, a program can understand and continually improve the relationship between benchmarked and fielded performance of acquired algorithms. A system supporting these program needs, deployed in an environment with sufficient computational power and necessary security controls is a powerful tool for ensuring due diligence in evaluation and acquisition of mission critical algorithms. This paper describes the Seascape system and its place in such a process.

关键词： Machine learning algorithm assurance algorithm acquisition artificial intelligence algorithm assessment

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assessment of algorithms for Estimating Chlorophyll-a Concentration in Inland Waters: A Round-Robin Scoring Method Based on the Optically Fuzzy Clustering

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷

作者： Bi, Shun Li, Yunmei Liu, Ge Song, Kaishan Xu, Jie Dong, Xianzhang Cai, Xiaolan Mu, Meng Miao, Song Lyu, Heng Nanjing Normal Univ Sch Geog Nanjing 210023 Peoples R China Nanjing Normal Univ Key Lab Virtual Geog Environm Minist Educ Nanjing 210023 Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Peoples R China Chinese Acad Sci Northeast Inst Geog & Agr Ecol Changchun 130102 Peoples R China

Recently, numerous bio-optical algorithms have been proposed to estimate chlorophyll-a (Chla) concentration in global surface waters. The surge of algorithms is mainly due to the heterogeneity of waters, especially in inland systems. To reduce redundant modeling work for inland waters and to enhance the usability of algorithms, it is urgent to evaluate the published algorithms for similar optical water types. However, approaches of the algorithm assessment in previous studies vary in error metrics usage or relative comparison, leading to the current lack of a standard or consensus in the community. Thus, we proposed a round-robin scoring method based on optically fuzzy clustering for algorithm assessment. An improved scoring approach (namely the sort-based method) was employed to aggregate the metrics of candidate algorithms. We generate a new set of optical water types using 1702 in situ samples, showing an improvement on our previous work [Bi et al. (2019)]. Then, an integrated Chla result was produced by blending the estimations of optimal algorithms weighted by the membership values, which showed better performance than that of any single algorithm. The blending framework presents more flexible and expandable than previous studies. We aim to illustrate the importance of assessment which considers key properties of algorithms (i.e., Accuracy, Precision, and Effectiveness) and to advocate more in-depth research on certain water types under the fuzzy clustering-based estimation framework (such as clean inland waters with low Chla). This article is accompanied by the open-source R package FCMm for implementation of the proposed method.

关键词： Clustering algorithms Measurement Adaptive optics Optical sensors Biomedical optical imaging Image color analysis Prediction algorithms algorithm assessment Chlorophyll-a (Chla) concentration error metrics optical water types remote sensing

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Establishing the reliability of algorithms

Establishing the reliability of algorithms

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26th Pacific Symposium on Biocomputing (PSB)

作者： Mangravite, Lara Mooney, Sean D. Friedberg, Iddo Guinney, Justin Sage Bionetworks Seattle WA 98121 USA Univ Washington Dept Biomed Informat & Med Educ Seattle WA 98195 USA Iowa State Univ Dept Vet Microbiol & Prevent Med Bioinformat & Computat Biol Program Ames IA USA

ISBN: (纸本)9789811232701;9789811232695

As rich biomedical data streams are accumulating across people and time, they provide a powerful opportunity to address limitations in our existing scientific knowledge and to overcome operational challenges in healthcare and life sciences. Yet the relative weighting of insights vs. methodologies in our current research ecosystem tends to skew the computational community away from algorithm evaluation and operationalization, resulting in a well-reported trend towards the proliferation of scientific outcomes of unknown reliability. algorithm selection and use is hindered by several problems that persist across our field. One is the impact of the self-assessment bias, which can lead to mis-representations in the accuracy of research results. A second challenge is the impact of data context on algorithm performance. Biology and medicine are dynamic and heterogeneous. Data is collected under varying conditions. For algorithms, this means that performance is not universal -- and need to be evaluated across a range of contexts. These issues are increasingly difficult as algorithms are trained and used on data collected in the real-world, outside of the traditional clinical research lab. In these cases, data collection is neither supervised nor well controlled and data access may be limited by privacy or proprietary reasons. Therefore, there is a risk that algorithms will be applied to data that are outside of the scope of the intent of the original training data provided. This workshop will focus on approaches that are emerging across the researcher community to quantify the accuracy of algorithms and the reliability of their outputs.

关键词： benchmarking algorithm assessment open science translational research

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The assessment of Landsat-8 OLI Atmospheric Correction algorithms for Inland Waters

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REMOTE SENSING 2019年第2期11卷 169-169页

作者： Wang, Dian Ma, Ronghua Xue, Kun Loiselle, Steven Arthur Chinese Acad Sci Nanjing Inst Geog & Limnol Key Lab Watershed Geog Sci Nanjing 210008 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Siena CSGI Dipartimento Biotecnol Chim & Farm Via Aldo Moro 2 I-53100 Siena Italy

The OLI (Operational Land Imager) sensor on Landsat-8 has the potential to meet the requirements of remote sensing of water color. However, the optical properties of inland waters are more complex than those of oceanic waters, and inland atmospheric correction presents additional challenges. We examined the performance of atmospheric correction (AC) methods for remote sensing over three highly turbid or hypereutrophic inland waters in China: Lake Hongze, Lake Chaohu, and Lake Taihu. Four water-AC algorithms (SWIR (Short Wave Infrared), EXP (Exponential Extrapolation), DSF (Dark Spectrum Fitting), and MUMM (Management Unit Mathematics Models)) and three land-AC algorithms (FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes), 6SV (a version of Second Simulation of the Satellite Signal in the Solar Spectrum), and QUAC (Quick Atmospheric Correction)) were assessed using Landsat-8 OLI data and concurrent in situ data. The results showed that the EXP (and DSF) together with 6SV algorithms provided the best estimates of the remote sensing reflectance (R-rs) and band ratios in water-AC algorithms and land-AC algorithms, respectively. AC algorithms showed a discriminating accuracy for different water types (turbid waters, in-water algae waters, and floating bloom waters). For turbid waters, EXP gave the best R-rs in visible bands. For the in-water algae and floating bloom waters, however, all water-algorithms failed due to an inappropriate aerosol model and non-zero reflectance at 1609 nm. The results of the study show the improvements that can be achieved considering SWIR bands and using band ratios, and the need for further development of AC algorithms for complex aquatic and atmospheric conditions, typical of inland waters.

关键词： remote sensing Landsat-8 OLI atmospheric correction inland waters algorithm assessment

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Filling the gap in quality assessment of video object tracking

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IMAGE AND VISION COMPUTING 2012年第9期30卷 630-640页

作者： Carvalho, Pedro Cardoso, Jaime S. Corte-Real, Luis INESC Porto P-4200465 Oporto Portugal

Current evaluation methods either rely heavily on reference information manually annotated or, by completely avoiding human input, provide only a rough evaluation of the performance of video object tracking algorithms. The main objective of this paper is to present a novel approach to the problem of evaluating video object tracking algorithms. It is proposed the use different types of reference information and the combination of heterogeneous metrics for the purpose of approximating the ideal error. This will enable a significant decrease of the required reference information, thus bridging the gap between metrics with different requirements concerning this type of data. As a result, evaluation frameworks can aggregate the benefits from individual approaches while overcoming their weaknesses, providing a flexible and powerful tool to assess and characterize the behavior of the tracking algorithms. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Computer vision Tracking algorithm assessment Evaluation metrics Information fusion

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Assessing algorithms to Remove Back-to-Front Interference in Documents

Assessing Algorithms to Remove Back-to-Front Interference in...

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2006 International Telecommunications Symposium (ITS)

作者： Lins, R. D. da Silva, J. M. M. Univ Fed Pernambuco Dept Elect & Syst Telecommun Grp Ctr Technol & Geosci Recife PE Brazil

ISBN: (纸本)9781424436446

Documents written on both sides on translucent paper make visible the ink from one side on the other. This artifact is called "back-to-front interference" or "bleeding". The direct binarization of documents with such interference yields unreadable documents. The literature presents several algorithms for suitably removing such artifact. This paper presents a quantitative method to assess algorithms to remove back-to-front interference.

关键词： algorithm assessment Back-to-front interference Bleeding

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High-resolution synthetic aperture radar experiments for ATR development and performance prediction

High-resolution synthetic aperture radar experiments for ATR...

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Conference on algorithms for Synthetic Aperture Radar Imagery IX

作者： Westerkamp, LA Morrison, SA Wild, TJ Mossing, JC Air Force Research Lab. (United States) JE Sverdrup Technology Inc. (United States)

ISBN: (纸本)0819444774

Commercial availability of very high-resolution synthetic aperture radar (SAR) imagery will enable development of automatic target recognition (ATR) algorithms to exploit its rich information content. This availability also permits exploration of both empirical and first principles approaches-for predicting ATR performance. This paper describes a recent collection of high resolution SAR imagery. It details the operating conditions represented by the data and provides recommended experiments designed to challenge ATR algorithms and performance prediction. This set of information, along with the imagery, is contained in a Problem Set that will be made available to the community. The imagery is from a Deputy Under Secretary of Defense (DUSD) for Science and Technology (S&T) sponsored collection using the Sandia National Laboratory and General Atomics Lynx Sensor. The Lynx is now available as a commercial off-the-shelf (COTS) sensor. It was designed for use in medium-altitude UAVs and manned platforms. It operates at Ku-band frequency in stripmap, spotlight, and ground moving target indicator modes. Imagery in this collection was collected at 4" resolution and was then also reprocessed to V resolution. The collection included several military vehicles with significant variation in target, sensor, and background conditions. Defined experiments in the Problem Set present ATR algorithm development challenges by defining development (training) sets with limited representation of operating conditions and test sets that explore the algorithm's ability to extend to more complex operating conditions. These challenges are critical to military employment of ATR because the real world contains much more variability than it will be possible to explicitly address in an algorithm. For example, neither the storage nor the search through an exhaustive bay of templates is achievable for any realistic application. Thus, advanced developments that allow robust performance in denied condit

关键词： high resolution SAR problem set ATR performance prediction algorithm assessment Lynx

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Performance prediction of vehicle detection algorithms

Performance prediction of vehicle detection algorithms

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11th Conference on Automatic Target Recognition

作者： Klausmann, P Kroschel, K Willersinn, D Fraunhofer Inst Informat & Data Proc D-76131 Karlsruhe Germany

ISBN: (纸本)0819440744

Performance prediction of computer vision algorithms is of increasing interest whenever robustness to illumination variations, shadows and different weather conditions has to be ensured. The statistical model which is presented in this contribution predicts the algorithm performance under the presence of noise, image clutter and perturbations and therefore provides an algorithm-specific measure of the underlying image quality. For the prediction of the detection performance logistic regression using covariates defined by the properties of the vehicle signatures is used. This approach provides an estimate of the probability of a single vehicle signature being detected by a given detection algorithm. To describe the relationship between background clutter and the false alarm rate of the algorithm a severity measure of the image background is presented. After the construction of the algorithm model, the probability of a vehicle signature being detected and the false alarm rate are estimated on new data. The model is evaluated and compared to the true algorithm performance.

关键词： automatic target detection performance prediction algorithm assessment logistic regression clutter measure

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