8ffd1240478623b23361a39a44b0a37d applsci-14-11059.pdf e470cee1588e30ba9364e961aad856dac95afc5a applsci-14-11059.pdf 2875fbdae9fdce182930bbd52e6730e9acf4a5ea11fab13c1f067c1e90b4a723 applsci-14-11059.pdf Title: Classification of Real-World Objects Using Supervised ML-Assisted Polarimetry: Cost/Benefit Analysis Subject: We study the problem of classification of various real-world objects using as input a database (DB) of laboratory polarimetric measures (Mueller matrix elements—MMEs). It can work as a complementary technology of surroundings' imaging that can be used, in particular, in autonomous driving. To this end, we look for an algorithm using less input parameters without great loss of the quality of classification. We start by analyzing the data in order to understand the attributes that are more important for associating the objects with one of several predefined classes. Different sets of attributes are studied using an artificial neural network (ANN), which is optimized in terms of the number of hidden layers and the activation function. After that, an improved machine learning (ML) architecture is built using the K-nearest neighbors (KNN) classifier on each cluster generated by applying the pre-trained ANN to the training set. This article focuses on the situation wherein one may not be able to measure all MMEs or it would be too expensive or challenging to implement when the measurement time is crucial. The results obtained for a reduced set of attributes using different ML architectures are very good, especially for the proposed combined ANN-KNN approach (wherein the ANN acts as a predictor and KNN as a corrector), which can help to avoid measuring all MMEs. Keywords: polarimetry; machine learning; object classification Author: Rui M. S. Pereira, Filipe Oliveira, Nazar Romanyshyn, Irene Estevez, Joel Borges, Stephane Clain and Mikhail I. Vasilevskiy Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25 CreationDate: Thu Nov 28 05:17:01 2024 CET ModDate: Thu Nov 28 05:20:00 2024 CET Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 13 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 1162996 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- AYHALM+URWPalladioL-Bold Type 1 Custom yes yes yes 10 0 HQYWYK+URWPalladioL-Roma Type 1 Custom yes yes yes 16 0 HPGHIN+URWPalladioL-Ital Type 1 Custom yes yes yes 21 0 HFXBRL+CMSY10 Type 1 Builtin yes yes yes 51 0 DURHUM+CMMI10 Type 1 Builtin yes yes yes 60 0 TSBOKK+CMEX10 Type 1 Builtin yes yes yes 68 0 IOLAWT+PazoMath-Italic Type 1 Builtin yes yes yes 73 0 GIGFZE+CMR10 Type 1 Builtin yes yes yes 78 0 CBWESR+URWPalladioL-BoldItal Type 1 Custom yes yes yes 83 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2025-02-20 02:14:43 CET RepresentationInformation: applsci-14-11059.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2025-02-19 10:17:38 CET Size: 1162996 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 313 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Materials and Methods Destination: section.2 Children: Item: Title: Data Collection Destination: subsection.2.1 Item: Title: Statistical Analysis of Input Attributes Destination: subsection.2.2 Item: Title: ML Architectures and Results Destination: section.3 Children: Item: Title: RFs with Different Sets of Input Attributes Destination: subsection.3.1 Item: Title: ANNs with Different Sets of Input Attributes Destination: subsection.3.2 Item: Title: ANN-KNN Classifier Destination: subsection.3.3 Item: Title: Recall Metric of Classifiers' Performance Destination: subsection.3.4 Item: Title: Discussion and Conclusions Destination: section.4 Item: Title: References Destination: section.5 Info: Title: Classification of Real-World Objects Using Supervised ML-Assisted Polarimetry: Cost/Benefit Analysis Author: Rui M. S. Pereira, Filipe Oliveira, Nazar Romanyshyn, Irene Estevez, Joel Borges, Stephane Clain and Mikhail I. Vasilevskiy Subject: We study the problem of classification of various real-world objects using as input a database (DB) of laboratory polarimetric measures (Mueller matrix elements MMEs). It can work as a complementary technology of surroundings' imaging that can be used, in particular, in autonomous driving. To this end, we look for an algorithm using less input parameters without great loss of the quality of classification. We start by analyzing the data in order to understand the attributes that are more important for associating the objects with one of several predefined classes. Different sets of attributes are studied using an artificial neural network (ANN), which is optimized in terms of the number of hidden layers and the activation function. After that, an improved machine learning (ML) architecture is built using the K-nearest neighbors (KNN) classifier on each cluster generated by applying the pre-trained ANN to the training set. This article focuses on the situation wherein one may not be able to measure all MMEs or it would be too expensive or challenging to implement when the measurement time is crucial. The results obtained for a reduced set of attributes using different ML architectures are very good, especially for the proposed combined ANN-KNN approach (wherein the ANN acts as a predictor and KNN as a corrector), which can help to avoid measuring all MMEs. 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