001448566 000__ 06691cam\a2200613\i\4500
001448566 001__ 1448566
001448566 003__ OCoLC
001448566 005__ 20230310004245.0
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001448566 008__ 220805s2022\\\\sz\a\\\\o\\\\\101\0\eng\d
001448566 019__ $$a1338196755
001448566 020__ $$a9783031124136$$q(electronic bk.)
001448566 020__ $$a3031124138$$q(electronic bk.)
001448566 020__ $$z9783031124129$$q(print)
001448566 020__ $$z303112412X
001448566 0247_ $$a10.1007/978-3-031-12413-6$$2doi
001448566 035__ $$aSP(OCoLC)1338644335
001448566 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP$$dOCLCF$$dOCLCQ
001448566 049__ $$aISEA
001448566 050_4 $$aTA1637
001448566 08204 $$a006.4/2$$223/eng/20220805
001448566 1112_ $$aInternational Conference on Image Processing and Capsule Networks$$n(3rd :$$d2022 :$$cOnline)
001448566 24510 $$aThird International Conference on Image Processing and Capsule Networks :$$bICIPCN 2022 /$$cJoy Iong-Zong Chen, João Manuel R. S. Tavares, Fuqian Shi, editors.
001448566 2463_ $$aICIPCN 2022
001448566 264_1 $$aCham, Switzerland :$$bSpringer,$$c2022.
001448566 300__ $$a1 online resource (xv, 841 pages) :$$billustrations (some color).
001448566 336__ $$atext$$btxt$$2rdacontent
001448566 337__ $$acomputer$$bc$$2rdamedia
001448566 338__ $$aonline resource$$bcr$$2rdacarrier
001448566 4901_ $$aLecture notes in networks and systems,$$x2367-3389 ;$$vvolume 514
001448566 500__ $$aIncludes author index.
001448566 5050_ $$aIntro -- Preface -- Contents -- Brain-Inspired Spatiotemporal Feature Extraction Using Convolutional Legendre Memory Unit -- 1 Introduction -- 1.1 Neuromorphic Computing -- 2 Related Works -- 3 Proposed Convolutional LMU Model -- 4 Synthetic Dataset and Evaluation Measures -- 5 Results and Analysis -- 6 Conclusion -- References -- Underwater Image Enhancement Using Image Processing -- 1 Introduction -- 1.1 Problem Statement -- 2 Literature Survey -- 3 Methodology -- 4 Architecture -- 5 Conclusion -- References -- Fake News Detection on Indian Sources -- 1 Introduction -- 2 Related Works
001448566 5058_ $$a3 Proposed Solution -- 3.1 Dataset -- 3.2 Data Cleaning -- 3.3 Data Analysis -- 3.4 Text Preprocessing and Text Transformation -- 3.5 Model -- 3.6 Testing -- 4 Results -- 5 Use Cases -- 6 Future Works -- 7 Conclusion -- References -- Exploring Self-supervised Capsule Networks for Improved Classification with Data Scarcity -- 1 Introduction -- 2 Related Work -- 2.1 Functionality of Capsule Networks -- 2.2 Self-supervision and Capsule Networks -- 2.3 Pretrained Capsule Networks -- 3 Methods -- 3.1 Data Set -- 3.2 Capsule Network Model -- 3.3 Self-supervision -- 4 Results and Discussion
001448566 5058_ $$a4.1 Data Scarcity -- 4.2 Learning Behaviour of the Self-supervised CapsNet -- 4.3 Data Scarcity and Imbalance -- 4.4 Correlation of Pretext and Downstream Accuracy -- 5 Conclusion -- References -- A Novel Architecture for Improving Tuberculosis Detection from Microscopic Sputum Smear Images -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Preprocessing -- 3.2 Mask Generation Using SegZNet Architecture -- 3.3 Data Augmentation -- 3.4 UNet Segmentation -- 4 Result and Discussion -- 5 Conclusion -- References -- TapasQA -- Question Answering on Statistical Plots Using Google TAPAS
001448566 5058_ $$a1 Purpose -- 2 Previous Work -- 3 Methodology -- 3.1 Dataset -- 3.2 Pipeline -- 4 Major Research Findings -- 4.1 Questions Handled by Our Model -- 4.2 Training Details -- 4.3 Evaluation Metric -- 5 Result Implications -- 5.1 Plot Element Detection Stage -- 5.2 Table Question Answering (QA) Stage -- 6 End-To-End Example -- 7 Value and Limitations -- 8 Conclusion and Future Work -- References -- Face Sketch-Photo Synthesis and Recognition -- 1 Introduction -- 2 Related Work -- 3 Research Gap -- 4 Data -- 4.1 CUFS Dataset -- 4.2 CelebA Dataset -- 4.3 ORL Dataset -- 5 Tools and Experimental Settings
001448566 5058_ $$a6 Proposed Methodology -- 6.1 Face-Sketch Synthesis -- 6.2 Face-Photo Synthesis -- 6.3 Facial Recognition -- 7 Results -- 7.1 Face-Sketch Synthesis -- 7.2 Face-Photo Synthesis -- 7.3 Facial Recognition -- 8 Evaluation -- 8.1 Face-Sketch Synthesis -- 8.2 Face-Photo Synthesis -- 8.3 Facial Recognition -- 9 Conclusion -- 10 Future Work -- References -- Toward Robust Image Pre-processing Steps for Vehicle Plate Recognition -- 1 Introduction -- 2 The Proposed Deskew Approach -- 3 Performance Evaluation -- 4 Conclusion -- References
001448566 506__ $$aAccess limited to authorized users.
001448566 520__ $$aThis book provides a collection of the state-of-the-art research attempts to tackle the challenges in image and signal processing from various novel and potential research perspectives. The book investigates feature extraction techniques, image enhancement methods, reconstruction models, object detection methods, recommendation models, deep and temporal feature analysis, intelligent decision support systems, and autonomous image detection models. In addition to this, the book also looks into the potential opportunities to monitor and control the global pandemic situations. Image processing technology has progressed significantly in recent years, and it has been commercialized worldwide to provide superior performance with enhanced computer/machine vision, video processing, and pattern recognition capabilities. Meanwhile, machine learning systems like CNN and CapsNet get popular to provide better model hierarchical relationships and attempts to more closely mimic biological neural organization. As machine learning systems prosper, image processing and machine learning techniques will be tightly intertwined and continuously promote each other in real-world settings. Adopting this trend, however, the image processing researchers are faced with few image reconstruction, analysis, and segmentation challenges. On the application side, the orientation of the image features and noise removal has become a huge burden.
001448566 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed August 5, 2022).
001448566 650_0 $$aImage processing$$xDigital techniques$$vCongresses.
001448566 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001448566 655_0 $$aElectronic books.
001448566 7001_ $$aChen, Joy Iong-Zong,$$eeditor.
001448566 7001_ $$aTavares, João Manuel R. S.,$$eeditor.$$1https://orcid.org/0000-0001-7603-6526
001448566 7001_ $$aShi, Fuqian,$$eeditor.
001448566 77608 $$iPrint version: $$z303112412X$$z9783031124129$$w(OCoLC)1332779607
001448566 830_0 $$aLecture notes in networks and systems ;$$vv. 514.$$x2367-3389
001448566 852__ $$bebk
001448566 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-12413-6$$zOnline Access$$91397441.1
001448566 909CO $$ooai:library.usi.edu:1448566$$pGLOBAL_SET
001448566 980__ $$aBIB
001448566 980__ $$aEBOOK
001448566 982__ $$aEbook
001448566 983__ $$aOnline
001448566 994__ $$a92$$bISE