001463721 000__ 04661cam\a22006737i\4500
001463721 001__ 1463721
001463721 003__ OCoLC
001463721 005__ 20230601003334.0
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001463721 007__ cr\cn\nnnunnun
001463721 008__ 230508s2023\\\\sz\\\\\\o\\\\\101\0\eng\d
001463721 019__ $$a1378010141$$a1378390993
001463721 020__ $$a9783031264313$$q(electronic bk.)
001463721 020__ $$a3031264312$$q(electronic bk.)
001463721 020__ $$z9783031264306
001463721 020__ $$z3031264304
001463721 0247_ $$a10.1007/978-3-031-26431-3$$2doi
001463721 035__ $$aSP(OCoLC)1378504703
001463721 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dYDX
001463721 049__ $$aISEA
001463721 050_4 $$aTA1634
001463721 08204 $$a006.3/7$$223/eng/20230508
001463721 1112_ $$aPSIVT (Symposium)$$n(10th :$$d2022 :$$cOnline)
001463721 24510 $$aImage and video technology :$$b10th Pacific-Rim Symposium, PSIVT 2022, virtual event, November 12-14, 2022, proceedings /$$cedited by Han Wang [and seven others].
001463721 2463_ $$aPSIVT 2022
001463721 264_1 $$aCham :$$bSpringer,$$c2023.
001463721 300__ $$a1 online resource.
001463721 336__ $$atext$$btxt$$2rdacontent
001463721 337__ $$acomputer$$bc$$2rdamedia
001463721 338__ $$aonline resource$$bcr$$2rdacarrier
001463721 4901_ $$aLecture notes in computer science ;$$v13763
001463721 500__ $$aIncludes author index.
001463721 504__ $$aReferences -- A Method for Face Image Inpainting Based on Autoencoder and Generative Adversarial Network -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Generative Network -- 3.2 Discriminator Network -- 3.3 Algorithms -- 4 Result Analysis -- 5 Conclusion -- References -- Traffic Sign Recognition from Digital Images by Using Deep Learning -- 1 Introduction -- 2 Literature Review -- 2.1 External Factors Affecting Traffic Sign Recognition -- 2.2 Related Algorithms -- 3 Our Method -- 3.1 Dark Channel Prior Defogging Algorithm -- 3.2 Guided Image Filtering
001463721 5050_ $$aWaste Classification from Digital Images Using ConvNeXt -- A Federated Learning approach for text classification using NLP -- A_Method_for_Face_Image_Inpainting_Based_on_Autoencoder_and_Adversarial_Generative_Networks -- Traffic Sign Recognition from Digital Images by Using Deep Learning -- Youtube Engagement Analytics via Deep Multimodal Hybrid Fusion -- Dynamic Point Cloud Compression with Cross-Sectional Approach -- Event-based visual sensing for human motion detection and classification at various distances -- On Low-Resolution Face Re-identification with High-Resolution-Mapping -- On Skin Lesion Recognition using Deep Learning 50 Ways to Choose Your Model -- Enhancing Automated Baggage Inspection Using Simulated X-ray Images of 3D Models -- A Wasserstein GAN for Joint Learning of Inpainting and Spatial Optimisation Rapid On-site Weed Identification with Machine Learning -- Remote Tiny Weeds Detection -- Combining_Multivision_Embedding_in_Contextual_Attention_for_Vietnamese_Visual_Question_Answering -- Depth Estimation of Traffic Scenes from Image Sequence Using Deep Learning.
001463721 506__ $$aAccess limited to authorized users.
001463721 520__ $$aThis book constitutes the conference proceedings of the 10th Pacific Rim Symposium on Image and Video Technology, PSIVT 2022, held in Bintan Island, Indonesia, in November 2022. A total of 15 papers were carefully reviewed and selected from 18 submissions. The main conference focuses on theoretical advances or practical implementations in image and video technology.
001463721 588__ $$aDescription based on print version record.
001463721 650_0 $$aComputer vision$$vCongresses.
001463721 650_0 $$aImage processing$$xDigital techniques$$vCongresses.
001463721 650_0 $$aDigital video$$vCongresses.
001463721 650_0 $$aImaging systems$$vCongresses.
001463721 655_0 $$aElectronic books.
001463721 7001_ $$aWang, Han,$$eeditor.
001463721 77608 $$iPrint version:$$aPSIVT (Symposium) (10th : 2022 : Online), creator.$$tImage and video technology.$$dCham : Springer, 2023$$z9783031264306$$w(OCoLC)1374596672
001463721 830_0 $$aLecture notes in computer science ;$$v13763.
001463721 852__ $$bebk
001463721 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-26431-3$$zOnline Access$$91397441.1
001463721 909CO $$ooai:library.usi.edu:1463721$$pGLOBAL_SET
001463721 980__ $$aBIB
001463721 980__ $$aEBOOK
001463721 982__ $$aEbook
001463721 983__ $$aOnline
001463721 994__ $$a92$$bISE