LEADER 04725nam 22008655 450 001 9910983299703321 005 20241101115739.0 010 $a9789819786923 010 $a9819786924 024 7 $a10.1007/978-981-97-8692-3 035 $a(MiAaPQ)EBC31749063 035 $a(Au-PeEL)EBL31749063 035 $a(CKB)36479387100041 035 $a(DE-He213)978-981-97-8692-3 035 $a(Exl-AI)31749063 035 $a(OCoLC)1467874998 035 $a(EXLCZ)9936479387100041 100 $a20241101d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPattern Recognition and Computer Vision $e7th Chinese Conference, PRCV 2024, Urumqi, China, October 18?20, 2024, Proceedings, Part IX /$fedited by Zhouchen Lin, Ming-Ming Cheng, Ran He, Kurban Ubul, Wushouer Silamu, Hongbin Zha, Jie Zhou, Cheng-Lin Liu 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (0 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v15039 311 08$a9789819786916 311 08$a9819786916 327 $a Preface -- Organization -- Contents ? Part IX -- Low-Level Vision and lmage Processing II -- Fine-Grained Adjustable Entropy Models for Rate-Complexity Jointly Adjustable Image Compression -- 1 Introduction -- 2 Proposed Method -- 2.1 Framework Construction of the Networks -- 2.2 Implementation and Optimization -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 R-D and Efficiency Performance -- 3.3 Visual Results -- 4 Conclusion -- References -- FPSNet: Focus-Perceptual-Semantic Full Flow Visual Redundancy Predicting for Camera Image -- 1 Instruction -- 2 Proposed Method -- 2.1 Overall Framework -- 2.2 Feature Pre-extraction -- 2.3 Network Design -- 3 Experiment -- 3.1 Protocol -- 3.2 Quantitative Analysis -- 3.3 Qualitative Analysis -- 3.4 Ablation Experiment -- 4 Conclusion -- References -- Semantic-Aware Global and Local Fusion Model for Image Enhancement -- 1 Introduction$7Generated by AI. 330 $aThis 15-volume set LNCS 15031-15045 constitutes the refereed proceedings of the 7th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2024, held in Urumqi, China, during October 18?20, 2024. The 579 full papers presented were carefully reviewed and selected from 1526 submissions. The papers cover various topics in the broad areas of pattern recognition and computer vision, including machine learning, pattern classification and cluster analysis, neural network and deep learning, low-level vision and image processing, object detection and recognition, 3D vision and reconstruction, action recognition, video analysis and understanding, document analysis and recognition, biometrics, medical image analysis, and various applications. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v15039 606 $aImage processing$xDigital techniques 606 $aComputer vision 606 $aArtificial intelligence 606 $aApplication software 606 $aComputer networks 606 $aComputer systems 606 $aMachine learning 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aArtificial Intelligence 606 $aComputer and Information Systems Applications 606 $aComputer Communication Networks 606 $aComputer System Implementation 606 $aMachine Learning 615 0$aImage processing$xDigital techniques. 615 0$aComputer vision. 615 0$aArtificial intelligence. 615 0$aApplication software. 615 0$aComputer networks. 615 0$aComputer systems. 615 0$aMachine learning. 615 14$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aArtificial Intelligence. 615 24$aComputer and Information Systems Applications. 615 24$aComputer Communication Networks. 615 24$aComputer System Implementation. 615 24$aMachine Learning. 676 $a006 700 $aLin$b Zhouchen$0908694 701 $aCheng$b Ming-Ming$01782756 701 $aHe$b Ran$0929219 701 $aUbul$b Kurban$01782757 701 $aSilamu$b Wushouer$01782758 701 $aZha$b Hongbin$01586081 701 $aZhou$b Jie$0671967 701 $aLiu$b Cheng-Lin$0861045 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910983299703321 996 $aPattern Recognition and Computer Vision$94309254 997 $aUNINA