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010   $a981-9537-20-7
024 7 $a10.1007/978-981-95-3720-4
035   $a(CKB)43368467900041
035   $a(MiAaPQ)EBC32420038
035   $a(Au-PeEL)EBL32420038
035   $a(DE-He213)978-981-95-3720-4
035   $a(OCoLC)1561174975
035   $a(EXLCZ)9943368467900041
100   $a20251116d2025      u|         0
101 0 $aeng
135   $aur|||||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aIntelligent Analysis of Optical Images /$fedited by Zhengjun Liu, Yutong Li
205   $a1st ed. 2025.
210  1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025.
215   $a1 online resource (0 pages)
225 1 $aScientific Computation,$x2198-2589
311 08$a981-9537-19-3 
327   $a1. Basic Programming Methods -- 2. Target Recognition -- 3. Autofocusing -- 4. Optical Speckle Analysis -- 5. Velocity Measurement from Motion Blurry Image.
330   $aThis book highlights intelligent analysis methods for optical images, with a particular emphasis on analytical techniques and programming. Automatic image processing and intelligent analysis represent the future trends of applications and are integral to machine vision analysis. By integrating optical imaging processes with computing technology, operations such as information extraction, modification, and organization can be effectively executed. The study of these intelligent analysis methods is intrinsically linked to various scientific and technological domains, including optics, mathematics, computing, and artificial intelligence. The collection and organization of relevant technologies for intelligent analysis hold significant application value across fields such as autonomous driving, computational vision, artificial intelligence, and multimodal information processing. The demand for accurate, automatic, and rapid image information acquisition is a pressing requirement in contemporary applications. As image processing technology develops rapidly, the distinguishing between image analysis and image processing is not straightforward. Given the vast amount of information contained in images, the necessity to extract pertinent information becomes even more pronounced, thereby conserving time and computational resources for subsequent applications. For optical images, the development and organization of intelligent analysis methods are of urgent importance, carrying substantial significance and social benefits for both application and research endeavors. Intelligent analysis methods are crucial for image acquisition and application, serving as a bridge between the two. When combined with deep learning technology, these methods can facilitate more comprehensive and in-depth research, enabling relevant information and technologies to better serve application tasks. This book is a valuable source of reference for researchers, engineers, and students engaged in the work and study in optical imaging fields.
410  0$aScientific Computation,$x2198-2589
606   $aOptics
606   $aComputer vision
606   $aImage processing
606   $aMaterials$xAnalysis
606   $aImaging systems
606   $aMathematics$xData processing
606   $aPattern recognition systems
606   $aApplied Optics
606   $aComputer Vision
606   $aImage Processing
606   $aImaging Techniques
606   $aComputational Science and Engineering
606   $aAutomated Pattern Recognition
615  0$aOptics.
615  0$aComputer vision.
615  0$aImage processing.
615  0$aMaterials$xAnalysis.
615  0$aImaging systems.
615  0$aMathematics$xData processing.
615  0$aPattern recognition systems.
615 14$aApplied Optics.
615 24$aComputer Vision.
615 24$aImage Processing.
615 24$aImaging Techniques.
615 24$aComputational Science and Engineering.
615 24$aAutomated Pattern Recognition.
676   $a621.36
700   $aLiu$b Zhengjun$01791052
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9911047824303321
996   $aIntelligent Analysis of Optical Images$94468663
997   $aUNINA