計(jì)算機(jī)視覺

內(nèi)容概要

　　羅杰波、湯曉鷗、徐東等編寫的《計(jì)算機(jī)視覺》是由一些綜述性或原始研究論文組成的，涉及了計(jì)算機(jī)視覺的各個(gè)領(lǐng)域，包括圖像分割和標(biāo)注、人臉和生物特征識(shí)別、圖像配準(zhǔn)、基于視頻內(nèi)容的分析和三維重建，每篇論文的作者至少有一位是中國(guó)科學(xué)技術(shù)大學(xué)信息學(xué)院的畢業(yè)生。 《計(jì)算機(jī)視覺》可供計(jì)算機(jī)專業(yè)高年級(jí)本科生、研究生以及相關(guān)領(lǐng)域的科研人員使用。

作者簡(jiǎn)介

羅杰波，云南昆明人，1989年于中國(guó)科學(xué)技術(shù)大學(xué)本科畢業(yè)，1995年于美國(guó)羅切斯特大學(xué)獲得博士學(xué)位。現(xiàn)任柯達(dá)公司研究院首席科學(xué)家，研究領(lǐng)域包括圖像處理、模式識(shí)剛、計(jì)算機(jī)視覺、多媒體信息挖掘、生物醫(yī)學(xué)信息學(xué)等。他以在電子成像和視頻通信方而的杰出成就當(dāng)選SPIE Fellow(2008)，以在語義圖像理解和智能圖像處理等方面的貢獻(xiàn)當(dāng)選IEEE Fellow(2009)，以在圖像視頻內(nèi)容識(shí)別中采用概率上下文模型的開創(chuàng)性工作當(dāng)選IAPR Fellow(2010)。湯曉鷗，遼寧鞍山人，1990年于中國(guó)科學(xué)技術(shù)大學(xué)本科畢業(yè)，1996年于麻省理工學(xué)院獲得博士學(xué)位?，F(xiàn)任香港中文大學(xué)工程學(xué)院副院長(zhǎng)及信息工程系教授，2005年至2008年于微軟亞洲研究院擔(dān)任視覺計(jì)算組主任，研究領(lǐng)域主要有計(jì)算機(jī)視覺、模式識(shí)別和視頻處理等。他以在模式識(shí)別和視頻處理等方面的貢獻(xiàn)當(dāng)選IEEE Fellow(2009)。徐東，四川大竹人，2001年和2005年于中國(guó)科學(xué)技術(shù)大學(xué)電子工程與信息科學(xué)系分別獲得本科和博士學(xué)位，2006年在美國(guó)哥倫比亞大學(xué)做博士后?，F(xiàn)任教于新加坡南洋理工大學(xué)計(jì)算機(jī)工程系，研究方向包括計(jì)算機(jī)視覺和多媒體信息處理，住國(guó)際著名學(xué)術(shù)期刊和學(xué)術(shù)會(huì)議上共發(fā)表了40余篇淪文。

書籍目錄

Preface to the USTC alumni’s seriesPrefacePart Ⅰ Segmentation and Registration Chapter 1 Graph Cuts Based Active Contours (GCBAC) Chapter 2 A Novel Region Constrained Non-Rigid Image Registration FramewQrkPart Ⅱ Face and Biometrics Chapter 3 Parallel Image Matrix Compression for Face Recognition Chapter 4 Facial Expression Recognition Based on Statistical Local Features Chapter 5 A Hierarchical Compositional Model for Face Representation and Sketching Chapter 6 A Brief Introduction to Skeleton-Based Fingerprint Minutiae ExtractionPart Ⅲ Image Annotation Chapter 7 Image Transform Bootstrapping and Its Applications to Semantic Scene Classification Chapter 8 Bipartite Graph Reinforcement Model for Web Image AnnotationPart Ⅳ Video Analysis Chapter 9 Motion Estimation Based on Trilinear and Optical Flow Constraints Chapter 10 Appearance Modeling for Visual Tracking Chapter 11 Robust Monocular 3D Tracking of Articulated Arm Movement Chapter 12 Video Classification via Local 3D Eigen Analysis Chapter 13 Video Annotation: Supervised, Semi-Supervised and Active Learning ApproachesPart Ⅴ 3D Reconstruction Chapter 14 Rapid 3D Modeling from a Single Image Based on Minimal 2D Control Points Chapter 15 Quasiconvex Optimization for Robust Geometric Reconstruction Chapter 16 Deformable Structure from Motion: A Factorization Scheme

章節(jié)摘錄

版權(quán)頁(yè)：插圖：Indeed we are far from the first who tried to patch spatial and temporal information together for a better understanding of the world: The 4D event space and light cone in special relativity put forward by Einstein have been the foundation of modern physics for about one century [34]. Even in video understanding several research groups have come to the realization of the benefits provided by patching videos in the temporal direction. Stauffer and Grimson developed the concept of pixel process , where a pixel process for a video, a 1D data, is formed by threading pixels of the same location in each frame in videos. They have applied this novel concept with valuable performance in a series of scene monitoring and tracking applications within the framework of background subtraction. Chin and colleagues proposed a temporal segmentation method based on a 2D image which is formed by stacking collinear pixels in a video. The cut detection is then transformed into a line detection problem in this 2D spatial-temporal image, which can be achieved more effectively. As can be seen these two methods entirely exploited temporal redundancies but fell short of taking full advantage of spatial redundancies. Nonetheless, the E1 time series for pixel process and E2 time frames are powerful enough to solve their respective problems elegantly.

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《計(jì)算機(jī)視覺(全英文)》：當(dāng)代科學(xué)技術(shù)基礎(chǔ)理論與前沿問題研究叢書:中國(guó)科學(xué)技術(shù)大學(xué)校友文庫(kù),“十一五”國(guó)家重點(diǎn)圖書

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