模式识别与人工智能
模式識彆與人工智能
모식식별여인공지능
Moshi Shibie yu Rengong Zhineng
2010年
1期
84-90
,共7页
立体匹配%信任度传播(BP)%自适应平滑约束%消息传输
立體匹配%信任度傳播(BP)%自適應平滑約束%消息傳輸
입체필배%신임도전파(BP)%자괄응평활약속%소식전수
Stereo Matching%Belief Propagation(BP)%Adapted Smoothness Constraint%Message Passing
针对信任度传播算法计算量大及误匹配率高的问题,提出一种高效的计算稠密视差图的全局优化算法首先,根据像素匹配代价的特点、视差不连续亮度变化的特征,定义具有适应性的数据约束和平滑约束,并对平滑约束进行分层调节后执行消息的传输.其次,讨论消息传输迭代过程中的冗余计算问题,通过检测消息的收敛性减少运行时间.最后,分析信任度传播算法中的误匹配问题,通过匹配的对称性检测遮挡,并提出重建数据项后,利用贪婪迭代法优化所得视差图,将图像中可靠像素的视差向不可靠像素扩散.实验结果表明,该算法能以较快的速度计算出更理想的视差图.
針對信任度傳播算法計算量大及誤匹配率高的問題,提齣一種高效的計算稠密視差圖的全跼優化算法首先,根據像素匹配代價的特點、視差不連續亮度變化的特徵,定義具有適應性的數據約束和平滑約束,併對平滑約束進行分層調節後執行消息的傳輸.其次,討論消息傳輸迭代過程中的冗餘計算問題,通過檢測消息的收斂性減少運行時間.最後,分析信任度傳播算法中的誤匹配問題,通過匹配的對稱性檢測遮擋,併提齣重建數據項後,利用貪婪迭代法優化所得視差圖,將圖像中可靠像素的視差嚮不可靠像素擴散.實驗結果錶明,該算法能以較快的速度計算齣更理想的視差圖.
침대신임도전파산법계산량대급오필배솔고적문제,제출일충고효적계산주밀시차도적전국우화산법수선,근거상소필배대개적특점、시차불련속량도변화적특정,정의구유괄응성적수거약속화평활약속,병대평활약속진행분층조절후집행소식적전수.기차,토론소식전수질대과정중적용여계산문제,통과검측소식적수렴성감소운행시간.최후,분석신임도전파산법중적오필배문제,통과필배적대칭성검측차당,병제출중건수거항후,이용탐람질대법우화소득시차도,장도상중가고상소적시차향불가고상소확산.실험결과표명,해산법능이교쾌적속도계산출경이상적시차도.
Large-scale computing and high matching error rate are two disadvantages of the existing algorithms based on belief propagation.An efficient global optimal algorithm for dense disparity mapping is presented.Firstly,according to the feature of match cost and the disparity discontinuities accompanying the intensity changes,the adapted data constraint and the smoothness constraint are defined,and the messages are passed after the smoothness constraint is adjusted in every level.Then,the redundancy in message passing iteration process is discussed,and the message convergence is checked to decrease the running time.Finally,match symmetry is used to detect occlusions according to the analysis of matching errors in belief propagation algorithm.After the data term is reconstructed,a greedy method is used to iteratively refine the disparity result to propagate disparity information from the stable pixels to the unstable ones.The experimental results show the proposed algorithm computes a disparity map accurately With relative less time.