{"id":102905,"date":"2019-05-21T12:44:36","date_gmt":"2019-05-21T04:44:36","guid":{"rendered":"http:\/\/www.fagao.me\/?p=102905"},"modified":"2019-05-21T12:44:36","modified_gmt":"2019-05-21T04:44:36","slug":"%e8%ae%ba%e6%96%87-cvpr-2017%e8%ae%ba%e6%96%87-%e5%9f%ba%e4%ba%8e%e7%bd%91%e6%a0%bc%e7%9a%84%e8%bf%90%e5%8a%a8%e7%bb%9f%e8%ae%a1-%e7%94%a8%e4%ba%8e%e5%bf%ab%e9%80%9f-%e8%b6%85%e9%b2%81%e6%a3%92","status":"publish","type":"post","link":"https:\/\/www.fagao.me\/p\/102905.htm","title":{"rendered":"\u8bba\u6587 CVPR 2017\u8bba\u6587 \u57fa\u4e8e\u7f51\u683c\u7684\u8fd0\u52a8\u7edf\u8ba1 \u7528\u4e8e\u5feb\u901f \u8d85\u9c81\u68d2\u7684\u7279\u5f81\u5339\u914d(\u9644\u4ee3\u7801)"},"content":{"rendered":"

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\u8f6c\u81ea\uff1a\u89c6\u89c9\u673a\u5668\u4eba<\/p>\n

\u73b0\u5728\uff0c\u673a\u5668\u5b66\u4e60\u7684\u8d8b\u52bf\u4ece\u4f20\u7edf\u65b9\u6cd5\u4e2d\u7684\u7b80\u5355\u6a21\u578b + \u5c11\u91cf\u6570\u636e\uff08\u4eba\u5de5\u6807<\/p>\n

\u4e00\u79cd\u89c6\u9891\u7684\u5feb\u901f\u641c\u7d22\u6280\u672f\uff0c\u6bd4SIFT\u8fd8\u5389\u5bb3\u3002\u57fa\u4e8e\u7f51\u683c\u7684\u8fd0\u52a8\u7edf\u8ba1\uff0c\u7528\u4e8e\u5feb\u901f\u3001\u8d85\u9c81\u68d2\u7684\u7279\u5f81\u5339\u914d\uff08\u529e\u516c\u6905\u6f14\u793a\uff09\uff0c\u8bba\u6587\u300aGrid-based Motion Statistics for Fast, Ultra-robust Feature Correspondence\u300b\u5c06\u5e73\u6ed1\u5ea6\u7ea6\u675f\u5f15\u5165\u7279\u5f81\u5339\u914d\u662f\u5df2\u77e5\u7684\u53ef\u4ee5\u5b9e\u73b0\u8d85\u5f3a\u9c81\u68d2\u5339\u914d\u3002 \u7136\u800c\uff0c\u8fd9\u6837\u7684\u5339\u914d\u65b9\u6848\u65e2\u590d\u6742\u53c8\u7f13\u6162\uff0c\u4f7f\u5f97\u5b83\u4eec\u4e0d\u9002\u5408\u4e8e\u89c6\u9891\u5e94\u7528\u3002 \u672c\u6587\u63d0\u51fa\u4e86GMS\uff08\u57fa\u4e8e\u7f51\u683c\u7684\u8fd0\u52a8\u7edf\u8ba1\uff09\uff0c\u4e00\u79cd\u7b80\u5355\u7684\u65b9\u6cd5\uff0c\u5c06\u8fd0\u52a8\u5e73\u6ed1\u5ea6\u4f5c\u4e3a\u4e00\u4e2a\u7edf\u8ba1\u91cf\uff0c\u8fdb\u884c\u5c40\u90e8\u533a\u57df\u7684\u5339\u914d\u3002GMS\u53ef\u4ee5\u5c06\u9ad8\u5339\u914d\u6570\u5b57\u8f6c\u6362\u6210\u9ad8\u5339\u914d\u8d28\u91cf\u3002 \u8fd9\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5b9e\u65f6\u3001\u8d85\u5f3a\u7684\u5339\u914d\u7cfb\u7edf\u3002 \u8bc4\u4f30\u4f4e\u8d28\u91cf\u3001\u6a21\u7cca\u7684\u89c6\u9891\u548c\u5e7f\u6cdb\u57fa\u7ebf\u663e\u793a\uff0cGMS\u59cb\u7ec8\u5982\u4e00\u5730\u4f18\u4e8e\u5176\u4ed6\u5b9e\u65f6\u5339\u914d\u5668\u3002<\/p>\n

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Incorporating smoothness constraints into feature matching is known to enable ultra-robust matching. However, such formulations are both complex and slow, making them unsuitable for video applications. This paper proposes GMS (Grid-based Motion Statistics), a simple means of encapsulating motion smoothness as the statistical likelihood of a certain number of matches in a region. GMS enables translation of high match numbers into high match quality. This provides a real-time, ultra-robust correspondence system. Evaluation on videos, with low textures, blurs and wide-baselines show GMS consistently out-performs other real-time matchers and can achieve parity with more sophisticated, much slower techniques.<\/p>\n<\/p>\n

\u9879\u76ee\u4e3b\u9875\uff1a<\/p>\n

http:\/\/jwbian.net\/gms<\/p>\n

\u539f\u6587\u94fe\u63a5\uff1a<\/p>\n

2019\/20190426A\/F0110084\n<\/p>\n

\u2193\u2193\u2193<\/p>\n

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