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image completion","{\"openalex\":\"W3001217199\",\"doi\":\"10.1145/882262.882267\"}","2026-05-20T09:20:07.198617851","2026-05-20T09:20:07.209238135","doi","10.1145/882262.882267","arxivId","openalexId","W3001217199","abstractText","We present a new method for completing missing parts caused by the removal of foreground or background elements from an image. Our goal is to synthesize a complete, visually plausible and coherent image. The visible parts of the image serve as a training set to infer the unknown parts. Our method iteratively approximates the unknown regions and composites adaptive image fragments into the image. Values of an inverse matte are used to compute a confidence map and a level set that direct an incremental traversal within the unknown area from high to low confidence. In each step, guided by a fast smooth approximation, an image fragment is selected from the most similar and frequent examples. As the selected fragments are composited, their likelihood increases along with the mean confidence of the image, until reaching a complete image. We demonstrate our method by completion of photographs and paintings.","publishedDate","2003-07-01","publishedYear",2003,"venue","ACM Transactions on Graphics",340,"authors","keywords",[167,168,169],"pdfUrl","topVenue","Image Processing and 3D Reconstruction","Generative Adversarial Networks and Image Synthesis","Advanced Image Processing Techniques","paper_W2998590421","Bilateral mesh denoising","{\"openalex\":\"W2998590421\",\"doi\":\"10.1145/882262.882368\"}","2026-05-16T06:43:26.208233792","2026-05-19T03:19:09.189682013","10.1145/882262.882368","W2998590421","We present an anisotropic mesh denoising algorithm that is effective, simple and fast. This is accomplished by filtering vertices of the mesh in the normal direction using local neighborhoods. Motivated by the impressive results of bilateral filtering for image denoising, we adopt it to denoise 3D meshes; addressing the specific issues required in the transition from two-dimensions to manifolds in three dimensions. We show that the proposed method successfully removes noise from meshes while preserving features. Furthermore, the presented algorithm excels in its simplicity both in concept and implementation.",485,[180,181,182],"Computer Graphics and Visualization Techniques","3D Shape Modeling and Analysis","Image and Signal Denoising Methods","paper_W2016331656","{\"openalex\":\"W2016331656\",\"doi\":\"10.1145/1201775.882267\"}","2026-05-16T03:34:56.443556466","2026-05-21T21:23:04.188515321","10.1145/1201775.882267","W2016331656",506,[167,168,169],"paper_W1984713107","{\"openalex\":\"W1984713107\",\"doi\":\"10.1145/1201775.882368\"}","2026-05-15T10:30:02.206495228","2026-05-21T07:30:06.187063771","10.1145/1201775.882368","W1984713107",623,[180,181,182],"paper_W2046658845","Sparse Solution of Underdetermined Systems of Linear Equations by Stagewise Orthogonal Matching Pursuit","{\"openalex\":\"W2046658845\",\"doi\":\"10.1109/tit.2011.2173241\"}","2026-05-14T06:59:04.316695249","2026-05-21T09:42:54.191473967","10.1109/tit.2011.2173241","W2046658845","Finding the sparsest solution to underdetermined systems of linear equations y = Φ _x is NP-hard in general. We show here that for systems with “typical”/“random” Φ, a good approximation to the sparsest solution is obtained by applying a fixed number of standard operations from linear algebra. Our proposal, Stagewise Orthogonal Matching Pursuit (StOMP), successively transforms the signal into a negligible residual. Starting with initial residual r ₀ = y , at the s -th stage it forms the “matched filter” Φ T _rs-1 , identifies all coordinates with amplitudes exceeding a specially chosen threshold, solves a least-squares problem using the selected coordinates, and subtracts the least-squares fit, producing a new residual. After a fixed number of stages (e.g., 10), it stops. In contrast to Orthogonal Matching Pursuit (OMP), many coefficients can enter the model at each stage in StOMP while only one enters per stage in OMP; and StOMP takes a fixed number of stages (e.g., 10), while OMP can take many (e.g., n ). We give both theoretical and empirical support for the large-system effectiveness of StOMP. We give numerical examples showing that StOMP rapidly and reliably finds sparse solutions in compressed sensing, decoding of error-correcting codes, and overcomplete representation.","2012-02-01",2012,"IEEE Transactions on Information Theory",1498,[212,213,182],"Matrix Theory and Algorithms","Sparse and Compressive Sensing Techniques",{"_60":215,"_62":216},[],[217],{"_65":218,"_67":219,"_69":220,"_71":221},1781135278579,{"_74":223,"_76":77,"_78":218,"_79":-5,"_80":81,"_82":81,"_83":81,"_84":-5,"_85":-5,"_86":87,"_35":88,"_89":90},[73,222],"[\"portal\",\"industries\"]","industries",[224,225,226,227,228,229,230,231,232,233,234,235],{"_15":297,"_29":298,"_31":-5,"_32":299,"_33":300,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":239,"_42":-5,"_44":-5,"_240":241,"_242":-5,"_243":77,"_244":245,"_246":301,"_248":81,"_249":87,"_250":81,"_251":-5,"_58":298,"_59":300},{"_15":293,"_29":294,"_31":-5,"_32":-5,"_33":295,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":239,"_42":-5,"_44":-5,"_240":241,"_242":-5,"_243":77,"_244":245,"_246":296,"_248":81,"_249":87,"_250":81,"_251":-5,"_58":294,"_59":295},{"_15":287,"_29":288,"_31":-5,"_32":289,"_33":290,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":239,"_42":-5,"_44":-5,"_240":241,"_242":-5,"_243":77,"_244":245,"_246":291,"_248":81,"_249":87,"_250":81,"_251":-5,"_58":288,"_59":290},{"_15":283,"_29":284,"_31":-5,"_32":-5,"_33":285,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":239,"_42":-5,"_44":-5,"_240":241,"_242":-5,"_243":77,"_244":245,"_246":286,"_248":81,"_249":87,"_250":81,"_251":-5,"_58":284,"_59":285},{"_15":279,"_29":280,"_31":-5,"_32":-5,"_33":281,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":239,"_42":-5,"_44":-5,"_240":241,"_242":-5,"_243":77,"_244":245,"_246":282,"_248":81,"_249":87,"_250":81,"_251":-5,"_58":280,"_59":281},{"_15":273,"_29":274,"_31":-5,"_32":275,"_33":276,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":239,"_42":-5,"_44":-5,"_240":241,"_242":-5,"_243":77,"_244":245,"_246":277,"_248":81,"_249":87,"_250":81,"_251":-5,"_58":274,"_59":276},{"_15":268,"_29":269,"_31":-5,"_32":-5,"_33":270,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":239,"_42":-5,"_44":-5,"_240":241,"_242":-5,"_243":77,"_244":245,"_246":271,"_248":81,"_249":272,"_250":81,"_251":-5,"_58":269,"_59":270},{"_15":264,"_29":265,"_31":-5,"_32":-5,"_33":266,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":239,"_42":-5,"_44":-5,"_240":241,"_242":-5,"_243":77,"_244":245,"_246":267,"_248":81,"_249":87,"_250":81,"_251":-5,"_58":265,"_59":266},{"_15":260,"_29":261,"_31":-5,"_32":-5,"_33":262,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":239,"_42":-5,"_44":-5,"_240":241,"_242":-5,"_243":77,"_244":245,"_246":263,"_248":81,"_249":87,"_250":81,"_251":-5,"_58":261,"_59":262},{"_15":256,"_29":257,"_31":-5,"_32":-5,"_33":258,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":239,"_42":-5,"_44":-5,"_240":241,"_242":-5,"_243":77,"_244":245,"_246":259,"_248":81,"_249":87,"_250":81,"_251":-5,"_58":257,"_59":258},{"_15":252,"_29":253,"_31":-5,"_32":-5,"_33":254,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":239,"_42":-5,"_44":-5,"_240":241,"_242":-5,"_243":77,"_244":245,"_246":255,"_248":81,"_249":87,"_250":81,"_251":-5,"_58":253,"_59":254},{"_15":236,"_29":237,"_31":-5,"_32":-5,"_33":238,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":239,"_42":-5,"_44":-5,"_240":241,"_242":-5,"_243":77,"_244":245,"_246":247,"_248":81,"_249":87,"_250":81,"_251":-5,"_58":237,"_59":238},"tax_ind_smartphone","智能手机","Smartphone","manual","taxonomyType","INDUSTRY","code","level","parentId","tax_ind_root","path","科技行业/智能手机","sortOrder","isLeaf","entityCount","children","tax_ind_robot","机器人","Robotics","科技行业/机器人","tax_ind_wearable","智能穿戴","Smart Wearables","科技行业/智能穿戴","tax_ind_energy","新能源与智能制造","New Energy & Smart Manufacturing","科技行业/新能源与智能制造","tax_ind_biotech","生物科技","Biotech","科技行业/生物科技","tax_ind_quantum","量子计算","Quantum Computing","科技行业/量子计算",true,"tax_ind_blockchain","区块链",[278],"Blockchain","科技行业/区块链","Web3","tax_ind_enterprise","企业软件","Enterprise Software","科技行业/企业软件","tax_ind_security","网络安全","Cybersecurity","科技行业/网络安全","tax_ind_semi","半导体",[292],"Semiconductor","科技行业/半导体","芯片","tax_ind_cloud","云计算","Cloud Computing","科技行业/云计算","tax_ind_ai","人工智能",[302,303],"Artificial Intelligence","科技行业/人工智能","AI","AI 技术","env",{"_306":24},"SITE_BASE_URL"]

Iddo Drori 人物

Iddo Drori · 相关论文