Yoav HaCohen

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This paper presents a new efficient method for recovering reliable local sets of dense correspondences between two images with some shared content. Our method is designed for pairs of images depicting similar regions acquired by different cameras and lenses, under non-rigid transformations, under different lighting, and over different backgrounds. We(More)
Image upsampling is a common yet challenging task, since it is severely underconstrained. While considerable progress was made in preserving the sharpness of salient edges, current methods fail to reproduce the fine detail typically present in the textured regions bounded by these edges, resulting in unrealistic appearance. In this paper we address this(More)
This paper presents a new method for deblurring photos using a sharp reference example that contains some shared content with the blurry photo. Most previous deblurring methods that exploit information from other photos require an accurately registered photo of the same static scene. In contrast, our method aims to exploit reference images where the shared(More)
With dozens or even hundreds of photos in today's digital photo albums, editing an entire album can be a daunting task. Existing automatic tools operate on individual photos without ensuring consistency of appearance between photographs that share content. In this paper, we present a new method for consistent editing of photo collections. Our method(More)
Matching algorithms play a fundamental role in many important but difficult software engineering activities, especially design evolution analysis and model comparison. We present MADMatch, a fast and scalable many-to-many approximate diagram matching approach based on an error-tolerant graph matching (ETGM) formulation. Diagrams are represented as graphs,(More)
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