Graphcut textures

Kwatra, Vivek; Schödl, Arno; Essa, Irfan; Turk, Greg; Bobick, Aaron F.

doi:10.1145/882262.882264

Cited by 716 publications

(83 citation statements)

References 24 publications

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“…The algorithm complexity (linear for the proposed method) has not been incorporated in the list because most discussed methods do not provide us with this information. Yet, according to [21], finding the best cut for a graph with n nodes appears to be Oðn logðnÞÞ, while the worst case is known to be Oðn 2 Þ, [31]. Table 1 summarizes the results of our comparison.…”

Section: Comparison With Other Methodologiesmentioning

confidence: 99%

“…The method by Peleg [29] concerns the elimination of existing seams rather than the actual composition of overlapping images, so it has not been included in the comparison. Finally, to reduce the size of the table, methods based on the computation of paths of minimal cost [28], [14], [17] have been grouped as well as those based on graph cuts [21], [2]. The table is binary in the sense that its scope is to indicate whether a given method satisfies a given property or not.…”

Section: Comparison With Other Methodologiesmentioning

confidence: 99%

“…A similar gradient-domain fusion based on Poisson equations and combined with a graph-cut optimization scheme relying on a minimization procedure described in [8] is developed by Agarwala et al [2]. The use of graph-cut techniques for image compositing was originally explored by Kwatra et al [21] in the context of texture synthesis, where patch regions from a sample image or video are transformed and stitched together along optimal seams to generate a new output. In our approach, the pointwise minimum of the morphological gradient of each input image ensures that gray-scale variations need to occur at the same place for both images in order to be considered.…”

Section: Principlementioning

confidence: 99%

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Morphological image compositing

Soille

2006

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-Image mosaicking can be defined as the registration of two or more images that are then combined into a single image. Once the images have been registered to a common coordinate system, the problem amounts to the definition of a selection rule to output a unique value for all those pixels that are present in more than one image. This process is known as image compositing. In this paper, we propose a compositing procedure based on mathematical morphology and its marker-controlled segmentation paradigm. Its scope is to position seams along salient image structures so as to diminish their visibility in the output mosaic even in the absence of radiometric corrections or blending procedures. We also show that it is suited to the seamless minimization of undesirable transient objects occurring in the regions where two or more images overlap. The proposed methodology and algorithms are illustrated for the composition of satellite images minimizing cloud cover.

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Section: Comparison With Other Methodologiesmentioning

confidence: 99%

Section: Comparison With Other Methodologiesmentioning

confidence: 99%

Section: Principlementioning

confidence: 99%

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Morphological image compositing

Soille

2006

IEEE Trans. Pattern Anal. Mach. Intell.

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“…Blending, for instance, introduces some new frequencies (over-blurring), thus deteriorating visual aspects. Better results are usually obtained using a clever "cutting trajectory" along the overlap, computed according to an errorminimizing factor [5,13]. Recent improvements in the field of texture synthesis focus on recovering even better some feature-related aspects [26] by using additionally Laplacian filters.…”

Section: Related Work and Motivationmentioning

confidence: 99%

Real-time structured texture synthesis and editing using image-mesh analogies

Dischler¹,

Zara

2006

Visual Comput

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We present a novel texture synthesis technique designed to reproduce at real-time frame-rates example texture images, with a special focus on patterns characterized by structural arrangements. Unlike current pixel-, patch-or texton-based schemes, that operate in image-space, our approach is structural. We propose to assimilate texture images to corresponding 2D geometric meshes (called texturemeshes). Our analysis mainly consists in generating automatically these meshes, while synthesis is then based on the creation of new vertex/polygon distributions matching some arrangement map. The output texture-image is obtained by rasterizing the previously generated polygons using graphics hardware capabilities, which guarantees high speed performance. By operating in geometry space instead of image / pixel-space, the proposed structural approach has a major advantage over current techniques: beyond pure texture reproduction, it permits us defining various tools, which allow users to further modify locally or globally and in realtime structural components of textures. By controlling the arrangement map, users can substitute new meshes in order to completely modify the structural appearance of input textures, yet maintaining a certain visual resemblance with the initial example image.

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“…The main research trend in these methods is to minimize the mismatches on the boundaries between patches, after their placement in the corresponding output. The new patch can be blended [11] with the already synthesized portion, or an optimal cut can be found for seaming the two patches [12,13]. The patch-based methods have shown the best synthesis results up to now.…”

Section: Introductionmentioning

confidence: 99%

Local spatiotemporal features for dynamic texture synthesis

et al. 2014

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In this paper, we study the use of local spatiotemporal patterns in a non-parametric dynamic texture synthesis method. Given a finite sample video of a texture in motion, dynamic texture synthesis may create a new video sequence, perceptually similar to the input, with an enlarged frame size and longer duration. In general, non-parametric techniques select and copy regions from the input sample to serve as building blocks by pasting them together one at a time onto the outcome. In order to minimize possible discontinuities between adjacent blocks, the proper representation and selection of such pieces become key issues. In previous synthesis methods, the block description has been based only on the intensities of pixels, ignoring the texture structure and dynamics. Furthermore, a seam optimization between neighboring blocks has been a fundamental step in order to avoid discontinuities. In our synthesis approach, we propose to use local spatiotemporal cues extracted with the local binary pattern from three orthogonal plane (LBP-TOP) operator, which allows us to include in the video characterization the appearance and motion of the dynamic texture. This improved representation leads us to a better fitting and matching between adjacent blocks, and therefore, the spatial similarity, temporal behavior, and continuity of the input can be successfully preserved. Moreover, the proposed method simplifies other approximations since no additional seam optimization is needed to get smooth transitions between video blocks. The experiments show that the use of the LBP-TOP representation outperforms other methods, without generating visible discontinuities or annoying artifacts. The results are evaluated using a double-stimulus continuous quality scale methodology, which is reproducible and objective. We also introduce results for the use of our method in video completion tasks. Additionally, we hereby present that the proposed technique is easily extendable to achieve the synthesis in both spatial and temporal domains.

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Graphcut textures

Cited by 716 publications

References 24 publications

Morphological image compositing

Morphological image compositing

Real-time structured texture synthesis and editing using image-mesh analogies

Local spatiotemporal features for dynamic texture synthesis

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