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Proceedings ArticleDOI

Saliency guided adaptive image abstraction

TL;DR: The adaptive image abstraction goal is finally achieved using a novel brush called point spread brush which is used to reproduce the action of brush with a varying spatial spread.
Abstract: We have developed a novel method for image abstraction which preserves more details present in the salient regions and removes details present in the non-salient regions from the given image of a natural scene. We define a region to be salient based on the saliency measure estimated in the region. We propose to preserve details in salient regions by dividing them into smaller groups of pixels and remove details from non-salient regions by dividing them in larger group of pixels. We achieve this kind of grouping by guiding an over-segmentation algorithm with spatially varying block size depending on the saliency measure. The adaptive image abstraction goal is finally achieved using a novel brush called point spread brush which is used to reproduce the action of brush with a varying spatial spread.
Citations
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01 Jan 2012
TL;DR: In this article, an automatic method that can be used to abstract high resolution images into very low resolution outputs with reduced color palettes in the style of pixel art is presented, simultaneously solving for a mapping of features and a reduced palette needed to construct the output image.
Abstract: We present an automatic method that can be used to abstract high resolution images into very low resolution outputs with reduced color palettes in the style of pixel art. Our method simultaneously solves for a mapping of features and a reduced palette needed to construct the output image. The results are an approximation to the results generated by pixel artists. We compare our method against the results of a naive process common to image manipulation programs, as well as the hand-crafted work of pixel artists. Through a formal user study and interviews with expert pixel artists we show that our results offer an improvement over the naive methods.

37 citations

Proceedings ArticleDOI
18 Dec 2016
TL;DR: A novel non-photorealistic rendering method which produces good quality stylization results for color images by using a scribble-based method and tracking the changes in saliency measure in the foreground and the background regions.
Abstract: In this work, we present a novel non-photorealistic rendering method which produces good quality stylization results for color images. The procedure is driven by saliency measure in the foreground and the background region. We start with generating saliency map and simple thresholding based segmentation to get rough estimation of the foreground-background mask. We improve this mask by using a scribble-based method where the scribbles for foreground-background regions are automatically generated from the previous rough estimation. Followed by the mask generation, we proceed with an iterative abstraction process which involves edge-preserving blurring and edge detection. The number of iterations of the abstraction process to be performed in the foreground and background regions are decided by tracking the changes in saliency measure in the foreground and the background regions. Performing unequal number of iterations helps to improve the average saliency measure in more salient region (foreground) while decreasing the average saliency measure in the non-salient region (background). Implementation results of our method shows the merits of this approach with other competing methods.

3 citations


Cites methods from "Saliency guided adaptive image abst..."

  • ...We have tested our method on MSRA10K benchmark dataset images and for most of the images good stylization results are obtained within few iterations [5]....

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References
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Journal ArticleDOI
TL;DR: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced, chosen to vary spatially in such a way as to encourage intra Region smoothing rather than interregion smoothing.
Abstract: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced. The diffusion coefficient is chosen to vary spatially in such a way as to encourage intraregion smoothing rather than interregion smoothing. It is shown that the 'no new maxima should be generated at coarse scales' property of conventional scale space is preserved. As the region boundaries in the approach remain sharp, a high-quality edge detector which successfully exploits global information is obtained. Experimental results are shown on a number of images. Parallel hardware implementations are made feasible because the algorithm involves elementary, local operations replicated over the image. >

12,560 citations


"Saliency guided adaptive image abst..." refers methods in this paper

  • ...…filters such as bilateral filter, difference of Gaussian filter, Kuwahara filter and morphological filters, and partial differential equation based methods such as anisotropic diffusion ([14], [15]), and mean curvature flow have also actively been used recently for image abstraction [16]....

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Journal ArticleDOI
TL;DR: A new superpixel algorithm is introduced, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels and is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.
Abstract: Computer vision applications have come to rely increasingly on superpixels in recent years, but it is not always clear what constitutes a good superpixel algorithm. In an effort to understand the benefits and drawbacks of existing methods, we empirically compare five state-of-the-art superpixel algorithms for their ability to adhere to image boundaries, speed, memory efficiency, and their impact on segmentation performance. We then introduce a new superpixel algorithm, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels. Despite its simplicity, SLIC adheres to boundaries as well as or better than previous methods. At the same time, it is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.

7,849 citations


"Saliency guided adaptive image abst..." refers background in this paper

  • ...In figure 2(b) we present the saliency map for the given input image....

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Book
01 Jan 1998
TL;DR: This work states that all scale-spaces fulllling a few fairly natural axioms are governed by parabolic PDEs with the original image as initial condition, which means that, if one image is brighter than another, then this order is preserved during the entire scale-space evolution.
Abstract: Preface Through many centuries physics has been one of the most fruitful sources of inspiration for mathematics. As a consequence, mathematics has become an economic language providing a few basic principles which allow to explain a large variety of physical phenomena. Many of them are described in terms of partial diierential equations (PDEs). In recent years, however, mathematics also has been stimulated by other novel elds such as image processing. Goals like image segmentation, multiscale image representation, or image restoration cause a lot of challenging mathematical questions. Nevertheless, these problems frequently have been tackled with a pool of heuristical recipes. Since the treatment of digital images requires very much computing power, these methods had to be fairly simple. With the tremendous advances in computer technology in the last decade, it has become possible to apply more sophisticated techniques such as PDE-based methods which have been inspired by physical processes. Among these techniques, parabolic PDEs have found a lot of attention for smoothing and restoration purposes, see e.g. 113]. To restore images these equations frequently arise from gradient descent methods applied to variational problems. Image smoothing by parabolic PDEs is closely related to the scale-space concept where one embeds the original image into a family of subsequently simpler , more global representations of it. This idea plays a fundamental role for extracting semantically important information. The pioneering work of Alvarez, Guichard, Lions and Morel 11] has demonstrated that all scale-spaces fulllling a few fairly natural axioms are governed by parabolic PDEs with the original image as initial condition. Within this framework, two classes can be justiied in a rigorous way as scale-spaces: the linear diiusion equation with constant dif-fusivity and nonlinear so-called morphological PDEs. All these methods satisfy a monotony axiom as smoothing requirement which states that, if one image is brighter than another, then this order is preserved during the entire scale-space evolution. An interesting class of parabolic equations which pursue both scale-space and restoration intentions is given by nonlinear diiusion lters. Methods of this type have been proposed for the rst time by Perona and Malik in 1987 190]. In v vi PREFACE order to smooth the image and to simultaneously enhance semantically important features such as edges, they apply a diiusion process whose diiusivity is steered by local image properties. These lters are diicult to analyse mathematically , as they may act locally like a backward diiusion process. …

2,484 citations


"Saliency guided adaptive image abst..." refers background in this paper

  • ...Non-linear filters such as bilateral filter, difference of Gaussian filter, Kuwahara filter and morphological filters, and partial differential equation based methods such as anisotropic diffusion ([14], [15]), and mean curvature flow have also actively been used recently for image abstraction [16]....

    [...]

Proceedings ArticleDOI
12 Dec 2011
TL;DR: This work presents a new image editing method, particularly effective for sharpening major edges by increasing the steepness of transition while eliminating a manageable degree of low-amplitude structures in an optimization framework making use of L0 gradient minimization.
Abstract: We present a new image editing method, particularly effective for sharpening major edges by increasing the steepness of transition while eliminating a manageable degree of low-amplitude structures. The seemingly contradictive effect is achieved in an optimization framework making use of L0 gradient minimization, which can globally control how many non-zero gradients are resulted in to approximate prominent structure in a sparsity-control manner. Unlike other edge-preserving smoothing approaches, our method does not depend on local features, but instead globally locates important edges. It, as a fundamental tool, finds many applications and is particularly beneficial to edge extraction, clip-art JPEG artifact removal, and non-photorealistic effect generation.

919 citations

Proceedings ArticleDOI
25 Jul 2011
TL;DR: The use of 1D operations leads to considerable speedups over existing techniques and potential memory savings; its computational cost is not affected by the choice of the filter parameters; and it is the first edge-preserving filter to work on color images at arbitrary scales in real time, without resorting to subsampling or quantization.
Abstract: We present a new approach for performing high-quality edge-preserving filtering of images and videos in real time. Our solution is based on a transform that defines an isometry between curves on the 2D image manifold in 5D and the real line. This transform preserves the geodesic distance between points on these curves, adaptively warping the input signal so that 1D edge-preserving filtering can be efficiently performed in linear time. We demonstrate three realizations of 1D edge-preserving filters, show how to produce high-quality 2D edge-preserving filters by iterating 1D-filtering operations, and empirically analyze the convergence of this process. Our approach has several desirable features: the use of 1D operations leads to considerable speedups over existing techniques and potential memory savings; its computational cost is not affected by the choice of the filter parameters; and it is the first edge-preserving filter to work on color images at arbitrary scales in real time, without resorting to subsampling or quantization. We demonstrate the versatility of our domain transform and edge-preserving filters on several real-time image and video processing tasks including edge-preserving filtering, depth-of-field effects, stylization, recoloring, colorization, detail enhancement, and tone mapping.

738 citations