Reflection symmetry aware image retargeting
TL;DR: A novel image Retargeting approach which preserves the reflection symmetry present in the image during the image retargeting process and shows better preservation of symmetry axis, preservation of shape of the symmetric object, and quality of image retTargeting when compared to the existing methods.
About: This article is published in Pattern Recognition Letters.The article was published on 2019-07-01. It has received 9 citations till now. The article focuses on the topics: Reflection symmetry & Seam carving.
Citations
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TL;DR: This work presents the methodology of detecting symmetry in 3D objects based on the three techniques: Eigenvalues and Eigenvectors, local surface discontinuity, and pixel orientation, and these methods have been modified suitably for the detection of symmetry in CH artifacts.
Abstract: Cultural Heritage (CH) artifacts generally possess symmetry of reflection, rotation, translation and glide reflection in their shape. Similarity measures are used to determine complex 3D models where symmetry is considered to be one of the similarity signatures. This work presents the methodology of detecting symmetry in 3D objects based on the three techniques: (1) Eigenvalues and Eigenvectors, (2) local surface discontinuity, and (3) pixel orientation. In this work, these methods have been modified suitably for the detection of symmetry in CH artifacts. Among these methods, it is found that the first two methods yield better performance on the symmetry signature estimation of 98 percent for complex models and up to 100% for primitive models. The execution time of the proposed methods is compared with the state-of-the-art approaches available in the literature. Three levels of random 3D models available in the internet repository are analyzed for efficiency, performance and robustness. At each level, the accuracy of the Eigenvalue method and the local discontinuity method is found to be better than the pixel orientation method. The modified algorithms have been tested for better performance with F-score, robustness, and execution time with 3D benchmark dataset and cultural heritage dataset available in the literature. Future work shall be extended by applying the symmetry features as constraints for the effective search of CH artifacts in digital repositories.
7 citations
TL;DR: In this paper , the authors provide a general classification of content-aware image retargeting (CAIR) methods and then discuss them in more detail, and the performance of CAIR approaches on the images of these datasets is qualitatively evaluated.
7 citations
TL;DR: In this article, a stable metric is proposed to extract subsets of consistently oriented candidate segments, whenever the underlying 2D signal appearance exhibits definite near symmetric correspondences, and the ranking of such segments on the basis of the surrounding gradient orientation specularity, in order to reflect real symmetric object boundaries.
Abstract: This work addresses the challenging problem of reflection symmetry detection in unconstrained environments. Starting from the understanding on how the visual cortex manages planar symmetry detection, it is proposed to treat the problem in two stages: i) the design of a stable metric that extracts subsets of consistently oriented candidate segments, whenever the underlying 2D signal appearance exhibits definite near symmetric correspondences; ii) the ranking of such segments on the basis of the surrounding gradient orientation specularity, in order to reflect real symmetric object boundaries. Since these operations are related to the way the human brain performs planar symmetry detection, a better correspondence can be established between the outcomes of the proposed algorithm and a human-constructed ground truth. When compared to the testing sets used in recent symmetry detection competitions, a remarkable performance gain can be observed. In additional, further validation has been achieved by conducting perceptual validation experiments with users on a newly built dataset.
6 citations
01 Nov 2019
TL;DR: Seam carving is searched in detail and the studies in the literature are examined under three groups and some further research topics are mentioned to help researchers who want to work on seam carving.
Abstract: Image resizing is a frequently used phenomenon in both image processing applications and adaptation to different aspect ratio. Using conventional resizing approaches like uniform scaling and cropping does not satisfy the needs of visual perfection expectations since they may cause distortions and data or importance loss in foreground objects. To overcome these deficiencies, researchers are looking for content-aware image resizing methods recently. Seam carving is the starring method to serve the purpose. It is a retargeting method which considers the images in a semantic manner for resizing to bring them to target aspect ratio. In this paper, seam carving is searched in detail and the studies in the literature are examined under three groups. Also, some further research topics are mentioned to help researchers who want to work on seam carving.
4 citations
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...considered in terms of visual enhancement [38]....
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1 citations
References
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TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Abstract: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.
46,906 citations
29 Jul 2007
TL;DR: In this article, seam carving is used for content-aware image resizing for both reduction and expansion, where an optimal 8-connected path of pixels on a single image from top to bottom, or left to right, where optimality is defined by an image energy function.
Abstract: Effective resizing of images should not only use geometric constraints, but consider the image content as well We present a simple image operator called seam carving that supports content-aware image resizing for both reduction and expansion A seam is an optimal 8-connected path of pixels on a single image from top to bottom, or left to right, where optimality is defined by an image energy function By repeatedly carving out or inserting seams in one direction we can change the aspect ratio of an image By applying these operators in both directions we can retarget the image to a new size The selection and order of seams protect the content of the image, as defined by the energy function Seam carving can also be used for image content enhancement and object removal We support various visual saliency measures for defining the energy of an image, and can also include user input to guide the process By storing the order of seams in an image we create multi-size images, that are able to continuously change in real time to fit a given size
1,652 citations
01 Aug 2008
TL;DR: This work replaces the dynamic programming method of seam carving with graph cuts that are suitable for 3D volumes and presents a novel energy criterion that improves the visual quality of the retargeted images and videos.
Abstract: Video, like images, should support content aware resizing. We present video retargeting using an improved seam carving operator. Instead of removing 1D seams from 2D images we remove 2D seam manifolds from 3D space-time volumes. To achieve this we replace the dynamic programming method of seam carving with graph cuts that are suitable for 3D volumes. In the new formulation, a seam is given by a minimal cut in the graph and we show how to construct a graph such that the resulting cut is a valid seam. That is, the cut is monotonic and connected. In addition, we present a novel energy criterion that improves the visual quality of the retargeted images and videos. The original seam carving operator is focused on removing seams with the least amount of energy, ignoring energy that is introduced into the images and video by applying the operator. To counter this, the new criterion is looking forward in time - removing seams that introduce the least amount of energy into the retargeted result. We show how to encode the improved criterion into graph cuts (for images and video) as well as dynamic programming (for images). We apply our technique to images and videos and present results of various applications.
775 citations
26 Dec 2007
TL;DR: The proposed algorithm is fully automatic and based on local saliency, motion detection and object detectors, and compared to the state of the art in image retargeting.
Abstract: Video retargeting is the process of transforming an existing video to fit the dimensions of an arbitrary display. A compelling retargeting aims at preserving the viewers' experience by maintaining the information content of important regions in the frame, whilst keeping their aspect ratio. An efficient algorithm for video retargeting is introduced. It consists of two stages. First, the frame is analyzed to detect the importance of each region in the frame. Then, a transformation that respects the analysis shrinks less important regions more than important ones. Our analysis is fully automatic and based on local saliency, motion detection and object detectors. The performance of the proposed algorithm is demonstrated on a variety of video sequences, and compared to the state of the art in image retargeting.
535 citations
06 Sep 2014
TL;DR: An approach for identifying a set of candidate objects in a given image that can be used for object recognition, segmentation, and other object-based image parsing tasks is presented.
Abstract: We present an approach for identifying a set of candidate objects in a given image. This set of candidates can be used for object recognition, segmentation, and other object-based image parsing tasks. To generate the proposals, we identify critical level sets in geodesic distance transforms computed for seeds placed in the image. The seeds are placed by specially trained classifiers that are optimized to discover objects. Experiments demonstrate that the presented approach achieves significantly higher accuracy than alternative approaches, at a fraction of the computational cost.
432 citations