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16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 16th International Conference on Computer Vision Theory and Applications (VISAPP)

作者： Wang, Jieying de Melo, Leonardo Falcao, Alexandre X. Kosinka, Jiri Telea, Alexandru Univ Groningen Bernoulli Inst NL-9747 AG Groningen Netherlands Univ Estadual Campinas Inst Comp Dept Informat Syst BR-13083852 Sao Paulo Brazil Univ Utrecht Dept Informat & Comp Sci NL-3584 CC Utrecht Netherlands Shandong Univ Sci & Technol Bernoulli Inst 579 Qianwangang Rd Qingdao 266590 Shandong Peoples R China

ISBN: (纸本)9783031254765;9783031254772

Medial descriptors have attracted increasing interest in image representation, simplification, and compression. Recently, such descriptors have been separately used to (a) increase the local quality of representing salient features in an image and (b) globally compress an entire image via a B-spline encoding. To date, the two desiderates, (a) high local quality and (b) high overall compression of images, have not been addressed by a single medial method. We achieve this integration by presenting Spatial Saliency Spline Dense Medial Descriptors (3S-DMD) for saliency-aware image simplification-and-compression. Our method significantly improves the trade-off between compression and image quality of earlier medial-based methods while keeping perceptually salient features. We also demonstrate the added-value of user-designed, as compared to automatically-computed, saliency maps. We show that our method achieves both higher compression and better quality than JPEG for a broad range of images and, for specific image types, yields higher compression and similar quality than JPEG 2000.

关键词： Medial descriptors Saliency maps B-splines image simplification image compression

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Evolutionary image simplification for lung nodule classification with convolutional neural networks

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INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2018年第10期13卷 1499-1513页

作者： Lueckehe, Daniel von Voigt, Gabriele Leibniz Univ Hannover Computat Hlth Informat Schlosswender Str 5 D-30159 Hannover NH Germany

Understanding decisions of deep learning techniques is important. Especially in the medical field, the reasons for a decision in a classification task are as crucial as the pure classification results. In this article, we propose a new approach to compute relevant parts of a medical image. Knowing the relevant parts makes it easier to understand decisions. In our approach, a convolutional neural network is employed to learn structures of images of lung nodules. Then, an evolutionary algorithm is applied to compute a simplified version of an unknown image based on the learned structures by the convolutional neural network. In the simplified version, irrelevant parts are removed from the original image. In the results, we show simplified images which allow the observer to focus on the relevant parts. In these images, more than 50% of the pixels are simplified. The simplified pixels do not change the meaning of the images based on the learned structures by the convolutional neural network. An experimental analysis shows the potential of the approach. Besides the examples of simplified images, we analyze the run time development. Simplified images make it easier to focus on relevant parts and to find reasons for a decision. The combination of an evolutionary algorithm employing a learned convolutional neural network is well suited for the simplification task. From a research perspective, it is interesting which areas of the images are simplified and which parts are taken as relevant.

关键词： Understanding deep learning image simplification Evolutionary algorithm Convolutional neural networks

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Salient object detection based on multi-scale contrast

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NEURAL NETWORKS 2018年 101卷 47-56页

作者： Wang, Hai Dai, Lei Cai, Yingfeng Sun, Xiaoqiang Chen, Long Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Peoples R China Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Peoples R China

Due to the development of deep learning networks, a salient object detection based on deep learning networks, which are used to extract the features, has made a great breakthrough compared to the traditional methods. At present, the salient object detection mainly relies on very deep convolutional network, which is used to extract the features. In deep learning networks, an dramatic increase of network depth may cause more training errors instead. In this paper, we use the residual network to increase network depth and to mitigate the errors caused by depth increase simultaneously. Inspired by image simplification, we use color and texture features to obtain simplified image with multiple scales by means of region assimilation on the basis of super-pixels in order to reduce the complexity of images and to improve the accuracy of salient target detection. We refine the feature on pixel level by the multi-scale feature correction method to avoid the feature error when the image is simplified at the above-mentioned region level. The final full connection layer not only integrates features of multi-scale and multi-level but also works as classifier of salient targets. The experimental results show that proposed model achieves better results than other salient object detection models based on original deep learning networks. (c) 2018 Elsevier Ltd. All rights reserved.

关键词： Saliency detection image simplification Deep learning Residual network

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A Robotic Grasping Algorithm based on Simplified image and Deep Convolutional Neural Network 4

A Robotic Grasping Algorithm based on Simplified Image and D...

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IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Mu, Tian Yuan, Bo Yu, Haibin Kang, Yu Univ Sci & Technol China Sch Informat Sci & Technol Hefei Anhui Peoples R China Chinese Acad Sci Shenyang Inst Automat Shenyang Liaoning Peoples R China Northeastern Univ Shenyang Liaoning Peoples R China

ISBN: (纸本)9781538653739

In this paper, a grasping method based on convolutional neural network (CNN) and image simplification is proposed to solve the problem of detecting the optimal grasping pose of a parallel-jaw gripper for unknown objects in an RGB-D view. First, the outlines of objects are simplified to remove blurred details caused by the 3D-sensing device. Further, the depth data at the edge of objects is clustered to simplify the complex 3D structure of objects. Then candidates for grasp on the image are selected by force closure and is sent into the Grasp Quality Convolutional Neural Network (GQ-CNN) to estimate the best estimation of grasping pose. A variety of common objects are used for the grasp experiment and the results demonstrate the effectiveness of the proposed method.

关键词： robotic grasp convolutional neural network grasp detection image simplification

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A Class of Elliptic Equations with Interior Degeneration Degenerate Diffusions and image Processing

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JOURNAL OF MATHEMATICAL IMAGING AND VISION 2017年第3期58卷 411-426页

作者： Guidotti, Patrick Univ Calif Irvine Irvine CA 92697 USA

A class of linear degenerate elliptic equations inspired by nonlinear diffusions of image processing is considered. It is characterized by an interior degeneration of the diffusion coefficient. It is shown that no particularly natural, unique interpretation of the equation is possible. This phenomenon is reflected in the behavior of numerical schemes for its resolution and points to similar issues that potentially affect its nonlinear counterpart.

关键词： Degenerate diffusion Nonuniqueness image simplification

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Orders for Simplifying Partial Partitions

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JOURNAL OF MATHEMATICAL IMAGING AND VISION 2017年第3期58卷 382-410页

作者： Ronse, Christian Univ Strasbourg CNRS ICube 300 Blvd Sebastien BrantCS 10413 F-67412 Illkirch Graffenstaden France

This paper is the third in a series devoted to orders on partial partitions in the framework of image analysis;the first two (with a view to image filtering and segmentation) identified 4 basic operations on blocks involved in such orders: merging, apportioning, creating and inflating blocks. Here, we consider orders where growing a partial partition decreases its support and diminishes the number of blocks. This can be done by a combination of merging (or apportioning) blocks with removing or deflating blocks (the opposite of creating or inflating blocks). We also introduce related operations on blocks: partial apportioning and partial merging. The new orders that we obtain can be useful in relation to skeletonization, image simplification, for processing segmentation markers or to describe the evolution of object boundaries in hierarchies. There are also possible applications in geographic information processing.

关键词： Partial partition Partial order image simplification Reduction

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A SPARSITY-BASED simplification METHOD FOR SEGMENTATION OF SPECTRAL-DOMAIN OPTICAL COHERENCE TOMOGRAPHY imageS 17

A SPARSITY-BASED SIMPLIFICATION METHOD FOR SEGMENTATION OF S...

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Conference on Wavelets and Sparsity XVII

作者： Meiniel, William Gan, Yu Olivo-Marin, Jean-Christophe Angelini, Elsa Inst Pasteur BioImage Anal Unit CNRS UMR 3691 Paris France Univ Paris Saclay Inst Telecom Paris France Columbia Univ Dept Elect Engn New York NY 10027 USA Imperial Coll London Fac Med Dept Surg & Canc ITMAT Data Sci GrpNIHR Imperial BRCCSM London England

ISBN: (数字)9781510612464

ISBN: (纸本)9781510612464;9781510612457

Optical coherence tomography (OCT) has emerged as a promising image modality to characterize biological tissues. With axio-lateral resolutions at the micron-level, OCT images provide detailed morphological information and enable applications such as optical biopsy and virtual histology for clinical needs. image enhancement is typically required for morphological segmentation, to improve boundary localization, rather than enrich detailed tissue information. We propose to formulate image enhancement as an image simplification task such that tissue layers are smoothed while contours are enhanced. For this purpose, we exploit a Total Variation sparsity-based image reconstruction, inspired by the Compressed Sensing (CS) theory, but specialized for images with structures arranged in layers. We demonstrate the potential of our approach on OCT human heart and retinal images for layers segmentation. We also compare our image enhancement capabilities to the state-of-the-art denoising techniques.

关键词： Optical Coherence Tomography Sparse sampling image simplification image segmentation

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Tangent-Based Binary image Abstraction

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JOURNAL OF IMAGING 2017年第2期3卷 16-16页

作者： Wu, Yi-Ta Yeh, Jeng-Sheng Wu, Fu-Che Chuang, Yung-Yu Natl Taiwan Univ Comp Sci & Informat Engn Taipei 10617 Taiwan Ming Chuan Univ Comp & Commun Engn Taoyuan 333 Taiwan Providence Univ Comp Sci & Commun Engn Taichung 43301 Taiwan

We present a tangent flow-based image abstraction framework that turns a color or grayscale image into a two-tone image, which contains only black and white color, but retains sufficient information such that the viewer can still recognize the main content of the original image by observing the output of a two-tone image. Usually, the relevant visual information is presented by the edge. Thus, the algorithm will enhance the edge content first by the tangent flow to preserve its detail. We use filters to smooth the input image to reduce the contrast in low contrast regions and enhance some important features. Then we turn the image into a two-tone image as output. We can reduce the size of the image significantly but retain sufficient information to capture the main point of the image through our method. At the end of our process, we provide a smoothing step to smooth the two-tone image. Through this step, we can get an artistic black-and-white image.

关键词： tangent flow image abstraction image simplification image stylization two-tone image binary image

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SPARSITY-BASED simplification OF SPECTRAL-DOMAIN OPTICAL COHERENCE TOMOGRAPHY imageS OF CARDIAC SAMPLES 13

SPARSITY-BASED SIMPLIFICATION OF SPECTRAL-DOMAIN OPTICAL COH...

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13th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Meiniel, William Gan, Yu Hendon, Christine P. Olivo-Marin, Jean-Christophe Laine, Andrew Angelini, Elsa D. Univ Paris Saclay Telecom ParisTech CNRS LTCI St Aubin France Inst Pasteur Unite Anal Images Biol Paris France Columbia Univ Dept Elect Engn New York NY 10027 USA Columbia Univ Dept Biomed Engn New York NY USA

ISBN: (纸本)9781479923496;9781479923502

We propose a sparsity-based simplification method for Spectral Domain Optical Coherence Tomography (SD-OCT) images of cardiac samples, displaying layers of tissue. Inspired by the Compressed Sensing (CS) theory, we implement a dedicated sparse sampling of SD-OCT samples achieving image simplification suited for layers segmentation, which is the target application. We compare our simplified images to state of the art denoising approaches. We also validate a straightforward segmentation approach on the variance map of the simplified images against manual delineation on raw SD-OCT images of in-vitro biological samples from four human hearts. Finally, we correlate average layer thickness with histopathological measures.

关键词： Optical Coherence Tomography Sparse sampling image simplification image segmentation

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Planting, Growing, and Pruning Trees: Connected Filters Applied to Document image Analysis

Planting, Growing, and Pruning Trees: Connected Filters Appl...

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11th IAPR International Workshop on Document Analysis Systems (DAS)

作者： Lazzara, Guillaume Geraud, Thierry Levillain, Roland EPITA Res & Dev Lab LRDE FR-94276 Le Kremlin Bicetre France

ISBN: (纸本)9781479932436

Mathematical morphology, when used in the field of document image analysis and processing, is often limited to some classical yet basic tools. The domain however features a lesser-known class of powerful operators, called connected filters. These operators present an important property: they do not shift nor create contours. Most connected filters are linked to a tree-based representation of an image's contents, where nodes represent connected components while edges express an inclusion relation. By computing attributes for each node of the tree from the corresponding connected component, then selecting nodes according to an attribute-based criterion, one can either filter or recognize objects in an image. This strategy is very intuitive, efficient, easy to implement, and actually well-suited to processing images of magazines. Examples of applications include image simplification, smart binarization, and object identification.

关键词： Document image Processing Mathematical Morphology Tree of Shapes image simplification Binarization Object Identification

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