显著物体形状结构保持的图像缩放方法

针对传统的缝裁剪图像缩放方法中可能出现对图像中显著物体形状结构的破坏问题,提出一种既考虑到显著物体内容保持又考虑到显著物体形状结构保持的新的图像缩放方法。该方法首先利用经典的图像显著度图模型,结合图像梯度直方图等信息构建形状结构更加清晰的图像重要度图;然后利用已构建的重要度图,对图像进行分块,按显著块的大小来确定缩放方法;最后结合经典缝裁剪方法和基于共形能量的变形方法对图像进行缩放。实验结果显示,该方法能够在图像缩放时更好地保持显著物体的内容和形状结构。     

基于主体区域保持的图像缩放算法

基于插值运算的缩放算法和经典的缝裁剪算法是两种常用的图像缩放算法,传统的 缩放算法在缩放比例不一致的情况下其效果不佳,而缝裁剪算法在主体区域较大或者图像背景较 为复杂时对图像的主体区域会造成一定破坏。针对以上问题,提出了一种基于主体区域保持的图 像缩放算法,使用高斯差分对图像进行角点检测,利用角点产生凸包,根据凸包对图像进行主体 区域检测,计算能量图并对位于主体区域像素点的能量给予相应的权重,根据权重的不同对主体 区域进行不同程度的保护。实验结果表明,该算法能更好地保持图像主体区域。     

利用梯度与人体区域信息的图像分条及缩放

为了保持图像中的人物和显著目标,避免局部区域过度压缩,提出一种基于能量图和条分割的图像缩放算法.首先计算图像中所有像素点的梯度值,结合人体检测并赋予人体区域较大权重来构建图像能量图,经优化迭代将相近能量的相邻像素列划分到同一图像条中实现图像分条;然后根据图像条的平均累积能量,采用缩放量与其能量成反比的关系计算图像条的缩放量;最后通过设置缩减比阈值判别图像条尺寸缩减程度;当缩减比超过阈值时,裁剪相应图像条,以防止产生局部扭曲变形并保持图像主要内容.实验结果表明,该算法能够保护图像的局部结构和全局视觉效果,获得高质量的缩放结果.     

基于贝叶斯模型的内容保持图像缩放算法

为解决图像在不同显示设备上进行缩放时显著目标易变形、微小目标易删除和多显著目标易融合等问题,提出一种基于贝叶斯模型的内容保持图像缩放算法。首先,用凸包和背景先验共同获得贝叶斯模型所需的先验概率和似然估计,代入贝叶斯模型算出显著图;其次,将梯度图与显著图相乘获得新梯度图,通过求新梯度图和显著图之和获得复合能量图;最后,利用该复合能量图进行缝缩放。实验结果表明,该缩放算法与原缝缩放算法相比解决了显著目标易变形和微小目标易删除的问题,减少了多显著目标易融合的现象。     

基于DCT域视觉显著性检测的图像缩放算法*

为适应不同终端显示多样化的要求,需对接收到的图像进行缩放调整。针对现有的基于内容感知(content-aware)的图像缩放方法中视觉内容的连贯性易被破环而出现失真的问题,提出了一个基于离散余弦变换(discrete cosine transform, DCT)域的视觉显著性检测的图像缩放算法。该算法利用DCT域的视觉显著性检测模型获取视觉显著图,然后结合视觉显著图和能量分布图进行线裁剪(Seam Carving),实现了图像的缩放。实验结果表明,该算法与现有的基于内容感知的图像缩放方法相比,不仅保护了视觉显著内容,还保证了图像内容的连贯性,算法质量指数也获得明显的提高。     

结合美学原则的内容感知图像缩放算法

针对目前内容感知的图像缩放算法存在美学效果考虑不足的问题,提出一种结合美学原则的缩放算法。算法首先采用图像协同分割的思想并结合视觉显著性检测获取图像的重要度图,以此作为图像重要内容的依据,在后续处理中防止其变形;其次针对移动终端常见的两类图像分别选择相适应的美学原则并建立其量化公式;借鉴经典的Seam Carving算法思想,利用美学构图原则和重要度图来指导裁剪线的复制与删除,达到图像缩放的效果。实验结果表明,与同类算法相比,该算法的缩放结果在保留原图重要信息的条件下,更具美感。     

基于内容划分和傅里叶分析的图像缩放算法

为满足图像缩放中保护重要区域和视觉连贯性的要求,算法通过对不同重要度的区域采用不同的采样率进行缩放.用显著区域、语义内容和结构信息的特征来检测图像中的重要区域,根据重要区域将原图像自适应地划分为多个子图,并根据傅里叶分析和视觉损失能量函数计算每个子图的采样率,对子图进行下采样得到最终的缩放图像.仿真实验表明,与Seam Carving等算法相比,该算法计算效率较高,而且对图像中的显著物体保护较好.     

结合显著度图和自适应能量线的快速图像缩放*

针对Seam Carving图像缩放方法中能量函数的局限性和动态规划算法的复杂性,提出了一种基于图像内容的快速图像缩放方法。结合基于视觉注意力模型的显著度图,完善图像内容的能量描述;同时,提出一种基于图像内容的粗、细能量线自适应机制,与层次加速动态规划一起,加速缩放效率。仿真实验结果表明,该算法在有效保护图像内容的同时,降低了算法的计算复杂度,减少了图像缩放时间。     

基于切缝对齐和变形校正的缝切割图像尺寸调整算法

针对图像尺寸调整的缝切割方法中存在的结构断裂和变形扭曲等问题,提出了一种基于切缝对齐和变形校正的切缝优化算法以获得更好的图像尺寸缩放效果.算法首先利用基于图割的切缝优化方法获得当前需要移除的一条切缝,然后采取动态规划算法,由粗到细地优化得到切缝上下像素之间的高精度匹配对应关系,最后通过逆向映射来对图像进行变形以校正发生扭曲甚至断裂的结构信息.实验结果表明,该方法能够很好地弥补缝切割所产生的扭曲变形,达到结构连续的视觉效果.基于切缝对齐和变形校正的切缝优化算法可用于调整图像尺寸使之与相应的显示设备相符,具有巨大的应用潜力.     

内容感知图像缩放技术综述

随着不同分辨率和纵横比的显示设备的迅猛增长,内容感知图像缩放技术逐渐成为图像处理领域新的研究热点之一。内容感知图像缩放的目标是在任意改变图像大小时保持图像中的主体特征不变。围绕其关键步骤:图像重要度识别和基于重要度的缩放,先概述重要度识别的相关方法,然后重点综述基于重要度的缩放技术。根据缩放是在像素级上操作还是亚像素级上操作,或者两者兼有,将其分为基于线裁剪缩放、基于图像变形缩放和多操作缩放3类,并比较各类方法的优缺点,同时给出各类方法所适合处理的图像类型。最后在分析各类研究方法的基础上,给出了内容感知缩放技术的可能发展方向。     

基于SIFT相似性度量的图像缩放

针对传统图像缩放算法存在的图像内容破坏与失真等问题,提出一种结合接缝雕刻和标准缩放的图像缩放算法。利用接缝雕刻算法对原始图像进行尺寸调整,分别从原始图像和缩放图像上提取尺度不变特征变换(SIFT)特征,并对2幅图像的SIFT特征进行匹配计算,得到两者的相似性距离。当原始图像与缩放图像之间的相似性距离达到某阈值时,即在图像重要目标或内容将要被破坏之前,停止使用接缝雕刻算法,采用标准缩放算法进行整体尺寸的调整操作。实验结果表明,该算法不仅可以有效避免图像内容的破坏与失真,而且可以较好地保护图像局部结构和全局视觉效果。     

Semantic aware sport image resizing jointly using seam carving and warping

In content aware image resizing, saliency map or gradient is usually used to determine the important regions of images. But for sport images such as basketball and football images, these methods may falsely classify parts of court fields as unimportant regions, while parts of grandstands as important regions. Such results are not consistent with human perception. In this paper, a semantic aware image resizing approach is proposed. We extract the semantic information automatically. We segment the court fields as important regions and detect the boundary of court fields as the semantic edges. Considering the complementary characteristic of discrete image resizing approaches such as seam carving and continuous approaches such as warping, seam carving and warping are jointly used in our scheme. We define the Semantic Weight Function (SWF) based on the semantically important regions. Then semantic aware seam carving (SASC) is proposed based on the SWF. Next we define the Deformation of Semantic Edges (DSE) to assess the image deformation caused by seam carving. Finally seam carving and warping are joined using the DSE. We compare our approach with approaches like scaling, seam carving and semantic aware seam carving (SASC). Experimental results show that our approach preserves more semantically important regions with less deformation. Our approach also preserves the aspect ratio of key objects.     

Multi-operator image retargeting with automatic integration of direct and indirect seam carving

Multi-operator image resizing can preserve important objects and structure in an image by combining multiple image resizing operators. However, traditional multi-operator methods do not take both horizontal and vertical content-aware resizing potential into consideration, which essentially leads to squeeze/stretch effect in the resultant images. In this paper, we propose a new multi-operator scheme that addresses aforementioned issue by integrating direct and indirect seam carving. Compared with previous methods, the proposed scheme remarkably reduces the cost of deciding when to change operators, by employing a newly defined image artifact measure. Furthermore, we propose a novel seam carving enhancement, named ACcumulated Energy Seam Carving (ACESC), as a basic operator to improve global structure preservation. By combining horizontal and vertical seam carving, our scheme preserves the shapes of important objects well. We present typical results to demonstrate the effectiveness of our method. User study shows that our method has high user preference.     

Optimized image resizing using flow-guided seam carving and an interactive genetic algorithm

In this paper, we introduce a novel method for content-aware image resizing based on flow-guided seam carving. It extends the existing seam carving framework by replacing the conventional energy field with a “structure-aware” energy field that takes into account the feature orientations in the image. Guided by this new energy field, our approach excels in preserving (i.e., avoiding the distortion of) important structures in the image, such as shape boundaries. We also present a simple user interface to further optimize the resizing result based on the genetic selection process among multiple resizing operators such as scaling, cropping, and flow-guided seam carving. We show that such simple user interaction, coupled with the genetic algorithm, dramatically increases the chances of producing the user-desired outcome.     

Semantic‐Preserving Word Clouds by Seam Carving

Word clouds are proliferating on the Internet and have received much attention in visual analytics. Although word clouds can help users understand the major content of a document collection quickly, their ability to visually compare documents is limited. This paper introduces a new method to create semantic‐preserving word clouds by leveraging tailored seam carving, a well‐established content‐aware image resizing operator. The method can optimize a word cloud layout by removing a left‐to‐right or top‐to‐bottom seam iteratively and gracefully from the layout. Each seam is a connected path of low energy regions determined by a Gaussian‐based energy function. With seam carving, we can pack the word cloud compactly and effectively, while preserving its overall semantic structure. Furthermore, we design a set of interactive visualization techniques for the created word clouds to facilitate visual text analysis and comparison. Case studies are conducted to demonstrate the effectiveness and usefulness of our techniques.     

基于边缘检测的保特征的图像缩放算法

针对Seam Carving算法在实现图像缩放的过程中,对图像边缘几何特征造成破坏的现象,提出一种基于边缘检测的改进Seam Carving算法。首先在Seam Carving算法计算低能量线的同时引入图像边缘检测,其次确定低能量线与边缘的交点,进而提升交点邻域的能量,分散过于集中的低能量线。仿真实验结果表明,该算法能够有效减少Seam Carving算法缩放图像后产生的畸变,很好地保持了图像的重要几何特征。