Free form deformation based face cartoon stylization

A novel free form based face cartoon stylization method is presented in this paper. First, a face cartoon library with marked feature points is constructed. And then select the input image as the target image and an appropriate cartoon image from cartoon library as the reference image, apply the deformation between the corresponding feature points of the images to the target image. Finally, we apply an image stylization process to the result image. As an alternative method, we also choose an appropriate cartoon image as the target image and the input image as the reference image to apply the free form deformation. The experimental results show that our method is straightforward and quick with diversified styles, delivering more infection.     

面向图像集复合质量的压缩采样分配算法

对于一个航拍图像集的压缩感知编码,现有方案只能采用固定的测量分配机制对其中每幅图像进行压缩采样,未考虑图像之间的差异性以及图像集的整体重构质量,因此难以充分利用有限的采样资源。在总的采样资源约束下,如何为航拍图像集中不同图像分配合理的采样率是一个需要解决的问题。首先,根据航拍图像集的通用需求提出了图像集复合质量的评价指标,用以计算图像集的整体重构质量;随后,根据图像集中不同图像的相对复杂度建立了一个图像方差模型,并基于该模型提出了一种图像集的压缩采样分配算法。实验结果表明相比于现有方案,在相同的采样资源约束下,所提算法有效地提升了航拍图像集的整体重构质量。     

基于熵的两区域图像分割

图像分割在多媒体,图像处理,计算机视觉领域扮演着重要角色。提出了基于图像分割熵的二区域图像分割方法。 首先,根据熵的特性:单个随机变量所对应的熵越大,所包含的信息量越大,图像是单一区域时,所含的信息量(熵)较小,引入图像分割熵(ISE)测度,用于度量两区域图像分割准确程度,将两区域图像分割问题转化成ISE最小值问题。然后,采用迭代图切(graph cut)算法给出ISE最小值问题的近似解,实现二区域图像分割。实验结果表明,基于图像分割熵的二区域图像分割方法是可行有效的。     

An Optimal Control Formulation of an Image Registration Problem

The basic idea of image registration is to find a reasonable transformation of an image so that the resulting difference between it and another image is made small. We derive an optimal control method for determining such a transformation; the approach is based on the grid deformation method and seeks to minimize an objective functional that measures the difference between the transformed image and the reference image. The existence of an optimal transformation is proved as is the applicability of the Lagrange multiplier method. Then, an optimality system from which optimal transformations can be obtained is derived.     

KIMS: knowledge-based image management system

A knowledge-based image management system called KIMS is developed for storage and retrieval of images. The aim of the system is to overcome some of the limitations inherent in Micro-IDBMS, a microcomputer-based image database management system. These limitations include abrupt failure and the inability to capture the concept of an image. Abrupt failure may occur during retrieval when there are no images that have the specified attribute value. The inability to capture the concept of an image is related to the general idea that an image is a representation.

KIMS basically consists of an image knowledge base and an inference mechanism, in addition to Micro-IDBMS. The paper describes the rolé of image knowledge and presents the design and implementation details of KIMS.     

Smart image design for large image databases

We propose an active image information system based upon the concept of smart images. A smart image is an image with an associated knowledge structure, consisting of protocols, hot spots, active indexes and attributes. This active image information system enables us to accomplish the objectives of timely delivery and easy accessibility by handling long-duration operations and supporting unitary image information usage. The experimental prototype of the smart image system is described.     

基于模糊数学的图像处理方法

基于模糊数学的图像处理技术是计算机图像处理中的重要技术。由于图像处理中图像分析和计算机视觉的影响很大,图像本质上具有模糊性,因此模糊信息处理技术在图像处理中的使用有其内在的合理性和必然性。     

一种改进型脉冲耦合神经网络及其图像分割

文章结合人类视觉系统(HVS)对图像各个区域敏感度不同这一特性,对通常的脉冲耦合神经网络模型(PC-NN--PulseCoupledNeuralNetwork)进行了改进,分析了改进模型的特性及其参数优化原理,提出了一种基于这种改进PCNN的图像分割算法。该算法可根据像素周边区域的灰度梯度大小发放不同值的脉冲,从而自适应地将图像分为多个不同等级的高低信息区域,较好地仿真了人类视觉系统特性。并将该算法应用于图像压缩,在压缩比和重建图像主观视觉感知质量上均达到了较好的性能。     

A novel unsupervised approach for multilevel image clustering from unordered image collection

A novel unsupervised approach to automatically constructing multilevel image clusters from unordered images is proposed in this paper. The whole input image collection is represented as an imaging sample space (ISS) consisting of globally indexed image features extracted by a new efficient multi-view image feature matching method. By making an analogy between image capturing and observation of ISS, each image is represented as a binary sequence, in which each bit indicates the visibility of a corresponding feature. Based on information theory-inspired image popularity and dissimilarity measures, we show that the image content and distance can be quantitatively described, guided by which an input image collection is organized into multilevel clusters automatically. The effectiveness and the efficiency of the proposed approach are demonstrated using three real image collections and promising results were obtained from both qualitative and quantitative evaluation.     

基于边缘提取的分形图象编码方法

为了提高图象边缘提取时分类的准确性,在利用四叉树进行自适应图象分割的同时,将Laplacian算子作用于原始图象,然后从得到的边缘图像中,除去给定阈值的特定灰度层的图像后,即得到一种去除冗余,并突出边缘特征的图象,再将它应用于Domain块分类之中,可使分类结果更准确和更具客观性,计算机仿真解码实验结果表明,与其他同类的自动分形方法相比,该方法在编码速度、压缩比和恢复图象质量等方面均有显著提高。     

3D convolutional neural network for object recognition: a review

Multimedia Tools and Applications - Recognition of an object from an image or image sequences is an important task in computer vision. It is an important low-level image processing operation and...     

基于边界几何特征的重叠颗粒图像分割算法

图像分割是图像处理中的重要问题,也是计算机视觉领域低层次视觉中的主要问题,是成功进行图像分析、理解和描述的关键技术。该文构造了一种针对二值图像边界几何特征的提取的算法,通过对图形边界的凹特性分析,实现了对不规则颗粒图像的分割。该算法仅对提取的图像数据作处理,不对图像本身作任何运算,避免了传统的基于图像形态学的分割算法所导致的图像信息丢失。     

构造标定自动机实现数字图像的Freeman链编码标定

图像的Freeman链编码是对图像边界的描述,这种链编码给我们图形一些基本特征,正在被广泛地应用到图像处理和图像识别中。本文给出了二值图像区域的标定方法。对于八近邻的图像,分别建立了一组最小的完备图。利用图像标定的基本图,为二值图像边界的识别构造了一个自动机,自动机的输出就是Freeman链编码,为二值图像区域的标定提供了一个有效算法。     

基于高斯模板的图象旋转算法

图象旋转是图象处理中的一个基本操作,根据图象在邻近区域内连续的特点,该文提出一种基于高斯模板的图象旋转算法。实验结果表明,该算法能较好地消除图象旋转所产生的锯齿现象,图象多次旋转后失真较少,与最近插值和双线性插值相比具有较好的性能。     

基于拉格朗日插值多项式的彩色图像分存方案

为了解决真彩色大秘密图像的安全传输问题,提出了一种基于拉格朗日插值多项式的图像分存方案;该方案通过对真彩色大秘密图像进行压缩、量化、编码、分解、译码等处理生成秘密图像的n个影子,再将每个影子隐藏于一幅尺寸是秘密图像的t分之一大小的可视的载体图像中,任取其中的t个嵌入秘密图像影子的载体图像,就能恢复原始秘密图像;同时给出了分存原理和直接恢复公式,提高了图像分存和恢复的速度,简化了计算过程。     

All-in-focus imaging using average filter-based relative focus measure

Digital images are normally taken by focusing on an object, resulting in defocused background regions. A popular approach to produce an all-in-focus image without defocused regions is to capture several input images at varying focus settings, and then fuse them into an image using offline image processing software. This paper describes an all-in-focus imaging method that can operate on digital cameras. The proposed method consists of an automatic focus-bracketing algorithm that determines at which focuses to capture images and an image-fusion algorithm that computes a high-quality all-in-focus image. While most previous methods use the focus measure calculated independently for each input image, the proposed method calculates the relative focus measure between a pair of input images. We note that a well-focused region in an image shows better contrast, sharpness, and details than the corresponding region that is defocused in another image. Based on the observation that the average filtered version of a well-focused region in an image shows a higher correlation to the corresponding defocused region in another image than the original well-focused version, a new focus measure is proposed. Experimental results of various sample image sequences show the superiority of the proposed measure in terms of both objective and subjective evaluation and the proposed method allows the user to capture all-in-focus images directly on their digital camera without using offline image processing software.     

人像变换的图象处理方法研究

本文研究基于径向函数的图象扭曲算法和边缘检测算法，在Ｗｉｎｄｏｗｓ环境下，编程实现人像变换的图象处理系统。同时，对给出的人像采用了基于知识的控制搜索策略。根据所选择的面部表情，搜索出与之相关的部位边界，对其进行扭曲变形，使之接近于自然的脸部表情。     

一种面向图象语义的主要区域提取方法

图象主要区域的提取是图象语义抽取及其应用的基础 .为了更好地进行图象语义的抽取 ,提出了一种面向图象语义的图象主要区域自动提取方法 .该方法首先将图象划分成固定大小的子块 ,并通过对子块特征进行聚类来获得图象的初始区域分割 ;而后 ,经过一系列的后处理来优化分割结果 ,并实现前景和背景区分 ;最后通过分析每个背景区域的重要程度 ,去除掉不相关的背景区域 .通过对包含有显著对象的户外图象进行的实验表明 :该方法不仅可以去除图象中 ,大量与图象语义不相关的内容 ,而且能保留图象的主要信息 ,这就为进一步的图象语义应用打好了基础 .     

视频序列中运动目标自动提取的研究

本文提出了一种从彩色视频序列中,有效分割运动目标的算法。首先将当前帧图像和背景参考图像在YUV彩色空间中进行距离差分得到距离差分图像,然后根据差分图像直方图的单峰聚集特性,提出了基于直方图的自适应聚类分割算法;对分割后的二值图像采用图像形态学方法去除无用的噪声斑点,并根据得到运动模板,提出了背景参考图像的更新策略。实验结果表明这是一种简单、有效的运动目标提取方法。     

通过脉冲耦合神经网络来增强图像

图像增强是指对图像进行加工,以获得更“好”的视觉效果的一种图像处理技术。这种“好”的评价是由图像的观察者给出的。该文提出了一个通过脉冲耦合神经网络来增强图像,使图像的边缘变得更清晰的方法,并用边缘检测算子对其结果进行了验证。实验表明这种方法具有良好的效果。