Retargeting images and video for preserving information saliency.

A nonphotorealistic algorithm for retargeting images adapts large images so that important objects in the image are still recognizable when displayed at a lower target resolution. Unlike existing image manipulation techniques such as cropping and scaling, the retargeting algorithm can handle multiple important objects in an image. To identify the important objects in an image, we must first segment the image. We use mean-shift image segmentation to decompose an image into homogeneous regions.     

View-Aware Image Object Compositing and Synthesis from Multiple Sources

Image compositing is widely used to combine visual elements from separate source images into a single image. Although recent image compositing techniques are capable of achieving smooth blending of the visual elements from different sources, most of them implicitly assume the source images are taken in the same viewpoint. In this paper, we present an approach to compositing novel image objects from multiple source images which have different viewpoints. Our key idea is to construct 3D proxies for meaningful components of the source image objects, and use these 3D component proxies to warp and seamlessly merge components together in the same viewpoint. To realize this idea, we introduce a coordinate-frame based single-view camera calibration algorithm to handle general types of image objects, a structure-aware cuboid optimization algorithm to get the cuboid proxies for image object components with correct structure relationship, and finally a 3D-proxy transformation guided image warping algorithm to stitch object components. We further describe a novel application based on this compositing approach to automatically synthesize a large number of image objects from a set of exemplars. Experimental results show that our compositing approach can be applied to a variety of image objects, such as chairs, cups, lamps, and robots, and the synthesis application can create novel image objects with significant shape and style variations from a small set of exemplars.     

地物提取的多尺度特征遥感应用分析

通过空间尺度效应分析,阐述不同属性景观地物在同一分辨率或同一尺度影像中提取的不合理性。为获得精确的地表信息,提出多尺度遥感影像分析方法,解决不同地物在不同空间尺度影像数据中提取的难题。通过多种分辨率影像的多尺度影像信息提取的应用实践,分析地物提取中的多尺度特性、尺度与分辨率关系等。     

含运动目标的红外图像非均匀校正算法

通过分析运动目标对基于图像配准的非均匀校正算法校正效果的影响,基于相邻帧之间运动目标辐射强度不变的假设,提出含运动目标的红外图像非均匀校正算法。通过临近帧补全运动目标遮挡的场景,消除运动目标的影响,进行非均匀校正得到场景校正帧,再利用运动目标的辐射信息对各帧运动目标进行非均匀校正,最终得到全图校正帧。仿真和实测红外图像校正实验表明：算法能够很好地解决含运动目标的红外序列图像的非均匀校正问题。     

A two step salient objects extraction framework based on image segmentation and saliency detection

Salient objects extraction from a still image is a very hot topic, as it owns a lot of useful applications (e.g., image compression, content-based image retrieval, digital watermarking). In this paper, targeted to improve the performance of the extraction approach, we propose a two step salient objects extraction framework based on image segmentation and saliency detection (TIS). Specially, during the first step, the image is segmented into several regions using image segmentation algorithm and the saliency map for the whole image is detected with saliency detection algorithm. In the second step, for each region, some features are extracted for the SVM algorithm to classify the region as a background region or a salient region twice. Experimental results show that our proposed framework can extract the salient objects more precisely and can achieve a good extraction results, compared with previous salient objects extraction methods.     

A modified object-oriented classification algorithm and its application in high-resolution remote-sensing imagery

High-resolution satellite images offer abundant information on the Earth's surface for remote-sensing applications. The traditional pixel-based image classification method only used by spectral information has been proved to have several drawbacks. To satisfactorily interpret high-resolution imagery, other important information such as geometry, texture and semantics must be used, which are represented not only in single pixels but in meaningful image objects. So, a modified high-resolution image classification algorithm with multi-characteristics based on objects is presented in this article. First, image objects are extracted by multi-scale multi-characteristic segmentation. Second, characteristics such as spectral information, geometry, texture and semantics are extracted by the corresponding extraction algorithm. Finally, the image objects are classified by means of fuzzy-logic classification with a weighted average calculation method. Preliminary results show promise in terms of classification quality and accuracy.     

基于形态学重建的粘连物体分割

提出一种基于形态学重建（Morphological Reconstruction）的图像分割方法。该方法先对待分割图像进行预处理,使边界点具有局部极大的灰度值;然后利用灰度形态学重建提取穹顶（Dome）,并根据其特性利用阚值对穹顶进行二值化获得候选边界点集;再利用二值形态学重建确定候选边界点集中的边界点,得到分割边界。实验结果表明,本分割方法所得边界连续性好、假边界少;该方法受噪声和对象内部灰度变化的影响较小,适合用于分割含有粘连对象的图像。     

Attention-driven image interpretation with application to image retrieval

Visual attention, a selective procedure of human's early vision, plays a very important role for humans to understand a scene by intuitively emphasizing some focused regions/objects. Being aware of this, we propose an attention-driven image interpretation method that pops out visual attentive objects from an image iteratively by maximizing a global attention function. In this method, an image can be interpreted as containing several perceptually attended objects as well as a background, where each object has an attention value. The attention values of attentive objectives are then mapped to importance factors so as to facilitate the subsequent image retrieval. An attention-driven matching algorithm is proposed in this paper based on a retrieval strategy emphasizing attended objects. Experiments on 7376 Hemera color images annotated by keywords show that the retrieval results from our attention-driven approach compare favorably with conventional methods, especially when the important objects are seriously concealed by the irrelevant background.     

应用于灰度图像分割的基于流体静力学原理活动轮廓模型

提出一种新的活动轮廓模型,应用于灰度图像分割。此模型建立在流体 静力学理论之上, 运用流体静力学理论直接驱动连续曲线,逼近被包围的目标。该模型能够 分割多重目标、能够分割嵌套的目标、能够有效地控制过分割现象。     

SimLocator: robust locator of similar objects in images

Similar objects commonly appear in natural images, and locating and cutting out these objects can be tedious when using classical interactive image segmentation methods. In this paper, we propose SimLocator, a robust method oriented to locate and cut out similar objects with minimum user interaction. After extracting an arbitrary object template from the input image, candidate locations of similar objects are roughly detected by distinguishing the shape and color features of each image. A novel optimization method is then introduced to select accurate locations from the two sets of candidates. Additionally, a matting-based method is used to improve the results and to ensure that all similar objects are located in the image. Finally, a method based on alpha matting is utilized to extract the precise object contours. To ensure the performance of the matting operation, this work has developed a new method for foreground extraction. Experiments show that SimLocator is more robust and more convenient to use compared to other more advanced repetition detection and interactive image segmentation methods, in terms of locating similar objects in images.     

针对小目标的遥感图像解译识别系统

当前各种遥感解译系统无法对多种不同目标提供一致的交互解译,且无法满足弱小目标特定的解译需求,针对该问题,基于GrabCut算法设计实现一种交互式的遥感解译识别系统。包括不同目标的特征数据库建立、感兴趣区域定位、交互式目标提取及目标解译识别等主要模块。对飞机目标的解译示例表明,该解译系统不依赖于具体的目标特征,可为遥感图像的解译工作提供良好的支持。     

运动目标检测的红外与可见光图像融合方法

在弱可见光条件下,对同一场景监控的红外与可见光图像进行融合,使融合图像即显示红外目标,又能保留可见光图像的细节结构信息,方便观察者对场景的观察与监控。充分利用红外成像的特点,热目标与背景的温度差会使目标在红外图像中的灰度值更大。使用红外序列建立稳定的背景模型,当前帧与背景的差得到运动目标区域,然后,将目标区域内的红外目标融合到可见光图像中,达到对红外运动目标检测的目的。     

A content based image retrieval system for a biological specimen collection

Digital photography and decreasing cost of storing data in digital form has led to an explosion of large digital image repositories. Since the number of images in image databases can be large (millions in some cases) it is important to develop automated tools to search them. In this paper, we present a content based image retrieval system for a database of parasite specimen images. Unlike most content based image retrieval systems, where the database consists of objects that vary widely in shape and size, the objects in our database are fairly uniform. These objects are characterized by flexible body shapes, but with fairly rigid ends. We define such shapes to be FleBoRE (Flexible Body Rigid Extremities) objects, and present a shape model for this class of objects. We have defined similarity functions to compute the degree of likeness between two FleBoRE objects and developed automated methods to extract them from specimen images. The system has been tested with a collection of parasite images from the Harold W. Manter Laboratory for Parasitology. Empirical and expert-based evaluations show that query by shape approach is effective in retrieving specimens of the same class.     

基于高分辨率遥感影像的土地覆盖信息提取

高空间分辨率遥感影像使得土地覆盖和土地利用信息的提取成为可能。以高分辨率遥感影像数据IKONOS为主要数据源,以多尺度分割与基于模糊逻辑分类的面向对象影像分析方法为主要技术,自动提取株洲市城乡结合部的土地覆盖和土地利用信息。达到了提取郊区丘陵地带林地信息和城市建筑、道路等土地覆盖信息的目的,而且精度高,速度快。结果表明利用该方法对复杂的城乡结合部信息获取是可行的。     

基于视频数据的电梯门防夹检测算法研究

针对电梯门现有防夹保护装置的缺陷,提出了一种基于视频数据的防夹检测算法,用于在轿厢门区内检测物体。首先,采用改进的双边滤波方法滤除视频帧和背景图像中的噪声。然后,差分视频帧与背景图像,并采用极大熵粒子群法二值化差分图像。接着,将二值图像进行形态学滤波以检测出轿厢门区范围内的物体。最后,利用Surendra算法更新背景图像。实验结果表明,提出算法可以在轿厢门区范围内实时且准确地检测出物体,为电梯门防夹功能的改进提供了可靠的保障。     

基于遗传算法的种子图象目标点模式匹配

在完成种子图象目标点坐标提取和由单摄像机、平面镜组成的摄像系统标定之后 ,关键问题是如何寻找图象中种子质心的对应点 .为了准确寻找到图象中种子质心的对应点 ,根据图象中种子区域的分布特点及遗传算法具有简单、通用、鲁棒性和适于并行处理等特性 ,提出了一种基于遗传算法的种子图象目标点模式匹配算法 ,即首先通过模糊聚类将单帧图象上的像点与虚像点划分为两类点群 ,然后通过在两类点群之间建立遗传机制来进行种子图象点模式匹配 .实验证明 ,采用该算法进行种子目标点群之间的匹配 ,具有快速准确的特点 ,并为种子空间点的恢复提供了准确的对应点寻找信息 .     

A formal framework for image indexing with triples: Toward a concept-based image retrieval

This paper provides a formal specification for concept-based image retrieval using triples. To effectively manage a vast amount of images, we may need an image retrieval system capable of indexing and searching images based on the characteristics of their content. However, such a content-based image retrieval technique alone may not satisfy user queries if retrieved images turn out to be relevant only when they are conceptually related with the queries. In this paper, we develop an image retrieval mechanism to extract semantics of images based on triples. The semantics can be captured by deriving concepts from its constituent objects and spatial relationships between them. The concepts are basically composite objects formed from the aggregation of the constituents. In our mechanism, all the spatial relationships between objects including the concepts are uniformly represented by triples, which are used for indexing images as well as capturing their semantics. We also develop a query evaluation for supporting the concept-based image retrieval. ©1999 John Wiley & Sons, Inc.     

多声部乐谱图象识别系统

文章提出一个完整的系统，将多声部乐谱原文转换成可编辑乐谱文本。系统首先在乐符检测阶段将图象切分为谱表，然后再其切分成个别对象。