Building Detection and Extraction in Recognition System

计算机视觉技术的迅速发展使得自动目标识别在当今的工业生产、日常生活娱乐以及国防军事等领域中的作用日益显著，应用前景越来越广泛。作为自动目标识别的组成部分之一，建筑物目标的提取与识别对于城市测绘与统计规划、三维数字地图重建、导航控制等诸多方面有着重要的意义。但是目前建筑目标提取与识别的研究工作中还存在许多难以解决的问题，是一个具有挑战性的课题。在建筑目标识别系统中，迅速地检测以及分割提取出图像中的主要建筑目标候选区域非常重要，它能缩小搜索范围并且有助于提高识别精度。但是由于受到天气、光线、建筑物密度等众多因素的影响，分割提取工作也具有相当的难度。本文针对基于单幅图像的建筑区域检测以及分割提取问题...With the rapid development of computer vision technology nowadays, automatic target recognition is playing a more important role in industry, entertainment, military realm and many other fields. Being a part of automatic target recognition technology, extraction and recognition technique for building target is significant for applications such as city statistics, reconstruction of 3D maps and stee...学位：工学硕士院系专业：计算机与信息工程学院计算机科学系_计算机应用技术学号：20022804

Approach to building extracti on based on two-way fusion mechanism

摘要: 模拟人类视觉中有意识主动寻找与无意识被动受吸引相交互的视觉过程,提出了一种基于双向融合机制的建筑目标检测方法。该方法综合了基于自底向上数据驱动的视觉显著性模式和自顶向下基于环境感知的目标搜索模式检测图像中的建筑目标区域。利用拍摄的自然图像进行实验表明:该方法能较好地检测出图像中的建筑目标,具有处理速度快、 准确性高的特点,能够满足处理复杂场景图像的实时性要求。 Abstract: This paper p roposed an app roach called two-way fusion mechanism, which detected the interested building in the image . it imitated the detecti on p rocess of human vision, in which searching subjectively and being attracted objectively happened at the same ti me . Thr ough the bottom-up methodswhich utilized visual salience model and the t op2 down methods based on envir onmental percep ti on technol ogy, the regions where building existweremarked in the image . Experiment on natural images validates that thismechanism has competitive accuracy in building detecti on in comp lex scene and favorable s peed, and satisfies the requirement of processing the natural image .基金项目: 国家创新研究群体资助项目( 60024301 ) ; 国家自然科学基金资助项目(60175008);厦门大学“985” 二期信息创新平台资助项

Approach to Building Recognition in Complex Scenes

提出了一种基于建筑目标的竖直线特征寻找图像中存在建筑目标区域的方法;考虑了目标特征的相互关系,给出了一种新的模板匹配算法。实验表明:利用该文提出的算法建立的识别系统与其它识别系统相比,大大减少了运算时间,有较好的抗噪声干扰和处理目标被遮挡问题的能力。A new approach to building extraction based on grouping the feature of the vertical lines of building object is proposed. Using it, the regions where building may exist are marked. A new template matching algorithm, which accounts for the dependencies between features of object, is presented. Tests show that the recognition system can save a lot of runtime, and yield substantial improvement over others for cluttered scenes and processing the partially occluded objects.国家创新研究群体资助项目(60024301);; 国家自然科学基金资助项目(60175008);; 厦门大学“985”二期信息创新平台资助项

Application of HSI Based on Visual Attention Model in Ship Detection

根据现有关于视觉心理学研究的相关成果和计算模型,提出了一种基于HSI颜色空间特征提取的视觉注意力模型,并应用于海上目标检测.首先把输入的RGB图像转换到HSI空间上,采用高斯金字塔和center-surround算子获得HSI三个分量下各自多尺度的视觉差异,通过对不同特征图的规格化和线性融合获得综合的显著图.该方法应用于多种海上目标图像均取得较好效果,背景中的海浪杂波得到了有效抑制,提取得到的显著区域包括了待检测的目标且范围较小,为后继的处理和分析提供了良好的基础.Visual attention analysis provides a mechanism to find the salient regions within the image.Based on this mechanism,a saliency detection method in HSI color space is proposed and applied to ship detection task.Firstly,an RGB image is transformed into HSI space.Gaussian pyramids are created for three features:hue,saturation and intensity.After calculating center-surround differences between different scales of the pyramid,some feature maps are obtained.Finally,these feature maps are combined into a saliency map.The experimental results indicate that this method is effective in ship detection task.Background noises are suppressed efficiently and the extracted salient regions are good for further processing.国家创新研究群体项目(60024301);; 国家自然科学基金(60175008);; 福建省自然科学基金资

Salient Building Detection Based on SVM

提出了一种针对自然图像中显著性建筑物的检测方法.首先,采用自底向上的注意力机制,对图像进行Haar小波分解,对得到的HL,LH分量进行平方求和,得到增强图像,然后对该增强图像在垂直方向上进行侧投影,基于得到的投影曲线进行多层阈值分割,找到显著性建筑物候选区域.进而,利用Sobel算子进行水平边缘与垂直边缘的检测,并统计较长的水平边缘与垂直边缘的数目,组成特征矢量.最后利用线性支持向量机对特征进行分类.实验证明了所提算法的有效性.This paper focuses on detecting salient buildings in a scenery image. A method based on bottom-up attention mechanism is proposed to detect salient buildings. Firstly, Haar wavelet decomposition is used to obtain the enhanced image which is the sum of the square of LH sub-image and HL sub-image. Secondly, the enhanced image is projected in the vertical direction to obtain the projection profile, and building candidates are separated from the background based on multi-level thresholding. Thirdly, the structure statistic features of buildings are extracted based on Sobel operator. The feature vector is formed by the number of long horizontal edges and that of vertical edges. Finally, linear support vector machines are used to classify buildings and the others. The proposed approach has been experimented on many real-world images with promising results.国家自然科学基金项目(60635050,60405004);; 国家自然科学创新研究群体基金项目(60021302