IMAGE OBJECT DETECTION BASED ON FRACTIONAL BROWNIAN MOTION

Fractional Brownian motion, continuous everywhere and differentiable nowhere, offers a convenient modeling for irregular nonstationary stochastic processes with long-term dependencies and power law behavior of spectrum over wide ranges of frequencies. It shows high correlation at coarse scale and varies slightly at fine scale, which is suitable for and successful in describing and modeling natural scenes. On the other hand, man-made objects can be constructively well described by using a set of regular simple shape primitives such as line, cylinder, etc. and are free of fractal. Based on the difference, a method to discriminate man-made objects from natural scenes is provided. Experiments are used to demonstrate the good efficiency of developed technique.     

基于分数布朗运动的图象目标检测

本文分析了处处连续而不可导的非平稳不规则信号模型分数布朗运动(fBm)。fBm具有时间(或空间)长程相关性和指数功率谱特性,在大尺度轮廓上,有较大的相关性;而观察尺度越小,变化幅度越小,越表现丰富的细节。这在自然景物、图形建模方面具有很好的应用前景。同时自然景物的较好符合fBm的分形模型与人造物体不符合分形模型之间的差异可以用来进行图象分割。文中给出了一个基于fBm分形参数的图象目标检测方案,并给出了实验结果。     

自然背景中人造目标的自适应检测

本文介绍了一种在可见光和红外图像中，自动检测自然背景中人造目标的方法。该方法以分形结构做为自然背景的数学模型，利用自然背景和人造目标在分形特征上的固有相对差异检测人造目标，本文还介绍了自适应分形特征增强和组合图像检测方法，并以对实际图像的处理的结果，说明本文介绍的人造目标检测方法的效果。     

基于部件的自动目标检测方法研究

该文提出了一种新的自动目标检测算法,实现对自然场景图像及高分辨率遥感图像中结构相对复杂的人造目标的自动检测。该方法基于组成物体的几何部件处理问题,降低了对训练样本数量的需求。首先选择两类典型特征,基于机器学习训练对应的分类器,有效地减少了背景中某些物体与前景目标部分特性相似对检测方法准确率的影响;然后利用标值点过程对问题建模,以对目标分布的先验约束和分类器的响应作为数据能量,自顶向下地自动检测目标。实验结果表明,该方法准确率高、鲁棒性好,具有较强的实际应用价值。     

基于分形模型的人造目标检测技术

介绍一种在红外和可见光图像中，自动检测自然背景中人造目标的方法．该方法以分形结构做为自然背景的数学模型，利用自然背景和人造目标在分形模型上的固有差异检测人造目标．本文还介绍了自适应分形特征的增强及图像检测方法；并以实际图像的处理结果，说明人造目标检测方法的效果．     

聚类与几何特征相结合的遥感图像多类人造目标检测算法

针对高分辨率光学遥感图像中人造目标的检测问 题,对传统的相位编组直线段提取算法和k-means 聚类算法了改进,提出了一种k-means聚类和几何特征 相结合的检测方法。根据自然物体和 人造目标在几何外形上表现出的不同特性,首先运用改进的相位编组算法对图像进行快速的 直线段提取; 然后以获取的直线段中心点为处理对象,运用k-means聚类算法 对提取的直线段进行密度聚类;最后,根 据每个类中的直线段数目和构成的几何基元情况,进行人造目标的判定。实验结果表明,本 文算法对遥感 图像中的房屋、汽车、船舰和飞机跑道等多类人造目标可达到90%以 上的检测精度,并具有较高的检测速度,对于一幅512pixel ×512pixel的图像,整个检测过程在100ms 以内。     

基于分形的红外图象目标自动检测

本文介绍了一种基于分形的复杂背景下红外图象目标自动检测方法,该方法以自然背景和人造目标在分形特征上的固有的差异为依据,利用了人造目标分形特征拟合误差大的特点,实现了复杂背景中红外图象目标的自动检测。实际检测的实验结果表明：该目标自动检测方法对环境的适应性强,是一种颇具发展前景的目标自动检测方法。     

Automatic target recognition organized via jump-diffusionalgorithms

Proposes a framework for simultaneous detection, tracking, and recognition of objects via data fused from multiple sensors. Complex dynamic scenes are represented via the concatenation of simple rigid templates. The variability of the infinity of pose is accommodated via the actions of matrix Lie groups extending the templates to individual instances. The variability of target number and target identity is accommodated via the representation of scenes as unions of templates of varying types, with the associated group transformations of varying dimension. We focus on recognition in the air-to-ground and ground-to-air scenarios. The remote sensing data is organized around both the coarse scale associated with detection as provided by tracking and range radars, along with the fine scale associated with pose and identity supported by high-resolution optical, forward looking infrared and delay-Doppler radar imagers. A Bayesian approach is adopted in which prior distributions on target scenarios are constructed via dynamical models of the targets of interest. These are combined with physics-based sensor models which define conditional likelihoods for the coarse/fine scale sensor data given the underlying scene. Inference via the Bayes posterior is organized around a random sampling algorithm based on jump-diffusion processes. New objects are detected and object identities are recognized through discrete jump moves through parameter space, the algorithm exploring scenes of varying complexity as it proceeds. Between jumps, the scale and rotation group transformations are generated via continuous diffusions in order to smoothly deform templates into individual instances of objects.     

基于DRF模型的自然背景中人造目标的检测算法

自然背景中人造目标的检测是计算机视觉、模式识别、机器学习和图像处理领域中非常活跃的研究课题.概率模型的方法为该问题的解决提供了新的研究途径,其中基于DRF模型的人造目标检测算法是一种最新发展的检测算法,仿真结果表明,该算法漏检率和误检率低,检测效果良好.     

基于分形的实现小目标检测的一种具体方法

介绍了基于分形的复杂背景下实现小目标检测的一种具体方法,该方法以自然背景和人造目标在分形特征上的固有差异为依据,利用了人造目标分形特征数大的特点,给出了进行小目标检测的具体实现过程。实际检测的实验结果表明：该小目标检测方法对环境的适应性强,检测结果精确,颇具发展前途。     

Multiscale gradient watersheds of color images

We present a new framework for the hierarchical segmentation of color images. The proposed scheme comprises a nonlinear scale-space with vector-valued gradient watersheds. Our aim is to produce a meaningful hierarchy among the objects in the image using three image components of distinct perceptual significance for a human observer, namely strong edges, smooth segments and detailed segments. The scale-space is based on a vector-valued diffusion that uses the Additive Operator Splitting numerical scheme. Furthermore, we introduce the principle of the dynamics of contours in scale-space that combines scale and contrast information. The performance of the proposed segmentation scheme is presented via experimental results obtained with a wide range of images including natural and artificial scenes.     

基于红外偏振成像的目标检测技术

红外偏振成像技术是利用物体偏振度上的差异来对复杂背景下的目标进行探测的。在战场上,由于人造物体和自然景物在偏振度上存在差异,红外偏振成像技术能够提高人们对自然景物及伪装的辨别能力。首先介绍了偏振成像理论及其系统的结构组成,然后对基于偏振图像处理的目标特征提取过程进行了分析,并对偏振图像像质评价方法以及图像融合、分割和特征提取方法进行了研究。最后给出了国外基于偏振成像目标检测技术的应用研究情况,并指出了该技术在军事领域中的应用价值。     

Image simulation of geometric targets for spaceborne syntheticaperture radar

A technique of synthetic aperture radar (SAR) image simulation is proposed. The method is based upon embedding of simulated man-made targets in a real background taken among the scenes captured by a spaceborne SAR. Scenes observed at 30 and 3 cm wavelength are used and the target dimensions are large enough with respect to the wavelength in order to compute the radar cross section from high frequency approximation techniques. Once the simulated image of the target is computed, the embedding process is performed bearing in mind the problems involved with this particular process. Although the simulation possibilities are restricted due to the simplifying assumptions, the results are sufficiently realistic to allow for the study of the detectability of a particular man-made target in a SAR environment     

PicToSeek: combining color and shape invariant features for imageretrieval

We aim at combining color and shape invariants for indexing and retrieving images. To this end, color models are proposed independent of the object geometry, object pose, and illumination. From these color models, color invariant edges are derived from which shape invariant features are computed. Computational methods are described to combine the color and shape invariants into a unified high-dimensional invariant feature set for discriminatory object retrieval. Experiments have been conducted on a database consisting of 500 images taken from multicolored man-made objects in real world scenes. From the theoretical and experimental results it is concluded that object retrieval based on composite color and shape invariant features provides excellent retrieval accuracy. Object retrieval based on color invariants provides very high retrieval accuracy whereas object retrieval based entirely on shape invariants yields poor discriminative power. Furthermore, the image retrieval scheme is highly robust to partial occlusion, object clutter and a change in the object's pose. Finally, the image retrieval scheme is integrated into the PicToSeek system on-line at http://www.wins.uva.nl/research/isis/PicToSeek/ for searching images on the World Wide Web.     

Visual servoing on unknown objects

We study visual servoing in a framework of detection and grasping of unknown objects. Classically, visual servoing has been used for applications where the object to be servoed on is known to the robot prior to the task execution. In addition, most of the methods concentrate on aligning the robot hand with the object without grasping it. In our work, visual servoing techniques are used as building blocks in a system capable of detecting and grasping unknown objects in natural scenes. We show how different visual servoing techniques facilitate a complete grasping cycle.     

非合作区域星载遥感图像的车辆目标检测

针对非合作区域遥感图像中复杂背景下车辆目标检 测困难的问题, 提出一种改进形态学重建的车辆目标检测方法。首先,利用遥感图像的近红外和红色波段的 信息,获取场 景的归一化植被指数(NDVI),去除植被背景的干扰;其次,设计方向模板, 对全色图像进行滤波,使人 造物背景得以完整保留,并生成标记图像,利用标记图像重建得到人造物背景;最后,利用 原图和重建背 景之间的差异,进行车辆检测,有效消除了复杂背景的干扰。将本文提出的方法应用于实际 遥感图像的车 辆目标检测,结果表明,检测效果好,鲁棒性强,无需先验信息,可用于大幅面遥感 图像的车辆目标自动提取。     

Reconstructing images from their most singular fractal manifold

Real-world images are complex objects, difficult to describe but at the same time possessing a high degree of redundancy. A previous on the statistical properties of natural images reveals that natural images can be viewed through different partitions which are essentially fractal in nature. One particular fractal component, related to the most singular (sharpest) transitions in the image, seems to be highly informative about the whole scene. We show how to decompose the image into their fractal components. We see that the most singular component is related to (but not coincident with) the edges of the objects present in the scenes. We propose a new, simple method to reconstruct the image with information contained in that most informative component. We see that the quality of the reconstruction is strongly dependent on the capability to extract the relevant edges in the determination of the most singular set. We discuss the results from the perspective of coding, proposing this method as a starting point for future developments.     

Voting-based separation of diffuse and specular pixels

A fast and efficient algorithm to separate diffuse and specular pixels in a single image without explicit colour segmentation or any prior information is proposed. It is applicable to highly textured objects/scenes under arbitrary illumination conditions. Experimental results show accurate separation results for natural images.