Characterization of slice-based H.264/AVC encoded video traffic using token buckets

An explicit slice-based mode type selection scheme for use in H.264/AVC has recently been developed, which reduces the burstiness effect of standard frame-based H.264/AVC by breaking up the Group of Picture structure. In this paper, slice-based encoded video streams are characterized using the token bucket traffic model and compared to standard frame-based encoded streams. Both lossless, loss bounded and delay bounded token bucket models are investigated and the high quantiles are found for the amount of loss. Loss above the amount given by the high quantiles will happen only with a very small probability. It is shown that the reduced burstiness for the slice-based video encoding leads to lower token bucket parameters compared to frame-based video encoding for a stream without scene changes, and a larger reduction in the token bucket parameters compared to the frame-based video encoding when a small amount of delay or loss is allowed for the stream with scene changes. Next, reshuffling of the frames of the video streams with scene changes is employed to better understand the effects of long-range dependence on the token bucket parameters. Only small effects are found from reshuffling the scenes, but reshuffling of the frames inside the scenes leads to lower token bucket parameters. Finally, an approach to estimate the parameters for the token bucket model using simple characteristics of the slice-based stream is developed.     

3-D model localization using high-resolution reconstruction ofmonocular image sequences

In this paper, we present a complete system for the recognition and localization of a three-dimensional (3-D) model from a sequence of monocular images with known motion. The originality of this system is twofold. First, it uses a purely 3-D approach, starting from the 3-D reconstruction of the scene and ending by the 3-D matching of the model. Second, unlike most monocular systems, we do not use token tracking to match successive images. Rather, subpixel contour matching is used to recover more precisely complete 3-D contours. In contrast with the token tracking approaches, which yield a representation of the 3-D scene based on disconnected segments or points, this approach provides us with a denser and higher level representation of the scene. The reconstructed contours are fused along successive images using a simple result derived from the Kalman filter theory. The fusion process increases the localization precision and the robustness of the 3-D reconstruction. Finally, corners are extracted from the 3-D contours. They are used to generate hypotheses of the model position, using a hypothesize-and-verify algorithm that is described in detail. This algorithm yields a robust recognition and precise localization of the model in the scene. Results are presented on infrared image sequences with different resolutions, demonstrating the precision of the localization as well as the robustness and the low computational complexity of the algorithms.     

一种新的基于模板匹配的车型识别

为提高当前车型检测在交通场景下的实时性和准确性,提出了一种新的基于模板匹配的车型识别方法。首先,基于车辆区域的显著性设计了非均匀采点,然后采用梯度量化,二进制图像,线性化内存等手段实现模板的并行化匹配,最后通过kmeans聚类产生多层次的车型模板索引,实现快速查表的车型匹配算法。实验表明,该算法能实时高效地实现交通场景下的车型识别。     

基于激光雷达及特征匹配的室内场景设计重建

针对当前采集到的场景出现缺损导致场景设计重建效率较低、准确性较差的问题,提出基于激光雷达扫描及关键点特征匹配的室内场景设计重建。通过Deleta 2B型激光雷达传感器直接采集室内场景图像,利用最小二乘拟合滤波算法曲线拟合室内场景图像的灰度值,根据图像特征向量匹配室内场景图像的关键点特征,通过德洛内三角化算法串联室内场景点云数据,构造三维网格模型,通过wallis滤波器提升重建辨识度,实现室内场景的重建。实验结果表明,该方法重建后的室内图像可辨识度较高,信噪比最高可达到498dB,室内场景图像覆盖率均在90以上,重建场景的结构相似度接近1,次卧室场景重建时间为592 h。     

基于形状及空间关系的场景相似性检索

为解决空间数据检索效率低、准确性差的问题,本文提出由空间对象形状描述模型、空间关系描述模型、场景相似性自适应计算模型构成的场景相似性检索方法.空间对象形状描述模型精准检索满足样例对象形状约束的数据库对象,提高空间对象形状的识别精度.空间关系描述模型检索满足样例场景关系约束的数据库场景,提高空间关系的描述精度.场景相似性自适应计算模型对满足形状及关系约束的完全匹配、局部匹配场景进行打分、排序,增加检索结果相似性打分的合理性.模拟场景、真实场景的实验表明本场景相似性检索方法具有良好的检索性能.     

基于空间金字塔匹配的单目热成像深度估计

热成像能够反映场景的温度分布,对热成像进行深度估计,可以恢复出场景的三维温度场,在故障诊断、夜视导航等领域具有重要意义。本文提出一种面向单目热成像深度估计的非参深度采样方法。为了克服热像纹理缺乏、轮廓模糊的缺点,使用了空间金字塔匹配(Spatial Pyramid Matching,SPM)来进行热像的特征分析。首先,基于SPM特征匹配,从数据库中筛选出与待估计深度的热像具有相似场景的候选热像;然后,采用SIFT Flow变形算法对候选热像的深度图进行采样,并将深度信息传递给待估计的热像。实验结果表明,这种方法能够对单目热像进行有效的深度估计,与同类算法相比具有明显优势。     

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.     

Determining scale and sea state from water video.

In most image processing and computer vision applications, real-world scale can only be determined when calibration information is available. Dynamic scenes further complicate most situations. However, some types of dynamic scenes provide useful information that can be used to recover real-world scale. In this paper, we focus on ocean scenes and propose a method for finding sizes in real-world units and the sea state from an uncalibrated camera. Fourier transforms in the space and time dimensions yield spatial and temporal frequency spectra. For water waves, the dispersion relation defines a square relationship between the wavelength and period of a wave. Our method applies this dispersion relation to recover the real-world scale of an ocean sequence. The sea state--including the peak wavelength and period, the wind speed that generated the waves, and the wave heights--is also determined from the frequency spectrum of the sequence combined with stochastic oceanography models. The process is demonstrated on synthetic and real sequences, validating the results with known scene geometry. This has wide applications in port monitoring and coastal surveillance.     

A 100-mega-access per second matching memory for a data-drivenmicroprocessor

A high-throughput matching memory (MM) for a data-driven microprocessor is discussed. An MM can be constructed using a hashing memory. However, one of the biggest problems with hashing memory is the necessity for selective processing whenever hashed address conflicts occur. To eliminate this problem, the MM incorporated a small amount of associative memory (32 words×50 b) as well as the hashing memory (512 words×42 b). The matching operation is subdivided into three pipeline stages, all controlled by the elastic pipeline scheme. With this structure, an MM with a high throughput of 100-mega-access/s MM can be realized     

基于特征点匹配和广义Hough变换的扩展目标跟踪方法

针对扩展目标在跟踪过程中存在的因旋转、尺度变化和目标部分区域离开视场时引起的漂移等问题,提出了一种在特征点匹配的基础上结合广义Hough变换的扩展目标跟踪方法.采用ORB特征处理相邻两帧图像,得到初始匹配特征点,然后再用随机采样一致处理筛选出匹配正确率高的特征点作为基础特征点,利用这些基础特征点进行广义Hough变换,提取出一个相对位置固定的跟踪点对目标进行跟踪.实验表明,该方法具有能保持跟踪点的相对位置固定的特性.     

基于单目相机与里程计的室内机器人定位研究

昂贵的定位传感器限制了室内服务机器人相关技术在实际生活中的发展.室内服务机器人定位技术的发展趋势之一是使用价格低廉的传感器.因此,基于单目相机与轮式里程计两种价格低廉的传感器,提出一种适用于室内动态环境的机器人定位解决方案.该方案将相机朝向天花板,以减少室内动态环境下信息多变带来的误差,改变特征匹配策略以提升重复纹理场...     

A contour-based approach to multisensor image registration

Image registration is concerned with the establishment of correspondence between images of the same scene. One challenging problem in this area is the registration of multispectral/multisensor images. In general, such images have different gray level characteristics, and simple techniques such as those based on area correlations cannot be applied directly. On the other hand, contours representing region boundaries are preserved in most cases. The authors present two contour-based methods which use region boundaries and other strong edges as matching primitives. The first contour matching algorithm is based on the chain-code correlation and other shape similarity criteria such as invariant moments. Closed contours and the salient segments along the open contours are matched separately. This method works well for image pairs in which the contour information is well preserved, such as the optical images from Landsat and Spot satellites. For the registration of the optical images with synthetic aperture radar (SAR) images, the authors propose an elastic contour matching scheme based on the active contour model. Using the contours from the optical image as the initial condition, accurate contour locations in the SAR image are obtained by applying the active contour model. Both contour matching methods are automatic and computationally quite efficient. Experimental results with various kinds of image data have verified the robustness of the algorithms, which have outperformed manual registration in terms of root mean square error at the control points.     

Georegistration of Landsat data via robust matching of multiresolution features

The goal of the project described in this paper is to build a prototype of an operational system, which will provide registration within subpixel accuracy of multitemporal Landsat data, acquired by either Landsat-5 or Landsat-7 Thematic Mapper instruments. Integrated within an automated mass processing system for Landsat data, the input to our registration system consists of scenes that have been geometrically and radiometrically corrected, as well as preprocessed for detection of clouds and cloud shadows. Such preprocessed scenes are then georegistered relative to a database of Landsat chips. This paper describes the entire registration process, including the use of landmark chips, feature extraction performed by an overcomplete wavelet representation, and feature matching using statistically robust techniques. Knowing the approximate longitudes and latitudes or the UTM coordinates of the four corners of each incoming scene, a subset of the chips that represent landmarks included in the scene are selected to perform the registration. For each of these selected landmark chips, a corresponding window is extracted from the incoming scene, and each chip-window pair is registered using a robust wavelet feature-matching methodology. Based on the transformations from the chip-window pairs, a global transformation is then computed for the entire scene using a variant of a robust least median of squares estimator. Empirical results of this registration process, which provided subpixel accuracy for several multitemporal scenes from different study areas, are presented and discussed.     

基于支持向量机的长波红外目标分类识别算法

红外图像的分辨率低和色彩单一,但由于红外设备的全天候工作特点,因而在某些场景具有重要作用.本文采用一种基于支持向量机(support vector machine,SVM)的长波红外目标图像分类识别的算法,在一幅图像中,将算法提取的边缘特征和纹理特征作为目标的识别特征,输入到支持向量机,最后输出目标的类别.在实验中,设...     

基于MSA直方图的景象匹配算法研究

提出了一种基于MSA直方图的景象匹配算法,用来解决景象匹配辅助导航系统中,由于拍摄的实时图背景复杂、存在畸变、易受遮挡等因素所带来的匹配困难。该算法利用灰度信息及其空间分布信息,分别计算目标区域及模板的MSA直方图,并计算匹配度,采用金字塔算法及中途停止策略进行搜索,对目标进行识别和定位。用cameraman图像、人脸图像和实际卫星拍摄图像进行了实验,实验结果表明：该算法对仿射变换、轻微透视投影变换、噪声、遮挡及视角变化具有鲁棒性,且比灰度直方图具有更好的性能,在实际拍摄的图像中能对目标进行准确的识别和定位。     

郊外低对比度图像线特征提取算法研究

景象匹配系统由于其精度高的特点,广泛应用于各种远程精确末制导系统。由于成像时的自然条件变化,使得基准图与实时图之间存在较大的对比度差别,增大了匹配难度。本文基于传统的特征提取,针对具有人工景物的郊外景象,利用Hough变换和数学形态学进行线特征的改进,从而提升了匹配质量。通过对实际情景图像的仿真分析,验证了该算法的有效性。     

基于Hausdorff距离的自动目标识别算法的研究

红外自动目标识别是智能化图像处理及应用开发的方法.由于传统的图像匹配方法受到诸如景物的遮挡、背景和噪声的影响比较大,并且需要建立模板与图像间的对应关系,因而使实际图像的匹配变得困难.为了克服上述缺陷,以便快速地进行图像匹配,提出了一种基于Hausdorff距离的自动目标识别算法.该算法不仅加快了匹配过程,提高了抗噪性能,而且能准确匹配遮挡图像.实验结果证明,该方法是正确有效的.     

Efficient image gradient based vehicle localization

This paper reports novel algorithms for the efficient localization and recognition of traffic in traffic scenes. The algorithms eliminate the need for explicit symbolic feature extraction and matching. The pose and class of an object is determined by a form of voting and one-dimensional (1-D) correlations based directly on image gradient data, which can be computed "on the fly." The algorithms are therefore very well suited to real-time implementation. The algorithms make use of two a priori sources of knowledge about the scene and the objects expected: (1) the ground-plane constraint and (2) the fact that the overall shape of road vehicles is strongly rectilinear. Additional efficiency is derived from making the weak perspective assumption. These assumptions are valid in the road traffic application domain. The algorithms are demonstrated and tested using routine outdoor traffic images. Success with a variety of vehicles in several traffic scenes demonstrates the efficiency and robustness of context-based image understanding in road traffic scene analysis. The limitations of the algorithms are also addressed.     

A review of speckle filtering in the context of estimation theory

Speckle filter performance depends strongly on the speckle and scene models used as the basis for filter development. These models implicitly incorporate certain assumptions about speckle, scene, and observed signals. In this study, the multiplicative and the product speckle models, which have been used for the development of most of the well-known filters, are analyzed, and their implicit assumptions with regard to the stationarity-nonstationarity nature of speckle are discussed. This leads to the definition of two categories of speckle filters: the stationary and the nonstationary multiplicative speckle model filters. The various approximate models used for the multiplicative speckle noise model are assessed as functions of speckle and scene characteristics to derive the requirements on scene signal variations for the validity of both the stationary and nonstationary multiplicative speckle models. Speckle filtering is then studied in the context of estimation theory, so as to develop a procedure for speckle filtering. It is shown that speckle filtering can be effective only in locally stationary scenes. Regions in which the signals are not stationary have to be filtered separately using a priori scene templates for the best matching of nonstationary scene features. The use of multiresolution techniques is crucial for accurate estimation of filter parameters. Under the guidance of the speckle filtering procedure, structural-multiresolution versions of the Lee (1980) and Frost et al. (1982) filters are developed for optimum application of these filters in the context of nonstationary scene signals.