Population-based continuous optimization, probabilistic modelling and mean shift

Evolutionary algorithms perform optimization using a population of sample solution points. An interesting development has been to view population-based optimization as the process of evolving an explicit, probabilistic model of the search space. This paper investigates a formal basis for continuous, population-based optimization in terms of a stochastic gradient descent on the Kullback-Leibler divergence between the model probability density and the objective function, represented as an unknown density of assumed form. This leads to an update rule that is related and compared with previous theoretical work, a continuous version of the population-based incremental learning algorithm, and the generalized mean shift clustering framework. Experimental results are presented that demonstrate the dynamics of the new algorithm on a set of simple test problems.

Can mean shift trackers perform better?

Many tracking algorithms have difficulties dealing with occlusions and background clutters, and consequently don't converge to an appropriate solution. Tracking based on the mean shift algorithm has shown robust performance in many circumstances but still fails e.g. when encountering dramatic intensity or colour changes in a pre-defined neighbourhood. In this paper, we present a robust tracking algorithm that integrates the advantages of mean shift tracking with those of tracking local invariant features. These features are integrated into the mean shift formulation so that tracking is performed based both on mean shift and feature probability distributions, coupled with an expectation maximisation scheme. Experimental results show robust tracking performance on a series of complicated real image sequences. © 2010 IEEE.

Innervation zone shift at different levels of isometric contraction in the biceps brachii muscle

Experiments were carried out to examine whether innervation zone (IZ) location remains stable at different levels of isometric contraction in the biceps brachii muscle (BB), and to determine how the proximity of the IZ affects common surface electromyography (sEMG) parameters. Twelve subjects performed maximal (MVC) and submaximal voluntary isometric contractions at 10%, 20%, 30%, 40%, 50% and 75% of MVC. sEMG signals were recorded with a 13 rows  5 columns grid of electrodes from the short head of BB. The IZ shifted in the proximal direction by up to 2.4 cm, depending upon the subject and electrode column. The mean shift of all the columns was 0.6 ± 0.4 cm (10% vs. 100% MVC, P < 0.001). This shift biased the average values of mean frequency (+21.8 ± 9.9 Hz, P < 0.001), root mean square (0.16 ± 0.15 mV, P < 0.05) and conduction velocity (1.15 ± 0.93 m/s, P < 0.01) in the channels immediately proximal to the IZ. The shift in IZ could be explained by shortening of the muscle fibers, and thus lengthening of the (distal) tendon due to increasing force. These results underline the importance of individual investigation of IZ locations before the placement of sEMG electrodes, even in isometric contractions.

An integrated approach for precise road reconstruction from aerial imagery and LiDAR data

Accurate and detailed road models play an important role in a number of geospatial applications, such as infrastructure planning, traffic monitoring, and driver assistance systems. In this thesis, an integrated approach for the automatic extraction of precise road features from high resolution aerial images and LiDAR point clouds is presented. A framework of road information modeling has been proposed, for rural and urban scenarios respectively, and an integrated system has been developed to deal with road feature extraction using image and LiDAR analysis. For road extraction in rural regions, a hierarchical image analysis is first performed to maximize the exploitation of road characteristics in different resolutions. The rough locations and directions of roads are provided by the road centerlines detected in low resolution images, both of which can be further employed to facilitate the road information generation in high resolution images. The histogram thresholding method is then chosen to classify road details in high resolution images, where color space transformation is used for data preparation. After the road surface detection, anisotropic Gaussian and Gabor filters are employed to enhance road pavement markings while constraining other ground objects, such as vegetation and houses. Afterwards, pavement markings are obtained from the filtered image using the Otsu's clustering method. The final road model is generated by superimposing the lane markings on the road surfaces, where the digital terrain model (DTM) produced by LiDAR data can also be combined to obtain the 3D road model. As the extraction of roads in urban areas is greatly affected by buildings, shadows, vehicles, and parking lots, we combine high resolution aerial images and dense LiDAR data to fully exploit the precise spectral and horizontal spatial resolution of aerial images and the accurate vertical information provided by airborne LiDAR. Objectoriented image analysis methods are employed to process the feature classiffcation and road detection in aerial images. In this process, we first utilize an adaptive mean shift (MS) segmentation algorithm to segment the original images into meaningful object-oriented clusters. Then the support vector machine (SVM) algorithm is further applied on the MS segmented image to extract road objects. Road surface detected in LiDAR intensity images is taken as a mask to remove the effects of shadows and trees. In addition, normalized DSM (nDSM) obtained from LiDAR is employed to filter out other above-ground objects, such as buildings and vehicles. The proposed road extraction approaches are tested using rural and urban datasets respectively. The rural road extraction method is performed using pan-sharpened aerial images of the Bruce Highway, Gympie, Queensland. The road extraction algorithm for urban regions is tested using the datasets of Bundaberg, which combine aerial imagery and LiDAR data. Quantitative evaluation of the extracted road information for both datasets has been carried out. The experiments and the evaluation results using Gympie datasets show that more than 96% of the road surfaces and over 90% of the lane markings are accurately reconstructed, and the false alarm rates for road surfaces and lane markings are below 3% and 2% respectively. For the urban test sites of Bundaberg, more than 93% of the road surface is correctly reconstructed, and the mis-detection rate is below 10%.

Tracking-based moving object detection

We present a novel approach for multi-object detection in aerial videos based on tracking. The proposed method mainly involves three steps. Firstly, the spatial-temporal saliency is employed to detect moving objects. Secondly, the detected objects are tracked by mean shift in the subsequent frames. Finally, the saliency results are fused with the weight map generated by tracking to get refined detection results, and in turn the modified detection results are used to update the tracking models. The proposed algorithm is evaluated on VIVID aerial videos, and the results show that our approach can reliably detect moving objects even in challenging situations. Meanwhile, the proposed method can process videos in real time, without the effect of time delay.

Kernel-based spatial-color modeling for fast moving object tracking

Visual tracking has been a challenging problem in computer vision over the decades. The applications of Visual Tracking are far-reaching, ranging from surveillance and monitoring to smart rooms. Mean-shift (MS) tracker, which gained more attention recently, is known for tracking objects in a cluttered environment and its low computational complexity. The major problem encountered in histogram-based MS is its inability to track rapidly moving objects. In order to track fast moving objects, we propose a new robust mean-shift tracker that uses both spatial similarity measure and color histogram-based similarity measure. The inability of MS tracker to handle large displacements is circumvented by the spatial similarity-based tracking module, which lacks robustness to object's appearance change. The performance of the proposed tracker is better than the individual trackers for tracking fast-moving objects with better accuracy.

Hierarchical Clustering Algorithm for Land Cover Mapping Using Satellite Images

This paper presents hierarchical clustering algorithms for land cover mapping problem using multi-spectral satellite images. In unsupervised techniques, the automatic generation of number of clusters and its centers for a huge database is not exploited to their full potential. Hence, a hierarchical clustering algorithm that uses splitting and merging techniques is proposed. Initially, the splitting method is used to search for the best possible number of clusters and its centers using Mean Shift Clustering (MSC), Niche Particle Swarm Optimization (NPSO) and Glowworm Swarm Optimization (GSO). Using these clusters and its centers, the merging method is used to group the data points based on a parametric method (k-means algorithm). A performance comparison of the proposed hierarchical clustering algorithms (MSC, NPSO and GSO) is presented using two typical multi-spectral satellite images - Landsat 7 thematic mapper and QuickBird. From the results obtained, we conclude that the proposed GSO based hierarchical clustering algorithm is more accurate and robust.

Quasi-based hierarchical clustering for land cover mapping using satellite images

This paper presents an improved hierarchical clustering algorithm for land cover mapping problem using quasi-random distribution. Initially, Niche Particle Swarm Optimization (NPSO) with pseudo/quasi-random distribution is used for splitting the data into number of cluster centers by satisfying Bayesian Information Criteria (BIC). Themain objective is to search and locate the best possible number of cluster and its centers. NPSO which highly depends on the initial distribution of particles in search space is not been exploited to its full potential. In this study, we have compared more uniformly distributed quasi-random with pseudo-random distribution with NPSO for splitting data set. Here to generate quasi-random distribution, Faure method has been used. Performance of previously proposed methods namely K-means, Mean Shift Clustering (MSC) and NPSO with pseudo-random is compared with the proposed approach - NPSO with quasi distribution(Faure). These algorithms are used on synthetic data set and multi-spectral satellite image (Landsat 7 thematic mapper). From the result obtained we conclude that use of quasi-random sequence with NPSO for hierarchical clustering algorithm results in a more accurate data classification.

Integration of speckle de-noising and image segmentation using synthetic aperture radar image for flood extent extraction

Flood is one of the detrimental hydro-meteorological threats to mankind. This compels very efficient flood assessment models. In this paper, we propose remote sensing based flood assessment using Synthetic Aperture Radar (SAR) image because of its imperviousness to unfavourable weather conditions. However, they suffer from the speckle noise. Hence, the processing of SAR image is applied in two stages: speckle removal filters and image segmentation methods for flood mapping. The speckle noise has been reduced with the help of Lee, Frost and Gamma MAP filters. A performance comparison of these speckle removal filters is presented. From the results obtained, we deduce that the Gamma MAP is reliable. The selected Gamma MAP filtered image is segmented using Gray Level Co-occurrence Matrix (GLCM) and Mean Shift Segmentation (MSS). The GLCM is a texture analysis method that separates the image pixels into water and non-water groups based on their spectral feature whereas MSS is a gradient ascent method, here segmentation is carried out using spectral and spatial information. As test case, Kosi river flood is considered in our study. From the segmentation result of both these methods are comprehensively analysed and concluded that the MSS is efficient for flood mapping.

A new distance for scale-invariant 3D shape recognition and registration

This paper presents a method for vote-based 3D shape recognition and registration, in particular using mean shift on 3D pose votes in the space of direct similarity transforms for the first time. We introduce a new distance between poses in this spacethe SRT distance. It is left-invariant, unlike Euclidean distance, and has a unique, closed-form mean, in contrast to Riemannian distance, so is fast to compute. We demonstrate improved performance over the state of the art in both recognition and registration on a real and challenging dataset, by comparing our distance with others in a mean shift framework, as well as with the commonly used Hough voting approach. © 2011 IEEE.

Scale-invariant vote-based 3D recognition and registration from point clouds

This chapter presents a method for vote-based 3D shape recognition and registration, in particular using mean shift on 3D pose votes in the space of direct similarity transformations for the first time. We introduce a new distance between poses in this spacethe SRT distance. It is left-invariant, unlike Euclidean distance, and has a unique, closed-form mean, in contrast to Riemannian distance, so is fast to compute. We demonstrate improved performance over the state of the art in both recognition and registration on a (real and) challenging dataset, by comparing our distance with others in a mean shift framework, as well as with the commonly used Hough voting approach. © 2013 Springer-Verlag Berlin Heidelberg.

视频监控中若干关键问题的研究

随着网络带宽、计算机处理能力和存储容量的迅速提高，以及各种视频信息处理技术的出现，全程数字化、网络化的视频监控系统优势愈发明显。其高度的开放性、集成性和灵活性为视频监控系统和设备的整体性能提升创造了必要的条件；同时也为整个安防产业的发展提供了更加广阔的发展空间，崭新的应用模式和市场机遇不断涌现。视频监控系统过程向着大型、连续、综合化发展，形成了复杂监控过程，监测控制的要求越来越高，需要更高性能的系统和采用更优秀的控制手段，面临着不能用传统方法解决的新问题。本文概述了目前视频监控中面临的挑战，简要介绍了与视频监控相关的研究领域和研究现状，研究了视频监控中若干亟待解决的问题，主要取得了以下几个方面的研究成果： 第一，提出了基于AdaBoost的改进的人脸检测算法，针对AdaBoost算法的训练速度慢的问题，提出了基于阈值控制的训练方法；同时研究了AdaBoost算法人脸检测方法，利用肤色模型检测人脸区域，并对颜色模型进行了光照补偿。实验结果表明本文的算法具有较好的检测结果。 第二，提出了基于Canny算法的一般目标检测算法，提出了改进的Canny边缘检测算法，研究了Canny算法中噪声抑制的方法，采用改进中心加权的MTM算法有效的抑制噪声。针对Canny检测算法中阈值设置的问题，提出改进的Canny阈值补偿的方法。实验结果表明，改进的Canny算法相比原算法具有更好的目标检测性能。 第三，提出了一种基于均值漂移（Mean Shift）的改进的目标跟踪算法，通过搜索窗口带宽的计算，加权背景信息以及卡尔曼滤波器建模改进了跟踪算法，避免了均值漂移算法中的一些关键问题。对比实验结果表明，本文的改进方法相比原算法具有较好的性能。 第四，研究了视频监控中基于可扩展视频编码(SVC)的技术。首先讨论了视频监控中采用可扩展视频编码(SVC)的优势，探讨了视频监控中采用可扩展视频编码(SVC)的框架。然后针对于视频质量评估问题，设计并实现了基于可扩展视频编码(SVC)的视频质量评估系统Evalvid-SVC，研究了基于可扩展视频编码(SVC)的视频质量评估。 第五，研究并实现了视频数据安全传输技术。提出了基于Diameter的统一认证方案。任何用户想要获取视频资源，都必须通过AAA子系统的认证和授权，授予合法用户以特定的方式使用资源。另外，为了保证监控数据从监控前端安全地传送到视频监控客户端，本文提出了一种有效的保证视频数据安全传输的方案。设计的数据加密算法应用DES算法对前端设备采集到的音视频数据进行加密，并通过定时更新和RSA加密的方式保护和传输DES密钥。

基于融合模板的均值移动头部跟踪算法

针对被跟踪头部目标特征状态随时间变化而与参考模板不匹配的问题,提出一种基于融合参考模板的均值移动算法,即将被跟踪目标在不同状态下所呈现出的不同特征使用采样的方法进行融合,如将头部跟踪过程中正面的肤色信息和后面的发色信息进行融合,从而形成一个包含不同特征的参考模板.在跟踪过程中,使用该融合模板可以有效地克服由被跟踪目标特征变化导致跟踪失败而不能实现头部连续跟踪的问题.通过头部跟踪实验可以看出,该算法实现了复杂环境下的具有360°旋转的头部跟踪,并且在一定程度上提高了跟踪精度.

基于直方图插值的均值移动小尺寸目标跟踪算法

小尺寸目标跟踪是视觉跟踪中的难题。该文首先指出了均值移动小尺寸目标跟踪算法中的两个主要问题:算法跟踪中断和丢失跟踪目标。然后,论文给出了相应的解决方法。对传统Parzen窗密度估计法加以改进,并用于对候选目标区域的直方图进行插值处理,较好地解决了算法跟踪中断问题。论文采用Kullback-Leibler距离作为目标模型和候选目标之间的新型相似性度量函数,并推导了其相应的权值和新位置计算公式,提高了算法的跟踪精度。多段视频序列的跟踪实验表明,该文提出的算法可以有效地跟踪小尺寸目标,能够成功跟踪只有6×12个像素的小目标,跟踪精度也有一定提高。

基于直方图处理和新型相似性度量函数的小尺寸目标跟踪算法

小尺寸目标跟踪是视觉跟踪中的难题。本文使用均值移动算法跟踪小尺寸目标。论文首先分析了基于均值移动的小尺寸目标跟踪算法的两个主要问题:跟踪算法中断和跟踪目标丢失。然后,论文在这两个方面对小尺寸目标跟踪算法进行改进。给出了一种新的直方图单元编号方法,使包含目标颜色分量的直方图单元分布得更为集中紧凑。当候选目标与目标模型不匹配时,给出一种平滑算法来处理候选目标的直方图。论文提出一种新的相似性度量函数,推导了相应的权值计算公式,在此基础上建立了基于均值移动的目标跟踪算法。多段真实场景视频序列的跟踪实验表明,本文提出的算法可以有效地跟踪小尺寸目标,跟踪精度也有一定提高。