Motion gradient vector flow: an external force for tracking rolling leukocytes with shape and size constrained active contours

Recording rolling leukocyte velocities from intravital microscopic video imagery is a critical task in inflammation research and drug validation. Since manual tracking is excessively time consuming, an automated method is desired. This paper illustrates an active contour based automated tracking method, where we propose a novel external force to guide the active contour that takes the hemodynamic flow direction into account. The construction of the proposed force field, referred to as motion gradient vector flow (MGVF), is accomplished by minimizing an energy functional involving the motion direction, and the image gradient magnitude. The tracking experiments demonstrate that MGVF can be used to track both slow- and fast-rolling leukocytes, thus extending the capture range of previously designed cell tracking techniques.     

Active contours for video object tracking using region, boundary and shape information

In this paper, we propose a robust model for tracking in video sequences with non-static backgrounds. The object boundaries are tracked on each frame of the sequence by minimizing an energy functional that combines region, boundary and shape information. The region information is formulated by minimizing the symmetric Kullback–Leibler (KL) distance between the local and global statistics of the objects versus the background. The boundary information is formulated using a color and texture edge map of the video frames. The shape information is calculated adaptively to the dynamic of the moving objects and permits tracking that is robust to background distractions and occlusions. Minimization of the energy functional is implemented using the level set method. We show the effectiveness of the approach for object tracking in color, infrared (IR), and fused color-infrared sequences.     

一种自循迹小车循迹方案优化系统

自循迹小车沿不同特征的曲线轨道运行,运行轨迹直接决定了小车能否快捷到达目的地.本文以高清摄像头捕获小车运行坐标,再利用LabVIEW根据坐标数组绘制运行轨迹,根据背景差分法对比轨道图和实际小车运行轨迹图,实现快速获得当前曲线特征的轨道对应的PID控制参数.     

Grayscale level connectivity: theory and applications

A novel notion of connectivity for grayscale images is introduced, defined by means of a binary connectivity assigned at image-level sets. In this framework, a grayscale image is connected if all level sets below a prespecified threshold are connected. The proposed notion is referred to as grayscale level connectivity and includes, as special cases, other well-known notions of grayscale connectivity, such as fuzzy grayscale connectivity and grayscale blobs. In contrast to those approaches, the present framework does not require all image-level sets to be connected. Moreover, a connected grayscale object may contain more than one regional maximum. Grayscale level connectivity is studied in the rigorous framework of connectivity classes. The use of grayscale level connectivity in image analysis applications, such as object extraction, image segmentation, object-based filtering, and hierarchical image representation, is discussed and illustrated.     

Vessel boundary tracking for intravital microscopy via multiscale gradient vector flow snakes

Due to movement of the specimen, vasodilation, and intense clutter, the intravital location of a vessel boundary from video microscopy is a difficult but necessary task in analyzing the mechanics of inflammation and the structure of the microvasculature. This paper details an active contour model for vessel boundary detection and tracking. In developing the method, two innovations are introduced. First, the B-spline model is combined with the gradient vector flow (GVF) external force. Second, a multiscale gradient vector flow (MSGVF) is employed to elude clutter and to reliably localize the vessel boundaries. Using synthetic experiments and video microscopy obtained via transillumination of the mouse cremaster muscle, we demonstrate that the MSGVF approach is superior to the fixed-scale GVF approach in terms of boundary localization. In each experiment, the fixed scale approach yielded at least a 50% increase in root mean squared error over the multiscale approach. In addition to delineating the vessel boundary so that cells can be detected and tracked, we demonstrate the boundary location technique enables automatic blood flow velocity computation in vivo.     

Formation of the Moving Analogy Target with C＋＋ Language in TV Tracking Equipment

Moving analogy target is a key component of the performance testing system in TV tracking equipment.A new method is provided to produce the moving analogy target whose motion speed,track,contrast and size can be varied.The video signal transformed by video switching card is used to test the performances of the electronic box of TV tracking equipment.These performances include minimal tracking contrast,minimal size of tracking target,maximal tracking speed and capture time.     

Robust contour tracking based on a coupling between geodesic active contours and conditional random fields

This paper presents a new general framework for contour tracking based on the synergy of two powerful segmentation tools, namely, spatial temporal conditional random fields (CRFs) and geodesic active contours (GACs). The contours of targets are modeled using a level set representation. The evolution of the level sets toward the target contours is formulated as one of the joint region-based (CRF) and boundary-based (GAC) segmentations under a unified Bayesian framework. A variational inference technique is used to solve this otherwise intractable inference problem, leading to approximate MAP solutions of both the new 3D spatial temporal CRF and the GAC model. The tracking result of the previous frame is used to initialize the curve in the current frame. Typical contour tracking problems are considered and experimental results are given to illustrate the robustness of the method against noise and its accurate performance in moving objects boundary localization.     

Tracking scale-space blobs for video description

We present a low-level estimation method based on simple features tracking as a first step to indexing audio-visual video sequences. Our method for video motion characterization uses scale-space blobs to track distinctive tokens, giving priority to temporal structure instead of spatial segmentation     

Mutual feedback scheme for face detection and tracking aimed at density estimation in demonstrations

Crowded video sequences, like those of demonstrations, offer an interesting challenge for object detection and tracking owing to their complexity: taken outdoors, often in different illumination conditions; showing faces not in frontal view, with perspective effects, complex background, etc. Tracking of individuals becomes a difficult task due to the high number of occlusions. The paper proposes a mutual feedback spatiotemporal detection strategy to tackle these problems. The system improves its efficiency thanks to a cooperative approach between spatial detection and temporal tracking. Spatial detection is based on skin colour classification and shape analysis by morphological tools. Temporal tracking is based on the analysis of the optical flow. The mutual feedback scheme benefits both spatial detection and temporal tracking. In order to deal with multiple occlusions, a graph-based tracking technique, which takes advantage of neighbourhood consistency, has been introduced.     

基于粒子滤波的行人跟踪研究及其性能分析

智能监控系统中的行人跟踪功能在很大程度上能够减轻工作人员的大量眼力工作,通过智能的方式自主地跟踪用户感兴趣的目标。在视频监控环境的基础上,利用改进的粒子滤波器,设计了一个行人跟踪系统,并使其具有系统性能分析的功能。通过使用Mean Shift算法进行目标位置的预估计和多特征融合的方式使该系统在视频背景复杂的情况下能够实现对行人的稳定跟踪。并在实际应用中,分析了在遮挡情况下,行人跟踪效果差的原因,并予以改进。在Visualc＋＋集成开发环境中,基于OpenCV和MATLAB编程实现了系统,能够实时地对目标进行跟踪,并对跟踪结果进行反馈。     

Performance measures for video object segmentation and tracking

We propose measures to evaluate quantitatively the performance of video object segmentation and tracking methods without ground-truth (GT) segmentation maps. The proposed measures are based on spatial differences of color and motion along the boundary of the estimated video object plane and temporal differences between the color histogram of the current object plane and its predecessors. They can be used to localize (spatially and/or temporally) regions where segmentation results are good or bad; and/or they can be combined to yield a single numerical measure to indicate the goodness of the boundary segmentation and tracking results over a sequence. The validity of the proposed performance measures without GT have been demonstrated by canonical correlation analysis with another set of measures with GT on a set of sequences (where GT information is available). Experimental results are presented to evaluate the segmentation maps obtained from various sequences using different segmentation approaches.     

Segmentation of Multi-Channel Image with Markov Random Field Based Active Contour Model

Segmentation is an important research area in image processing and computer vision. The essential purpose of research work is to achieve two goals: (i) partition the image into homogeneous regions based on certain properties, and (ii) accurately track the boundary for each region. In this study, we will present a novel framework that is designed to fulfill these requirements. Distinguished from most existing approaches, our method consists of three steps in the segmentation processes: global region segmentation, control points searching and object boundary tracking. In step one, we apply Markov Random Field (MRF) modeling to multi-channel images and propose a robust energy minimization approach to solve the multi-dimensional Markov Random Field. In step two, control points are found along the target region boundary by using a maximum reliability criterion and deployed to automatically initialize a Minimum Path Approach (MPA). Finally, the active contour evolves to the optimal solution in the fine-tuning process. In this study, we have applied this framework to color images and multi-contrast weighting magnetic resonance image data. The experimental results show encouraging performance. Moreover, the proposed approach also has the potential to deal with topology changing and composite object problems in boundary tracking.     

Fluid Vector Flow and Applications in Brain Tumor Segmentation

In this paper, we propose a new approach that we call the ldquofluid vector flowrdquo (FVF) active contour model to address problems of insufficient capture range and poor convergence for concavities. With the ability to capture a large range and extract concave shapes, FVF demonstrates improvements over techniques like gradient vector flow, boundary vector flow, and magnetostatic active contour on three sets of experiments: synthetic images, pediatric head MRI images, and brain tumor MRI images from the Internet brain segmentation repository.     

Action change detection in video using a bilateral spatial–temporal constraint

Action change detection in video aims to detect action discontinuity in video. The silhouettes-based features are desirable for action change detection. This paper studies the problem of silhouette-quality assessment. For that, a non-reference approach without the need for ground truth is proposed in this paper to evaluate the quality of silhouettes, by exploiting both the boundary contrast of the silhouettes in the spatial domain and the consistency of the silhouettes in the temporal domain. This is in contrast to that either only spatial information or only temporal information of silhouettes is exploited in conventional approaches. Experiments are conducted using artificially generated degraded silhouettes to show that the proposed approach outperforms conventional approaches to achieve more accurate quality assessment. Furthermore, experiments are performed to show that the proposed approach is able to improve the accuracy performance of conventional action change approaches in two human action video data-sets. The average runtime of the proposed approach for Weizmann action video data-set is 0.08 second for one frame using Matlab programming language. It is computationally efficient and potential to real-time implementations.     

Image stabilization and registration for tracking cells in the microvasculature

We propose a registration system to be used for tracking cells in intravital video microscopy that 1) stabilizes jitter-the undesired translational displacement of frames due to respiratory movement, etc., and 2) registers frames in a moving field of view (FOV) to allow for cell tracking over an extended range. For the first time, tracking of rolling leukocytes in vivo over a moving FOV is demonstrated. In a fixed FOV, stable background regions are located using a morphological approach. Template subregions are then selected from the stable regions and matched to corresponding locations in a reference frame. We show the effectiveness of the stabilization algorithm by using an active contour to track 15 leukocytes previously untrackable due to jitter. For 30 fixed FOV sequences containing rolling leukocytes, the resulting root-mean-square error (RMSE) is less than 0.5 microm. To align frames in a moving FOV, we present a modified correlation approach to estimate the common region between two consecutive fixed FOVs. We correlate the overlapping regions of the initial frame of the current fixed FOV and the final frame of the previous fixed FOV to register the images in the adjoining moving FOV. The RMSE of our moving FOV registration technique was less than 0.6 mmicrom. In 10 sequences from different venules, we were able to track 11 cells using an active contour approach over moving FOVs.     

Tracking leukocytes in vivo with shape and size constrained active contours

Inflammatory disease is initiated by leukocytes (white blood cells) rolling along the inner surface lining of small blood vessels called postcapillary venules. Studying the number and velocity of rolling leukocytes is essential to understanding and successfully treating inflammatory diseases. Potential inhibitors of leukocyte recruitment can be screened by leukocyte rolling assays and successful inhibitors validated by intravital microscopy. In this paper, we present an active contour or snake-based technique to automatically track the movement of the leukocytes. The novelty of the proposed method lies in the energy functional that constrains the shape and size of the active contour. This paper introduces a significant enhancement over existing gradient-based snakes in the form of a modified gradient vector flow. Using the gradient vector flow, we can track leukocytes rolling at high speeds that are not amenable to tracking with the existing edge-based techniques. We also propose a new energy-based implicit sampling method of the points on the active contour that replaces the computationally expensive explicit method. To enhance the performance of this shape and size constrained snake model, we have coupled it with Kalman filter so that during coasting (when the leukocytes are completely occluded or obscured), the tracker may infer the location of the center of the leukocyte. Finally, we have compared the performance of the proposed snake tracker with that of the correlation and centroid-based trackers. The proposed snake tracker results in superior performance measures, such as reduced error in locating the leukocyte under tracking and improvements in the percentage of frames successfully tracked. For screening and drug validation, the tracker shows promise as an automated data collection tool.     

Real-Time Video Compression Algorithm for Hadamard Transform Processing

A real-time digital video processor using Hadamard transform techniques to reduce video bandwidth is described. The processor can be programmed with different parameters to investigate various algorithms for bandwidth compression. The processor is also adaptive in that it can select different parameter sets to trade-off spatial resolution for temporal resolution in the regions of the picture that are moving. Algorithms used in programming the system are described along with results achieved at various levels of compression. The algorithms relate to spatial compression, temporal compression, and the adaptive selection of parameter sets.     

Group-based spatio-temporal video analysis and abstraction using wavelet parameters

In this paper, we present a spatio-temporal event-based approach to video signal analysis and abstraction employing wavelet transform features. The video signal is assumed to be a sequence of overlapping independent visual components called events, which typically are temporally overlapping compact functions that describe temporal evolution of a given set of the spatial parameters of the video signal. We utilize event-based temporal decomposition technique to resolve the overlapping arrangement of the video signal that is known to be one of the main concerns in video analysis via conventional frame-based schemes. In our method, a set of spatial parameters, extracted from the video, is expressed as a linear combination of a set of temporally overlapping compact functions, called events, through an optimization process. First, to reduce computational complexity, the video sequence is divided into overlapped groups. Next, Generalized Gaussian Density (GGD) parameters, extracted from 2D wavelet transform subbands, are used as the spatial parameters. Temporal decomposition is then applied to the GGD parameters, structured as a frame-based matrix of GGD vectors, to compute the event functions and associated orthogonal GGD parameters. Frames located at event centroids, which are much smaller in number than the number of frames in the original video, are taken as candidates for the keyframes that are selected based on a distance criterion in the feature space. Our contribution is that this still image video abstraction scheme does not need shot or cluster boundary detection, unlike current methods. Experimental results confirm the efficiency and accuracy of our approach.     

Robust online multi-target visual tracking using a HISP filter with discriminative deep appearance learning

We propose a novel online multi-target visual tracker based on the recently developed Hypothesized and Independent Stochastic Population (HISP) filter. The HISP filter combines advantages of traditional tracking approaches like MHT and point-process-based approaches like PHD filter, and it has linear complexity while maintaining track identities. We apply this filter for tracking multiple targets in video sequences acquired under varying environmental conditions and targets density using a tracking-by-detection approach. We also adopt deep CNN appearance representation by training a verification-identification network (VerIdNet) on large-scale person re-identification data sets. We construct an augmented likelihood in a principled manner using this deep CNN appearance features and spatio-temporal information. Furthermore, we solve the problem of two or more targets having identical label considering the weight propagated with each confirmed hypothesis. Extensive experiments on MOT16 and MOT17 benchmark data sets show that our tracker significantly outperforms several state-of-the-art trackers in terms of tracking accuracy.     

使用线性分割和序列合并的视频镜头分割方法

本文提出一种双向帧距离模型,基于帧距离双向对比,采用较低的全局阈值,使用线性遍历的方法对视频内容进行粗镜头划分成碎片序列集合和边界序列集合.然后通过比对每一个边界序列相邻的碎片序列内容的相似度,对粗划分的内容进行再合并,从而实现了用线性方法同时解决突变类镜头切换和渐变类镜头切换的镜头分割问题.