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 speaker tracking using particle filter sensor fusion

Sensor fusion for object tracking has become an active research direction during the past few years. But how to do it in a robust and principled way is still an open problem. In this paper, we propose a new fusion framework that combines both the bottom-up and top-down approaches to probabilistically fuse multiple sensing modalities. At the lower level, individual vision and audio trackers are designed to generate effective proposals for the fuser. At the higher level, the fuser performs reliable tracking by verifying hypotheses over multiple likelihood models from multiple cues. Unlike traditional fusion algorithms, the proposed framework is a closed-loop system where the fuser and trackers coordinate their tracking information. Furthermore, to handle nonstationary situations, the proposed framework evaluates the performance of the individual trackers and dynamically updates their object states. We present a real-time speaker tracking system based on the proposed framework by fusing object contour, color and sound source location. We report robust tracking results.     

基于运动跟踪匹配技术的视频对象提取

本文提出一种自动分割VOP的技术。其方法是：先对初始帧使用形态运动滤波技术提取出初始运动对象的二值轮廓模型,并在后继帧中使用豪斯道夫对象跟踪器跟踪运动以对象模型;而为了适应对象的形状变化,本文使用活动轮廓模型（snake）技术对运动心合匹配;最后根据一系列精确的二值轮廓引导提取运动对象序列。实验结果表明,我们的算法可有效地提取视频对象平面。     

融合局部三值数量和色度信息的均值漂移跟踪

该文提出了局部三值数量(Local Ternary Number, LTN)这一新的局部显著性纹理算子,并将其与色度信息相结合得到一种新的目标跟踪方法。该方法充分利用目标像素与其八邻域像素灰度值的大小关系,将局部显著性算子 (Local Similarity Number, LSN)加以拓展,设计了局部三值数量这一新的局部显著性纹理算子,该算子能区分目标像素在同一局部显著度下的不同纹理结构;LTN掩膜提取边缘、线和角点上关键像素以提高纹理特征的区分能力,同时能够较完整地保留目标信息;在此基础上,将掩膜内目标像素的LTN特征与色度信息融合生成一种新的目标模型,并嵌入到均值漂移(Mean Shift, MS)框架完成目标的跟踪。实验结果表明,该文提出的目标跟踪方法在场景中存在相似颜色和光照变化干扰的情况下,仍能持续准确地实现目标的定位,提高了传统均值漂移跟踪算法的性能。     

Robust shape tracking with multiple models in ultrasound images

This paper addresses object tracking in ultrasound images using a robust multiple model tracker. The proposed tracker has the following features: 1) it uses multiple dynamic models to track the evolution of the object boundary, and 2) it models invalid observations (outliers), reducing their influence on the shape estimates. The problem considered in this paper is the tracking of the left ventricle which is known to be a challenging problem. The heart motion presents two phases (diastole and systole) with different dynamics, the multiple models used in this tracker try to solve this difficulty. In addition, ultrasound images are corrupted by strong multiplicative noise which prevents the use of standard deformable models. Robust estimation techniques are used to address this difficulty. The multiple model data association (MMDA) tracker proposed in this paper is based on a bank of nonlinear filters, organized in a tree structure. The algorithm determines which model is active at each instant of time and updates its state by propagating the probability distribution, using robust estimation techniques.     

End-to-end DeepNCC framework for robust visual tracking

In this paper, we propose an NCC-based object tracking deep framework, which can be well initialized with the limited target samples in the first frame. The proposed framework contains a pretrained model, online feature fine-tuning layers and tracking processes. The pretrained model provides rich feature representations while online feature fine-tuning layers select discriminative and generic features for the tracked object. We choose normalized cross-correlation as a template tracking layer to perform the tracking process. To enable the learned features representation closely coordinated to the tracked target, we jointly train the feature representation network and tracking processes. In online tracking, an adaptive template and a fixed template are fused to find the optimal tracking results. Scale estimation and a high-confidence model update scheme are perfectly integrated into the framework to adapt to the target appearance changes. The extensive experiments demonstrate that the proposed tracker achieves superior performance compared with other state-of-the-art trackers.     

基于初级视皮层抑制的轮廓检测方法

轮廓检测是基于形状目标识别任务的关键,然而从自然场景中自动地检测出目标的轮廓是非常困难的,因为背景中存在着大量的无关干扰成分.生理学与解剖学的研究表明初级视皮层中具有方位选择性的神经元的响应受到其周围环境中同方位刺激的抑制,因此方位发生变化的地方受到的抑制程度相对较少,这使得孤立的边缘或区域的边界更为显著.基于此我们提出了一个视觉生理机制的轮廓检测模型,其目的是减少背景纹理的干扰,同时保留感兴趣的对象.针对模拟及真实图像的实验结果表明这种抑制措施有效地抑制了纹理边缘并减少了轮廓自身的破坏,极大地提高了复杂背景中轮廓检测的性能.     

An integrated region-, boundary-, shape-based active contour for multiple object overlap resolution in histological imagery

Active contours and active shape models (ASM) have been widely employed in image segmentation. A major limitation of active contours, however, is in their 1) inability to resolve boundaries of intersecting objects and to 2) handle occlusion. Multiple overlapping objects are typically segmented out as a single object. On the other hand, ASMs are limited by point correspondence issues since object landmarks need to be identified across multiple objects for initial object alignment. ASMs are also are constrained in that they can usually only segment a single object in an image. In this paper, we present a novel synergistic boundary and region-based active contour model that incorporates shape priors in a level set formulation with automated initialization based on watershed. We demonstrate an application of these synergistic active contour models using multiple level sets to segment nuclear and glandular structures on digitized histopathology images of breast and prostate biopsy specimens. Unlike previous related approaches, our model is able to resolve object overlap and separate occluded boundaries of multiple objects simultaneously. The energy functional of the active contour is comprised of three terms. The first term is the prior shape term, modeled on the object of interest, thereby constraining the deformation achievable by the active contour. The second term, a boundary-based term detects object boundaries from image gradients. The third term drives the shape prior and the contour towards the object boundary based on region statistics. The results of qualitative and quantitative evaluation on 100 prostate and 14 breast cancer histology images for the task of detecting and segmenting nuclei and lymphocytes reveals that the model easily outperforms two state of the art segmentation schemes (geodesic active contour and Rousson shape-based model) and on average is able to resolve up to 91% of overlapping/occluded structures in the images.     

基于相关滤波扩展记忆系统模型的智能目标跟踪

将目标跟踪过程看作一个多重记忆系统模型,提 出了基于相关滤波的扩展记忆系统模型,实现了基 于记忆系统模型的智能目标跟踪。首先,通过提取跟踪目标特征学习目标信息,生成短时相 关滤波器,产 生短时记忆;然后利用每一帧短期记忆的不断重复与更新,产生长时记忆,生成长时相关滤 波器。短时与 长时记忆构成相关滤波记忆系统模型,完成目标跟踪。在此模型基础上,分析与挖掘模型中 的相关滤波数 据,加入四种智能化控制信息,构建扩展记忆系统模型,实现智能化的目标跟踪。基于相关 滤波的扩展记 忆系统模型利用生物记忆的原理使目标跟踪更加自动化、智能化,增强目标跟踪的准确性。 实验结果表明, 与当前流行的相关滤波跟踪算法相比,本文算法提高了目标跟踪的抗干扰性、抗遮挡性与抗 形变能力,同时保证了在尺度跟踪的有效性。     

Level set analysis for leukocyte detection and tracking

We propose a cell detection and tracking solution using image-level sets computed via threshold decomposition. In contrast to existing methods where manual initialization is required to track individual cells, the proposed approach can automatically identify and track multiple cells by exploiting the shape and intensity characteristics of the cells. The capture of the cell boundary is considered as an evolution of a closed curve that maximizes image gradient along the curve enclosing a homogeneous region. An energy functional dependent upon the gradient magnitude along the cell boundary, the region homogeneity within the cell boundary and the spatial overlap of the detected cells is minimized using a variational approach. For tracking between frames, this energy functional is modified considering the spatial and shape consistency of a cell as it moves in the video sequence. The integrated energy functional complements shape-based segmentation with a spatial consistency based tracking technique. We demonstrate that an acceptable, expedient solution of the energy functional is possible through a search of the image-level lines: boundaries of connected components within the level sets obtained by threshold decomposition. The level set analysis can also capture multiple cells in a single frame rather than iteratively computing a single active contour for each individual cell. Results of cell detection using the energy functional approach and the level set approach are presented along with the associated processing time. Results of successful tracking of rolling leukocytes from a number of digital video sequences are reported and compared with the results from a correlation tracking scheme.     

Tracking visible boundary of objects using occlusion adaptive motion snake

We propose a novel technique for tracking the visible boundary of a video object in the presence of occlusion. Starting with an initial contour that is interactively specified by the user and may be automatically refined by using intra-energy terms, the proposed technique employs piecewise contour prediction using local motion and color information on both sides of the contour segment, and contour snapping using scale-invariant intra-frame and inter-frame energy terms. The piecewise (segmented) nature of the contour prediction scheme and modeling of the motion on both sides of each contour segment enable accurate determination of whether and where the tracked boundary is occluded by another object. The proposed snake energy terms are associated with contour segments (as opposed to node points) and they are scale/resolution independent to allow multi-resolution contour tracking without the need to retune the weights of the energy terms at each resolution level. This facilitates contour prediction at coarse resolution and snapping at fine resolution with high accuracy. Experimental results are provided to illustrate the performance of the proposed occlusion detection algorithm and the novel snake energy terms that enable visible boundary tracking in the presence of occlusion.     

Robust tracking based on local structural cell graph

Structure information has been increasingly incorporated into computer vision, however most trackers have ignored the inner spatial structure of the object. In this paper, we develop a simple yet robust tracking algorithm based on local structural cell graph (LSCG). This approach exploits both partial and spatial information of the target via representing the object with local structural cells (LSCs) and constructing a graph to model the spatial structure between the inner parts of the object. The tracking is formulated as matching LSCG, whose nodes are target parts and edges are the interaction between two parts. Within the Bayesian framework, we achieve object tracking by matching graphs between the reference and candidates. Eventually, the candidate with the highest similarity is the target. In addition, an updating strategy is adopted to help our tracker adapt to the fast time-varying object appearance. Experimental results demonstrate that the proposed method outperforms several state-of-the-art trackers.     

基于多个非刚体目标跟踪的视频对象平面生成算法

提出了一种提取运动对象的新的视频序列分割算法。算法的核心是一个对象跟踪器,它利用一种基于对象行为的跟踪算法对多个非刚体目标有效地进行对象跟踪,并与后续帧进行匹配,然后采用一种基于运动相连成分的模型刷新方法对模型的每一帧进行刷新,初始的模型自动产生,再利用滤波技术滤除静止背景,最后,利用边界图像模型从序列中提取出视频对象平面（VOP）。     

基于多注意力图的孪生网络视觉目标跟踪

在视觉跟踪应用中,目标外观通常由包含目标的矩形区域来建模,这种矩形化边框的描述方式不可避免地引入了背景干扰,并随着场景变化导致跟踪关注点的模糊及歧义,进而产生跟踪漂移。针对以上问题,提出了一种基于多注意力图的孪生网络视觉目标跟踪算法。首先,建立了一种关注于前景目标区域特征表达的孪生网络。该网络通过构建梯度注意力图损失函数项来引导网络训练,提升网络区分目标和干扰背景的能力。此外,嵌入通道注意力和空间注意力进一步强化目标的特征表达,自动发掘有区分的特征表示。在多个公共数据集上的实验验证了提出算法的有效性,以及算法可完成实时的视觉目标跟踪。      

Particle filtering for multiple object tracking in dynamic fluorescence microscopy images: application to microtubule growth analysis

Quantitative analysis of dynamic processes in living cells by means of fluorescence microscopy imaging requires tracking of hundreds of bright spots in noisy image sequences. Deterministic approaches, which use object detection prior to tracking, perform poorly in the case of noisy image data. We propose an improved, completely automatic tracker, built within a Bayesian probabilistic framework. It better exploits spatiotemporal information and prior knowledge than common approaches, yielding more robust tracking also in cases of photobleaching and object interaction. The tracking method was evaluated using simulated but realistic image sequences, for which ground truth was available. The results of these experiments show that the method is more accurate and robust than popular tracking methods. In addition, validation experiments were conducted with real fluorescence microscopy image data acquired for microtubule growth analysis. These demonstrate that the method yields results that are in good agreement with manual tracking performed by expert cell biologists. Our findings suggest that the method may replace laborious manual procedures.     

Combining learning-based intensity distributions with nonparametric shape priors for image segmentation

Integration of shape prior information into level set formulations has led to great improvements in image segmentation in the presence of missing information, occlusion, and noise. However, most shape-based segmentation techniques incorporate image intensity through simplistic data terms. A common underlying assumption of such data terms is that the foreground and the background regions in the image are homogeneous, i.e., intensities are piecewise constant or piecewise smooth. This situation makes integration of shape priors inefficient in the presence of intensity inhomogeneities. In this paper, we propose a new approach for combining information from shape priors with that from image intensities. More specifically, our approach uses shape priors learned by nonparametric density estimation and incorporates image intensity distributions learned in a supervised manner. Such a combination has not been used in previous work. Sample image patches are used to learn the intensity distributions, and segmented training shapes are used to learn the shape priors. We present an active contour algorithm that takes these learned densities into account for image segmentation. Our experiments on synthetic and real images demonstrate the robustness of the proposed approach to complicated intensity distributions, and occlusions, as well as the improvements it provides over existing methods.     

Video object tracking and segmentation with box annotation

This paper presents a two-stage approach, track and then segment, to perform semi-supervised video object segmentation (VOS) with only bounding box annotations. The proposed reverse optimization for VOS (ROVOS) which leverages a fully convolutional Siamese network performs tracking and segmentation in the tracker. The segmentation cues are able to reversely optimize the location of the tracker and the object segmentation masks are produced by the two-branch system online. The experimental results on DAVIS 2016 and DAVIS 2017 demonstrate significant improvements of the proposed algorithm over the state-of-the-art methods.     

Robust object tracking via deformation samples generator

In object tracking applications, it is common for trackers to experience drift problems when the object of interest becomes deformed, which compromises the ability of the tracker to track the object. It is therefore desirable to develop a learning tracker classifier that is robust to deformations. The performance of existing trackers that employ deep classification networks degrades when the amount of training data is limited and does not cover all possible scenarios. While these limitations can be mitigated in part by using larger training datasets, these datasets may still not cover all situations and the positive samples are still monotonous. To overcome this problem, we propose a novel deformation samples generator that generates samples that would normally be difficult for the tracker to classify. In the proposed framework, both the classifier and deformation samples generator learn in a joint manner. Our experiments show that the proposed approach outperforms state-of-the-art methods in both quantitative and qualitative evaluations for the visual object tracking task.