TARA: Tracking with Aspect Ratio Adaptability Ma, Haoyi; Acton, Scott T.; Lin, Zongli
2020 54th Asilomar Conference on Signals, Systems, and Computers,
2020-Nov.-1
Conference Proceeding
In visual object tracking (VOT), accurate and robust scale estimation of a target object is a challenging task. The discriminative correlation filter (DCF) is widely employed in VOT due to its high ...efficiency and accuracy. However, DCF based trackers do not have inherent scale adaptability. Most existing scale estimation methods for DCF based trackers cannot accommodate aspect ratio variation and thus result in inferior performance. In this paper, we propose to address the scale estimation problem and enable aspect ratio adaptability by utilizing a group of DCFs to localize the boundaries of the target object. Deep hierarchical convolutional features are exploited to improve the accuracy and robustness. The resulting system is named TARA: tracking with aspect ratio adaptability. Extensive empirical evaluation using the publicly available tracking benchmark datasets demonstrates that TARA can meet the demand of scale variation challenges and obtains favorable performance compared to state-of-the-art trackers.
In order to understand the brain, we need to first understand the morphology of neurons. In the neurobiology community, there have been recent pushes to analyze both neuron connectivity and the ...influence of structure on function. Currently, a technical roadblock that stands in the way of these studies is the inability to automatically trace neuronal structure from microscopy. On the image processing side, proposed tracing algorithms face difficulties in low contrast, indistinct boundaries, clutter, and complex branching structure. To tackle these difficulties, we develop Tree2Tree, a robust automatic neuron segmentation and morphology generation algorithm. Tree2Tree uses a local medial tree generation strategy in combination with a global tree linking to build a maximum likelihood global tree. Recasting the neuron tracing problem in a graph-theoretic context enables Tree2Tree to estimate bifurcations naturally, which is currently a challenge for current neuron tracing algorithms. Tests on cluttered confocal microscopy images of Drosophila neurons give results that correspond to ground truth within a margin of ±2.75% normalized mean absolute error.
Accurate detection and segmentation of single cells in three-dimensional (3D) fluorescence time-lapse images is essential for observing individual cell behaviors in large bacterial communities called ...biofilms. Recent progress in machine-learning-based image analysis is providing this capability with ever-increasing accuracy. Leveraging the capabilities of deep convolutional neural networks (CNNs), we recently developed bacterial cell morphometry in 3D (BCM3D), an integrated image analysis pipeline that combines deep learning with conventional image analysis to detect and segment single biofilm-dwelling cells in 3D fluorescence images. While the first release of BCM3D (BCM3D 1.0) achieved state-of-the-art 3D bacterial cell segmentation accuracies, low signal-to-background ratios (SBRs) and images of very dense biofilms remained challenging. Here, we present BCM3D 2.0 to address this challenge. BCM3D 2.0 is entirely complementary to the approach utilized in BCM3D 1.0. Instead of training CNNs to perform voxel classification, we trained CNNs to translate 3D fluorescence images into intermediate 3D image representations that are, when combined appropriately, more amenable to conventional mathematical image processing than a single experimental image. Using this approach, improved segmentation results are obtained even for very low SBRs and/or high cell density biofilm images. The improved cell segmentation accuracies in turn enable improved accuracies of tracking individual cells through 3D space and time. This capability opens the door to investigating time-dependent phenomena in bacterial biofilms at the cellular level.
Calcium signaling plays a significant role in microglial activation. Genetically encoded calcium indicators (GECI) have been widely used for calcium imaging studies in many brain cell types, ...including neurons, astrocytes, and oligodendrocytes. However, microglial calcium imaging approaches have been hampered by idiosyncrasies of their gene expression and malleable cell properties. The generation of PC::G5-tdT, a Polr2a locus-based conditional mouse reporter of calcium, facilitated the deployment of GECI in microglia. When crossed with the Iba1(Aif1)-IRES-Cre line, all brain microglia of the progeny are labeled with the calcium indicator variant GCaMP5G and the red fluorescent protein tdTomato. This reporter system has enabled in vivo studies of intracellular calcium in large microglial cell populations in cerebral pathologies such as ischemic stroke. In this chapter, we outline specific guidelines for genetic, surgical, imaging, and data analysis aspects of microglial calcium monitoring of the ischemic cortex following middle cerebral artery occlusion.
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.
Registration of an in vivo microscopy image sequence is necessary in many significant studies, including studies of atherosclerosis in large arteries and the heart. Significant cardiac and ...respiratory motion of the living subject, occasional spells of focal plane changes, drift in the field of view, and long image sequences are the principal roadblocks. The first step in such a registration process is the removal of translational and rotational motion. Next, a deformable registration can be performed. The focus of our study here is to remove the translation and/or rigid body motion that we refer to here as coarse alignment. The existing techniques for coarse alignment are unable to accommodate long sequences often consisting of periods of poor quality images (as quantified by a suitable perceptual measure). Many existing methods require the user to select an anchor image to which other images are registered. We propose a novel method for coarse image sequence alignment based on minimum weighted spanning trees (MISTICA) that overcomes these difficulties. The principal idea behind MISTICA is to reorder the images in shorter sequences, to demote nonconforming or poor quality images in the registration process, and to mitigate the error propagation. The anchor image is selected automatically making MISTICA completely automated. MISTICA is computationally efficient. It has a single tuning parameter that determines graph width, which can also be eliminated by the way of additional computation. MISTICA outperforms existing alignment methods when applied to microscopy image sequences of mouse arteries.
In this article, we introduce an approach for detecting evolving geophysical features within interferometric synthetic aperture radar (InSAR)-derived point cloud data sets. This approach is based on ...the availability of models describing both spatial and temporal behaviours of the geophysical features of interest. The model parameters are used to generate a multidimensional space that is then scanned with a user-defined resolution. For each point in the parameter space, a spatiotemporal template is reconstructed from the original model. This template is then used to scan the point cloud data set for regions matching the spatiotemporal behaviour.We also introduce a proportional measure where the residual for each point in the data set is compared to both the data and the template to provide a scale invariant measure of the behavioural matching. The matching is evaluated for every point in the parameter over a region of influence determined by the parameters. The resulting multidimensional space is then collapsed onto geographical coordinates to produce an overlay map identifying regions whose spatiotemporal behaviour matches the feature of interest.We tailored our approach to the detection of subsidence behaviour, indicative of the development of sinkholes, modelled as Gaussian with amplitude linearly increasing with time. We verified the validity of our model using both synthetic and actual InSAR data sets. The latter was obtained by processing imagery of a region near Wink, Texas, containing ground truth sinkhole data.We applied this framework to a 40 km × 40 km area of interest located in western Virginia and performed ground validation on a subset of the identified regions. The results show good agreement between the locations detected by our algorithm and the evidence of subsidence observed during the ground validation campaign.
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Classroom videos are a common source of data for educational researchers studying classroom interactions as well as a resource for teacher education and professional development. Over the last ...several decades emerging technologies have been applied to classroom videos to record, transcribe, and analyze classroom interactions. With the rise of machine learning, we report on the development and validation of neural networks to classify instructional activities using video signals, without analyzing speech or audio features, from a large corpus of nearly 250 h of classroom videos from elementary mathematics and English language arts instruction. Results indicated that the neural networks performed fairly-well in detecting instructional activities, at diverse levels of complexity, as compared to human raters. For instance, one neural network achieved over 80% accuracy in detecting four common activity types: whole class activity, small group activity, individual activity, and transition. An issue that was not addressed in this study was whether the fine-grained and agnostic instructional activities detected by the neural networks could scale up to supply information about features of instructional quality. Future applications of these neural networks may enable more efficient cataloguing and analysis of classroom videos at scale and the generation of fine-grained data about the classroom environment to inform potential implications for teaching and learning.
•Classroom activities can automatically be classified with neural networks.•Neural networks specific to computer vision tasks were found to be highly accurate.•This application is promising for facilitating classroom video research studies.
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Automatic glia reconstruction is essential for the dynamic analysis of microglia motility and morphology, notably so in research on neurodegenerative diseases. In this paper, we propose an automatic ...3D tracing algorithm called C3VFC that uses vector field convolution to find the critical points along the centerline of an object and trace paths that traverse back to the soma of every cell in an image. The solution provides detection and labeling of multiple cells in an image over time, leading to multi-object reconstruction. The reconstruction results can be used to extract bioinformatics from temporal data in different settings. The C3VFC reconstruction results found up to a 53% improvement on the next best performing state-of-the-art tracing method. C3VFC achieved the highest accuracy scores, in relation to the baseline results, in four of the five different measures: Entire structure average, the average bi-directional entire structure average, the different structure average, and the percentage of different structures.
The development of social behavior is poorly understood. Many animals adjust their behavior to environmental conditions based on a social context. Despite having relatively simple visual systems, ...Drosophila larvae are capable of identifying and are attracted to the movements of other larvae. Here, we show that Drosophila larval visual recognition is encoded by the movements of nearby larvae, experienced during a specific developmental critical period. Exposure to moving larvae, only during a specific period, is sufficient for later visual recognition of movement. Larvae exposed to wild-type body movements, during the critical period, are not attracted to the movements of tubby mutants, which have altered morphology. However, exposure to tubby, during the critical period, results in tubby recognition at the expense of wild-type recognition indicating that this is true learning. Visual recognition is not learned in excessively crowded conditions, and this is emulated by exposure, during the critical period, to food previously used by crowded larvae. We propose that Drosophila larvae have a distinct critical period, during which they assess both social and resource conditions, and that this irreversibly determines later visually guided social behavior. This model provides a platform towards understanding the regulation and development of social behavior.
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