Craig Przybyla

Groupwise Tracking of Crowded Similar-Appearance Targets from Low-Continuity Image Sequences

Automatic tracking of large-scale crowded targets are of particular importance in many applications, such as crowded people/vehicle tracking in video surveillance, fiber tracking in materials science, and cell tracking in biomedical imaging. This problem becomes very challenging when the targets show similar appearance and the interslice/ inter-frame continuity is low due to sparse sampling, camera motion and target occlusion. The main challenge comes from the step of association which aims at matching the predictions and the observations of the multiple targets. In this paper we propose a new groupwise method to explore the target group information and employ the within-group correlations for association and tracking. In particular, the within-group association is modeled by a nonrigid 2D Thin-Plate transform and a sequence of group shrinking, group growing and group merging operations are then developed to refine the composition of each group. We apply the proposed method to track large-scale fibers from microscopy material images and compare its performance against several other multi-target tracking methods. We also apply the proposed method to track crowded people from videos with poor inter-frame continuity.

Failure prediction in ceramic composites using acoustic emission and digital image correlation

The objective of the work performed here was to develop a methodology for linking in-situ detection of localized matrix cracking to the final failure location in continuous fiber reinforced CMCs. First, the initiation and growth of matrix cracking are measured and triangulated via acoustic emission (AE) detection. High amplitude events at relatively low static loads can be associated with initiation of large matrix cracks. When there is a localization of high amplitude events, a measurable effect on the strain field can be observed. Full field surface strain measurements were obtained using digital image correlation (DIC). An analysis using the combination of the AE and DIC data was able to predict the final failure location.

Large-Scale Fiber Tracking Through Sparsely Sampled Image Sequences of Composite Materials

Fast and accurate characterization of fiber micro-structures plays a central role for material scientists to analyze physical properties of continuous fiber reinforced composite materials. In materials science, this is usually achieved by continuously cross-sectioning a 3D material sample for a sequence of 2D microscopic images, followed by a fiber detection/tracking algorithm through the obtained image sequence. To speed up this process and be able to handle larger size material samples, this paper proposes sparse sampling with larger inter-slice distance in cross sectioning and develops a new algorithm that can robustly track large-scale fibers from such a sparsely sampled image sequence. In particular, the problem is formulated as multi-target tracking, and the Kalman filters are applied to track each fiber along the image sequence. One main challenge in this tracking process is to correctly associate each fiber to its observation given that: fiber observations are of large scale, crowded, and show very similar appearances in a 2D slice and there may be a large gap between the predicted location of a fiber and its observation in the sparse sampling. To address this challenge, a novel group-wise association algorithm is developed by leveraging the fact that fibers are implanted in bundles and the fibers in the same bundle are highly correlated through the image sequence. In experiments, the proposed algorithm is tested on three tiles of 100-slice S200 material samples and the tracking performance is evaluated using 1136 human annotated ground-truth fiber tracks. Both quantitative and qualitative results show that the proposed algorithm clearly outperforms the state-of-the-art multiple-target tracking algorithms on sparsely sampled image sequences.

Anomaly detection of microstructural defects in continuous fiber reinforced composites

Ceramic matrix composites (CMC) with continuous fiber reinforcements have the potential to enable the next generation of high speed hypersonic vehicles and/or significant improvements in gas turbine engine performance due to their exhibited toughness when subjected to high mechanical loads at extreme temperatures (2200F+). Reinforced fiber composites (RFC) provide increased fracture toughness, crack growth resistance, and strength, though little is known about how stochastic variation and imperfections in the material effect material properties. In this work, tools are developed for quantifying anomalies within the microstructure at several scales. The detection and characterization of anomalous microstructure is a critical step in linking production techniques to properties, as well as in accurate material simulation and property prediction for the integrated computation materials engineering (ICME) of RFC based components. It is desired to find statistical outliers for any number of material characteristics such as fibers, fiber coatings, and pores. Here, fiber orientation, or ‘velocity’, and ‘velocity’ gradient are developed and examined for anomalous behavior. Categorizing anomalous behavior in the CMC is approached by multivariate Gaussian mixture modeling. A Gaussian mixture is employed to estimate the probability density function (PDF) of the features in question, and anomalies are classified by their likelihood of belonging to the statistical normal behavior for that feature.

Characterization and Simulation of Time-Dependent Response of Structural Materials for Aero Structures and Turbine Engines

When considering structural materials used in aerospace applications and time-dependent behavior, primary concern are material/microstructural changes and damage initiation and growth as a result of complex loading (creep and/or fatigue) scenarios and/or environmental attack. The degradation and damage in the material can result in a decrease in load-carrying capability. It is the decrease of capability as a function of time/usage/exposure that must be understood and predicted to optimize the design and life management strategies of aerospace components that comprise aircraft structures and turbine engines. Historically predictive models in these domains were empirically based; relying on accelerated test methods, extensive amounts of test data, and mathematical fits to that data. More recent research in time-dependent material properties has shifted the focus to understanding the underlying mechanisms of material degradation and developing predictive capabilities incorporating that understanding. Specifically, to realize more accurate and robust performance prognosis for structural materials, a shift from empirical descriptions of time-dependent material behavior to more mechanistic-based models that capture the physics of failure is needed.

Role of exterior statistics-based boundary conditions for property-based statistically equivalent representative volume elements of polydispersed elastic composites

The property-based statistically equivalent RVE or P-SERVE has been introduced in the literature as the smallest microstructural volume element in non-uniform microstructures that has effective material properties equivalent to those of the entire microstructure. An important consideration is the application of appropriate boundary conditions for optimal property-based statistically equivalent representative volume element domains. The exterior statistics-based boundary conditions have been developed, accounting for the statistics of fiber distributions and interactions in the domain exterior to the property-based statistically equivalent representative volume element. This paper is intended to validate the efficacy of the exterior statistics-based boundary condition-based property-based statistically equivalent representative volume elements for evaluating homogenized stiffnesses of a unidirectional polymer matrix composite with a polydispersed microstructure characterized by nonuniform dispersion of carbon fibers of varying sizes in an epoxy matrix. Experimental tests and microstructural characterization of the polymer matrix composite are conducted for calibration and validation of the model. Statistically equivalent microstructural volume elements are constructed from experimental micrographs for direct numerical simulations. The performance of the property-based statistically equivalent representative volume element with exterior statistics-based boundary conditions is compared with other boundary conditions, as well as with the statistical volume elements. The tests clearly show the significant advantages of the exterior statistics-based boundary conditions in terms of accuracy of the homogenized stiffness and efficiency.

On-the-Fly Performance Evaluation of Large-Scale Fiber Tracking.

Large-scale fiber tracking in the images serial-sectioned from material samples is a critical step to analyze physical properties of continuous fiber reinforced composite materials. In serial-section imaging, increasing the sampling sparsity, i.e., the inter-slice intervals, can lead to significant speedups in data collection. However, increasing the sampling sparsity leads to difficulties in tracking large-scale crowded and similar-appearance fibers through the serial-section slices. One way to address this issue is to dynamically adjust the sampling rate by balancing the tracking accuracy with the data collection time. For this purpose, it is necessary to develop methods for estimating the tracking accuracy on the fly, i.e., immediately after tracking is updated on a new serial-section slice. Typical tracking accuracy metrics require ground truths, which are usually constructed by human annotations and unavailable on the fly. In this paper, we present a new approach to evaluate the performance of online largescale fiber tracking without involving the ground truth. Specifically, we explore the local spatial consistency of the fibers between adjacent slices and define a new performance-evaluation metric based on this spatial consistency. A set of experiments on real composite-material images are conducted to illustrate the effectiveness and accuracy of the proposed performance-evaluation metric for large-scale fiber tracking. Introduction In materials science and research, an important problem is to quickly and accurately reconstruct and characterize the underlying microstructure of a material sample [6]. For fiber-reinforced composite materials, the microstructure feature of interest is the fibers, whose shapes, orientations, and distributions directly affect the mechanical properties [10, 13]. One typical approach to reconstruct the large-scale fibers is to serial section the 3D material sample, take high-resolution microscopic images for each slice, and finally detect/track all the fibers through the slices [14, 16]. However, dense sampling of serial-sectioning, i.e., with very small inter-slice intervals, is time consuming and prevents the quick processing of large-sized material sample. On the other hand, overly sparse sampling of serial-sectioning, i.e., with very large inter-slice intervals, introduces uncertainty and ambiguity in tracking fibers across slices. One effective approach to address this issue is to dynamically adjust the sampling rate, i.e, the interslice intervals, in serial-sectioning by balancing the tracking accuracy with the data collection time. To achieve this goal, it is necessary to have a reliable evaluation of the tracking performance on the available slices with their inter-slice intervals before moving to serial-sectioning the next slice. This requires the use of an online tracking algorithm and the development of an on-the-fly tracking performance evaluation metric. By using an online tracking algorithm, such as Kalman filters, the fibers can be tracked based on the available slices and updated as soon as a new serial-sectioned slice is available. The on-the-fly performance evaluation continuously and quickly assesses the fiber-tracking performance as soon as a new slice is serial-sectioned and the online fiber tracking is updated using this new slice. The estimated tracking performance can then be used to adjust the inter-slice interval for the next slice. In material science, materials are reinforced by embedded objects such as small particles, fibers, or boundaries between different crystals. In order to be effective, these objects must be fairly dense, leading to a common characteristic that the microscopic structure is composed of crowded or densely packed mixtures of embedded objects. The objects are mainly fibers in this paper. The fact that the fibers are crowded induces a spatial consistency for fiber neighbors in different slices, so we take advantage of that spatial consistency in estimating the performance of large-scale fiber tracking. In this paper, we focus on addressing the problem of on-the-fly performance evaluation such that the fiber tracking performance can be quickly estimated without any interruption after a new slice is serial sectioned and the online tracking is extended to this new slice. In particular, this requires to exclude the human interactions from the tracking performance evaluation. Unfortunately, existing multi-target tracking performance evaluations metrics, such as the widely used Multiple Object Tracking Accuracy (MOTA) [5], usually require the ground-truth tracking results. For fiber tracking, these evaluation metrics count the coincidence between the tracked fibers and ground-truth fibers to evaluate the tracking performance. The construction of the ground-truth fibers requires manual annotation of fibers on each slice and manual linking of the fibers between slices. Manually annotating ground truth for large-scale fibers (about 500 similar-appearance fibers in one slice) in an image sequence makes the whole system not automatic. The objective of this paper is to develop a new metric that can evaluate the performance of online large-scale fiber tracking without requiring the ground truth. Related Works In most of the previous work [15, 9, 1, 8, 14], performance evaluation for object tracking requires manually annotated ground-truth tracking. By comparing generated tracking results with the ground-truth tracking on each image through the 142 IS&T International Symposium on Electronic Imaging 2017 Computational Imaging XV https://doi.org/10.2352/ISSN.2470-1173.2017.17.COIMG-437