In-situ crystal morphology identification using imaging analysis with application to the L-glutamic acid crystallization

A synthetic image analysis strategy is proposed for in-situ crystal size measurement and shape identification for monitoring crystallization processes, based on using a real-time imaging system. The proposed method consists of image processing, feature analysis, particle sieving, crystal size measurement, and crystal shape identification. Fundamental image features of crystals are selected for efficient classification. In particular, a novel shape feature, referred to as inner distance descriptor, is introduced to quantitatively describe different crystal shapes, which is relatively independent of the crystal size and its geometric direction in an image captured for analysis. Moreover, a pixel equivalent calibration method based on subpixel edge detection and circle fitting is proposed to measure crystal sizes from the captured images. In addition, a kernel function based method is given to deal with nonlinear correlations between multiple features of crystals, facilitating computation efficiency for real-time shape identification. Case study and experimental results from the cooling crystallization of l-glutamic acid demonstrate that the proposed image analysis method can be effectively used for in-situ crystal size measurement and shape identification with good accuracy

Investigation of Computer Vision Concepts and Methods for Structural Health Monitoring and Identification Applications

This study presents a comprehensive investigation of methods and technologies for developing a computer vision-based framework for Structural Health Monitoring (SHM) and Structural Identification (St-Id) for civil infrastructure systems, with particular emphasis on various types of bridges. SHM is implemented on various structures over the last two decades, yet, there are some issues such as considerable cost, field implementation time and excessive labor needs for the instrumentation of sensors, cable wiring work and possible interruptions during implementation. These issues make it only viable when major investments for SHM are warranted for decision making. For other cases, there needs to be a practical and effective solution, which computer-vision based framework can be a viable alternative. Computer vision based SHM has been explored over the last decade. Unlike most of the vision-based structural identification studies and practices, which focus either on structural input (vehicle location) estimation or on structural output (structural displacement and strain responses) estimation, the proposed framework combines the vision-based structural input and the structural output from non-contact sensors to overcome the limitations given above. First, this study develops a series of computer vision-based displacement measurement methods for structural response (structural output) monitoring which can be applied to different infrastructures such as grandstands, stadiums, towers, footbridges, small/medium span concrete bridges, railway bridges, and long span bridges, and under different loading cases such as human crowd, pedestrians, wind, vehicle, etc. Structural behavior, modal properties, load carrying capacities, structural serviceability and performance are investigated using vision-based methods and validated by comparing with conventional SHM approaches. In this study, some of the most famous landmark structures such as long span bridges are utilized as case studies. This study also investigated the serviceability status of structures by using computer vision-based methods. Subsequently, issues and considerations for computer vision-based measurement in field application are discussed and recommendations are provided for better results. This study also proposes a robust vision-based method for displacement measurement using spatio-temporal context learning and Taylor approximation to overcome the difficulties of vision-based monitoring under adverse environmental factors such as fog and illumination change. In addition, it is shown that the external load distribution on structures (structural input) can be estimated by using visual tracking, and afterward load rating of a bridge can be determined by using the load distribution factors extracted from computer vision-based methods. By combining the structural input and output results, the unit influence line (UIL) of structures are extracted during daily traffic just using cameras from which the external loads can be estimated by using just cameras and extracted UIL. Finally, the condition assessment at global structural level can be achieved using the structural input and output, both obtained from computer vision approaches, would give a normalized response irrespective of the type and/or load configurations of the vehicles or human loads

Using Self-Contradiction to Learn Confidence Measures in Stereo Vision

Learned confidence measures gain increasing importance for outlier removal and quality improvement in stereo vision. However, acquiring the necessary training data is typically a tedious and time consuming task that involves manual interaction, active sensing devices and/or synthetic scenes. To overcome this problem, we propose a new, flexible, and scalable way for generating training data that only requires a set of stereo images as input. The key idea of our approach is to use different view points for reasoning about contradictions and consistencies between multiple depth maps generated with the same stereo algorithm. This enables us to generate a huge amount of training data in a fully automated manner. Among other experiments, we demonstrate the potential of our approach by boosting the performance of three learned confidence measures on the KITTI2012 dataset by simply training them on a vast amount of automatically generated training data rather than a limited amount of laser ground truth data.Comment: This paper was accepted to the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. The copyright was transfered to IEEE (https://www.ieee.org). The official version of the paper will be made available on IEEE Xplore (R) (http://ieeexplore.ieee.org). This version of the paper also contains the supplementary material, which will not appear IEEE Xplore (R

A local algorithm for the computation of image velocity via constructive interference of global Fourier components

A novel Fourier-based technique for local motion detection from image sequences is proposed. In this method, the instantaneous velocities of local image points are inferred directly from the global 3D Fourier components of the image sequence. This is done by selecting those velocities for which the superposition of the corresponding Fourier gratings leads to constructive interference at the image point. Hence, image velocities can be assigned locally even though position is computed from the phases and amplitudes of global Fourier components (spanning the whole image sequence) that have been filtered based on the motion-constraint equation, reducing certain aperture effects typically arising from windowing in other methods. Regularization is introduced for sequences having smooth flow fields. Aperture effects and their effect on optic-flow regularization are investigated in this context. The algorithm is tested on both synthetic and real image sequences and the results are compared to those of other local methods. Finally, we show that other motion features, i.e. motion direction, can be computed using the same algorithmic framework without requiring an intermediate representation of local velocity, which is an important characteristic of the proposed method.Postprint (author’s final draft

Model-Based Environmental Visual Perception for Humanoid Robots

The visual perception of a robot should answer two fundamental questions: What? and Where? In order to properly and efficiently reply to these questions, it is essential to establish a bidirectional coupling between the external stimuli and the internal representations. This coupling links the physical world with the inner abstraction models by sensor transformation, recognition, matching and optimization algorithms. The objective of this PhD is to establish this sensor-model coupling

Patterns of Tree Defoliation and Mortality from Insect Damage Using Multi-Scale Remote Sensing

With the climate rapidly changing, coniferous trees in North America face many threats, and both native and invasive insects are contributing to their decline and mortality. As insects, particularly bark beetles, successfully attack trees, the foliage of those trees undergoes a color shift from green to red to gray. Attacks from other insects, such as defoliators, can result in defoliation, crown thinning, and loss of needles. These changes may be detected by remote sensing instruments such as satellites and drones. Tree mortality may also come from multiple other variables, such as fire or drought, which then causes tree stress, making plants more susceptible to insect infestation. I analyzed an area with tree disturbance and mortality from three distinct types of insects in Montana, United States to study the detection of forest disturbance by insect outbreaks. This study aims to examine the patterns displayed across a section of forest at different spatial resolutions and scales. Field studies consisted of measuring variables such as diameter, health, and needle color on both trees inside eight-meter fixed-radius plots as well as individual trees not within plots. I analyzed and classified imagery from various sensors, including data from an unmanned aerial vehicle and multiple satellites. Pixels from these data sets are classified using two modeling techniques: maximum likelihood and random forest. This resulted in maps of different tree health classes and other land classes such as bare ground and herbaceous vegetation. I evaluated tree disturbance with classifications of finer spatial resolution pixels (subpixels), which were aggregated to the size of coarser spatial resolution pixels (superpixels) by calculating the percentage of unhealthy trees within, and then comparing them to the classification of the actual classified superpixels. By comparing classification results at different resolution levels, it is possible to extract what information was retained or lost at each step down in spatial resolution, and field measurements provided corroborating evidence of tree disturbance.Random forest models outperformed maximum likelihood models based on accuracy of withheld evaluation points, with overall accuracies ranging from 81.5% to 94.5%. Corroboration of individual trees from the field data was only easily feasible with UAV data, plausible with WorldView-3 data, and not possible with any imagery of 10-m spatial resolution or coarser. Total percent area affected of unhealthy trees was not consistent across resolutions, although coarser imagery tended to underestimate mortality or damage for most intensities of finer imagery disturbance when grouped into distinct disturbance bins but predict more mortality or disturbance across an entire landscape. This study will assist forest managers and natural resource scientists in understanding detection of insect-affected forests, in particular when insect outbreaks are more diffuse and not severe across the entire landscape, giving managers guidelines for where to invest time and resources. This research will also allow for general trends for areas with insect-specific mortality, allowing for potential future comparisons with other causes of tree mortality