Robust Techniques for Feature-based Image Mosaicing

Since the last few decades, image mosaicing in real time applications has been a challenging field for image processing experts. It has wide applications in the field of video conferencing, 3D image reconstruction, satellite imaging and several medical as well as computer vision fields. It can also be used for mosaic-based localization, motion detection & tracking, augmented reality, resolution enhancement, generating large FOV etc. In this research work, feature based image mosaicing technique using image fusion have been proposed. The image mosaicing algorithms can be categorized into two broad horizons. The first is the direct method and the second one is based on image features. The direct methods need an ambient initialization whereas, Feature based methods does not require initialization during registration. The feature-based techniques are primarily followed by the four steps: feature detection, feature matching, transformation model estimation, image resampling and transformation. SIFT and SURF are such algorithms which are based on the feature detection for the accomplishment of image mosaicing, but both the algorithms has their own limitations as well as advantages according to the applications concerned. The proposed method employs this two feature based image mosaicing techniques to generate an output image that works out the limitations of the both in terms of image quality The developed robust algorithm takes care of the combined effect of rotation, illumination, noise variation and other minor variation. Initially, the input images are stitched together using the popular stitching algorithms i.e. Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF). To extract the best features from the stitching results, the blending process is done by means of Discrete Wavelet Transform (DWT) using the maximum selection rule for both approximate as well as detail-components

Fundamental understanding of the transient melt pool dynamics, solidification kinetics and build texture in spot-melt additive manufacturing of Ti-6Al-4V

The overarching goal of this dissertation is to better understand the underlying process-structure relationships in play during the implementation of a spot melt strategy for metal additive manufacturing, which has become a popular alternative to the conventional raster melt strategy for site-specific microstructure control. In the first part of this dissertation, the effect of a spot melt strategy on the solidification texture, variant selection, phase fraction, and their variations along the build height of an E-PBF Ti-6Al-4V is investigated in comparison to a conventional linear melt strategy using high-energy synchrotron x-ray diffraction. In spite of the thermal excursions involved, the a [alpha] phase exhibit a Burgers orientation relationship (BOR) with the parent b [beta] phase for both melt strategies. Overall, the novel spot melt strategy produces a more homogeneous microstructure in terms of both the phase fraction and texture across the build height. Motivated by these findings, the second part of this dissertation tries to understand the fundamentals of microstructure formation in laser spot melts (on Ti-6Al-4V alloy, as a function of laser power) by probing the transient evolution of melt pool dimensions and solid-liquid interface velocity using a rapid (mm-ms resolution) in-situ, dynamic synchrotron x-ray radiography (DXR) and post-mortem EBSD. Solidification kinetics was observed to follow three stages (with a prominent steady state) in both the conduction and keyhole mode. Further, the steady-state solidification velocity was found to decrease with increasing laser power. Finally, physical processes operational during the melting and solidification events are ascertained using dimensional analysis and their bearing on solidification microstructure is discussed. In the last part of this dissertation, the conversion of radiography images of a spot melting event to density maps (using Beer-Lambert’s law as the physical basis) and further, to transient sub-surface temperature evolution is demonstrated. In summary, a deeper understanding of the fundamental processes behind the spot melt strategy has been achieved in this work through experimental means such as in-situ radiography, ex-situ diffraction, and post-mortem EBSD. This understanding to serve as a basis to validate existing and improve spot melt pool models and help AM process design

Efficient Experimental and Data-Centered Workflow for Microstructure-Based Fatigue Data – Towards a Data Basis for Predictive AI Models

Background Early fatigue mechanisms for various materials are yet to be unveiled for the (very) high-cycle fatigue (VHCF) regime. This can be ascribed to a lack of available data capturing initial fatigue damage evolution, which continues to adversely affect data scientists and computational modeling experts attempting to derive microstructural dependencies from small sample size data and incomplete feature representations. Objective The aim of this work is to address this lack and to drive the digital transformation of materials such that future virtual component design can be rendered more reliable and more efficient. Achieving this relies on fatigue models that comprehensively capture all relevant dependencies. Methods To this end, this work proposes a combined experimental and data post-processing workflow to establish multimodal fatigue crack initiation and propagation data sets efficiently. It evolves around fatigue testing of mesoscale specimens to increase damage detection sensitivity, data fusion through multimodal registration to address data heterogeneity, and image-based data-driven damage localization. Results A workflow with a high degree of automation is established, that links large distortion-corrected microstructure data with damage localization and evolution kinetics. The workflow enables cycling up to the VHCF regime in comparatively short time spans, while maintaining unprecedented time resolution of damage evolution. Resulting data sets capture the interaction of damage with microstructural features and hold the potential to unravel a mechanistic understanding. Conclusions The proposed workflow lays the foundation for future data mining and data-driven modeling of microstructural fatigue by providing statistically meaningful data sets extendable to a wide range of materials

A large-scale analysis of mRNAs expressed by primary mesenchyme cells of the sea urchin embryo

The primary mesenchyme cells (PMCs) of the sea urchin embryo have been an important model system for the analysis of cell behavior during gastrulation. To gain an improved understanding of the molecular basis of PMC behavior, a set of 8293 expressed sequenced tags (ESTs) was derived from an enriched population of mid-gastrula stage PMCs. These ESTs represented approximately 1200 distinct proteins, or about 15% of the mRNAs expressed by the gastrula stage embryo. 655 proteins were similar (P<10-7 by BLAST comparisons) to other proteins in GenBank, for which some information is available concerning expression and/or function. Another 116 were similar to ESTs identified in other organisms, but not further characterized. We conservatively estimate that sequences encoding at least 435 additional proteins were included in the pool of ESTs that did not yield matches by BLAST analysis. The collection of newly identified proteins includes many candidate regulators of primary mesenchyme morphogenesis, including PMC-specific extracellular matrix proteins, cell surface proteins, spicule matrix proteins and transcription factors. This work provides a basis for linking specific molecular changes to specific cell behaviors during gastrulation. Our analysis has also led to the cloning of several key components of signaling pathways that play crucial roles in early sea urchin development

Multisource Data Integration in Remote Sensing

Papers presented at the workshop on Multisource Data Integration in Remote Sensing are compiled. The full text of these papers is included. New instruments and new sensors are discussed that can provide us with a large variety of new views of the real world. This huge amount of data has to be combined and integrated in a (computer-) model of this world. Multiple sources may give complimentary views of the world - consistent observations from different (and independent) data sources support each other and increase their credibility, while contradictions may be caused by noise, errors during processing, or misinterpretations, and can be identified as such. As a consequence, integration results are very reliable and represent a valid source of information for any geographical information system

Vision Sensors and Edge Detection

Vision Sensors and Edge Detection book reflects a selection of recent developments within the area of vision sensors and edge detection. There are two sections in this book. The first section presents vision sensors with applications to panoramic vision sensors, wireless vision sensors, and automated vision sensor inspection, and the second one shows image processing techniques, such as, image measurements, image transformations, filtering, and parallel computing

Emotion Quantification Using Variational Quantum State Fidelity Estimation

Sentiment analysis has been instrumental in developing artificial intelligence when applied to various domains. However, most sentiments and emotions are temporal and often exist in a complex manner. Several emotions can be experienced at the same time. Instead of recognizing only categorical information about emotions, there is a need to understand and quantify the intensity of emotions. The proposed research intends to investigate a quantum-inspired approach for quantifying emotional intensities in runtime. The inspiration comes from manifesting human cognition and decision-making capabilities, which may adopt a brief explanation through quantum theory. Quantum state fidelity was used to characterize states and estimate emotion intensities rendered by subjects from the Amsterdam Dynamic Facial Expression Set (ADFES) dataset. The Quantum variational classifier technique was used to perform this experiment on the IBM Quantum Experience platform. The proposed method successfully quantifies the intensities of joy, sadness, contempt, anger, surprise, and fear emotions of labelled subjects from the ADFES dataset