Multi-scale data fusion for surface metrology

The major trends in manufacturing are miniaturization, convergence of the traditional research fields and creation of interdisciplinary research areas. These trends have resulted in the development of multi-scale models and multi-scale surfaces to optimize the performance. Multi-scale surfaces that exhibit specific properties at different scales for a specific purpose require multi-scale measurement and characterization. Researchers and instrument developers have developed instruments that are able to perform measurements at multiple scales but lack the much required multi- scale characterization capability. The primary focus of this research was to explore possible multi-scale data fusion strategies and options for surface metrology domain and to develop enabling software tools in order to obtain effective multi-scale surface characterization, maximizing fidelity while minimizing measurement cost and time. This research effort explored the fusion strategies for surface metrology domain and narrowed the focus on Discrete Wavelet Frame (DWF) based multi-scale decomposition. An optimized multi-scale data fusion strategy ‘FWR method’ was developed and was successfully demonstrated on both high aspect ratio surfaces and non-planar surfaces. It was demonstrated that the datum features can be effectively characterized at a lower resolution using one system (Vision CMM) and the actual features of interest could be characterized at a higher resolution using another system (Coherence Scanning Interferometer) with higher capability while minimizing the measurement time

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

Automatic near real-time flood detection in high resolution X-band synthetic aperture radar satellite data using context-based classification on irregular graphs

This thesis is an outcome of the project “Flood and damage assessment using very high resolution SAR data” (SAR-HQ), which is embedded in the interdisciplinary oriented RIMAX (Risk Management of Extreme Flood Events) programme, funded by the Federal Ministry of Education and Research (BMBF). It comprises the results of three scientific papers on automatic near real-time flood detection in high resolution X-band synthetic aperture radar (SAR) satellite data for operational rapid mapping activities in terms of disaster and crisis-management support. Flood situations seem to become more frequent and destructive in many regions of the world. A rising awareness of the availability of satellite based cartographic information has led to an increase in requests to corresponding mapping services to support civil-protection and relief organizations with disaster-related mapping and analysis activities. Due to the rising number of satellite systems with high revisit frequencies, a strengthened pool of SAR data is available during operational flood mapping activities. This offers the possibility to observe the whole extent of even large-scale flood events and their spatio-temporal evolution, but also calls for computationally efficient and automatic flood detection methods, which should drastically reduce the user input required by an active image interpreter. This thesis provides solutions for the near real-time derivation of detailed flood parameters such as flood extent, flood-related backscatter changes as well as flood classification probabilities from the new generation of high resolution X-band SAR satellite imagery in a completely unsupervised way. These data are, in comparison to images from conventional medium-resolution SAR sensors, characterized by an increased intra-class and decreased inter-class variability due to the reduced mixed pixel phenomenon. This problem is addressed by utilizing multi-contextual models on irregular hierarchical graphs, which consider that semantic image information is less represented in single pixels but in homogeneous image objects and their mutual relation. A hybrid Markov random field (MRF) model is developed, which integrates scale-dependent as well as spatio-temporal contextual information into the classification process by combining hierarchical causal Markov image modeling on automatically generated irregular hierarchical graphs with noncausal Markov modeling related to planar MRFs. This model is initialized in an unsupervised manner by an automatic tile-based thresholding approach, which solves the flood detection problem in large-size SAR data with small a priori class probabilities by statistical parameterization of local bi-modal class-conditional density functions in a time efficient manner. Experiments performed on TerraSAR-X StripMap data of Southwest England and ScanSAR data of north-eastern Namibia during large-scale flooding show the effectiveness of the proposed methods in terms of classification accuracy, computational performance, and transferability. It is further demonstrated that hierarchical causal Markov models such as hierarchical maximum a posteriori (HMAP) and hierarchical marginal posterior mode (HMPM) estimation can be effectively used for modeling the inter-spatial context of X-band SAR data in terms of flood and change detection purposes. Although the HMPM estimator is computationally more demanding than the HMAP estimator, it is found to be more suitable in terms of classification accuracy. Further, it offers the possibility to compute marginal posterior entropy-based confidence maps, which are used for the generation of flood possibility maps that express that the uncertainty in labeling of each image element. The supplementary integration of intra-spatial and, optionally, temporal contextual information into the Markov model results in a reduction of classification errors. It is observed that the application of the hybrid multi-contextual Markov model on irregular graphs is able to enhance classification results in comparison to modeling on regular structures of quadtrees, which is the hierarchical representation of images usually used in MRF-based image analysis. X-band SAR systems are generally not suited for detecting flooding under dense vegetation canopies such as forests due to the low capability of the X-band signal to penetrate into media. Within this thesis a method is proposed for the automatic derivation of flood areas beneath shrubs and grasses from TerraSAR-X data. Furthermore, an approach is developed, which combines high resolution topographic information with multi-scale image segmentation to enhance the mapping accuracy in areas consisting of flooded vegetation and anthropogenic objects as well as to remove non-water look-alike areas

Cumulative index to NASA Tech Briefs, 1986-1990, volumes 10-14

Tech Briefs are short announcements of new technology derived from the R&D activities of the National Aeronautics and Space Administration. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This cumulative index of Tech Briefs contains abstracts and four indexes (subject, personal author, originating center, and Tech Brief number) and covers the period 1986 to 1990. The abstract section is organized by the following subject categories: electronic components and circuits, electronic systems, physical sciences, materials, computer programs, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

NASA Tech Briefs, December 1988

This month's technical section includes forecasts for 1989 and beyond by NASA experts in the following fields: Integrated Circuits; Communications; Computational Fluid Dynamics; Ceramics; Image Processing; Sensors; Dynamic Power; Superconductivity; Artificial Intelligence; and Flow Cytometry. The quotes provide a brief overview of emerging trends, and describe inventions and innovations being developed by NASA, other government agencies, and private industry that could make a significant impact in coming years. A second bonus feature in this month's issue is the expanded subject index that begins on page 98. The index contains cross-referenced listings for all technical briefs appearing in NASA Tech Briefs during 1988

An intelligent system for the classification and selection of novel and efficient lossless image compression algorithms

We are currently living in an era revolutionised by the development of smart phones and digital cameras. Most people are using phones and cameras in every aspect of their lives. With this development comes a high level of competition between the technology companies developing these devices, each one trying to enhance its products to meet the new market demands. One of the most sought-after criteria of any smart phone or digital camera is the camera’s resolution. Digital imaging and its applications are growing rapidly; as a result of this growth, the image size is increasing, and alongside this increase comes the important challenge of saving these large-sized images and transferring them over networks. With the increase in image size, the interest in image compression is increasing as well, to improve the storage size and transfer time. In this study, the researcher proposes two new lossless image compression algorithms. Both proposed algorithms focus on decreasing the image size by reducing the image bit-depth through using well defined methods of reducing the coloration between the image intensities.The first proposed lossless image compression algorithm is called Column Subtraction Compression (CSC), which aims to decrease the image size without losing any of the image information by using a colour transformation method as a pre-processing phase, followed by the proposed Column Subtraction Compression function to decrease the image size. The proposed algorithm is specially designed for compressing natural images. The CSC algorithm was evaluated for colour images and compared against benchmark schemes obtained from (Khan et al., 2017). It achieved the best compression size over the existing methods by enhancing the average storage saving of the BBWCA, JPEG 2000 LS, KMTF– BWCA, HEVC and basic BWCA algorithms by 2.5%, 15.6%, 41.6%, 7.8% and 45.07% respectively. The CSC algorithm simple implementation positively affects the execution time and makes it one of the fastest algorithms, since it needed less than 0.5 second for compressing and decompressing natural images obtained from (Khan et al., 2017). The proposed algorithm needs only 19.36 seconds for compressing and decompressing all of the 10 images from the Kodak image set, while the BWCA, KMTF – BWCA and BBWCA need 398.5s, 429.24s and 475.38s respectively. Nevertheless, the CSC algorithm achieved less compression ratio, when compressing low resolution images since it was designed for compressing high resolution images. To solve this issue, the researcher proposed the Low-Resolution Column Subtraction Compression algorithm (LRCSC) to enhance the CSC low compression ratio related to compressing low-resolution images.The LRCSC algorithm starts by using the CSC algorithm as a pre-processing phase, followed by the Huffman algorithm and Run-Length Coding (RLE) to decrease the image size as a final compression phase. The LRCSC enhanced the average storage saving of the CSC algorithm for raster map images by achieving 13.68% better compression size. The LRCSC algorithm decreases the raster map image set size by saving 96% from the original image set size but did not reach the best results when compared with the PNG, GIF, BLiSE and BBWCA where the storage saving is 97.42%, 98.33%, 98.92% and 98.93% respectively. The LRCSC algorithm enhanced the compression execution time with acceptable compression ratio. Both of the proposed algorithms are effective with any image types such as colour or greyscale images. The proposed algorithms save a lot of memory storage and dramatically decreased the execution time.Finally, to take full benefits of the two newly developed algorithms, anew system is developed based on running both of the algorithm for the same input image and then suggest the appropriate algorithm to be used for the de-compression phase