Pixelated detectors and improved efficiency for magnetic imaging in STEM differential phase contrast

The application of differential phase contrast imaging to the study of polycrystalline magnetic thin films and nanostructures has been hampered by the strong diffraction contrast resulting from the granular structure of the materials. In this paper we demonstrate how a pixelated detector has been used to detect the bright field disk in aberration corrected scanning transmission electron microscopy (STEM) and subsequent processing of the acquired data allows efficient enhancement of the magnetic contrast in the resulting images. Initial results from a charged coupled device (CCD) camera demonstrate the highly efficient nature of this improvement over previous methods. Further hardware development with the use of a direct radiation detector, the Medipix3, also shows the possibilities where the reduction in collection time is more than an order of magnitude compared to the CCD. We show that this allows subpixel measurement of the beam deflection due to the magnetic induction. While the detection and processing is data intensive we have demonstrated highly efficient DPC imaging whereby pixel by pixel interpretation of the induction variation is realised with great potential for nanomagnetic imaging

Modeling edges at subpixel accuracy using the local energy approach

In this paper we described new technique for 1-D and 2-D edge feature extraction to subpixel accuracy using edge models and the local energy approach. A candidate edge is modeled as one of a number of parametric edge models, and the fit is refined by a least-squared error fitting technique

Edge Detection with Sub-pixel Accuracy Based on Approximation of Edge with Erf Function

Edge detection is an often used procedure in digital image processing. For some practical applications is desirable to detect edges with sub-pixel accuracy. In this paper we present edge detection method for 1-D images based on approximation of real image function with Erf function. This method is verified by simulations and experiments for various numbers of samples of simulated and real images. Results of simulations and experiments are also used to compare proposed edge detection scheme with two often used moment-based edge detectors with sub-pixel precision

2-D edge feature extraction to subpixel accuracy using the generalized energy approach

Precision edge feature extraction is a very important step in vision, Researchers mainly use step edges to model an edge at subpixel level. In this paper we describe a new technique for two dimensional edge feature extraction to subpixel accuracy using a general edge model. Using six basic edge types to model edges, the edge parameters at subpixel level are extracted by fitting a model to the image signal using least-.squared error fitting technique.<br /

The image processing for the target centre detection in digital image

This thesis comprises of five chapters. Chapter one describes basic principles of the digital image, digital image construction and the present status of the digital photogrammetry system, named PHOENICS (PHOtogrammetric ENgineering and Industrial digital Camera System), as developed by H. Rüther (1989). The target's shape analysis in the digital image are presented in chapter two. Chapter three presents the algorithms to detect and locate target on the digital image. These are the least squares adjustment technique, moment method, moment-preserving for edge detection as well as test methods for the evaluation of the various alglorithms. The novel RG method is presented in chapter four. Chapter five introduces the theory of some image processing methods

Image understanding and feature extraction for applications in industry and mapping

Bibliography: p. 212-220.The aim of digital photogrammetry is the automated extraction and classification of the three dimensional information of a scene from a number of images. Existing photogrammetric systems are semi-automatic requiring manual editing and control, and have very limited domains of application so that image understanding capabilities are left to the user. Among the most important steps in a fully integrated system are the extraction of features suitable for matching, the establishment of the correspondence between matching points and object classification. The following study attempts to explore the applicability of pattern recognition concepts in conjunction with existing area-based methods, feature-based techniques and other approaches used in computer vision in order to increase the level of automation and as a general alternative and addition to existing methods. As an illustration of the pattern recognition approach examples of industrial applications are given. The underlying method is then extended to the identification of objects in aerial images of urban scenes and to the location of targets in close-range photogrammetric applications. Various moment-based techniques are considered as pattern classifiers including geometric invariant moments, Legendre moments, Zernike moments and pseudo-Zernike moments. Two-dimensional Fourier transforms are also considered as pattern classifiers. The suitability of these techniques is assessed. These are then applied as object locators and as feature extractors or interest operators. Additionally the use of fractal dimension to segment natural scenes for regional classification in order to limit the search space for particular objects is considered. The pattern recognition techniques require considerable preprocessing of images. The various image processing techniques required are explained where needed. Extracted feature points are matched using relaxation based techniques in conjunction with area-based methods to 'obtain subpixel accuracy. A subpixel pattern recognition based method is also proposed and an investigation into improved area-based subpixel matching methods is undertaken. An algorithm for determining relative orientation parameters incorporating the epipolar line constraint is investigated and compared with a standard relative orientation algorithm. In conclusion a basic system that can be automated based on some novel techniques in conjunction with existing methods is described and implemented in a mapping application. This system could be largely automated with suitably powerful computers

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

Divergence Model for Measurement of Goos-Hanchen Shift

In this effort a new measurement technique for the lateral Goos-Hanchen shift is developed, analyzed, and demonstrated. The new technique uses classical image formation methods fused with modern detection and analysis methods to achieve higher levels of sensitivity than obtained with prior practice. Central to the effort is a new mathematical model of the dispersion seen at a step shadow when the Goos-Hanchen effect occurs near critical angle for total internal reflection. Image processing techniques are applied to measure the intensity distribution transfer function of a new divergence model of the Goos-Hanchen phenomena providing verification of the model. This effort includes mathematical modeling techniques, analytical derivations of governing equations, numerical verification of models and sensitivities, optical design of apparatus, image processin

A non-contact vision-based system for multi-point displacement monitoring in a cable-stayed footbridge

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.Vision-based monitoring receives increased attention for measuring displacements of civil infrastructure such as towers and bridges. Currently, most field applications rely on artificial targets for video processing convenience, leading to high installation effort and focus on only single-point displacement measurement e.g. at mid-span of a bridge. This study proposes a low-cost and non-contact vision-based system for multi-point displacement measurement based on a consumer-grade camera for video acquisition and a custom-developed package for video processing. The system has been validated on a cable-stayed footbridge for deck deformation and cable vibration measurement under pedestrian loading. The analysis results indicate that the system provides valuable information about bridge deformation of the order of a few cm induced, in this application, by pedestrian passing. The measured data enables accurate estimation of modal frequencies of either the bridge deck or the bridge cables and could be used to investigate variations of modal frequencies under varying pedestrian loads