Optical and interferometric studies of growth phenomena on carborundum crystals

The theory of the growth of a perfect crystal is outlined and a brief description is given of the development of this theory, taking into account the presence of imperfections, especially dislocations, in the crystal. The molecular 'growth spirals' and other features predicted by this theory put forward by Burton, Cabrera and Prank require improved experimental techniques for their study, which are described. The experimental study of the growth features divides itself into two parts: (1) Microscopic studies. (2) Interferometric studies. The different growth features observed on the faces of silicon-carbide (Si-C) crystals are illustrated and explained. The observed 'growth spirals' can be divided into three types: (1) Elementary spirals with step heights equal to the size of the X-ray unit cell. (2) Spirals originating from dislocations of multiple strength, the step heights being a multiple of the X-ray unit cell. (3) Interlaced spirals in which the step heights are a fraction of the unit cell. The microscopic studies illustrate the information about the shape of the spirals, the behaviour and interaction of growth fronts with one another, originating from different sources, the growth pattern for a number of screw dislocations emerging on the crystal face fault surfaces, and their statiscal properties such as density of dis-locations etc. From these studies the type of information obtainable about the conditions of growth is the size of the critical nucleus and the supersaturation. The interferometric techniques utilized for the measurement of step heights are discussed. A study of the measured step heights leads to an understanding of the interesting property of 'polytypism' as observed in silicon-carbide crystals which occur in different types as shown by X-ray diffraction data. The 'growth spirals' demonstrate the X-ray predictions and confirm them.<p

3D Object Detection for Autonomous Driving: A Survey

Autonomous driving is regarded as one of the most promising remedies to shield human beings from severe crashes. To this end, 3D object detection serves as the core basis of such perception system especially for the sake of path planning, motion prediction, collision avoidance, etc. Generally, stereo or monocular images with corresponding 3D point clouds are already standard layout for 3D object detection, out of which point clouds are increasingly prevalent with accurate depth information being provided. Despite existing efforts, 3D object detection on point clouds is still in its infancy due to high sparseness and irregularity of point clouds by nature, misalignment view between camera view and LiDAR bird's eye of view for modality synergies, occlusions and scale variations at long distances, etc. Recently, profound progress has been made in 3D object detection, with a large body of literature being investigated to address this vision task. As such, we present a comprehensive review of the latest progress in this field covering all the main topics including sensors, fundamentals, and the recent state-of-the-art detection methods with their pros and cons. Furthermore, we introduce metrics and provide quantitative comparisons on popular public datasets. The avenues for future work are going to be judiciously identified after an in-deep analysis of the surveyed works. Finally, we conclude this paper.Comment: 3D object detection, Autonomous driving, Point cloud

Representations for Cognitive Vision : a Review of Appearance-Based, Spatio-Temporal, and Graph-Based Approaches

The emerging discipline of cognitive vision requires a proper representation of visual information including spatial and temporal relationships, scenes, events, semantics and context. This review article summarizes existing representational schemes in computer vision which might be useful for cognitive vision, a and discusses promising future research directions. The various approaches are categorized according to appearance-based, spatio-temporal, and graph-based representations for cognitive vision. While the representation of objects has been covered extensively in computer vision research, both from a reconstruction as well as from a recognition point of view, cognitive vision will also require new ideas how to represent scenes. We introduce new concepts for scene representations and discuss how these might be efficiently implemented in future cognitive vision systems

Combining segmentation and attention: a new foveal attention model

Artificial vision systems cannot process all the information that they receive from the world in real time because it is highly expensive and inefficient in terms of computational cost. Inspired by biological perception systems, artificial attention models pursuit to select only the relevant part of the scene. On human vision, it is also well established that these units of attention are not merely spatial but closely related to perceptual objects (proto-objects). This implies a strong bidirectional relationship between segmentation and attention processes. While the segmentation process is the responsible to extract the proto-objects from the scene, attention can guide segmentation, arising the concept of foveal attention. When the focus of attention is deployed from one visual unit to another, the rest of the scene is perceived but at a lower resolution that the focused object. The result is a multi-resolution visual perception in which the fovea, a dimple on the central retina, provides the highest resolution vision. In this paper, a bottom-up foveal attention model is presented. In this model the input image is a foveal image represented using a Cartesian Foveal Geometry (CFG), which encodes the field of view of the sensor as a fovea (placed in the focus of attention) surrounded by a set of concentric rings with decreasing resolution. Then multi-resolution perceptual segmentation is performed by building a foveal polygon using the Bounded Irregular Pyramid (BIP). Bottom-up attention is enclosed in the same structure, allowing to set the fovea over the most salient image proto-object. Saliency is computed as a linear combination of multiple low level features such as color and intensity contrast, symmetry, orientation and roundness. Obtained results from natural images show that the performance of the combination of hierarchical foveal segmentation and saliency estimation is good in terms of accuracy and speed

Why population forecasts should be probabilistic - illustrated by the case of Norway

Deterministic population forecasts do not give an appropriate indication of forecast uncertainty. Forecasts should be probabilistic, rather than deterministic, so that their expected accuracy can be assessed. We review three main methods to compute probabilistic forecasts, namely time series extrapolation, analysis of historical forecast errors, and expert judgement. We illustrate, by the case of Norway up to 2050, how elements of these three methods can be combined when computing prediction intervals for a population’s future size and age-sex composition. We show the relative importance for prediction intervals of various sources of variance, and compare our results with those of the official population forecast computed by Statistics Norway.cohort component method, forecast errors, forecasting, simulation, stochastic population forecast, time series, uncertainty

Phase 1 of the automated array assembly task of the low cost silicon solar array project

The results of a study of process variables and solar cell variables are presented. Interactions between variables and their effects upon control ranges of the variables are identified. The results of a cost analysis for manufacturing solar cells are discussed. The cost analysis includes a sensitivity analysis of a number of cost factors

Text localization and recognition in natural scene images

Text localization and recognition (text spotting) in natural scene images is an interesting task that finds many practical applications. Algorithms for text spotting may be used in helping visually impaired subjects during navigation in unknown environments; building autonomous driving systems that automatically avoid collisions with pedestrians or automatically identify speed limits and warn the driver about possible infractions that are being committed; and to ease or solve some tedious and repetitive data entry tasks that are still manually carried out by humans. While Optical Character Recognition (OCR) from scanned documents is a solved problem, the same cannot be said for text spotting in natural images. In fact, this latest class of images contains plenty of difficult situations that algorithms for text spotting need to deal with in order to reach acceptable recognition rates. During my PhD research I focused my studies on the development of novel systems for text localization and recognition in natural scene images. The two main works that I have presented during these three years of PhD studies are presented in this thesis: (i) in my first work I propose a hybrid system which exploits the key ideas of region-based and connected components (CC)-based text localization approaches to localize uncommon fonts and writings in natural images; (ii) in my second work I describe a novel deep-based system which exploits Convolutional Neural Networks and enhanced stable CC to achieve good text spotting results on challenging data sets. During the development of both these methods, my focus has always been on maintaining an acceptable computational complexity and a high reproducibility of the achieved results