Differential Operators for Edge Detection

We present several results characterizing two differential operators used for edge detection: the Laplacian and the second directional derivative along the gradient. In particular, (a)we give conditions for coincidence of the zeros of the two operators, and (b) we show that the second derivative along the gradient has the same zeros of the normal curvature in the gradient direction. Biological implications are also discussed. An experiment is suggested to test which of the two operators may be used by the human visual system.MIT Artificial Intelligence Laborator

Accelerating object extraction and detection using a hierarchical approach with shape descriptors

Automatic object recognition is a fundamental problem in the fields of computer vision and machine learning, that has received a lot of research attention lately. Miniaturization and affordability, of both, high resolution digital cameras and advanced computing hardware, have further advanced the scope and applications of object recognition methods. While there are different methods, that build upon various low level features to construct object models, this work explores and implements the use of closed-contours as formidable object features. A hierarchical technique is employed to extract the contours, exploiting the inherent spatial relationships between the parent and child contours of an object, and later describing them as part of the query feature vector. Fourier Descriptors are used to effectively and invariantly describe the extracted contours. A diverse database of shapes is created and later used to train standard classification algorithms, for shape-labeling. A simple-hierarchical, shape label and spatial descriptor matching method is implemented, to find the nearest object-model, from a collection of stored templates. Multi-threaded architecture and GPU efficient image-processing functions are adopted wherever possible, speeding up the running time of the proposed technique, and making it efficient for use in real world applications. The technique is successfully tested on common traffic signs in real world images, with overall good performance and robustness being obtained as an end result

Pressure-based large-eddy simulation of under-expanded hydrogen jets for engine applications

An assessment of Large-Eddy Simulations (LES) of non-reactive under-expanded hydrogen jets by using a pressure-based algorithm is presented. Such jets feature strong compressible discontinuities often considered to be best dealt with by a density-based solver. The crucial contribution of this work is to evaluate the suitability of the pressure- based solver to correctly describe the flow field of gaseous hydrogen jets for engine ap- plications, despite the presence of shock waves in the under-expanded near-orifice region. Inherently, the paper aims at providing guidance on the corresponding numerical aspects to simulate these flows. Hydrogen jets in an argon atmosphere at three different injection pressures are simulated and the results are compared to experiments in literature. Jet tip penetration and cone angle are the main investigated parameters. A good match is found, confirming the solidity of the proposed model. Different LES sub-grid scale models and discretisation schemes are then investigated in order to find the best approach in terms of accuracy and required computational cost. In particular, it is found that the WALE model coupled with a 4th-order cubic scheme for the convective terms yields the most suitable configuration

A sub-pixel edge detector: an implementation of the Canny/Devernay Algorithm

An image edge detector is described which produces chained edge points with sub-pixel accuracy. The method incorporates the main ideas of the classic Canny and Devernay algorithms. The analysis shows that a slight modification to the original formulation improves the sub-pixel accuracy of the edge points

Investigation of Spray Angle Measurement Techniques

The in-cylinder fluid-dynamic processes of fuel injection and air entrainment influence the structure and shape of evolving fuel sprays, which can subsequently alter the ignition, combustion and pollutant formation processes in diesel engines. Different spray angle detection methods have been used in the literature to investigate the global and local spray characteristics. In this work, the five most widely used diesel spray angle detection methods were identified, and used to evaluate the characteristic features of each detection method: methods with a detection range based on the spray penetration length, methods with a fixed detection range in the near and far-field spray region, triangular-based methods, and methods based on averaging local data points. The sprays were acquired from our spray chamber and processed with different thresholding techniques to explore the differences between spray angle detection methods. All five methods generated a similar global trend of spray angle variation for temporally evolving sprays over the complete injection period. However, the actual spray angle values detected by each method were not always comparable. The differences in spray angle values between the different detection methods were larger during the early start of injection, and these differences systematically decreased as the spray approached a steady-state. The methods that detected the angle in the far-field demonstrated lower spatiotemporal variability when compared to the methods that detected the angle in the near-field. An assessment ofthecomparabilitybetweenangledetectionmethodswasmadeandtheoutcomeprovidesguidanceforthe selection of the spray angle detection method.Engineering and Physical Sciences Research Council in the UK (EPSRC

BEMDEC: An Adaptive and Robust Methodology for Digital Image Feature Extraction

The intriguing study of feature extraction, and edge detection in particular, has, as a result of the increased use of imagery, drawn even more attention not just from the field of computer science but also from a variety of scientific fields. However, various challenges surrounding the formulation of feature extraction operator, particularly of edges, which is capable of satisfying the necessary properties of low probability of error (i.e., failure of marking true edges), accuracy, and consistent response to a single edge, continue to persist. Moreover, it should be pointed out that most of the work in the area of feature extraction has been focused on improving many of the existing approaches rather than devising or adopting new ones. In the image processing subfield, where the needs constantly change, we must equally change the way we think. In this digital world where the use of images, for variety of purposes, continues to increase, researchers, if they are serious about addressing the aforementioned limitations, must be able to think outside the box and step away from the usual in order to overcome these challenges. In this dissertation, we propose an adaptive and robust, yet simple, digital image features detection methodology using bidimensional empirical mode decomposition (BEMD), a sifting process that decomposes a signal into its two-dimensional (2D) bidimensional intrinsic mode functions (BIMFs). The method is further extended to detect corners and curves, and as such, dubbed as BEMDEC, indicating its ability to detect edges, corners and curves. In addition to the application of BEMD, a unique combination of a flexible envelope estimation algorithm, stopping criteria and boundary adjustment made the realization of this multi-feature detector possible. Further application of two morphological operators of binarization and thinning adds to the quality of the operator

Image partition and video segmentation using the Mumford-Shah functional

2010 - 2011The aim of this Thesis is to present an image partition and video segmentation procedure, based on the minimization of a modified version of Mumford-Shah functional. The Mumford-Shah functional used for image partition has been then extended to develop a video segmentation procedure. Differently by the image processing, in video analysis besides the usual spatial connectivity of pixels (or regions) on each single frame, we have a natural notion of “temporal” connectivity between pixels (or regions) on consecutive frames given by the optical flow. In this case, it makes sense to extend the tree data structure used to model a single image with a graph data structure that allows to handle a video sequence. The video segmentation procedure is based on minimization of a modified version of a Mumford-Shah functional. In particular the functional used for image partition allows to merge neighboring regions with similar color without considering their movement. Our idea has been to merge neighboring regions with similar color and similar optical flow vector. Also in this case the minimization of Mumford-Shah functional can be very complex if we consider each possible combination of the graph nodes. This computation becomes easy to do if we take into account a hierarchy of partitions constructed starting by the nodes of the graph.[edited by author]X n.s

Computerised stereoscopic measurement of the human retina

The research described herein is an investigation into the problems of obtaining useful clinical measurements from stereo photographs of the human retina through automation of the stereometric procedure by digital stereo matching and image analysis techniques. Clinical research has indicated a correlation between physical changes to the optic disc topography (the region on the retina where the optic nerve enters the eye) and the advance of eye disease such as hypertension and glaucoma. Stereoscopic photography of the human retina (or fundus, as it is called) and the subsequent measurement of the topography of the optic disc is of great potential clinical value as an aid in observing the pathogenesis of such disease, and to this end, accurate measurements of the various parameters that characterise the changing shape of the optic disc topography must be provided. Following a survey of current clinical methods for stereoscopic measurement of the optic disc, fundus image data acquisition, stereo geometry, limitations of resolution and accuracy, and other relevant physical constraints related to fundus imaging are investigated. A survey of digital stereo matching algorithms is presented and their strengths and weaknesses are explored, specifically as they relate to the suitability of the algorithm for the fundus image data. The selection of an appropriate stereo matching algorithm is discussed, and its application to four test data sets is presented in detail. A mathematical model of two-dimensional image formation is developed together with its corresponding auto-correlation function. In the presense of additive noise, the model is used as a tool for exploring key problems with respect to the stereo matching of fundus images. Specifically, measures for predicting correlation matching error are developed and applied. Such measures are shown to be of use in applications where the results of image correlation cannot be independently verified, and meaningful quantitative error measures are required. The application of these theoretical tools to the fundus image data indicate a systematic way to measure, assess and control cross-correlation error. Conclusions drawn from this research point the way forward for stereo analysis of the optic disc and highlight a number of areas which will require further research. The development of a fully automated system for diagnostic evaluation of the optic disc topography is discussed in the light of the results obtained during this research

Visual tracking over multiple temporal scales

Visual tracking is the task of repeatedly inferring the state (position, motion, etc.) of the desired target in an image sequence. It is an important scientific problem as humans can visually track targets in a broad range of settings. However, visual tracking algorithms struggle to robustly follow a target in unconstrained scenarios. Among the many challenges faced by visual trackers, two important ones are occlusions and abrupt motion variations. Occlusions take place when (an)other object(s) obscures the camera's view of the tracked target. A target may exhibit abrupt variations in apparent motion due to its own unexpected movement, camera movement, and low frame rate image acquisition. Each of these issues can cause a tracker to lose its target. This thesis introduces the idea of learning and propagation of tracking information over multiple temporal scales to overcome occlusions and abrupt motion variations. A temporal scale is a specific sequence of moments in time Models (describing appearance and/or motion of the target) can be learned from the target tracking history over multiple temporal scales and applied over multiple temporal scales in the future. With the rise of multiple motion model tracking frameworks, there is a need for a broad range of search methods and ways of selecting between the available motion models. The potential benefits of learning over multiple temporal scales are first assessed by studying both motion and appearance variations in the ground-truth data associated with several image sequences. A visual tracker operating over multiple temporal scales is then proposed that is capable of handling occlusions and abrupt motion variations. Experiments are performed to compare the performance of the tracker with competing methods, and to analyze the impact on performance of various elements of the proposed approach. Results reveal a simple, yet general framework for dealing with occlusions and abrupt motion variations. In refining the proposed framework, a search method is generalized for multiple competing hypotheses in visual tracking, and a new motion model selection criterion is proposed