On Using Physical Analogies for Feature and Shape Extraction in Computer Vision

There is a rich literature of approaches to image feature extraction in computer vision. Many sophisticated approaches exist for low- and high-level feature extraction but can be complex to implement with parameter choice guided by experimentation, but impeded by speed of computation. We have developed new ways to extract features based on notional use of physical paradigms, with parameterisation that is more familiar to a scientifically-trained user, aiming to make best use of computational resource. We describe how analogies based on gravitational force can be used for low-level analysis, whilst analogies of water flow and heat can be deployed to achieve high-level smooth shape detection. These new approaches to arbitrary shape extraction are compared with standard state-of-art approaches by curve evolution. There is no comparator operator to our use of gravitational force. We also aim to show that the implementation is consistent with the original motivations for these techniques and so contend that the exploration of physical paradigms offers a promising new avenue for new approaches to feature extraction in computer vision

The image ray transform for structural feature detection

The use of analogies to physical phenomena is an exciting paradigm in computer vision that allows unorthodox approaches to feature extraction, creating new techniques with unique properties. A technique known as the "image ray transform" has been developed based upon an analogy to the propagation of light as rays. The transform analogises an image to a set of glass blocks with refractive index linked to pixel properties and then casts a large number of rays through the image. The course of these rays is accumulated into an output image. The technique can successfully extract tubular and circular features and we show successful circle detection, ear biometrics and retinal vessel extraction. The transform has also been extended through the use of multiple rays arranged as a beam to increase robustness to noise, and we show quantitative results for fully automatic ear recognition, achieving 95.2% rank one recognition across 63 subjects

Circle Detection Using the Image Ray Transform

Physical analogies are an exciting paradigm for creating techniques for image feature extraction. A transform using an analogy to light rays has been developed for the detection of circular and tubular features. It uses a 2D ray tracing algorithm to follow rays through an image, interacting at a low level, to emphasise higher level features. It has been empirically tested as a pre-processor to aid circle detection with the Hough Transform and has been shown to provide a clear improvement over standard techniques. The transform was also used on natural images and we show its ability to highlight circles even in complex scenes. We also show the flexibility available to the technique through adjustment of parameters

The image ray transform

Image feature extraction is a fundamental area of image processing and computer vision. There are many ways that techniques can be created that extract features and particularly novel techniques can be developed by taking influence from the physical world. This thesis presents the Image Ray Transform (IRT), a technique based upon an analogy to light, using the mechanisms that define how light travels through different media and analogy to optical fibres to extract structural features within an image. Through analogising the image as a transparent medium we can use refraction and reflection to cast many rays inside the image and guide them towards features, transforming the image in order to emphasise tubular and circular structures.The power of the transform for structural feature detection is shown empirically in a number of applications, especially through its ability to highlight curvilinear structures. The IRT is used to enhance the accuracy of circle detection through use as a preprocessor, highlighting circles to a greater extent than conventional edge detection methods. The transform is also shown to be well suited to enrolment for ear biometrics, providing a high detection and recognition rate with PCA, comparable to manual enrolment. Vascular features such as those found in medical images are also shown to be emphasised by the transform, and the IRT is used for detection of the vasculature in retinal fundus images.Extensions to the basic image ray transform allow higher level features to be detected. A method is shown for expressing rays in an invariant form to describe the structures of an object and hence the object itself with a bag-of-visual words model. These ray features provide a complementary description of objects to other patch-based descriptors and have been tested on a number of object categorisation databases. Finally a different analysis of rays is provided that can produce information on both bilateral (reflectional) and rotational symmetry within the image, allowing a deeper understanding of image structure. The IRT is a flexible technique, capable of detecting a range of high and low level image features, and open to further use and extension across a range of applications

Application of Analogical Reasoning for Use in Visual Knowledge Extraction

There is a continual push to make Artificial Intelligence (AI) as human-like as possible; however, this is a difficult task because of its inability to learn beyond its current comprehension. Analogical reasoning (AR) has been proposed as one method to achieve this goal. Current literature lacks a technical comparison on psychologically-inspired and natural-language-processing-produced AR algorithms with consistent metrics on multiple-choice word-based analogy problems. Assessment is based on “correctness” and “goodness” metrics. There is not a one-size-fits-all algorithm for all textual problems. As contribution in visual AR, a convolutional neural network (CNN) is integrated with the AR vector space model, Global Vectors (GloVe), in the proposed, Image Recognition Through Analogical Reasoning Algorithm (IRTARA). Given images outside of the CNN’s training data, IRTARA produces contextual information by leveraging semantic information from GloVe. IRTARA’s quality of results is measured by definition, AR, and human factors evaluation methods, which saw consistency at the extreme ends. The research shows the potential for AR to facilitate more a human-like AI through its ability to understand concepts beyond its foundational knowledge in both a textual and visual problem space