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

Watershed based region growing algorithm

This paper presents a solution to a major drawback of watershed transformation: over segmentation. The solution utilizes one of its main advantages - very good edge extraction. It is a method that simulates pouring water onto a landscape created on a basis of a digital image. Unfortunately transformation produces a region for each local minimum so, usually, the number of watersheds (catchment basins) is too big. Watershed region growing is based on a minimum variance region growing algorithm [1-3]. It differs from the original in that it grows a homogenous region by adding and removing entire watersheds (catchment basins) and not separate pixels. The generalized watershed based region dilation and contraction are presented. Thanks to the use of watershed transformation, the region growing process is not able to grow a region easily outside the object boundaries. Test segmentations of two class images and a comparison between the minimum variance and the watershed based region growing are presented

Detección del espacio glotal en imágenes laríngeas mediante transformada Watershed y Merging JND

El presente artículo describe un nuevo método para la detección del espacio glotal en imágenes laríngeas obtenidas de vídeos de alta o baja velocidad. El proceso de detección basa su eficacia en la combinación de varias técnicas de gran relevancia en el campo del tratamiento digital de imágenes. Una de estas técnicas es la transformada Watershed que junto con varios tipos de Merging y un proceso final de predicción lineal, hacen posible la detección automática en un 99% de las imágenes analizadas. La potencia del método se ve incrementada por la ausencia de cualquier tipo de inicialización y por no necesitar condiciones estrictas sobre las características de las imágenes a procesar. Evidentemente es importante que el algoritmo integre información a priori del espacio glotal, pero este conocimiento es bastante relajado comparado con las condiciones impuestas por otros trabajos que también intentan la segmentación

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 for high-level feature extraction but can be complex to implement with parameter choice guided by experimentation, but with performance analysis and optimization impeded by speed of computation. We have developed new feature extraction techniques on notional use of physical paradigms, with parametrization aimed to be more familiar to a scientifically trained user, aiming to make best use of computational resource. This paper is the first unified description of these new approaches, outlining the basis and results that can be achieved. We describe how gravitational force can be used for low-level analysis, while analogies of water flow and heat can be deployed to achieve high-level smooth shape detection, by determining features and shapes in a selection of images, comparing results with those by stock approaches from the literature. 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

Plateau Problem in the Watershed Transform

The watershed transform is one of best known and widely used methods for image segmentation in mathematical morphology. Since the definition, deriving from geology and nature observation is quite intuitive and straightforward to implement; many fast and powerful algorithms for watershed transform have already been presented. However, there still occur problems when one wishes to achieve a precise solution on blurred or noised image. The same range of problems is faced when a plateau occurs in the image. In this paper several methods for plateau reduction are discussed and some novel ideas proposed. All algorithms are performed on a set of both natural and synthetic images

Segmentation and feature extraction of fluid-filled uterine fibroid–A knowledge-based approach

Uterine fibroids are the most common pelvic tumours in females. Ultrasound images of fibroids require image segmentation and feature extraction for analysis. This paper proposes a new method for segmenting the fluid-filled fibroid found in the uterus. It presents a fully automatic approach in which there is no need for human intervention. The method used in this paper employs a number of knowledge-based rules to locate the object and also utilises the concepts in mathematical morphology. It also extracts the necessary features of the fibroid which can be used to prepare the radiological report. The performance of this method is evaluated using area-based metrics