Jan Gaura

Active Contours without Edges and with Simple Shape Priors

In this paper, we introduce two simple shape priors into the Chan and Vese level-set method, namely, a prescribed area and a prescribed area-to-perimeter ratio of particular objects. It is remarkable that these priors may be easily incorporated into the Euler-Lagrange equation of the original method. As a side effect of our experimenting with the method, we also introduce a new probability-based level-set function, which removes the need for reinitialisation and usually, according to our experience, speeds up the computation. Finally, we also propose a method how to treat, in a simple way, the situation in which the particular objects differ in brightness. Although the mentioned changes make the segmentation more reliable, they almost do not complicate the computation. The results of experiments are also presented.

Resistance-Geodesic Distance and Its Use in Image Segmentation

Measuring the distance is an important task in many clustering and image-segmentation algorithms. The value of the distance decides whether two image points belong to a single or, respectively, to two different image segments. The Euclidean distance is used quite often. In more complicated cases, measuring the distances along the surface that is defined by the image function may be more appropriate. The geodesic distance, i.e. the shortest path in the corresponding graph, has become popular in this context. The problem is that it is determined on the basis of only one path that can be viewed as infinitely thin and that can arise accidentally as a result of imperfections in the image. Considering the k shortest paths can be regarded as an effort towards the measurement of the distance that is more reliable. The drawback remains that measuring the distance along several paths is burdened with the same problems as the original geodesic distance. Therefore, it does not guarantee significantly better results. In addition to this, the approach is computationally demanding. This paper introduces the resistance-geodesic distance with the goal to reduce the possibility of using a false accidental path for measurement. The approach can be briefly characterised in such a way that the path of a certain chosen width is sought for, which is in contrast to the geodesic distance. Firstly, the effective conductance is computed for each pair of the neighbouring nodes to determine the local width of the path that could possibly run through the arc connecting them. The width computed in this way is then used for determining the costs of arcs; the arcs whose use would lead to a small width of the final path are penalised. The usual methods for computing the shortest path in a graph are then used to compute the final distances. The corresponding theory and the experimental results are presented in this paper.

An algorithm for iris extraction

In this paper, we describe a new method for detecting iris in digital images. Our method is simple yet effective. It is based on statistical point of view when searching for limbic boundary and rather analytical approach when detecting pupillary boundary. It can be described in three simple steps; firstly, the bright point inside the pupil is detected; secondly, outer limbic boundary is found via statistical measurements of outer boundary points; and thirdly, inner boundary points are found by means of defined cost function maximization. Performance of the presented method is evaluated on series of iris close-up images and compared with the traditional Hough method as well.

Three-Dimensional Reconstruction of Macroscopic Features in Biological Materials

This paper covers the topic of three dimensional reconstruction of small textureless formations usually found in biological samples. Generally used reconstructing algorithms do not provide sufficient accuracy for surface analysis. In order to achieve better results, combined strategy was developed, linking stereo matching algorithms with monocular depth cues such as depth from focus and depth from illumination.

Image segmentation based on electrical proximity in a resistor-capacitor network

Measuring the distances is an important problem in many image-segmentation algorithms. The distance should tell whether two image points belong to a single or, respectively, to two different image segments. The paper deals with the problem of measuring the distance along the manifold that is defined by image. We start from the discussion of difficulties that arise if the geodesic distance, diffusion distance, and some other known metrics are used. Coming from the diffusion equation and inspired by the diffusion distance, we propose to measure the proximity of points as an amount of substance that is transferred in diffusion process. The analogy between the images and electrical circuits is used in the paper, i.e., we measure the proximity as an amount of electrical charge that is transported, during a certain time interval, between two nodes of a resistor-capacitor network. We show how the quantity we introduce can be used in the algorithms for supervised (seeded) and unsupervised image segmentation. We also show that the distance between the areas consisting of more than one point (pixel) can also be easily introduced in a meaningful way. Experimental results are also presented.

A New Method for Bryophyte Canopy Analysis Based on 3D Surface Reconstruction

Recent studies concerning bryophyte canopy structure applied various modern, computer analysis methods for determining moss layer characteristics drawing on the outcomes of a previous research on surface of soil. Surface roughness index Lr was hereby used as a monitor of quality (and condition) of bryophyte canopy. We explored stereo photogrammetry, a non-contact passive method of collecting distance information about surfaces, as a method to acquire 3D model of bryophyte layer and thus Lr and compared it with methods already performed by other authors - contact probe, LED scanning and 3D laser scanning. In contrast to active methods, this method relies on detecting reflected ambient light, therefore, it does not emit any kind of radiation, which can lead to interference with moss photosynthetic pigments, nor does it affect the structure of its layer. This makes it much more suitable for usage in natural environment.

A Complex Network Based Classification of Covered Conductors Faults Detection

Presence of partial discharges implies the fault behavior on insulation system of medium voltage overhead lines, especially with covered conductors (CC). This paper covers the machine learning model based on features, which are derived from complex networks. These features are applied to predict whether the measured signal contains phenomenon indicating CC fault behavior or not. The comparison of different threshold levels of similarity values brings more information about complex network modeling. The final performance of the Random Forest classification algorithm shows valuable results for future research.

Diffusion-Based Similarity for Image Analysis

Measuring the distances is a key problem in many image-analysis algorithms. This is especially true for image segmentation. It provides a basis for the decision whether two image points belong to a single or to two different image segments. Many algorithms use the Euclidean distance, which may not be the right choice. The geodesic distance or the k shortest paths measure the distance along the surface that is defined by the image function. The diffusion distance seems to provide better properties since all the paths are taken into account. In this paper, we show that the diffusion distance has the properties that make it difficult to use in some image processing algorithms, mainly in image segmentation, which extends the recent observations of some other authors. We propose a new measure called normalised diffusion cosine similarity that overcomes some problems of diffusion distance. Lastly, we present the necessary theory and the experimental results.

Normalised Diffusion Cosine Similarity and Its Use for Image Segmentation

In many image-segmentation algorithms, measuring the distances is a key problem since the distance is often used to decide whether two image points belong to a single or, respectively, to two different image segments. The usual Euclidean distance need not be the best choice. Measuring the distances along the surface that is defined by the image function seems to be more relevant in more complicated images. Geodesic distance, i.e. the shortest path in the corresponding graph, or the k shortest paths can be regarded as the simplest methods. It might seem that the diffusion distance should provide the properties that are better since all the paths (not only their limited number) are taken into account. In this paper, we firstly show that the diffusion distance has the properties that make it difficult to use it image segmentation, which extends the recent observations of some other authors. Afterwards, we propose a new measure called normalised diffusion cosine similarity that is more suitable. We present the corresponding theory as well as the experimental results.

Resistance-Geodesic Distance and Its Use in Image Processing and Segmentation

In many clustering and image-segmentation algorithms, measuring distances is used to decide whether two image points belong to a single or, respectively, to two different image segments. In more complicated images, measuring the distances along the surface that is defined by the image function may be more relevant than the Euclidean distance. The geodesic distance, i.e. the shortest path in the corresponding graph, has become popular. The problem is that it is determined on the basis of only one path that can arise accidentally as a result of imperfections in image. Using the k shortest paths solves the problem only partially since more than one path can arise accidentally too. In this paper, we introduce the resistance-geodesic distance with the goal to reduce the possibility that a false accidental path will be used for computing. Firstly, the effective conductance is computed for each pair of neighbouring nodes to determine the local width of the path that could run through the arc connecting them. The width is then used for determining the costs of arcs; the arcs with a small width are penalised. The usual methods for computing the shortest path in graph are then used to compute the final distances. We present the corresponding theory as well as the experimental results.