Jouni Sampo

Segmentation of Overlapping Elliptical Objects in Silhouette Images

Segmentation of partially overlapping objects with a known shape is needed in an increasing amount of various machine vision applications. This paper presents a method for segmentation of clustered partially overlapping objects with a shape that can be approximated using an ellipse. The method utilizes silhouette images, which means that it requires only that the foreground (objects) and background can be distinguished from each other. The method starts with seedpoint extraction using bounded erosion and fast radial symmetry transform. Extracted seedpoints are then utilized to associate edge points to objects in order to create contour evidence. Finally, contours of the objects are estimated by fitting ellipses to the contour evidence. The experiments on one synthetic and two different real data sets showed that the proposed method outperforms two current state-of-art approaches in overlapping objects segmentation.

Segmentation of Partially Overlapping Nanoparticles Using Concave Points

This paper presents a novel method for the segmentation of partially overlapping nanoparticles with a convex shape in silhouette images. The proposed method involves two main steps: contour evidence extraction and contour estimation. Contour evidence extraction starts with contour segmentation where contour segments are recovered from a binarized image by detecting concave points. After this, contour segments which belong to the same object are grouped by utilizing properties of fitted ellipses. Finally, the contour estimation is implemented through a non-linear ellipse fitting problem in which partially observed objects are modeled in the form of ellipse-shape objects. The experiments on a dataset consisting of nanoparticles demonstrate that the proposed method outperforms two current state-of-art approaches in overlapping nanoparticles segmentation. The method relies only on edge information and can be applied to any segmentation problems where the objects are partially overlapping and have an approximately elliptical shape, such as cell segmentation.

Using the fibre structure of paper to determine authenticity of the documents: Analysis of transmitted light images of stamps and banknotes

A novel method is presented for distinguishing postal stamp forgeries and counterfeit banknotes from genuine samples. The method is based on analyzing differences in paper fibre networks. The main tool is a curvelet-based algorithm for measuring overall fibre orientation distribution and quantifying anisotropy. Using a couple of more appropriate parameters makes it possible to distinguish forgeries from genuine originals as concentrated point clouds in two- or three-dimensional parameter space.

Curvelet-based method for orientation estimation of particles from optical images

Abstract. A method based on the curvelet transform is introduced to estimate the orientation distribution from two-dimensional images of small anisotropic particles. Orientation of fibers in paper is considered as a particular application of the method. Theoretical aspects of the suitability of this method are discussed and its efficiency is demonstrated with simulated and real images of fibrous systems. Comparison is made with two traditionally used methods of orientation analysis, and the new curvelet-based method is shown to perform better than these traditional methods.

Measuring Translation Shiftability of Frames

The shiftability property has been shown to be advantageous in certain signal processing tasks, such as feature extraction, and thus, practical shiftability measures are needed. In this study, translation shiftability measures for frames of regular translates are revisited, novel measures are proposed, and numerical examples are shown. In addition, a simple lower bound for shiftability of dual frames is presented and the measures are also considered in the finite-dimensional case.

Curvelet-based method for orientation estimation of particles

A method based on the curvelet transform is introduced for estimating from two-dimensional images the orientation distribution of small anisotropic particles. Orientation of fibers in paper is considered as a particular application of the method. Theoretical aspects of the suitability of this method are discussed and its efficiency is demonstrated with simulated and real images of fibrous systems. Comparison is made with two traditionally used methods of orientation analysis, and the new curvelet-based method is shown to perform clearly better than these traditional methods.

Stability of the classifier based on modification on Schweizer and Sklars equations

In this article we have applied Schweizer & Sklars implications with extension to generalized mean to classification task. We show that classification results are not so sensitive to p values with Schweizer & Sklars measures, which indicates generalized form of equations. Investigation for correct mean values is carried out. In this article we have also tested stability of the classifier. Two different tests for stability are made: in one test stability was checked respect to weight parameters and other test was carried out for ideal vectors

Segmentation of Partially Overlapping Convex Objects Using Branch and Bound Algorithm

This paper presents a novel method for the segmentation of partially overlapping convex shape objects in silhouette images. The proposed method involves two main steps: contour evidence extraction and contour estimation. Contour evidence extraction starts by recovering contour segments from a binarized image using concave contour point detection. The contour segments which belong to the same objects are grouped by utilizing a criterion defining the convexity, symmetry and ellipticity of the resulting object. The grouping is formulated as a combinatorial optimization problem and solved using the well-known branch and bound algorithm. Finally, the contour estimation is implemented through a non-linear ellipse fitting problem in which partially observed objects are modeled in the form of ellipse-shape objects. The experiments on a dataset of consisting of nanoparticles demonstrate that the proposed method outperforms four current state-of-art approaches in overlapping convex objects segmentation. The method relies only on edge information and can be applied to any segmentation problems where the objects are partially overlapping and have an approximately convex shape.

Use of hybrid method in wavelet base selection for signal compression

In this paper we study a problem of signal compression how to choose a best mother wavelet from the set S of wavelets. The approach is following: First we calculate a discrete wavelet transform of signal by using one standard wavelet. Then we form coefficients mi for each scale i from the wavelet expansions coefficients. Coefficients mi are used for selecting best wavelet from the set S. Selection is classification problem and we have constructed classification algorithm that uses fuzzy similarity that is based on a continuous t-norm called Lukasiewicz algebra. We are using normal and cumulative forms of generalized Lukasiewicz algebra and we have also applied a genetic algorithm into the our classifier to choose appropriate weights in our classification tasks. There are many advantages what we get by using t-norm called Lukasiewicz in classification: 1) Structure has a promising mathematical background 2) Mean of many fuzzy similarities is still a fuzzy similarity 3) Any pseudo-metric induces fuzzy similarity on a given non-empty set X with respect to the Lukasiewicz-conjunction. Algorithm is efficient especially because we have to calculate wavelet transform only once and classification is simple and fast. Algorithm is also very flexible, cause we can implement any type metrics or mean measures into it. As our results we will present a new method to select best mother wavelet from a given set S. We will also show that proposed hybrid method can be used in this kind of analytical problems. The best way to form coefficients mi and choose metric or measure is depended of class of signals we are working with, which is still unclear.

Comparison of Concave Point Detection Methods for Overlapping Convex Objects Segmentation

Segmentation of overlapping convex objects has gained a lot of attention in numerous biomedical and industrial applications. A partial overlap between two or more convex shape objects leads to a shape with concave edge points that correspond to the intersections of the object boundaries. Therefore, it is a common practice to utilize these concave points to segment the contours of overlapping objects. Although a concave point has a clear mathematical definition, the task of concave point detection (CPD) from noisy digital images with limited resolution is far from trivial. This work provides the first comprehensive comparison of CPD methods with both synthetic and real world data. We further propose a modification to an earlier CPD method and show that it outperforms the other methods. Finally, we demonstrate that by using the enhanced concave points we obtain segmentation results that outperform the state-of-the-art in the task of partially overlapping convex object segmentation.