Learning Connectedness and Convexity of Binary Images from Their Projections
نویسندگان
چکیده
In this paper we investigate the retrieval of geometrical information (especially, convexity and connectedness) of binary images from their projections which can be useful in binary tomography to facilitate the task of reconstruction. Supposing that the projections are the features of the images, we study how decision trees, neural networks, and nearest neighbor learning algorithms perform in classifying binary images with different connectedness and convexity properties.
منابع مشابه
DISCRETE TOMOGRAPHY AND FUZZY INTEGER PROGRAMMING
We study the problem of reconstructing binary images from four projections data in a fuzzy environment. Given the uncertainly projections,w e want to find a binary image that respects as best as possible these projections. We provide an iterative algorithm based on fuzzy integer programming and linear membership functions.
متن کاملDecision Trees in Binary Tomography for Supporting the Reconstruction of hv-Convex Connected Images
In binary tomography, several algorithms are known for reconstructing binary images having some geometrical properties from their projections. In order to choose the appropriate reconstruction algorithm it is necessary to have a priori information of the image to be reconstructed. In this way we can improve the speed and reduce the ambiguity of the reconstruction. Our work is concerned with the...
متن کاملAlgorithm for Reconstructing 3D-Binary Matrix with Periodicity Constraints from Two Projections
We study the problem of reconstructing a three dimensional binary matrices whose interiors are only accessible through few projections. Such question is prominently motivated by the demand in material science for developing tool for reconstruction of crystalline structures from their images obtained by high-resolution transmission electron microscopy. Various approaches have been suggested to r...
متن کاملLearning Connectedness in Binary Images
This paper proposes a new Eye-based Recurrent Network Architecture (ERNA) for image classification. The new architecture is trained by a combination of Qlearning and RPROP. The classification performance is compared with other network architectures on the task of determining connectedness between pixels in small binary images. The experiments show that ERNA outperforms both the standard multi-l...
متن کاملApproximate Testing of Visual Properties
We initiate a study of property testing as applied to visual properties of images. Property testing is a rapidly developing area investigating algorithms that, with a small number of local checks, distinguish objects satisfying a given property from objects which need to be modified significantly to satisfy the property. We study visual properties of discretized images represented by n× n matri...
متن کامل