Multifocus image fusion by establishing focal connectivity

ABSTRACT Multifocus fusion is the process of unifying focal information from a set of input images acquired with limited depth of field. In this effort, we present a general purpose multifocus fusion algorithm, which can be applied to varied applications ranging from microscopic to long range scenes. The main contribution in this paper is the segmentation of the input images into partitions based on focal connectivity. Focal connectivity is established by isolating regions in an input image that fall on the same focal plane. Our method uses focal connectivity and does not rely on physical properties like edges directly for segmentation. Our method establishes sharpness maps to the input images, which are used to isolate and attribute image partitions to input images. The partitions are mosaiced seamlessly to form the fused image. Illustrative examples of multifocus fusion using our method are shown. Comparisons against existing methods are made and the results are discussed. Index Terms-Depth of focus, focal connectivity, image fusion, image partitioning, multifocus fusion

Tensor voting for robust color edge detection

The final publication is available at Springer via http://dx.doi.org/10.1007/978-94-007-7584-8_9This chapter proposes two robust color edge detection methods based on tensor voting. The first method is a direct adaptation of the classical tensor voting to color images where tensors are initialized with either the gradient or the local color structure tensor. The second method is based on an extension of tensor voting in which the encoding and voting processes are specifically tailored to robust edge detection in color images. In this case, three tensors are used to encode local CIELAB color channels and edginess, while the voting process propagates both color and edginess by applying perception-based rules. Unlike the classical tensor voting, the second method considers the context in the voting process. Recall, discriminability, precision, false alarm rejection and robustness measurements with respect to three different ground-truths have been used to compare the proposed methods with the state-of-the-art. Experimental results show that the proposed methods are competitive, especially in robustness. Moreover, these experiments evidence the difficulty of proposing an edge detector with a perfect performance with respect to all features and fields of application.This research has been supported by the Swedish Research Council under the project VR 2012-3512

Eine Methodenbank zur Evaluierung von Stereo-Vision-Verfahren

SIGLEAvailable from TIB Hannover: RN 2856(91-09) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman

Perception-based 3D Triangle Mesh Segmentation Using Fast Marching

In this paper, we describe an algorithm called Fast Marching Watersheds that segments a triangle mesh into visual parts. This computer vision algorithm leverages a human vision theory known as the minima rule. Our implementation computes the principal curvatures and principal directions at each vertex of a mesh, and then our hillclimbing watershed algorithm identifies regions bounded by contours of negative curvature minima. These regions fit the definition of visual parts according to the minima rule. We present evaluation analysis and experimental results for the proposed algorithm

Linking Feature Lines on 3D Triangle Meshes with Artificial Potential Fields

We propose artificial potential fields as a support theory for a feature linking algorithm. This algorithm operates on 3D triangle meshes derived from multiple range scans of an object, and the features of interest are curvature extrema on the object’s surface. A problem that arises with detecting these features is that results from standard algorithms are often incomplete in that feature lines are broken and discontinuous. Our novel linking algorithm closes these broken feature lines to form a more complete feature description. The main contribution of this algorithm is the use of artificial potential fields to govern the linking process. In this paper, we discuss the feature detection process itself and then define the linking procedure in the context of potential fields. We present results for both synthetic and scanned models. 1

Shape analysis algorithm based on information theory

In this paper, we describe an algorithm to measure the shape complexity for discrete approximations of planar curves in 2D images and manifold surfaces for 3D triangle meshes. We base our algorithm on shape curvature, and thus we compute shape information as the entropy of curvature. We present definitions to estimate curvature for both discrete curves and surfaces and then formulate our theory of shape information from these definitions. We demonstrate our algorithm with experimental results. 1