Fast anisotropic Gauss filtering

Abstract. We derive the decomposition of the anisotropic Gaussian in a one dimensional Gauss filter in the x-direction followed by a one dimensional filter in a non-orthogonal direction ϕ. So also the anisotropic Gaussian can be decomposed by dimension. This appears to be extremely efficient from a computing perspective. An implementation scheme for normal convolution and for recursive filtering is proposed. Also directed derivative filters are demonstrated. For the recursive implementation, filtering an 512 × 512 image is performed within 65 msec, independent of the standard deviations and orientation of the filter. Accuracy of the filters is still reasonable when compared to truncation error or recursive approximation error. The anisotropic Gaussian filtering method allows fast calculation of edge and ridge maps, with high spatial and angular accuracy. For tracking applications, the normal anisotropic convolution scheme is more advantageous, with applications in the detection of dashed lines in engineering drawings. The recursive implementation is more attractive in feature detection applications, for instance in affine invariant edge and ridge detection in computer vision. The proposed computational filtering method enables the practical applicability of orientation scale-space analysis

A local Gaussian filter and adaptive morphology as tools for completing partially discontinuous curves

This paper presents a method for extraction and analysis of curve--type structures which consist of disconnected components. Such structures are found in electron--microscopy (EM) images of metal nanograins, which are widely used in the field of nanosensor technology. The topography of metal nanograins in compound nanomaterials is crucial to nanosensor characteristics. The method of completing such templates consists of three steps. In the first step, a local Gaussian filter is used with different weights for each neighborhood. In the second step, an adaptive morphology operation is applied to detect the endpoints of curve segments and connect them. In the last step, pruning is employed to extract a curve which optimally fits the template

Describing Textures in the Wild

Patterns and textures are defining characteristics of many natural objects: a shirt can be striped, the wings of a butterfly can be veined, and the skin of an animal can be scaly. Aiming at supporting this analytical dimension in image understanding, we address the challenging problem of describing textures with semantic attributes. We identify a rich vocabulary of forty-seven texture terms and use them to describe a large dataset of patterns collected in the wild.The resulting Describable Textures Dataset (DTD) is the basis to seek for the best texture representation for recognizing describable texture attributes in images. We port from object recognition to texture recognition the Improved Fisher Vector (IFV) and show that, surprisingly, it outperforms specialized texture descriptors not only on our problem, but also in established material recognition datasets. We also show that the describable attributes are excellent texture descriptors, transferring between datasets and tasks; in particular, combined with IFV, they significantly outperform the state-of-the-art by more than 8 percent on both FMD and KTHTIPS-2b benchmarks. We also demonstrate that they produce intuitive descriptions of materials and Internet images.Comment: 13 pages; 12 figures Fixed misplaced affiliatio

A Distributed Weighted Voting Approach for Accurate Eye Center Estimation

This paper proposes a novel approach for accurate estimation of eye center in face images. A distributed voting based approach in which every pixel votes is adopted for potential eye center candidates. The votes are distributed over a subset of pixels which lie in a direction which is opposite to gradient direction and the weightage of votes is distributed according to a novel mechanism.  First, image is normalized to eliminate illumination variations and its edge map is generated using Canny edge detector. Distributed voting is applied on the edge image to generate different eye center candidates. Morphological closing and local maxima search are used to reduce the number of candidates. A classifier based on spatial and intensity information is used to choose the correct candidates for the locations of eye center. The proposed approach was tested on BioID face database and resulted in better Iris detection rate than the state-of-the-art. The proposed approach is robust against illumination variation, small pose variations, presence of eye glasses and partial occlusion of eyes.Defence Science Journal, 2013, 63(3), pp.292-297, DOI:http://dx.doi.org/10.14429/dsj.63.276