Shape is probably the single most important feature for object detection and much research has gone into developing deformable shape models. However, contours extracted by bottom-up edge detectors are notoriously unreliable, especially in natural images. In this paper, we present a very simple, yet powerful method for model creation, hypothesis generation, and hypothesis verification, which is competitive with much more complex methods. Let s be a descriptor in some high-dimensional space of an edge fragment representing part of an object contour. In Bayesian terms, this fragment was likely to be generated by some class c∈C if the conditional likelihood P(s|c) is greater for c than that for any other class, P(s|c′ 6= c), including the background class. P(s|c) can be estimated non-parametrically, for example by using Gaussian kernels associated with some nearby samples in the feature space. In [2] it was shown that a good approximation can be obtained by using only the nearest sample. A fragment is discriminative if it is much more likely to belong to a class c than any other class c′ 6= c. We thus define the discriminative power of a fragment by:

We present a fast Bayesian algorithm for category-level object detection in natural images. We modify the popular Naive Bayes Nearest Neighbour classification algorithm to make it suitable for evaluating multiple sub-regions in an image, and offer a fast, filtering-based alternative to the multi-scale sliding window approach. Our algorithm is example-based and requires no learning. Tests on standard datasets and robotic scenarios show competitive detection rates and real-time performance of our algorithm.

Coloured regions can be segregated from each other by using colour-opponent mechanisms, colour contrast, saturation and luminance. Here we address segmentation by using end-stopped cells tuned to colour instead of to colour contrast. Colour information is coded in separate channels. By using multi-scale cortical endstopped cells tuned to colour, keypoint information in all channels is coded and mapped by multi-scale peaks. Unsupervised segmentation is achieved by analysing the branches of these peaks, which yields the best-fitting image regions. Copyright © 2014 SCITEPRESS.

Best-performing object recognition algorithms employ a large number features extracted on a dense grid, so they are too slow for real-time and active vision. In this paper we present a fast cortical keypoint detector for extracting meaningful points from images. It is competitive with state-of-the-art detectors and particularly well-suited for tasks such as object recognition. We show that by using these points we can achieve state-of-the-art categorization results in a fraction of the time required by competing algorithms.

We present a new method for characterising 2D shapes, specifically elliptical ones, that can be successfully applied to any star-shaped object. Rather than using the classical centroidal profile approach, we dynamically fit an ellipse to the contour points and compute the distances of every point on the contour to the two ellipse foci. The sum of the two distances minus the length of the ellipse's major axis, as a function of the angle for one full rotation, is the new contour-selection. This profile is normalised with respect to the object orientation by taking the orientation of the fitted ellipse's major axis as a reference. Our method can perform better if a contour has defects or is partially occluded, it has no problem with very elongated objects, and it can also cope with objects with identical shapes but different sizes.

A range image segmentation contest was organized in conjunction with ICPR'2000. The goal is to continue the effort of experimentally evaluating range image segmentation algorithms initiated by Hoover et al. (1996) and Powell et al. (1998). This paper summarizes the results of the contest.

Lines and edges play an important role in pattern recognition because they mark object surfaces and boundaries. In spite of many attempts to construct optimal detectors, e.g. an edge detector, it appears that all known algorithms have problems at locations where lines and edges are very close and/or intersect. Furthermore, there are still very few schemes which can detect and classify lines and edges (events) simultaneously. In view of the fact that the human visual system does not seem to suffer from any problems, our aim is to develop an event detection scheme that makes use of biologically-motivated operators and, therefore, to overcome the problems known from the literature. In this scheme we apply 2D complex Gabor filters and exploit the local phase information. In order to cope with events having a different curvature, we develop an adaptive scheme that is based on compound Gabor filter kernels with different orientation bandwidths.

Complex moments of the Gabor power spectrum yield estimates of the N-folded symmetry of the local image content at different frequency scales, that is, they allow to detect linear, rectangular, hexagonal/triangular, and so on, structures with very fine to very coarse resolutions. Results from experiments on the unsupervised segmentation of real textures indicate their importance for image processing applications. Real geometric moments computed in Gabor space also provide for very powerful texture features, but lack the clear geometrical interpretation of complex moments.>

An approach to image feature extraction is proposed. Complex moments of the Gabor power spectrum are used to detect linear, rectangular, hexagonal/triangular, and other structures with very fine to very coarse resolutions. When the method is applied to texture segmentation, good results are obtained.>

Median-based filtering techniques are compared by considering a test image which contains a central disk-shaped region with a step or a ramp edge against a uniform background. Free parameters are the amplitude of Gaussian noise added,'the edge slope, and the number of filtering iterations. The quantitative comparison measure is the normalized squared error between the filtered noisy image and the noise-free image, on the flat image regions and on the transition region separately.