Article history: Received 30 July 2016 Received in revised form 30 October 2016 Accepted 30 December 2016 Available online 3 January 2017

In this paper we present an improved scheme for line and edge detection in cortical area V1, based on responses of simple and complex cells, truly multi-scale with no free parameters. We illustrate the multi-scale representation for visual reconstruction, and show how object segregation can be achieved with coarse-to-finescale groupings. A two-level object categorization scenario is tested in w...

Antarctica is surrounded by a variety of large, medium and small sized ice shelves, glacier tongues and coastal areas without offshore floating ice masses. We used the mosaic of the Radarsat-1 Antarctica Mapping Project (RAMP) Antarctic Mapping Mission 1 (AMM) to classify the coastline of Antarctica in terms of surface structure patterns close to the calving front. With the aid of an automated ...

Intensity-based edge detectors cannot distinguish whether an edge is caused by material changes, shadows, surface orientation changes or by highlights. Therefore, our aim is to classify the physical cause of an edge using hyperspectra obtained by a spectrograph. Methods are presented to detect edges in hyperspectral images. In theory, the effect of varying imaging conditions is analyzed for ”ra...

A new method for line detection in two-dimensional image data is presented. Based on the moment-preserving principle, the method is developed to estimate line location and width to the subpixel accuracy within a circular mask. Experimental results are given to show the effectiveness of the proposed line detector.

We present a novel method that finds edges between certain image features, e.g. gray-levels, and disregards edges between other features. The method uses a channel representation of the features and performs normalized convolution using the channel values as certainties. This means that areas with certain features can be disregarded by the

Local properties of image (phase, amplitude and orientation) can be estimated practically using quadrature filters kernel and can be easily represented in two dimensions using the monogenic signal. This powerful feature representation has given rise to robust phase-based edge detection. Nonetheless, it is limited to the class of intrinsically onedimensional signals, such as lines and edges. All...

An edge detection technique based on local smoothing and statistical hypothesis testing for the detection and localization of step edges and roof edges is proposed. Smoothing and statistical hypothesis testing procedures for detection and localization of step edges and roof edges are formulated. Experimental results on gray-scale images are presented. The merits, limitations and factors critica...