Broad-scale mapping of seafloor habitats in the north-east Atlantic using existing environmental data

If marine management policies and actions are to achieve long-term sustainable use and management of the marine environment and its resources, they need to be informed by data giving the spatial distribution of seafloor habitats over large areas. Broad-scale seafloor habitat mapping is an approachwhich has the benefit of producing maps covering large extents at a reasonable cost. This approach was first investigated by Roff et al. (2003), who, acknowledging that benthic communities are strongly influenced by the physical characteristics of the seafloor, proposed overlaying mapped physical variables using a geographic information system (GIS) to produce an integrated map of the physical characteristics of the seafloor. In Europe the method was adapted to the marine section of the EUNIS (European Nature Information System) classification of habitat types under the MESH project, andwas applied at an operational level in 2011 under the EUSeaMap project. The present study compiled GIS layers for fundamental physical parameters in the northeast Atlantic, including (i) bathymetry, (ii) substrate type, (iii) light penetration depth and (iv) exposure to near-seafloor currents andwave action. Based on analyses of biological occurrences, significant thresholds were fine-tuned for each of the abiotic layers and later used in multi-criteria raster algebra for the integration of the layers into a seafloor habitat map. The final result was a harmonised broad-scale seafloor habitat map with a 250 m pixel size covering four extensive areas, i.e. Ireland, the Bay of Biscay, the Iberian Peninsula and the Azores. The map provided the first comprehensive perception of habitat spatial distribution for the Iberian Peninsula and the Azores, and fed into the initiative for a pan- European map initiated by the EUSeaMap project for Baltic, North, Celtic and Mediterranean seas.

Multi-scale keypoint annotation: a biological approach

The primary visual cortex employs simple, complex and end-stopped cells to create a scale space of 1D singularities (lines and edges) and of 2D singularities (line and edge junctions and crossings called keypoints). In this paper we show first results of a biological model which attributes information of the local image structure to keypoints at all scales, ie junction type (L, T, +) and main line/edge orientations. Keypoint annotation in combination with coarse to fine scale processing facilitates various processes, such as image matching (stereo and optical flow), object segregation and object tracking.

Cortical object segregation and categorization by multi-scale line and edge coding

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 which pre-categorization is based on coarse scales only, and final categorization on coarse plus fine scales. Processing schemes are discussed in the framework of a complete cortical architecture.

Fine arts edutainment: the amateur painter

A new scheme for painterly rendering (NPR) has been developed. This scheme is based on visual perception, in particular themulti-scale line/edge representation in the visual cortex. The Amateur Painter (TAP) is the user interface on top of the rendering scheme. It allows to (semi)automatically create paintings from photographs, with different types of brush strokes and colour manipulations. In contrast to similar painting tools, TAP has a set of menus that reflects the procedure followed by a normal painter. In addition, menus and options have been designed such that they are very intuitive, avoiding a jungle of sub-menus with options from image processing that children and laymen do not understand. Our goal is to create a tool that is extremely easy to use, with the possibility that the user becomes interested in painting techniques, styles, and fine arts in general.

Multi-scale lines and edges in V1 and beyond: brightness, object categorization and recognition, and consciousness

In this paper we present an improved model for line and edge detection in cortical area V1. This model is based on responses of simple and complex cells, and it is multi-scale with no free parameters. We illustrate the use of the multi-scale line/edge representation in different processes: visual reconstruction or brightness perception, automatic scale selection and object segregation. A two-level object categorization scenario is tested in which pre-categorization is based on coarse scales only and final categorization on coarse plus fine scales. We also present a multi-scale object and face recognition model. Processing schemes are discussed in the framework of a complete cortical architecture. The fact that brightness perception and object recognition may be based on the same symbolic image representation is an indication that the entire (visual) cortex is involved in consciousness.

BIMP: A real-time biological model of multi-scale keypoint detection in V1

We present an improved, biologically inspired and multiscale keypoint operator. Models of single- and double-stopped hypercomplex cells in area V1 of the mammalian visual cortex are used to detect stable points of high complexity at multiple scales. Keypoints represent line and edge crossings, junctions and terminations at fine scales, and blobs at coarse scales. They are detected by applying first and second derivatives to responses of complex cells in combination with two inhibition schemes to suppress responses along lines and edges. A number of optimisations make our new algorithm much faster than previous biologically inspired models, achieving real-time performance on modern GPUs and competitive speeds on CPUs. In this paper we show that the keypoints exhibit state-of-the-art repeatability in standardised benchmarks, often yielding best-in-class performance. This makes them interesting both in biological models and as a useful detector in practice. We also show that keypoints can be used as a data selection step, significantly reducing the complexity in state-of-the-art object categorisation. (C) 2014 Elsevier B.V. All rights reserved.