Invariant multi-scale object categorisation and recognition

Object recognition requires that templates with canonical views are stored in memory. Such templates must somehow be normalised. In this paper we present a novel method for obtaining 2D translation, rotation and size invariance. Cortical simple, complex and end-stopped cells provide multi-scale maps of lines, edges and keypoints. These maps are combined such that objects are characterised. Dynamic routing in neighbouring neural layers allows feature maps of input objects and stored templates to converge. We illustrate the construction of group templates and the invariance method for object categorisation and recognition in the context of a cortical architecture, which can be applied in computer vision.

Evolution of matrix and bone gamma-carboxyglutamic acid proteins in vertebrates

The evolution of calcified tissues is a defining feature in vertebrate evolution. Investigating the evolution of proteins involved in tissue calcification should help elucidate how calcified tissues have evolved. The purpose of this study was to collect and compare sequences of matrix and bone γ-carboxyglutamic acid proteins (MGP and BGP, respectively) to identify common features and determine the evolutionary relationship between MGP and BGP. Thirteen cDNAs and genes were cloned using standard methods or reconstructed through the use of comparative genomics and data mining. These sequences were compared with available annotated sequences (a total of 48 complete or nearly complete sequences, 28 BGPs and 20 MGPs) have been identified across 32 different species (representing most classes of vertebrates), and evolutionarily conserved features in both MGP and BGP were analyzed using bioinformatic tools and the Tree-Puzzle software. We propose that: 1) MGP and BGP genes originated from two genome duplications that occurred around 500 and 400 million years ago before jawless and jawed fish evolved, respectively; 2) MGP appeared first concomitantly with the emergence of cartilaginous structures, and BGP appeared thereafter along with bony structures; and 3) BGP derives from MGP. We also propose a highly specific pattern definition for the Gla domain of BGP and MGP. Previous Section Next Section BGP1 (bone Gla protein or osteocalcin) and MGP (matrix Gla protein) belong to the growing family of vitamin K-dependent (VKD) proteins, the members of which are involved in a broad range of biological functions such as skeletogenesis and bone maintenance (BGP and MGP), hemostasis (prothrombin, clotting factors VII, IX, and X, and proteins C, S, and Z), growth control (gas6), and potentially signal transduction (proline-rich Gla proteins 1 and 2). VKD proteins are characterized by the presence of several Gla residues resulting from the post-translational vitamin K-dependent γ-carboxylation of specific glutamates, through which they can bind to calcium-containing mineral such as hydroxyapatite. To date, VKD proteins have only been clearly identified in vertebrates (1) although the presence of a γ-glutamyl carboxylase has been reported in the fruit fly Drosophila melanogaster (2) and in marine snails belonging to the genus Conus (3). Gla residues have also been found in neuropeptides from Conus venoms (4), suggesting a wider prevalence of γ-carboxylation.

Multi-scale cortical keypoint representation for attention and object detection

Keypoints (junctions) provide important information for focus-of-attention (FoA) and object categorization/recognition. In this paper we analyze the multi-scale keypoint representation, obtained by applying a linear and quasi-continuous scaling to an optimized model of cortical end-stopped cells, in order to study its importance and possibilities for developing a visual, cortical architecture.We show that keypoints, especially those which are stable over larger scale intervals, can provide a hierarchically structured saliency map for FoA and object recognition. In addition, the application of non-classical receptive field inhibition to keypoint detection allows to distinguish contour keypoints from texture (surface) keypoints.

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.