Multi-scale keypoints in V1 and face detection

End-stopped cells in cortical area V1, which combine out- puts of complex cells tuned to different orientations, serve to detect line and edge crossings (junctions) and points with a large curvature. In this paper we study the importance of the multi-scale keypoint representa- tion, i.e. retinotopic keypoint maps which are tuned to different spatial frequencies (scale or Level-of-Detail). We show that this representation provides important information for Focus-of-Attention (FoA) and object detection. In particular, we show that hierarchically-structured saliency maps for FoA can be obtained, and that combinations over scales in conjunction with spatial symmetries can lead to face detection through grouping operators that deal with keypoints at the eyes, nose and mouth, especially when non-classical receptive field inhibition is employed. Al- though a face detector can be based on feedforward and feedback loops within area V1, such an operator must be embedded into dorsal and ventral data streams to and from higher areas for obtaining translation-, rotation- and scale-invariant face (object) detection.

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 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.

Disparity energy model with keypoint disparity validation

A biological disparity energy model can estimate local depth information by using a population of V1 complex cells. Instead of applying an analytical model which explicitly involves cell parameters like spatial frequency, orientation, binocular phase and position difference, we developed a model which only involves the cells’ responses, such that disparity can be extracted from a population code, using only a set of previously trained cells with random-dot stereograms of uniform disparity. Despite good results in smooth regions, the model needs complementary processing, notably at depth transitions. We therefore introduce a new model to extract disparity at keypoints such as edge junctions, line endings and points with large curvature. Responses of end-stopped cells serve to detect keypoints, and those of simple cells are used to detect orientations of their underlying line and edge structures. Annotated keypoints are then used in the leftright matching process, with a hierarchical, multi-scale tree structure and a saliency map to segregate disparity. By combining both models we can (re)define depth transitions and regions where the disparity energy model is less accurate.

Differential requirements of aPKC activity within different epithelial tissues

Epithelial tissues are essential during morphogenesis and organogenesis. During development, epithelial tissues undergo several different remodeling processes, from cell intercalation to cell change shape. An epithelial cell has a highly polarized structure, which is important to maintain tissue integrity. The mechanisms that regulate and maintain apicobasal polarity and epithelial integrity are mostly conserved among all species and in different tissues within the same organism. aPKC-PAR complex localizes in the apical domain of polarized cells, and its function is essential for apicobasal polarization and epithelial integrity. In this work we characterized two novel alleles of aPKC: a temperature sensitive allele (aPKCTS), which has a point mutation on a kinase domain, and another allele with a point mutation on a highly conserved amino acid within the PB1 domain of aPKC (aPKCPB1). Analysis of the aPKCTS mutant phenotypes, lead us to propose that during development different epithelial tissues have differential requirements of aPKC activity. More specifically, our work suggests de novo formation of adherens junctions (AJs) is particularly sensitive to sub-optimal levels of apkc activity. Analysis of the aPKCPB1 allele, suggests that aPKC is likely to have an apical structural function mostly independent of its kinase activity. Altogether our work suggests that although loss of aPKC function is associated to similar epithelial phenotypes (e.g., loss of apicobasal polarization and epithelial integrity), the requirements of aPKC activity within these tissues are nevertheless likely to vary.

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.

Electronic levels in MEH-PPV

pn-Junctions of MEH-PPV on top of heavily doped n-type silicon were used in electrical measurements. Through deep-level transient-spectroscopy (DLTS)-like measurements, four traps (two majority and two minority traps) could be identified on top of the shallow acceptor level responsible for conduction. Furthermore, evidence is found for interface states. (C) 2000 Published by Elsevier Science S.A. All rights reserved.