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 hierarchy for Focus-of-Attention and object detection

Hypercolumns in area V1 contain frequency- and orientation-selective simple and complex cells for line (bar) and edge coding, plus end-stopped cells for key- point (vertex) detection. A single-scale (single-frequency) mathematical model of single and double end-stopped cells on the basis of Gabor filter responses was developed by Heitger et al. (1992 Vision Research 32 963-981). We developed an improved model by stabilising keypoint detection over neighbouring micro- scales.

Fast line, arc/circle and leg detection from laser scan data in a player driver

A feature detection system has been developed for real-time identification of lines, circles and people legs from laser range data. A new method sutable for arc/circle detection is proposed: the Inscribed Angle Variance (IAV). Lines are detected using a recursive line fitting method.

Improved line/edge detection and visual reconstruction

Lines and edges provide important information for object categorization and recognition. In addition, one brightness model is based on a symbolic interpretation of the cortical multi-scale line/edge representation. In this paper we present an improved scheme for line/edge extraction from simple and complex cells and we illustrate the multi-scale representation. This representation can be used for visual reconstruction, but also for nonphotorealistic rendering. Together with keypoints and a new model of disparity estimation, a 3D wireframe representation of e.g. faces can be obtained in the future.

Face segregation and recognition by cortical multi-scale line and edge coding

Models of visual perception are based on image representations in cortical area V1 and higher areas which contain many cell layers for feature extraction. Basic simple, complex and end-stopped cells provide input for line, edge and keypoint detection. In this paper we present an improved method for multi-scale line/edge detection based on simple and complex cells. We illustrate the line/edge representation for object reconstruction, and we present models for multi-scale face (object) segregation and recognition that can be embedded into feedforward dorsal and ventral data streams (the “what” and “where” subsystems) with feedback streams from higher areas for obtaining translation, rotation and scale invariance.

Face recognition by cortical multi-scale line and edge representations

Empirical studies concerning face recognition suggest that faces may be stored in memory by a few canonical representations. Models of visual perception are based on image representations in cortical area V1 and beyond, which contain many cell layers for feature extraction. Simple, complex and end-stopped cells provide input for line, edge and keypoint detection. Detected events provide a rich, multi-scale object representation, and this representation can be stored in memory in order to identify objects. In this paper, the above context is applied to face recognition. The multi-scale line/edge representation is explored in conjunction with keypoint-based saliency maps for Focus-of-Attention. Recognition rates of up to 96% were achieved by combining frontal and 3/4 views, and recognition was quite robust against partial occlusions.

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.

Visual cortex frontend: integrating lines, edges, keypoints and disparaty

We present a 3D representation that is based on the pro- cessing in the visual cortex by simple, complex and end-stopped cells. We improved multiscale methods for line/edge and keypoint detection, including a method for obtaining vertex structure (i.e. T, L, K etc). We also describe a new disparity model. The latter allows to attribute depth to detected lines, edges and keypoints, i.e., the integration results in a 3D \wire-frame" representation suitable for object recognition.

Nanoparticle-based biosensors: development and application

Tese dout., Ciências Biotecnológicas, Universidade do Algarve, 2009

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.

Recognition of facial expressions by cortical multi-scale line and edge coding

Empirical studies concerning face recognition suggest that faces may be stored in memory by a few canonical representations. Models of visual perception are based on image representations in cortical area V1 and beyond, which contain many cell layers for feature extraction. Simple, complex and end-stopped cells provide input for line, edge and keypoint detection. Detected events provide a rich, multi-scale object representation, and this representation can be stored in memory in order to identify objects. In this paper, the above context is applied to face recognition. The multi-scale line/edge representation is explored in conjunction with keypoint-based saliency maps for Focus-of-Attention. Recognition rates of up to 96% were achieved by combining frontal and 3/4 views, and recognition was quite robust against partial occlusions.

Application of molecular tools for detection of plant viruses

Grapevine leafroll disease (GLRD) is one of the most important virus diseases of grapevines worldwide, causing major economical impact. The disease has a complex aetiology and currently eleven phloem-limited viruses, termed in general Grapevine leafroll-associated virus (GLRaVs), have been identified. Two of the GLRaVs, GLRaV-1 and GLRaV-3, are included in the European certification scheme of propagation material. However, the flawed notion that GLRaV-3 is more frequent than GLRaV-1 and that all other GLRaVs are possibly not as relevant for GLRD, has until now precluded the development of specific serological and molecular detection assays and limited the scope of molecular characterization of the viruses known to be associated with the disease. Hence, few studies have addressed the phylodynamics of GLRaVs or even characterized the genetic structure of their natural populations. This generalized lack of molecular information, in turn underlie the deficient capacity to detect the viruses. The phylogenetic analyses were conducted on the basis of the heat shock protein 70 homologue (HSP70h) and the coat protein (CP) genes for GLRaV-1 and the HSP70h, the heat shock protein 90 homologue (HSP90h) and the CP genes for GLRaV-5. The data obtained for GLRaV-1 contributed 83 new CP sequences. This information was combined with previous analysis by other authors and used for the production of new polyclonal IgG, capable of detecting CP variants from all the phylogroups observed. Successful testing of this new tool included tissue print immunoblotting (TPIB) and in situ immunoassay (ISIA). The data obtained for GLRaV-5, contributed 61 new CP and 28 new HSP90h gene sequences. Eight phylogenetic groups were identified on the basis of the CP. Characterization of the genetic structure of the isolates revealed a higher diversity than previously reported and allowed the identification of dominant virus variants. For both GLRaV-1 and GLRaV-5, the effect of vegetative propagation on the virus transmission dynamics was addressed.

Modelfest and contrast-interrelation-function data predicted by a retinal model

The automatic implementation of decoders for a visual perception is achieved as follows. The action described by a production rule is realized by means of the decoder in which a pattern of connections coreesponds to that of stimuli. According to "S.Karasawa,(Proc. of CCCT, Vol.5, pp.194-1999, Austin, Texas, August, 2004)", each program mable controllable connection among inputs is realized by a floating gate avalanche injection MOS FET, where inverted signals are used at writing, and the detection of matching between inputs and connections is carried out by using the signal source in which low level signal is provided via comparatively smaller resistance than high level.