2,874 research outputs found
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
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
Image morphology: from perception to rendering
Complete image ontology can be obtained by formalising a top-down meta-language wich must address all possibilities, from global message and composition to objects and local surface properties
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
A cortical framework for invariant object categorization and recognition
In this paper we present a new model for invariant object categorization and recognition. It is based on explicit
multi-scale features: lines, edges and keypoints are extracted from responses of simple, complex and endstopped cells in cortical area V1, and keypoints are used to construct saliency maps for Focus-of-Attention.
The model is a functional but dichotomous one, because keypoints are employed to model the “where” data stream, with dynamic routing of features from V1 to higher areas to obtain translation, rotation and size
invariance, whereas lines and edges are employed in the “what” stream for object categorization and recognition. Furthermore, both the “where” and “what” pathways are dynamic in that information at coarse
scales is employed first, after which information at progressively finer scales is added in order to refine the processes, i.e., both the dynamic feature routing and the categorization level. The construction of group and object templates, which are thought to be available in the prefrontal cortex with “what” and “where” components in PF46d and PF46v, is also illustrated. The model was tested in the framework of an integrated and biologically plausible architecture
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
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
Recognition of Facial Expressions by Cortical Multi-scale Line and Edge Coding
Face-to-face communications between humans involve emotions, which often are unconsciously conveyed by facial expressions and body gestures. Intelligent human-machine interfaces, for example in cognitive robotics, need to recognize emotions. This paper addresses facial expressions and their neural correlates on the basis of a model of the visual cortex: the multi-scale line and edge coding. The recognition model links the cortical representation with Paul Ekman's Action Units which are related to the different facial muscles. The model applies a top-down categorization with trends and magnitudes of displacements of the mouth and eyebrows based on expected displacements relative to a neutral expression. The happy vs. not-happy categorization yielded a. correct recognition rate of 91%, whereas final recognition of the six expressions happy, anger, disgust, fear, sadness and surprise resulted in a. rate of 78%
Object categorisations using templates constructed from multi-scale line and edge representations
Object categorisation is linked to detection, segregation and recognition. In the visual system, these processes are achieved in the ventral \what"and dorsal \where"pathways [3], with bottom-up feature extractions in areas V1, V2, V4 and IT (what) in parallel with top-down attention from PP via MT to V2 and V1 (where). The latter is steered by object templates in memory, i.e. in prefrontal cortex with a what component in PF46v and a where component in PF46d
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
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