351 research outputs found
Invariant Categorisation of Polygonal Objects using Multi-resolution Signatures
With the increasing use of 3D objects and models, mining of 3D databases is becoming an important issue. However, 3D object recognition is very time consuming because of variations due to position, rotation, size and mesh resolution. A fast categorisation can be used to discard non-similar objects, such that only few objects need to be compared in full detail. We present a simple method for characterising 3D objects with the goal of performing a fast similarity search in a set of polygonal mesh models. The method constructs, for each object, two sets of multi-scale signatures: (a) the progression of deformation due to iterative mesh smoothing and, similarly, (b) the influence of mesh dilation and erosion using a sphere with increasing radius. The signatures are invariant to 3D translation, rotation and scaling, also to mesh resolution because of proper normalisation. The method was validated on a set of 31 complex objects, each object being represented with three mesh resolutions. The results were measured in terms of Euclidian distance for ranking all objects, with an overall average ranking rate of 1.29
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
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
Arquitectura do córtex visual com aplicações na visão por computador
O estudo da visão humana atrai o interesse de muitos cientistas ao longo dos séculos, como por
exemplo em 1704 por Newton na visão a cores e 1910 por Helmholtz na óptica fisiológica. No
entanto, as primeiras contribuições na visão computacional começaram por volta de 40 anos
atrás quando os primeiros computadores apareceram. Por volta de 1980, David Marr
estabeleceu as bases para a moderna teoria de visão computacional
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
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
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
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
- …