IEEE Transactions on Pattern Analysis and Machine Intelligence
Integration of visual modules: an extension of the Marr paradigm
Integration of visual modules: an extension of the Marr paradigm
Performance of optical flow techniques
International Journal of Computer Vision
Signal Processing for Computer Vision
Signal Processing for Computer Vision
Pattern Recognition Letters
Early Cognitive Vision: Using Gestalt-Laws for Task-Dependent, Active Image-Processing
Natural Computing: an international journal
Optical flow estimation from monogenic phase
IWCM'04 Proceedings of the 1st international conference on Complex motion
IEEE Transactions on Signal Processing
Continuous dimensionality characterization of image structures
Image and Vision Computing
Probabilistic Pose Recovery Using Learned Hierarchical Object Models
Cognitive Vision
Probabilistic object models for pose estimation in 2D images
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Three dilemmas of signal- and symbol-based representations in computer vision
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
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In this paper, we describe a biological motivated image representation in terms of local multi–modal primitives. These primitives are functional abstractions of hypercolumns in V1 [13]. The efficient and generic coding of visual information in terms of local symbolic descriptiones allows for a wide range of applications. For example, they have been used to investigate the multi–modal character of Gestalt laws in natural scenes [14], to code a multi–modal stereo matching and to investigate the role of different visual modalities for stereo [11], and to use a combination of stereo and grouping as well as Rigid Body Motion to acquire reliable 3D information as demonstrated in this publication.