1,059 research outputs found
Efficient spatial-domain implementation of a multiscale image representation based on Gabor functions
Contiene tablas y fórmulasGabor schemes of multiscale image representation are useful in many computer
vision applications. However, the classic Gabor expansion is computationally
expensive due to the lack of orthogonality of Gabor functions. Some alternative
schemes, based on the application of a bank of Gabor filters, have important
advantages such as computational efficiency and robustness, at the cost of
redundancy and lack of completeness. In a previous work we proposed a
quasicomplete Gabor transform, suitable for fast implementations in either space or
frequency domains. Reconstruction was achieved by simply adding together the
even Gabor channels. In this work, we develop an optimized spatial-domain
implementation, using one-dimensional, 11-tap filter masks, that is faster and more
flexible than Fourier implementations. The reconstruction method is improved by
applying fixed and independent weights to the Gabor channels before adding them.
Finally, we analyze and implement, in the spatial domain, two ways to incorporate
a high-pass residual, which permits a visually complete representation of the image.Peer reviewe
Self-organized learning in multi-layer networks
We present a framework for the self-organized formation of high level learning by a statistical preprocessing of features. The paper focuses first on the formation of the features in the context of layers of feature processing units as a kind of resource-restricted associative multiresolution learning We clame that such an architecture must reach maturity by basic statistical proportions, optimizing the information processing capabilities of each layer. The final symbolic output is learned by pure association of features of different levels and kind of sensorial input. Finally, we also show that common error-correction learning for motor skills can be accomplished also by non-specific associative learning. Keywords: feedforward network layers, maximal information gain, restricted Hebbian learning, cellular neural nets, evolutionary associative learnin
Radon-Gabor Barcodes for Medical Image Retrieval
In recent years, with the explosion of digital images on the Web,
content-based retrieval has emerged as a significant research area. Shapes,
textures, edges and segments may play a key role in describing the content of
an image. Radon and Gabor transforms are both powerful techniques that have
been widely studied to extract shape-texture-based information. The combined
Radon-Gabor features may be more robust against scale/rotation variations,
presence of noise, and illumination changes. The objective of this paper is to
harness the potentials of both Gabor and Radon transforms in order to introduce
expressive binary features, called barcodes, for image annotation/tagging
tasks. We propose two different techniques: Gabor-of-Radon-Image Barcodes
(GRIBCs), and Guided-Radon-of-Gabor Barcodes (GRGBCs). For validation, we
employ the IRMA x-ray dataset with 193 classes, containing 12,677 training
images and 1,733 test images. A total error score as low as 322 and 330 were
achieved for GRGBCs and GRIBCs, respectively. This corresponds to retrieval accuracy for the first hit.Comment: To appear in proceedings of the 23rd International Conference on
Pattern Recognition (ICPR 2016), Cancun, Mexico, December 201
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