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This paper proposes a new rotation-invariant and scale-invariant representation for texture image retrieval based on steerable pyramid decomposition. By calculating the mean and standard deviation of decomposed image subbands, the texture feature vectors are extracted. To obtain rotation or scale invariance, the feature elements are aligned by considering(More)
The aim of this work is to extract the road network from aerial images. What makes the problem challenging is the complex structure of the prior: roads form a connected network of smooth, thin segments which meet at junctions and crossings. This type of a-priori knowledge is more difficult to turn into a tractable model than standard smoothness or(More)
This paper proposes a new texture classification system, which is distinguished by: (1) a new rotation-invariant image descriptor based on Steerable Pyramid Decomposition, and (2) by a novel multi-class recognition method based on Optimum Path Forest. By combining the discriminating power of our image descriptor and classifier, our system uses small size(More)
— Learning how to extract texture features from non-controlled environments characterized by distorted images is a still-open task. By using a new rotation-invariant and scale-invariant image descriptor based on Steerable Pyramid Decomposition , and a novel multi-class recognition method based on Optimum-Path Forest, a new texture recognition system is(More)
This paper presents an unified framework for fast interactive segmentation of natural images using the image foresting transform (IFT) — a tool for the design of image processing operators based on connectivity functions (path-value functions) in graphs derived from the image. It mainly consists of three tasks: recognition, enhancement, and(More)
This paper proposes a new texture descriptor to guide the search and retrieval in image databases. It extracts rich information from global and local primitives of tex-tured images. At a higher level, the global macro-features in textured images are characterized by exploiting the multi-resolution properties of the Steerable Pyramid Decomposition. By doing(More)
In this paper we propose an approach to multi-class semantic segmentation of urban areas in high-resolution aerial images with class-specific object priors for buildings and roads. What makes model design challenging are highly heterogeneous object appearances and shapes that call for priors beyond standard smoothness or co-occurrence assumptions. The data(More)
Images for 3D mapping are always recorded in such a way that relevant scene parts are seen from multiple viewpoints, so as to facilitate camera orientation and 3D point triangulation. Beyond geometric reconstruction, automatic mapping also requires the semantic interpretation of the image content, and for that task the redundancy provided by overlapping(More)