Fractals everywhere
Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
NETRA: a toolbox for navigating large image databases
NETRA: a toolbox for navigating large image databases
Computer and Robot Vision
A region based representation for image and video retrieval
A region based representation for image and video retrieval
ICAI'09 Proceedings of the 10th WSEAS international conference on Automation & information
Web-based enrichment of bike sensor data for automatic geo-annotation
Proceedings of the Second ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information
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The paper presents an image processing algorithm based on statistical methods in order to evaluate the road delimiting by textured region similarity measurement and defect texture detection and localization. With the purpose of algorithm validation, the images are divided in sixteen equivalent regions. For the proper region identification and classification, a decision theoretic method and two types of statistic texture feature are used. The first type features derive from the medium co-occurrence matrices: contrast, energy, entropy, homogeneity, and variance, but in normalized form. The second type feature is the edge density per unit of area. The algorithms are implemented in Visual C++ and Matlab and allows the simultaneously display of both the investigated region, and the Euclidian distance between them and a reference image region. The basic texture (reference) is considered an asphalt one and the different textures are considered like the grass and the pebble. The result is the classification of the tested texture in road and non-road type, based on the similarity evaluation and the localization of the defect regions.