1,721,164 research outputs found
Proceedings - 14th International conference on Image Analysis and Processing, ICIAP 2007: Foreword
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Cluster partitioning in image analysis classification: A genetic algorithm approach
No full textA classification of data by using the genetic algorithm computational paradigm is proposed. The best data partition is defined to be the one minimizing the sum of Pythagorean distances between each datum in a cluster and the relative center of class or center of mass. Background is given, and the relevant genetic algorithm description is provided. The model for the genetic application is presented. Simulation results confirm genetic algorithms to be powerful tools for the solution of optimization problems
A highly selective HT based algorithm for detecting extended, almost rectilinear shapes
No full textThe paper presents an highly selective algorithm for detecting extended and almost rectilinear shapes in digital images, in presence of structured and unstructured noise; it exploits the Gradient-based Hough Transform, followed by a special purpose correlation process in the parameter space. The paper discusses the algorithm and its application in a quality inspection task for detecting fabrication defects in mechanical pieces
Eliciting visual primitives for detection of elongated shapes
No full textThis paper deals with the problem of eliciting visual primitives for visual search with the aim of detecting 2D objects characterized, primarily, by an elongated shape. The paper proposes a new visual primitive obtained by combining in a suitable correlation, a basic set of standard local features. This primitive is able to synthesize the information associated with local features and, as a more effective ensemble of proprieties of the considered model, enhance detection. The paper discusses the approach, presents the new primitive and evaluates its robustness in the case of non-ideal and noisy images. Finally an application to the context of visual inspection is presented
The vector-gradient Hough transform for identifying straight-translation generated shapes
No full textThe paper introduces the vector-gradient Hough transform (VGHT), a modified version of the gradient weighted Hough transform (GWHT), defined in vector space and able to exploit all the vector information of the gradient of luminosity. The new formulation, directly derived from the Radon transform, is analyzed and compared with the GWHT, in order to point out the improvement in selectivity provided by the VGHT in a strictly polar parametric space, without any relevant increase in computational complexity. This approach can be very suitable for identifying a specifically defined model of shapes in gray level images, ideally generated by a translation in the 2D space of a 1D luminosity profile. Finally, the suitability of the VGHT in real applications is shown with examples in the area of defect identification for automated visual inspection. © 1996 IEEE
Exploiting image processing locality in cache pre-fetching
No full textEmerging trends in computer design attempt to include specific solutions for handling images also in general-purpose computers, because of the current spread of multimedia, image processing and computer graphics applications. In this context, we propose hardware pre-fetching techniques specific for caching images: The main issue we state is that most algorithms working on images exhibit a 2D spatial locality that is not taken into account in current cache organization and data access strategies. To this aim we propose an adaptive local pre-fetching for the image data type; this technique, mirroring the two-dimensional spatial locality of image processing algorithms, results in being more efficient than other approaches, such as sequential pre-fetching and adaptive pre-fetching. Performance is evaluated on different classes of image processing algorithms, namely raster-scan and propagative algorithms, common in computer vision and multimedia applications
1st ACM Workshop on Multimedia in Forensics - MiFor 09, Co-located with the 2009 ACM International Conference on Multimedia, MM 09: Foreword
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