2016, Articolo in rivista, ENG
Righi M.; D'Acunto M.; Salvetti O.
PRIAR (Pattern Recognition Image Augmented Resolution) is an innovative approach to single-frame super-resolution that combines common single-frame super-resolution with pattern-recognition algorithms. PRIAR uses the information gained through pattern-recognition to enhance resolution for low quality images, and to allow the end user to explore, recognize and super-resolve low-resolution images. In this paper, we present the basic functionality of the PRIAR algorithm that we have implemented. The program is modular and each module is easily combined. In addition, such modularity permits us to work on images where single modules can be changed in order to resolve different classes of problems. In this paper, we firstly present the features of the PRIAR program processing images reproducing animal cells recorded with a scanning probe microscope.
2015, Rapporto tecnico, ENG
Bolettieri P.
This report describes the final implementation of the Image Retrieval System infrastructure developed for the EAGLE (Europeana network of Ancient Greek and Latin Epigraphy) project. The EAGLE project is gathering a comprehensive collection of inscriptions (about 80 % of the surviving material) and making it accessible through a user-friendly portal, which supports searching and browsing of the epigraphic material. In this document we will describe the Image Retrieval System and its API.
2004, Contributo in atti di convegno, ENG
Le Saux B.; Amato G.
The semantic interpretation of natural scenes, generally so obvious and effortless for humans, still remains a challenge in computer vision. We intend to design classifiers able to annotate images with keywords. Firstly, we propose an image representation appropriate for scene description: images are segmented into regions and indexed according to the presence of given region types. Secondly, we propound a classification scheme de- signed to separate images in the descriptor space. This is achieved by combining feature selection and kernel-method-based classification.
2004, Contributo in atti di convegno, ENG
Le Saux B.; Amato G.
The interpretation of natural scenes, generally so obvious and effortless for humans, still remains a challenge in computer vision. To allow the search of image-based documents in digital libraries, we propose to design classifiers able to annotate images with keywords. First, we propose an image representation appropriate for scene description. Images are segmented into regions, and then indexed according to the presence of given region types. Second, we propound a classification scheme designed to separate images in the descriptor space. This is achieved by combining feature selection and kernel-method-based classification.
2004, Rapporto tecnico, ENG
Le Saux B.; Amato G.
The interpretation of natural scenes, generally so obvious and effortless for humans, still remains a challenge in computer vision. To allow the search of image-based documents in digital libraries, we propose to design classifiers able to annotate images with keywords. First, we propose an image representation appropriate for scene description. Images are segmented into regions, and then indexed according to the presence of given region types. Second, we propound a classification scheme designed to separate images in the descriptor space. This is achieved by combining feature selection and kernel-method-based classification.