View planning for efficient contour-based 3D object recognition

This paper presents a method for capture planning in view based 3D recognition. Views are represented by their contours, encoded into curvature functions, which are reduced into compact feature vectors by Principal Component Analysis. These vectors are very resistant against transformations, so they can be assumed to be distributed over the surface of a sphere with the object in its center. After clustering these vectors, 3D objects are represented via Hidden Markov Models where classes are states. To recognize an object in a minimum number of steps, we propose to align candidate cluster representations and then subtracting their cluster maps to decide in which locations they differ the most. Then, a TSP is used to decide in which order these distinctive locations are visited. The proposed approach has been successfully tested with several artificial 3D object databases, even though it still presents some errors in objects with strong symmetries.Ministerio de Ciencia e Innovación (MICINN) project TEC-2008-06734 Junta de Andalucia (JA) project TIC-0310

Scene text segmentation based on thresholding

This research deals with the problem of text segmentation in scene images. Introduction deals with the information contained in an image and the different properties that will be useful for image segmentation. After that, the process of extraction of textual information is explained step by step. Furthermore, the problem of scene text segmentation is described more precisely and an overview of more popular existing methods is given. Text segmentation method is created and implemented using C++ programming language with OpenCV library. Finally, algorithm is evaluated with images from ICDAR 2013 test dataset