Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

Advances in Character Recognition

This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject

Drawing, Handwriting Processing Analysis: New Advances and Challenges

International audienceDrawing and handwriting are communicational skills that are fundamental in geopolitical, ideological and technological evolutions of all time. drawingand handwriting are still useful in defining innovative applications in numerous fields. In this regard, researchers have to solve new problems like those related to the manner in which drawing and handwriting become an efficient way to command various connected objects; or to validate graphomotor skills as evident and objective sources of data useful in the study of human beings, their capabilities and their limits from birth to decline

A generic approach for desining on-line handwritten shapes recognizers

This paper presents a generic approach for designing on-line handwritten shapes recognizers. Our approach allows designing very different recognition engines that correspond to various needs in pen-based interfaces. In particular, it allows dealing with a wide class of symbols and characters. We present in detail our system and make the link between our models and more standard statistical models such as Hierarchical Hidden Markov Models and Dynamic Bayesian Networks. We then evaluate fundamental properties of our approach: learning from scratch any symbol, learning from very few training sample. We show experimentally that, using our approach, one can learn both a state-of-the-art writerindependent recognizer for alphanumeric characters, and a writer-dependent recognizer working with any twodimensional shapes that learns a new symbol with a few training samples and requires very few machines resources.Dans ce papier, nous présentons une approche générique pour le développement de moteurs de reconnaissance de symboles manuscrits en ligne. Cette approche permet de concevoir des systèmes de reconnaissance de types très variés correspondant à différents contextes des interfaces stylo, pouvant notamment fonctionner sur diverses classes de caractères ou symboles. Nous présentons en détail notre approche et faisons le lien avec d’une part les modèles de Markov hiérarchiques et d’autre part les réseaux bayésiens dynamiques. Nous évaluons ensuite les propriétés fondamentales de notre approche qui lui confèrent une grande flexibilité. Puis nous montrons que l’on peut, avec cette approche générique, concevoir aussi bien des systèmes omni-scripteur rivalisant avec les meilleurs systèmes actuels sur des caractères alphanumériques usuels, que des systèmes mono-scripteur pour des symboles graphiques quelconques, nécessitant très peu d’exemples d’apprentissage et peu gourmands en ressources machine