Advances in Adaptive Composite Filters for Object Recognition
H. Victor, Leonardo Trujillo, Sergio Pinto-Fernández · InTech eBooks · 2012
The problem of object recognition is one of the most common problems that is addressed by researchers and engineers that want to develop artificial vision or image analysis systems. In order to recognize an object within an image or video sequence we must basically solve two different but related tasks. Firstly, it is essential to detect the target object within the scene image, and secondly its exact location within the image must be estimated. While the general concept of object recognition is straightforward, even a brief review of modern literature reveals a wide range of proposals and systems (Goudail & Refregier, 2004; Szeliski, 2010). However, one of the most common and successful approaches are local feature-based systems that normally employ two basic steps (Lowe, 2004; Tuytelaars & Mikolajczyk, 2008). First, object features are extracted from the scene image, and afterwards a classification step is used to determine if the observed features belong to the target object; a process known as feature matching. Feature-based systems have achieved very good results and are widely used in many application domains. Nevertheless, feature based systems suffer from two noteworthy drawbacks. First, they can be computationally expensive1, and second their overall performance depends upon some ad-hoc decisions that might require optimization (Brown et al., 2011; Olague & Trujillo, 2011; Perez & Olague, 2008; Theodoridis & Koutroumbas, 2008; Trujillo & Olague, 2008).