Fusion of visual salience maps for object acquisition

The paradigm of visual attention has been widely investigated and applied to many computer vision applications. In this study, the authors propose a new saliency-based visual attention algorithm applied to object acquisition. The proposed algorithm automatically extracts points of visual attention (PVA) in the scene, based on different feature saliency maps. Each saliency map represents a specific feature domain, such as textural, contrast, and statistical-based features. A feature selection, based on probability of detection and false alarm rate and repeatability criteria, is proposed to choose the most efficient feature combination for saliency map. Motivated by the assumption that the extracted PVA represents the most visually salient regions in the image, they suggest using the visual attention approach for object acquisition. A comparison with other well-known algorithms for point of interest detection shows that the proposed algorithm performs better. The proposed algorithm was successfully tested on synthetic, charge-coupled device (CCD), and infrared (IR) images. Evaluation of the algorithm for object acquisition, based on ground truth, is carried out using synthetic images, which contain multiple examples of objects, with various sizes and brightness levels. A high probability of correct detection (greater than 90%) with a low false alarm rate (about 20 false alarms per image) was achieved.