Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Robust Real-Time Face Detection
International Journal of Computer Vision
Pedestrian Detection via Classification on Riemannian Manifolds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Salient region detection and segmentation
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Performance Evaluation of the Covariance Descriptor for Target Detection
SCCC '09 Proceedings of the 2009 International Conference of the Chilean Computer Science Society
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Tracking Based on Covariance Descriptors and On-Line Naive Bayes Nearest Neighbor Classifier
PSIVT '10 Proceedings of the 2010 Fourth Pacific-Rim Symposium on Image and Video Technology
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Region covariance: a fast descriptor for detection and classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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One of the challenges of computer vision is to improve the automatic systems for the recognition and tracking of objects in a set of images. One approach that has recently gained importance is based on extracting descriptors, such as the covariance descriptor, because they manage to remain invariant in the regions of these images despite changes of translation, rotation and scale. In this work we propose, using the Covariance Descriptor, a novel saliency system able to find the most relevant regions in an image, which can be used for recognition and tracking objects. Our method is based on the amount of information from each point in the image, and allows us to adapt the regions to maximize the difference of information between the region and its environment. The results show that this tool's improvements can boost trackers precision up to 90% (with initial precision of 50%) without compromising the recall.