Covariance descriptor multiple object tracking and re-identification with colorspace evaluation
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Scalable mobile search with binary phrase
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Evaluation of salient point methods
Proceedings of the 21st ACM international conference on Multimedia
Golden retriever: a Java based open source image retrieval engine
Proceedings of the 21st ACM international conference on Multimedia
Improved binary feature matching through fusion of hamming distance and fragile bit weight
Proceedings of the 3rd ACM international workshop on Interactive multimedia on mobile & portable devices
SIFER: Scale-Invariant Feature Detector with Error Resilience
International Journal of Computer Vision
A comparative study on mobile visual recognition
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
Local features and histogram based planar object recognition
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
Retina enhanced SURF descriptors for spatio-temporal concept detection
Multimedia Tools and Applications
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A large number of vision applications rely on matching keypoints across images. The last decade featured an arms-race towards faster and more robust keypoints and association algorithms: Scale Invariant Feature Transform (SIFT)[17], Speed-up Robust Feature (SURF)[4], and more recently Binary Robust Invariant Scalable Keypoints (BRISK)[I6] to name a few. These days, the deployment of vision algorithms on smart phones and embedded devices with low memory and computation complexity has even upped the ante: the goal is to make descriptors faster to compute, more compact while remaining robust to scale, rotation and noise. To best address the current requirements, we propose a novel keypoint descriptor inspired by the human visual system and more precisely the retina, coined Fast Retina Keypoint (FREAK). A cascade of binary strings is computed by efficiently comparing image intensities over a retinal sampling pattern. Our experiments show that FREAKs are in general faster to compute with lower memory load and also more robust than SIFT, SURF or BRISK. They are thus competitive alternatives to existing keypoints in particular for embedded applications.