Book ChapterDOI
CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching
Motilal Agrawal,Kurt Konolige,Morten Rufus Blas +2 more
- pp 102-115
Reads0
Chats0
TLDR
A suite of scale-invariant center-surround detectors (CenSurE) that outperform the other detectors, yet have better computational characteristics than other scale-space detectors, and are capable of real-time implementation are introduced.Abstract:
We explore the suitability of different feature detectors for the task of image registration, and in particular for visual odometry, using two criteria: stability (persistence across viewpoint change) and accuracy (consistent localization across viewpoint change). In addition to the now-standard SIFT, SURF, FAST, and Harris detectors, we introduce a suite of scale-invariant center-surround detectors (CenSurE) that outperform the other detectors, yet have better computational characteristics than other scale-space detectors, and are capable of real-time implementation.read more
Citations
More filters
Proceedings ArticleDOI
High-performance visual odometry with two-stage local binocular BA and GPU
Wei Lu,Zhiyu Xiang,Jilin Liu +2 more
TL;DR: A two-stage local binocular bundle adjustment algorithm doing the optimization and construct a parallel pipeline using GPU acceleration to achieve high accuracy and high frequency visual odometry.
Journal ArticleDOI
Saliency Detection Based on Multiscale Extrema of Local Perceptual Color Differences
TL;DR: Experimental validation on the extensive CAT2000 data set demonstrates that the proposed saliency detection algorithm either outperforms or is highly competitive with prior approaches, and can perform well across different categories and object sizes, while remaining training-free.
Book ChapterDOI
Ground Truth Data, Content, Metrics, and Analysis
TL;DR: This chapter proposes a method and corresponding ground truth dataset for measuring interest point detector response as compared to human visual system response and human expectations and looks at the current state of the art, its best practices, and a survey of available ground truth datasets.
Proceedings ArticleDOI
Image features and seasons revisited
TL;DR: An evaluation of standard image features in the context of long-term visual teach-and-repeat mobile robot navigation, where the environment exhibits significant changes in appearance caused by seasonal weather variations and daily illumination changes, proposes a novel feature descriptor, GRIEF, which outperforms the other ones while being computationally more efficient.
Journal ArticleDOI
Illumination-Robust remote sensing image matching based on oriented self-similarity
Amin Sedaghat,Nazila Mohammadi +1 more
TL;DR: Experimental analysis on three categories of synthetic and real remote sensing images from various sensors demonstrate the superior capability of the HOSS over state-of-the-art descriptors, including DOBSS, AB-SIFT, PIIFD, and DAISY, in terms of the recall, precision, and positional accuracy.
References
More filters
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Journal ArticleDOI
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
TL;DR: New results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form that provide the basis for an automatic system that can solve the Location Determination Problem under difficult viewing.
Proceedings ArticleDOI
A Combined Corner and Edge Detector
Chris Harris,Mike Stephens +1 more
TL;DR: The problem the authors are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work.
Book ChapterDOI
SURF: speeded up robust features
TL;DR: A novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Proceedings ArticleDOI
Robust real-time face detection
Paul A. Viola,Michael Jones +1 more
TL;DR: A new image representation called the “Integral Image” is introduced which allows the features used by the detector to be computed very quickly and a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions.