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Image Matching Using SIFT, SURF, BRIEF and ORB: Performance Comparison for Distorted Images
Fast and robust image matching is a very important task with various app...
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Training Deep Neural Networks to Detect Repeatable 2D Features Using Large Amounts of 3D World Capture Data
Image space feature detection is the act of selecting points or parts of...
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Evaluation of Feature Detector-Descriptor for Real Object Matching under Various Conditions of Ilumination and Affine Transformation
This study attempts to provide explanations, descriptions and evaluation...
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On the Comparison of Classic and Deep Keypoint Detector and Descriptor Methods
The purpose of this study is to give a performance comparison between se...
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FFD: Fast Feature Detector
Scale-invariance, good localization and robustness to noise and distorti...
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Image Identification Using SIFT Algorithm: Performance Analysis against Different Image Deformations
Image identification is one of the most challenging tasks in different a...
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A Fast Ellipse Detector Using Projective Invariant Pruning
Detecting elliptical objects from an image is a central task in robot na...
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Analysis of feature detector and descriptor combinations with a localization experiment for various performance metrics
The purpose of this study is to provide a detailed performance comparison of feature detector/descriptor methods, particularly when their various combinations are used for image-matching. The localization experiments of a mobile robot in an indoor environment are presented as a case study. In these experiments, 3090 query images and 127 dataset images were used. This study includes five methods for feature detectors (features from accelerated segment test (FAST), oriented FAST and rotated binary robust independent elementary features (BRIEF) (ORB), speeded-up robust features (SURF), scale invariant feature transform (SIFT), and binary robust invariant scalable keypoints (BRISK)) and five other methods for feature descriptors (BRIEF, BRISK, SIFT, SURF, and ORB). These methods were used in 23 different combinations and it was possible to obtain meaningful and consistent comparison results using the performance criteria defined in this study. All of these methods were used independently and separately from each other as either feature detector or descriptor. The performance analysis shows the discriminative power of various combinations of detector and descriptor methods. The analysis is completed using five parameters: (i) accuracy, (ii) time, (iii) angle difference between keypoints, (iv) number of correct matches, and (v) distance between correctly matched keypoints. In a range of 60, covering five rotational pose points for our system, the FAST-SURF combination had the lowest distance and angle difference values and the highest number of matched keypoints. SIFT-SURF was the most accurate combination with a 98.41 fastest algorithm was ORB-BRIEF, with a total running time of 21,303.30 s to match 560 images captured during motion with 127 dataset images.
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