Feature Matching with Improved SIRB using RANSAC

Object recognition has a wide domain of applications such as content-based image classification, video data mining, video surveillance and more. Object recognition accuracy has been a significant concern. Although deep learning had automated the feature extraction but hand crafted features continue to deliver consistent performance. This paper aims at efficient object recognition using hand crafted features based on Oriented Fast & Rotated BRIEF (Binary Robust Independent Elementary Features) and Scale Invariant Feature Transform features. Scale Invariant Feature Transform (SIFT) are particularly useful for analysis of images in light of different orientation and scale. Locality Preserving Projection (LPP) dimensionality reduction algorithm is explored to reduce the dimensions of obtained image feature vector. The execution of the proposed work is tested by using k-NN, decision tree and random forest classifiers. A dataset of 8000 samples of 100-class objects has been considered for experimental work. A precision rate of 69.8% and 76.9% has been achieved using ORB and SIFT feature descriptors, respectively. A combination of ORB and SIFT feature descriptors is also considered for experimental work. The integrated technique achieved an improved precision rate of 85.6% for the same.

Extremely variant image pairs include distorted, deteriorated, and corrupted scenes that have experienced severe geometric, photometric, or non-geometric-non-photometric transformations with respect to their originals. Real world visual data can become extremely dusty, smoky, dark, noisy, motion-blurred, affine, JPEG compressed, occluded, shadowed, virtually invisible, etc. Therefore, matching of extremely variant scenes is an important problem and computer vision solutions must have the capability to yield robust results no matter how complex the visual input is. Similarly, there is a need to evaluate feature detectors for such complex conditions. With standard settings, feature detection, description, and matching algorithms typically fail to produce significant number of correct matches in these types of images. Though, if full potential of the algorithms is applied by using extremely low thresholds, very encouraging results are obtained. In this paper, potential of 14 feature detectors: SIFT, SURF, KAZE, AKAZE, ORB, BRISK, AGAST, FAST, MSER, MSD, GFTT, Harris Corner Detector based GFTT, Harris Laplace Detector, and CenSurE has been evaluated for matching 10 extremely variant image pairs. MSD detected more than 1 million keypoints in one of the images and SIFT exhibited a repeatability score of 99.76% for the extremely noisy image pair but failed to yield high quantity of correct matches. Rich information is presented in terms of feature quantity, total feature matches, correct matches, and repeatability scores. Moreover, computational costs of 25 diverse feature detectors are reported towards the end, which can be used as a benchmark for comparison studies.

2013

Post-processing after matching technique such as RANSAC is useful for reducing outliners. However, such methods may not be able to increase the number of correctly matched pairs that is important in some application such as image stitching. In this work, a post-processing technique for increasing the number of correct matched points between two images with on-plane rotation is proposed. The proposed method makes use of the dominant rotational degree between two corresponding images to increase the number of matched points after feature extraction process using state-of-the-art methods such as Scale Invariant Feature Transform (SIFT) or Speeded-Up Robust Feature (SURF). The proposed method can generally increase the number of matched points around 10% to 20%. Furthermore, it can also correct the false matching which is caused by similar appearance of features.

2011 International Conference on Digital Image Computing: Techniques and Applications, 2011

Scene classification in indoor and outdoor environments is a fundamental problem to the vision and robotics community. Scene classification benefits from image features which are invariant to image transformations such as rotation, illumination, scale, viewpoint, noise etc. Selecting suitable features that exhibit such invariances plays a key part in classification performance. This paper summarizes the performance of two robust feature detection algorithms namely Scale Invariant Feature Transform (SIFT) and Speeded up Robust Features (SURF) on several classification datasets. In this paper, we have proposed three shorter SIFT descriptors. Results show that the proposed 64D and 96D SIFT descriptors perform as well as traditional 128D SIFT descriptors for image matching at a significantly reduced computational cost. SURF has also been observed to give good classification results on different datasets.

Feature detection and matching are used in image registration, object tracking, object retrieval etc. There are number of approaches used to detect and matching of features as SIFT (Scale Invariant Feature Transform), SURF (Speeded up Robust Feature), FAST, ORB etc. SIFT and SURF are most useful approaches to detect and matching of features because of it is invariant to scale, rotate, translation, illumination, and blur. In this paper, there is comparison between SIFT and SURF approaches are discussed. SURF is better than SIFT in rotation invariant, blur and warp transform. SIFT is better than SURF in different scale images. SURF is 3 times faster than SIFT because using of integral image and box filter. SIFT and SURF are good in illumination changes images. Keywords-SIFT (Scale Invariant Feature Transform), SURF (Speeded up Robust Feature), invariant, integral image, box filter

2012

Standard RANSAC does not perform very well for contaminated sets, when there is a majority of outliers. We present a method that overcomes this problem by transforming the problem into a 2D position vector space, where an ordinary cluster algorithm can be used to find a set of putative inliers. This set can then easily be handled by a modified version of RANSAC that draws samples from this set only and scores using the entire set. This approach works well for moderate differences in scale and rotation. For contaminated sets the increase in performance is in several orders of magnitude. We present results from testing the algorithm using the Direct Linear Transformation on aerial images and photographs used for panographs.

International Journal of Innovative Research in Computer and Communication Engineering, 2013

Accurate, robust and automatic image registration is critical task in many applications. To perform image registration/alignment, required steps are: Feature detection, Feature matching, derivation of transformation function based on corresponding features in images and reconstruction of images based on derived transformation function. Accuracy of registered image depends on accurate feature detection and matching. So these two intermediate steps are very important in many image applications: image registration, computer vision, image mosaic etc. This paper presents two different methods for scale and rotation invariant interest point/feature detector and descriptor: Scale Invariant Feature Transform (SIFT) and Speed Up Robust Features (SURF). It also presents a way to extract distinctive invariant features from images that can be used to perform reliable matching between different views of an object/scene.

IEEE Transactions on Robotics, 2012

Keypoint detection and matching is of fundamental importance for many applications in computer and robot vision. The association of points across different views is problematic because image features can undergo significant changes in appearance. Unfortunately, state-of-the-art methods, like the scale-invariant feature transform (SIFT), are not resilient to the radial distortion that often arises in images acquired by cameras with microlenses and/or wide field-of-view. This paper proposes modifications to the SIFT algorithm that substantially improve the repeatability of detection and effectiveness of matching under radial distortion, while preserving the original invariance to scale and rotation. The scale-space representation of the image is obtained using adaptive filtering that compensates the local distortion, and the keypoint description is carried after implicit image gradient correction. Unlike competing methods, our approach avoids image resampling (the processing is carried out in the original image plane), it does not require accurate camera calibration (an approximate modeling of the distortion is sufficient), and it adds minimal computational overhead. Extensive experiments show the advantages of our method in establishing point correspondence across images with radial distortion.

2017 Artificial Intelligence and Signal Processing Conference (AISP), 2017

This paper describes the method to increase the speed of SIFT feature extraction by feature approximation instead of feature calculation in various layers. SIFT has been proven to be the most robust local rotation and illumination invariant feature descriptor. Additionally, it supports affine transformation. Being fully scale invariant is the most important advantage of this descriptor. The most major SIFT'sdrawback is time-consuming which prevents utilizing SIFTin real time applications. This research attempts to decrease computational cost withoutsacrificing performance. The recent researches in this area approved that direct feature computation is more expensive than extrapolation. Consequently, contribution of this research reduces processing time considerablywithout losing accuracy.

In this paper, we present a novel scale-and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features). It approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster. This is achieved by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (in casu, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps. The paper presents experimental results on a standard evaluation set, as well as on imagery obtained in the context of a real-life object recognition application. Both show SURF's strong performance.

International Journal of Computer Vision, 2004

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. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.

Most descriptor-based keypoint recognition methods require computationally expensive patch preprocessing to obtain insensitivity to various kinds of deformations. This limits their applicability towards realtime applications on low-powered devices such as mobile phones. In this paper, we focus on descriptors which are relatively weak (i.e. sensitive to scale and rotation), and present a classification-based approach to improve their robustness and efficiency to achieve real-time matching. We demonstrate our method by applying it to BRIEF [7] resulting in comparable robustness to SIFT [4], while outperforming several state-ofthe-art descriptors like SURF [6], ORB [8], and FREAK [10].

International Journal of …, 2009

Abstract— The SIFT algorithm produces keypoint descriptors. This paper analyzes that the SIFT algorithm generates the number of keypoints when we increase a parameter (number of sublevels per octave). SIFT has a good hit rate for this analysis. The algorithm was tested over a ...

IEEE Transactions on Image Processing, 2011

This paper presents a novel algorithm for matching image interest points. Potential interest points are identified by searching for local peaks in Difference-of-Gaussian (DoG) images. We refine and assign rotation, scale and location for each keypoint by using the SIFT algorithm [1]. Pseudo log-polar sampling grid is then applied to properly scaled image patches around each keypoint, and a weighted adaptive lifting scheme transform is designed for each ring of the log-polar grid. The designed adaptive transform for a ring in the reference keypoint and the general non-adaptive transform are applied to the corresponding ring in a test keypoint. Similarity measure is calculated by comparing the corresponding transform domain coefficients of the adaptive and nonadaptive transforms. We refer to the proposed versatile system of Rotation And Scale Invariant Matching as RASIM. Our experiments show that the accuracy of RASIM is more than SIFT, which is the most widely used interest point matching algorithm in the literature. RASIM is also more robust to image deformations while its computation time is comparable to SIFT.

IEEE International Conference on Intelligent Robots and Systems, 2007

Numerous scale-invariant feature matching algorithms using scale-space analysis have been proposed for use with perspective cameras, where scale-space is defined as convolution with a Gaussian. The contribution of this work is a method suitable for use with wide angle cameras. Given an input image, we map it to the unit sphere and obtain scale-space images by convolution with the solution of the spherical diffusion equation on the sphere which we implement in the spherical Fourier domain. Using such an approach, the scale-space response of a point in space is independent of its position on the image plane for a camera subject to pure rotation. Scale-invariant features are then found as local extrema in scale-space. Given this set of scale-invariant features, we then generate feature descriptors by considering a circular support region defined on the sphere whose size is selected relative to the feature scale. We compare our method to a naive implementation of SIFT where the image is treated as perspective, where our results show an improvement in matching performance.