SIFT and SURF Performance Evaluation against Various Image Deformations on Benchmark Dataset (original) (raw)
Related papers
Speeded-Up Robust Features (SURF
This article presents a novel scale-and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features). SURF 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 (specifically, 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.
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.
SURF: Speeded Up Robust Features
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.
Comparison of Feature Detection and Matching Approaches: SIFT and SURF
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
Scale Invariant Feature Transform
Scale Invariant Feature Transform (SIFT) is an image descriptor for image-based matching developed by David Lowe (1999, 2004). This descriptor as well as related image descriptors are used for a large number of purposes in computer vision related to point matching between different views of a 3-D scene and view-based object recognition. The SIFT descriptor is invariant to translations, rotations and scaling transformations in the image domain and robust to moderate perspective transformations and illumination variations. Experimentally, the SIFT descriptor has been proven to be very useful in practice for image matching and object recognition under real-world conditions. In its original formulation, the SIFT descriptor comprised a method for detecting interest points from a grey-level image at which statistics of local gradient directions of image intensities were accumulated to give a summarizing description of the local image structures in a local neighbourhood around each interes...
Object Recognition: Performance evaluation using SIFT and SURF
International Journal of Computer Applications, 2013
Object Recognition has become one of the most attractive areas of research for most of the scientists over the past few decades. Object recognition has extensive applications in numerous areas of interest. In this paper, the importance of object recognition in different applications has been highlighted. The very famous and impressive technique by David Lowe which is Scale Invariant Feature Transform (SIFT) has been implemented for object recognition and an attempt has been done to compare the results obtained from it with the another very important technique called Speeded-Up Robust Feature Transform (SURF) to conclude with certain interesting results.
AN EFFICIENT WAY FOR SHAPE RECOGNITION BY USING SPEEDED-UP ROBUST FEATURE (SURF)
IJCSMC, 2018
In this paper, we present a novel scale-and rotation-invariant interest point detector and descriptor, coined CONTOUR 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. 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. The calculation has properties of picture scaling-, interpretation, and revolution invariants. Contour-SURF feature extraction and matching technique all along with a matching practice are given for the contour based shape detection. The algorithm automatically extracts local features in the certain entity outline with no any limitation of definite neighborhood location. A Contour recognition experiment was done with different datasets. All images were taken as a target and resultant to the further model. The detection correctness reaches 100% for images having distinctive contour feature, and lesser for images having common shapes.
Journal of Electronic Imaging, 2014
Robust local descriptors usually consist of high dimensional feature vectors to describe distinctive characteristics of images. The high dimensionality of a feature vector incurs into considerable costs in terms of computational time and storage. It also results in the curse of dimensionality, which affects the performance of several tasks that use such feature vectors, such as matching, retrieval and classification of images. To address these problems, it is possible to employ some dimensionality reduction techniques, leading frequently to information lost and, consequently, accuracy reduction. This work aims at applying linear dimensionality reduction to the SIFT and SURF descriptors. The objective is to demonstrate that even risking to decrease the accuracy of the feature vectors, it results in a satisfactory trade-off between computational time and storage requirements. We perform linear dimensionality reduction through Random Projections (RP), Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Partial Least Squares (PLS) in order to create lower dimensional feature vectors. These new reduced descriptors lead us to less computational time and memory storage requirements, even improving accuracy in some cases. We evaluate such reduced feature vectors in a matching application, as well as their distinctiveness in image retrieval. Finally, we assess the computational time and storage requirements by comparing the original and the reduced feature vectors.
Performance Evaluation of SIFT-Based Descriptors for Object Recognition
2010
Object recognition has become one of the most active research topics in computer vision in recent years. The set of features extracted from the training image is critical for good object recognition performance. The Scale Invariant Feature Transform (SIFT) was proposed by David Lowe in 1999; the SIFT features are local and effective for object recognition. In this paper we conducted a survey of recent related work on the SIFT descriptor, analyzed the evaluation criteria for object recognition, and analyzed the performance of the SIFT descriptor and extended SIFT descriptors based on common properties and evaluation criterion. The paper documents improvement strategies and trends of the SIFT descriptor and proposed extensions.
Combining Harris interest points and the SIFT descriptor for fast scale-invariant object recognition
2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009
In the recent past, the recognition and localization of objects based on local point features has become a widely accepted and utilized method. Among the most popular features are currently the SIFT features, the more recent SURF features, and region-based features such as the MSER. For time-critical application of object recognition and localization systems operating on such features, the SIFT features are too slow (500-600 ms for images of size 640×480 on a 3 GHz CPU). The faster SURF achieve a computation time of 150-240 ms, which is still too slow for active tracking of objects or visual servoing applications. In this paper, we present a combination of the Harris corner detector and the SIFT descriptor, which computes features with a high repeatability and very good matching properties within approx. 20 ms. While just computing the SIFT descriptors for computed Harris interest points would lead to an approach that is not scaleinvariant, we will show how scale-invariance can be achieved without a time-consuming scale space analysis. Furthermore, we will present results of successful application of the proposed features within our system for recognition and localization of textured objects. An extensive experimental evaluation proves the practical applicability of our approach.