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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.
Camera calibration, 3D reconstruction, image registration, and object recognition are just a few. The search for discrete image correspondences the goal of this work can be divided into three main steps. First, interest points are selected at distinctive locations in the image, such as corners, blobs, and T-junctions.
The most valuable property of an interest point detector is its repeatability, i. Next, the neighbourhood of every interest point is represented by a feature vector.
This descriptor has to be distinctive and, at the same time, robust to noise, detection errors, and geometric and photometric deformations. Finally, the descriptor vectors are matched between different images. The matching is often based on a distance between the vectors, e.
The dimension of the descriptor has a direct impact on the time this takes, and a lower number of dimensions is therefore desirable. It has been our goal to develop both a detector and descriptor, which in comparison to the state-of-the-art are faster to compute, while not sacrificing performance.
In order to succeed, one has to strike a balance between the above requirements, like reducing the descriptor s dimension and complexity, while keeping it sufficiently distinctive.
Van Gool A wide variety of detectors and descriptors have already been proposed in the literature e. Also, detailed comparisons and evaluations on benchmarking datasets have been performed [7 9]. While constructing our fast detector and descriptor, we built on the insights gained from this previous work in order to get a feel for what are the aspects contributing to performance.
In our experiments on benchmark image sets as well as on a real object recognition application, the resulting detector and descriptor are not only faster, but also more distinctive and equally repeatable.
When working with local features, a first issue that needs to be settled is the required level of invariance. Clearly, this depends on the expected geometric and photometric deformations, which in turn are determined by the possible changes in viewing conditions. Here, we focus on scale and image rotation invariant detectors and descriptors.
These seem to offer a good compromise between feature complexity and robustness to commonly occurring deformations. Skew, anisotropic scaling, and perspective effects are assumed to be second-order effects, that are covered to some degree by the overall robustness of the descriptor.
As also claimed by Lowe , the additional complexity of full affine-invariant features often has a negative impact on their robustness and does not pay off, unless really large viewpoint changes are to be expected. Indeed, in quite a few applications, like mobile robot navigation or visual tourist guiding, the camera often only rotates about the vertical axis.
The benefit of avoiding the overkill of rotation invariance in such cases is not only increased speed, but also increased discriminative power. Concerning the photometric deformations, we assume a simple linear model with a scale factor and offset.
Notice that our detector and descriptor don t use colour.Computational Modeling of Age-Differences in a Visually Demanding Driving Task: Vehicle Detection, SMC-A(30), No.
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