Detecting interest points is a key component of vision-based estimation algorithms, such as visual odometry or visual SLAM. Classically, interest point detection has been done with methods such as Harris, FAST, or DoG. Recently, better detectors have been proposed based on Neural Networks. Traditionally, interest point detectors have been designed to maximize repeatability or matching score. Instead, we pursue another metric, which we call succinctness. This metric captures the minimum amount of interest points that need to be extracted in order to achieve accurate relative pose estimation. Extracting a minimum amount of interest points is attractive for many applications, because it reduces computational load, memory, and, potentially, data transmission. We propose a novel reinforcement- and ranking-based training framework, which uses a full relative pose estimation pipeline during training. It can be trained in an unsupervised manner, without pose or 3D point ground truth. Using this training framework, we present a detector which outperforms previous interest point detectors in terms of succinctness on a variety of publicly available datasets.
Classically, interest points have been selected from distinctive image locations, that is, image locations which significantly differ from neighboring image locations. Whether an image location is distinctive can be determined explicitly @cite_35 or using a first-order approximation such as the Harris @cite_14 or Shi-Tomasi @cite_8 detector. Alternatively, distinctive interest points can be detected by convolving the image with a suitable kernel, such as the Laplacian of Gaussian (LoG), or its faster approximation, the Difference of Gaussians (DoG) kernel, prominently used in @cite_50 . Other filter-based detectors have been proposed by @cite_46 @cite_45 . Finally, another method for detecting interest points is to explicitly identify image regions that resemble corners. Iteratively, gradually more efficient ways to do this have been proposed by @cite_32 @cite_29 @cite_27 and have found widespread popularity with @cite_23 due to its highly efficient implementation. An interesting challenge in interest point detection is ensuring that the detection is independent of scale and affine transformations. Scale invariance can be achieved using multi-scale detection @cite_42 , while affine invariance, in particular invariance to rotation is already given for most aforementioned detectors.
@cite_35 @cite_14 @cite_8 @cite_29 @cite_42 @cite_32 @cite_27 @cite_45 @cite_50 @cite_23 @cite_46
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In this paper we propose a novel approach for detecting interest points invariant to scale and affine transformations. Our scale and affine invariant detectors are based on the following recent results: (1) Interest points extracted with the Harris detector can be adapted to affine transformations and give repeatable results (geometrically stable). (2) The characteristic scale of a local structure is indicated by a local extremum over scale of normalized derivatives (the Laplacian). (3) The affine shape of a point neighborhood is estimated based on the second moment matrix. Our scale invariant detector computes a multi-scale representation for the Harris interest point detector and then selects points at which a local measure (the Laplacian) is maximal over scales. This provides a set of distinctive points which are invariant to scale, rotation and translation as well as robust to illumination changes and limited changes of viewpoint. The characteristic scale determines a scale invariant region for each point. We extend the scale invariant detector to affine invariance by estimating the affine shape of a point neighborhood. An iterative algorithm modifies location, scale and neighborhood of each point and converges to affine invariant points. This method can deal with significant affine transformations including large scale changes. The characteristic scale and the affine shape of neighborhood determine an affine invariant region for each point. We present a comparative evaluation of different detectors and show that our approach provides better results than existing methods. The performance of our detector is also confirmed by excellent matching resultss the image is described by a set of scale affine invariant descriptors computed on the regions associated with our points. This paper presents a new method for detecting scale invariant interest points. The method is based on two recent results on scale space: (1) Interest points can be adapted to scale and give repeatable results (geometrically stable). (2) Local extrema over scale of normalized derivatives indicate the presence of characteristic local structures. Our method first computes a multi-scale representation for the Harris interest point detector. We then select points at which a local measure (the Laplacian) is maximal over scales. This allows a selection of distinctive points for which the characteristic scale is known. These points are invariant to scale, rotation and translation as well as robust to illumination changes and limited changes of viewpoint. For indexing, the image is characterized by a set of scale invariant points; the scale associated with each point allows the computation of a scale invariant descriptor. Our descriptors are, in addition, invariant to image rotation, of affine illumination changes and robust to small perspective deformations. Experimental results for indexing show an excellent performance up to a scale factor of 4 for a database with more than 5000 images. We present a generic detection localization algorithm capable of searching for a visual object of interest without training. The proposed method operates using a single example of an object of interest to find similar matches, does not require prior knowledge (learning) about objects being sought, and does not require any preprocessing step or segmentation of a target image. Our method is based on the computation of local regression kernels as descriptors from a query, which measure the likeness of a pixel to its surroundings. Salient features are extracted from said descriptors and compared against analogous features from the target image. This comparison is done using a matrix generalization of the cosine similarity measure. We illustrate optimality properties of the algorithm using a naive-Bayes framework. The algorithm yields a scalar resemblance map, indicating the likelihood of similarity between the query and all patches in the target image. By employing nonparametric significance tests and nonmaxima suppression, we detect the presence and location of objects similar to the given query. The approach is extended to account for large variations in scale and rotation. High performance is demonstrated on several challenging data sets, indicating successful detection of objects in diverse contexts and under different imaging conditions. In this paper, we describe an interest point detector using edge foci. Unlike traditional detectors that compute interest points directly from image intensities, we use normalized intensity edges and their orientations. We hypothesize that detectors based on the presence of oriented edges are more robust to non-linear lighting variations and background clutter than intensity based techniques. Specifically, we detect edge foci, which are points in the image that are roughly equidistant from edges with orientations perpendicular to the point. The scale of the interest point is defined by the distance between the edge foci and the edges. We quantify the performance of our detector using the interest point's repeatability, uniformity of spatial distribution, and the uniqueness of the resulting descriptors. Results are found using traditional datasets and new datasets with challenging non-linear lighting variations and occlusions.