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.

/pdf/surf-speeded-up-robust-features-1fje09jteo.pdf

SURF: speeded up robust features

https://www.cs.jhu.edu/~misha/ReadingSeminar/Papers/Bay08.pdf

Speeded-Up Robust Features (SURF)

The rapid increase in the performance of graphics hardware, coupled with recent improvements in its programmability, have made graphics hardware a compelling platform for computationally demanding tasks in a wide variety of application domains. In this report, we describe, summarize, and analyze the latest research in mapping general-purpose computation to graphics hardware. We begin with the technical motivations that underlie general-purpose computation on graphics processors (GPGPU) and describe the hardware and software developments that have led to the recent interest in this field. We then aim the main body of this report at two separate audiences. First, we describe the techniques used in mapping general-purpose computation to graphics hardware. We believe these techniques will be generally useful for researchers who plan to develop the next generation of GPGPU algorithms and techniques. Second, we survey and categorize the latest developments in general-purpose application development on graphics hardware. This survey should be of particular interest to researchers who are interested in using the latest GPGPU applications in their systems of interest.

/pdf/a-survey-of-general-purpose-computation-on-graphics-hardware-2phx1q9xxr.pdf

A Survey of General-Purpose Computation on Graphics Hardware

A Survey of General-Purpose Computation on Graphics Hardware.

We present a generic objectness measure, quantifying how likely it is for an image window to contain an object of any class. We explicitly train it to distinguish objects with a well-defined boundary in space, such as cows and telephones, from amorphous background elements, such as grass and road. The measure combines in a Bayesian framework several image cues measuring characteristics of objects, such as appearing different from their surroundings and having a closed boundary. These include an innovative cue to measure the closed boundary characteristic. In experiments on the challenging PASCAL VOC 07 dataset, we show this new cue to outperform a state-of-the-art saliency measure, and the combined objectness measure to perform better than any cue alone. We also compare to interest point operators, a HOG detector, and three recent works aiming at automatic object segmentation. Finally, we present two applications of objectness. In the first, we sample a small numberof windows according to their objectness probability and give an algorithm to employ them as location priors for modern class-specific object detectors. As we show experimentally, this greatly reduces the number of windows evaluated by the expensive class-specific model. In the second application, we use objectness as a complementary score in addition to the class-specific model, which leads to fewer false positives. As shown in several recent papers, objectness can act as a valuable focus of attention mechanism in many other applications operating on image windows, including weakly supervised learning of object categories, unsupervised pixelwise segmentation, and object tracking in video. Computing objectness is very efficient and takes only about 4 sec. per image.

/pdf/measuring-the-objectness-of-image-windows-1pdjv11led.pdf

Measuring the Objectness of Image Windows

In this work we scrutinize a low level computer vision task - non-maximum suppression (NMS) - which is a crucial preprocessing step in many computer vision applications. Especially in real time scenarios, efficient algorithms for such preprocessing algorithms, which operate on the full image resolution, are important. In the case of NMS, it seems that merely the straightforward implementation or slight improvements are known. We show that these are far from being optimal, and derive several algorithms ranging from easy-to-implement to highly-efficient.

Efficient Non-Maximum Suppression

We present a GPU-based foreground-background segmentation that processes image sequences in less than 4ms per frame. Change detection wrt. the background is based on a color similarity test in a small pixel neighbourhood, and is integrated into a Bayesian estimation framework. An iterative MRFbased model is applied, exploiting parallelism on modern graphics hardware. Resulting segmentation exhibits compactness and smoothness in foreground areas as well as for inter-frame temporal contiguity. Further renements extend the colinearity criterion with compensation for dark foreground and background areas and thus improving overall performance.

GPU-Based Foreground-Background Segmentation using an Extended Colinearity Criterion

This paper investigates several aspects of 3D-2D camera pose estimation, aimed at robot navigation in poorly-textured scenes. The major contribution is a fast, linear algorithm for the general case with six or more points. We show how to specialise this to work with only four or five points, which is of utmost importance in a test and hypothesis framework. Our formulation allows for an easy inclusion of lines, as well as the handling of other camera geometries, such as stereo rigs. We also treat the special case of planar motion, a valid restriction for most indoor environments. We conclude the paper with extensive simulated tests and a real test case, which substantiate the algorithm’s usability for our application domain.

/pdf/generalised-linear-pose-estimation-1p6wi9csgn.pdf

Generalised linear pose estimation

Textures often result from complicated surface geometries. We propose a method that extracts such geometry from raw BTF data. Exploiting the huge amount of input data, only a few and rather weak assumptions about reflectance and geometry suffice. A key element of our approach are viewpoint robustness of reflectance features. We propose a few and compare them for 3D reconstruction.

http://www.macs.hw.ac.uk/texture2005/programme/papers/035.pdf

3D Texture Reconstruction from Extensive BTF Data

This paper addresses the problem of camera self-calibration, bundle adjustment and 3D reconstruction from line segments in two images of poorly-textured indoor scenes. First, we generate line segment correspondences, using an extended version of our previously proposed matching scheme. The first main contribution is a new method to identify polyhedral junctions resulting from the intersections of the line segments. At the same time, the images are segmented into planar polygons. This is done using an algorithm based on a Binary Space Partitioning (BSP) tree. The junctions are matched end points of the detected line segments and hence can be used to obtain the epipolar geometry. The essential matrix is considered for metric camera calibration. For better stability, the second main contribution consists in a bundle adjustment on the line segments and the camera parameters that reduces the number of unknowns by a maximum flow algorithm. Finally, a piecewise-planar 3D reconstruction is computed based on the segmentation of the BSP tree. The system?s performance is tested on some challenging examples.

Alexander Neubeck

Papers

Efficient Non-Maximum Suppression

GPU-Based Foreground-Background Segmentation using an Extended Colinearity Criterion

Generalised linear pose estimation

3D Texture Reconstruction from Extensive BTF Data

3D from Line Segments in Two Poorly-Textured, Uncalibrated Images