Radon-based Structure from Motion Without Correspondences

We present a novel approach for the estimation of 3Dmotion directly from two images using the Radon transform. We assume a similarity function defined on the crossproduct of two images which assigns a weight to all feature pairs. This similarity function is integrated over all feature pairs that satisfy the epipolar constraint. This integration is equivalent to filtering the similarity function with a Dirac function embedding the epipolar constraint. The result of this convolution is a function of the five unknownmotion parameters with maxima at the positions of compatible rigid motions. The breakthrough is in the realization that the Radon transform is a filtering operator: If we assume that images are defined on spheres and the epipolar constraint is a group action of two rotations on two spheres, then the Radon transform is a convolution/correlation integral. We propose a new algorithm to compute this integral from the spherical harmonics of the similarity and Dirac functions. The resulting resolution in the motion space depends on the bandwidth we keep from the spherical transform. The strength of the algorithm is in avoiding a commitment to correspondences, thus being robust to erroneous feature detection, outliers, and multiple motions. The algorithm has been tested in sequences of real omnidirectional images and it outperforms correspondence-based structure from motion

Geometric Inference with Microlens Arrays

This dissertation explores an alternative to traditional fiducial markers where geometric information is inferred from the observed position of 3D points seen in an image. We offer an alternative approach which enables geometric inference based on the relative orientation of markers in an image. We present markers fabricated from microlenses whose appearance changes depending on the marker\u27s orientation relative to the camera. First, we show how to manufacture and calibrate chromo-coding lenticular arrays to create a known relationship between the observed hue and orientation of the array. Second, we use 2 small chromo-coding lenticular arrays to estimate the pose of an object. Third, we use 3 large chromo-coding lenticular arrays to calibrate a camera with a single image. Finally, we create another type of fiducial marker from lenslet arrays that encode orientation with discrete black and white appearances. Collectively, these approaches oer new opportunities for pose estimation and camera calibration that are relevant for robotics, virtual reality, and augmented reality

Optical-Flow Based Detection of Moving Objects in Traffic Scenes

Traffic is increasing continuously. Nevertheless the number of traffic fatalities decreased in the past. One reason for this are the passive safety systems, such as side crash protection or airbag, which have been engineered the last decades and which are standard in today's cars. Active safety systems are increasingly developed. They are able to avoid or at least to mitigate accidents. For example, the adaptive cruise control (ACC) original designed as a comfort system is developed towards an emergency brake system. Active safety requires sensors perceiving the vehicle environment. ACC uses radar or laser scanner. However, cameras are also interesting sensors as they are capable of processing visual information such as traffic signs or lane markings. In traffic moving objects (cars, bicyclists, pedestrians) play an important role. To perceive them is essential for active safety systems. This thesis deals with the detection of moving objects utilizing a monocular camera. The detection is based on the motions within the video stream (optical flow). If the ego-motion and the location of the camera with respect to the road plane are known the viewed scene can be 3D reconstructed exploiting the measured optical flow. In this thesis an overview of existing algorithms estimating the ego-motion is given. Based on it a suitable algorithm is selected and extended by a motion model. The latter one considerably increases the accuracy as well as the robustness of the estimate. The location of the camera with respect to the road plane is estimated using the optical flow on the road. The road might be temporary low-textured making it hard to measure the optical flow. Consequently, the road homography estimate will be poor. A novel Kalman filtering approach combining the estimate of the ego-motion and the estimate of the road homography leads to far better results. The 3D reconstruction of the viewed scene is performed pointwise for each measured optical flow vector. A point is reconstructed through intersection of the viewing rays which are determined by the optical flow vector. This only yields a correct result for static, i.e. non-moving, points. Further, static points fulfill four constraints: epipolar constraint, trifocal constraint, positive depth constraint, and positive height constraint. If at least one constraint is violated the point is moving. For the first time an error metric is developed exploiting all four constraints. It measures the deviation from the constraints quantitatively in a unified manner. Based on this error metric the detection limits are investigated. It is shown that overtaking objects are detected very well whereas objects being overtaken are detected hardly. Oncoming objects on a straight road are not detected by means of the available constraints. Only if one assumes that these objects are opaque and touch the ground the detection becomes feasible. An appropriate heuristic is introduced. In conclusion, the developed algorithms are a system to detect moving points robustly. The problem of clustering the detected moving points to objects is outlined. It serves as a starting point for further research activities

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition