This paper presents a systematic study of the scan skewing problem. Scan skewing is the non-rigid deformation of point clouds acquired by LiDAR's and is the result of their sequential scanning nature. We theoretically analyze the impact of skewing on scan matching and subsequently quantify the impact using synthetic LiDAR data with controlled skew distortions. We also show how the Geometric-Algebra LMS, an iterative point set registration filter, can be tuned to incorporate skewing aware weights to reduce the impact of skewing on scan matching. Results with real 3D LiDAR data are also presented.

In this paper we show that a Geometric Algebra-based least-mean-squares adaptive filter (GA-LMS) can be used to recover the 6-degree-of-freedom alignment of two point clouds related by a set of point correspondences. We present a series of techniques that endow the GA-LMS with outlier (false correspondence) resilience to outperform standard least squares (LS) methods that are based on Singular Value Decomposition (SVD). We furthermore show how to derive and compute the step size of the GA-LMS.

This letter exploits geometric (Clifford) algebra (GA) theory to devise and introduce a new adaptive filtering strategy. From a least-squares cost function, the gradient is calculated following results from geometric calculus (GC), the extension of GA to handle differential and integral calculus. The novel GA least-mean-squares (GA-LMS) adaptive filter, which inherits properties from standard adaptive filters and from GA, is developed to recursively estimate a rotor (multivector), a hypercomplex quantity able to describe rotations in any dimension. The adaptive filter (AF) performance is assessed via a 3-D point-clouds registration problem, which contains a rotation estimation step. Calculating the AF computational complexity suggests that it can contribute to reduce the cost of a full-blown 3-D registration algorithm, especially when the number of points to be processed grows. Moreover, the employed GA/GC framework allows for easily applying the resulting filter to estimating rotors in higher dimensions.

A Multi-View Video (MVV) is a set of related videos that capture an interesting scene from different perspectives at overlapping times. The work at hand is concerned with the design of innovative user interfaces (UI) for viewing MVVs. As a first contribution four different MVV UIs are designed. While different in design, their common aim is to allow a pleasant viewing and perspective switching experience by reducing the cognitive effort associated with constructing a mental map of the scene. This is achieved by incorporating the spatial relationships of the available views in the UI elements. As a second contribution a quality model is developed and a methodical evaluation process is designed. This is used to evaluate and compare the UIs. In a third contribution we use principal component analysis (PCA) to reveal information about the perceptual quality space which helps validating our proposed quality model. Based on the findings, a series of conclusions for best design practices are provided.

We study the impact of JPEG compression on the performance of an image retrieval system for different feature detector-descriptor combinations. The VLBenchmarks retrieval framework is used to compare a total of 60 detector-descriptor combinations for a dataset with JPEG-encoded query images. Our results show that among all tested detectors, the Hessian-Affine detector leads to the most robust performance in the presence of strong JPEG compression. Additionally, we compare the retrieval gains of the different detector-descriptor pairs after processing the JPEG-encoded query images with different deblocking filters. The results illustrate that for the MSER, MFD and WαSH detectors, the retrieval results benefit from two of the deblocking approaches at low bit rate irrespective of what descriptor the detectors are combined with. The same two deblocking filters are found to increase the retrieval performance for the MROGH descriptor when combined with most of the tested detectors.

Video synchronization is an essential processing step in many multimedia applications, and various methods have been proposed in the literature each of which addresses the problem from a different point of vantage. In this article, we present an information theoretic approach to video synchronization, based on the state-of-the-art in hybrid video coding. Time series derived from the videos' instantaneous bitrate demand are correlated in a robust manner employing the recently published ConCor algorithm. We enhance ConCor with integrated normalization capabilities in order to improve its shape-oriented matching performance. Furthermore, we present a mathematical framework to derive the most suitable ConCor parameters given a specific class of input videos. In an extensive experimental analysis, we give an insight into the representation of synchronization-relevant scene changes with bitrate data, and examine the influence of encoding parameters on the synchronization performance. Experiments on diverse video input substantiate the reliable performance of our easy to implement, yet effective video synchronization algorithm which distinguishes itself in that it operates largely without manual intervention.

Consensus-based Cross-correlation (ConCor) is a recently presented algorithm for robust synchronization of noisy and corrupted signals. ConCor has a number of interdependent parameters that need to be set correctly to guarantee good performance. In this paper we analyse the effects of the individual parameters on ConCor's behaviour and performance. As a second contribution, we show that a parameter sweep with subsequent majority voting can be used to boost ConCor's performance and produce a trustworthy confidence measure. As a final contribution we show how the proposed extension also allows performing multi-modal (joint audio-video) synchronization of casual multi-perspective video recordings enabling superior matching performance.

Recognizing the location and orientation of a mobile device from captured images is a promising application of image retrieval algorithms. Matching the query images to an existing georeferenced database like Google Street View enables mobile search for location related media, products, and services. Due to the rapidly changing field of view of the mobile device caused by constantly changing user attention, very low retrieval times are essential. These can be significantly reduced by performing the feature quantization on the handheld and transferring compressed Bag-of-Feature vectors to the server. To cope with the limited processing capabilities of handhelds, the quantization of high dimensional feature descriptors has to be performed at very low complexity. To this end, we introduce in this paper the novel Multiple Hypothesis Vocabulary Tree (MHVT) as a step towards real-time mobile location recognition. The MHVT increases the probability of assigning matching feature descriptors to the same visual word by introducing an overlapping buffer around the separating hyperplanes to allow for a soft quantization and an adaptive clustering approach. Further, a novel framework is introduced that allows us to integrate the probability of correct quantization in the distance calculation using an inverted file scheme. Our experiments demonstrate that our approach achieves query times reduced by up to a factor of 10 when compared to the state-of-the-art.