Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results

In this paper, automated user verification techniques for smartphones are investigated. A unique non-commercial dataset, the University of Maryland Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentication research is introduced. This paper focuses on three sensors - front camera, touch sensor and location service while providing a general description for other modalities. Benchmark results for face detection, face verification, touch-based user identification and location-based next-place prediction are presented, which indicate that more robust methods fine-tuned to the mobile platform are needed to achieve satisfactory verification accuracy. The dataset will be made available to the research community for promoting additional research.Comment: 8 pages, 12 figures, 6 tables. Best poster award at BTAS 201

Real-Time Panoramic Tracking for Event Cameras

Event cameras are a paradigm shift in camera technology. Instead of full frames, the sensor captures a sparse set of events caused by intensity changes. Since only the changes are transferred, those cameras are able to capture quick movements of objects in the scene or of the camera itself. In this work we propose a novel method to perform camera tracking of event cameras in a panoramic setting with three degrees of freedom. We propose a direct camera tracking formulation, similar to state-of-the-art in visual odometry. We show that the minimal information needed for simultaneous tracking and mapping is the spatial position of events, without using the appearance of the imaged scene point. We verify the robustness to fast camera movements and dynamic objects in the scene on a recently proposed dataset and self-recorded sequences.Comment: Accepted to International Conference on Computational Photography 201

Semiautomated Skeletonization of the Pulmonary Arterial Tree in Micro-CT Images

We present a simple and robust approach that utilizes planar images at different angular rotations combined with unfiltered back-projection to locate the central axes of the pulmonary arterial tree. Three-dimensional points are selected interactively by the user. The computer calculates a sub- volume unfiltered back-projection orthogonal to the vector connecting the two points and centered on the first point. Because more x-rays are absorbed at the thickest portion of the vessel, in the unfiltered back-projection, the darkest pixel is assumed to be the center of the vessel. The computer replaces this point with the newly computer-calculated point. A second back-projection is calculated around the original point orthogonal to a vector connecting the newly-calculated first point and user-determined second point. The darkest pixel within the reconstruction is determined. The computer then replaces the second point with the XYZ coordinates of the darkest pixel within this second reconstruction. Following a vector based on a moving average of previously determined 3- dimensional points along the vessel\u27s axis, the computer continues this skeletonization process until stopped by the user. The computer estimates the vessel diameter along the set of previously determined points using a method similar to the full width-half max algorithm. On all subsequent vessels, the process works the same way except that at each point, distances between the current point and all previously determined points along different vessels are determined. If the difference is less than the previously estimated diameter, the vessels are assumed to branch. This user/computer interaction continues until the vascular tree has been skeletonized

Non-Euclidean geometry in nature

I describe the manifestation of the non-Euclidean geometry in the behavior of collective observables of some complex physical systems. Specifically, I consider the formation of equilibrium shapes of plants and statistics of sparse random graphs. For these systems I discuss the following interlinked questions: (i) the optimal embedding of plants leaves in the three-dimensional space, (ii) the spectral statistics of sparse random matrix ensembles.Comment: 52 pages, 21 figures, last section is rewritten, a reference to chaotic Hamiltonian systems is adde

Localization Algorithms of Underwater Wireless Sensor Networks: A Survey

In Underwater Wireless Sensor Networks (UWSNs), localization is one of most important technologies since it plays a critical role in many applications. Motivated by widespread adoption of localization, in this paper, we present a comprehensive survey of localization algorithms. First, we classify localization algorithms into three categories based on sensor nodes’ mobility: stationary localization algorithms, mobile localization algorithms and hybrid localization algorithms. Moreover, we compare the localization algorithms in detail and analyze future research directions of localization algorithms in UWSNs

Effects of amplitude modulation on sound localization in reverberant environments.

Auditory localization involves different cues depending on the spatial domain. Azimuth localization cues include interaural time differences (ITDs), interaural level differences (ILDs) and pinnae cues. Auditory distance perception (ADP) cues include intensity, spectral cues, binaural cues, and the direct-to-reverberant energy ratio (D/R). While D/R has been established as a primary ADP cue, it is unlikely that it is directly encoded in the auditory system because it can be difficult to extract from ongoing signals. It is also noteworthy that no neuronal population has been identified that specifically codes D/R. It has therefore been proposed that D/R is indirectly encoded in the auditory system, through sensitivity to other acoustic parameters that are correlated with D/R, such as temporal cues (Zahorik, 2002b), spectral properties (Jetzt, 1979; Larsen, 2008), and interaural correlation (Bronkhorst and Houtgast, 1999). An additional D/R correlate relies on attenuation of amplitude modulation (AM) as a function of distance. Room modulation transfer functions act as low-pass filters on AM signals, and therefore the direct portion of a signal will have less modulation depth attenuation than the reverberant portion. Although recent neural and behavioral work has demonstrated that this cue can provide distance information monaurally, the extent to which the modulation attenuation cue contributes to ADP relative to other ADP cues is not fully understood. It is also possible modulation attenuation by the room can provide additional directional localization information, perhaps through the resulting dynamic fluctuation of the ILD cue. The role of AM in directional sound localization has not been extensively studied, particularly in reverberant soundfields which can affect the modulation reaching the two ears in a directionally-dependent fashion. Three human psychophysical experiments assessed the role of AM signals in distance and directional auditory localization in reverberant soundfields. Experiment I focused on validating a graphical response method to be used in subsequent experiments. In Experiment II, an auditory distance estimation task was performed which yielded measures of the relative perceptual contributions of the modulation depth cue during ADP in a reverberant room. Experiment III investigated the effect of AM on binaural localization in the horizontal plane in a reverberant room

Three-dimensional multi-source localization of underwater objects using convolutional neural networks for artificial lateral lines

This research focuses on the signal processing required for a sensory system that can simultaneously localize multiple moving underwater objects in a three-dimensional (3D) volume by simulating the hydrodynamic flow caused by these objects. We propose a method for localization in a simulated setting based on an established hydrodynamic theory founded in fish lateral line organ research. Fish neurally concatenate the information of multiple sensors to localize sources. Similarly, we use the sampled fluid velocity via two parallel lateral lines to perform source localization in three dimensions in two steps. Using a convolutional neural network, we first estimate a two-dimensional image of the probability of a present source. Then we determine the position of each source, via an automated iterative 3D-aware algorithm. We study various neural network architectural designs and different ways of presenting the input to the neural network; multi-level amplified inputs and merged convolutional streams are shown to improve the imaging performance. Results show that the combined system can exhibit adequate 3D localization of multiple sources