Density-Based Region Search with Arbitrary Shape for Object Localization

Region search is widely used for object localization. Typically, the region search methods project the score of a classifier into an image plane, and then search the region with the maximal score. The recently proposed region search methods, such as efficient subwindow search and efficient region search, %which localize objects from the score distribution on an image are much more efficient than sliding window search. However, for some classifiers and tasks, the projected scores are nearly all positive, and hence maximizing the score of a region results in localizing nearly the entire images as objects, which is meaningless. In this paper, we observe that the large scores are mainly concentrated on or around objects. Based on this observation, we propose a method, named level set maximum-weight connected subgraph (LS-MWCS), which localizes objects with arbitrary shapes by searching regions with the densest score rather than the maximal score. The region density can be controlled by a parameter flexibly. And we prove an important property of the proposed LS-MWCS, which guarantees that the region with the densest score can be searched. Moreover, the LS-MWCS can be efficiently optimized by belief propagation. The method is evaluated on the problem of weakly-supervised object localization, and the quantitative results demonstrate the superiorities of our LS-MWCS compared to other state-of-the-art methods

Time-Frequency multipliers for sound synthesis

International audienceTime-frequency analysis and wavelet analysis are generally used for providing signal expansions that are suitable for various further tasks such as signal analysis, de-noising, compression, source separation, ... However, time-frequency analysis and wavelet analysis also provide efficient ways for constructing signals' transformations. They are modelled as linear operators that can be designed directly in the transformed domain, i.e. the time-frequency plane, or the time-scale half plane. Among these linear operators, transformations that are diagonal in the time-frequency or time scale spaces, i.e. that may be expressed by multiplications in these domains, deserve particular attention, as they are extremely simple to implement, even though their properties are not necessarily easy to control. This work is a first attempt for exploring such approaches in the context of the analysis and the design of sound signals. We study more specifically the transformations that may be interpreted as linear time-varying (LTV) systems (often called time-varying filters). It is known that under certain assumptions, the latter may be conveniently represented by pointwise multiplication with a certain time frequency transfer function in the time-frequency domain. The purpose of this work is to examine such representations in practical situations, and investigate generalizations. The originality of this approach for sound synthesis lies in the design of practical operators that can be optimized to morph a given sound into another one, at a very high sound quality

Four- and twelve-band low-energy symmetric Hamiltonians and Hubbard parameters for twisted bilayer graphene using ab-initio input

A computationally efficient workflow for obtaining low-energy tight-binding Hamiltonians for twisted bilayer graphene, obeying both crystal and time-reversal symmetries, is presented in this work. The Hamiltonians at the first magic angle are generated using the Slater-Koster approach with parameters obtained by a fit to ab-initio data at larger angles. Low-energy symmetric four-band and twelve-band Hamiltonians are constructed using the Wannier90 software. The advantage of our scheme is that the low-energy Hamiltonians are purely real and are obtained with the maximum-localization procedure to reduce the spread of the basis functions. Finally, we compute extended Hubbard parameters for both models within the constrained random phase approximation (cRPA) for screening, which again respect the symmetries. The relevant data and results of this work are freely available via an online repository. The workflow is straightforwardly transferable to other twisted multi-layer materials.Comment: 14 pages, 8 figure

A computer vision-based lane detection technique using gradient threshold and hue-lightness-saturation value for an autonomous vehicle

Automatic lane detection for driver assistance is a significant component in developing advanced driver assistance systems and high-level application frameworks since it contributes to driver and pedestrian safety on roads and highways. However, due to several limitations that lane detection systems must rectify, such as the uncertainties of lane patterns, perspective consequences, limited visibility of lane lines, dark spots, complex background, illuminance, and light reflections, it remains a challenging task. The proposed method employs vision-based technologies to determine the lane boundary lines. We devised a system for correctly identifying lane lines on a homogeneous road surface. Lane line detection relies heavily on the gradient and hue lightness saturation (HLS) thresholding which detects the lane line in binary images. The lanes are shown, and a sliding window searching method is used to estimate the color lane. The proposed system achieved 96% accuracy in detecting lane lines on the different roads, and its performance was assessed using data from several road image databases under various illumination circumstances

Detecting Long-Duration Narrow-Band Gravitational Wave Transients Associated with Soft Gamma Repeater Quasi-Periodic Oscillations

We have performed an in-depth concept study of a gravitational wave data analysis method which targets repeated long quasi-monochromatic transients (triggers) from cosmic sources. The algorithm concept can be applied to multi-trigger data sets in which the detector-source orientation and the statistical properties of the data stream change with time, and does not require the assumption that the data is Gaussian. Reconstructing or limiting the energetics of potential gravitational wave emissions associated with quasi-periodic oscillations (QPOs) observed in the X-ray lightcurve tails of soft gamma repeater flares might be an interesting endeavour of the future. Therefore we chose this in a simplified form to illustrate the flow, capabilities, and performance of the method. We investigate performance aspects of a multi-trigger based data analysis approach by using O(100 s) long stretches of mock data in coincidence with the times of observed QPOs, and by using the known sky location of the source. We analytically derive the PDF of the background distribution and compare to the results obtained by applying the concept to simulated Gaussian noise, as well as off-source playground data collected by the 4-km Hanford detector (H1) during LIGO's fifth science run (S5). We show that the transient glitch rejection and adaptive differential energy comparison methods we apply succeed in rejecting outliers in the S5 background data. Finally, we discuss how to extend the method to a network containing multiple detectors, and as an example, tune the method to maximize sensitivity to SGR 1806-20 flare times.Comment: 11 pages, 8 figure

Quantitative analysis of microscopy

Particle tracking is an essential tool for the study of dynamics of biological processes. The dynamics of these processes happens in three-dimensional (3D) space as the biological structures themselves are 3D. The focus of this thesis is on the development of single particle tracking methods for analysis of the dynamics of biological processes through the use of image processing techniques. Firstly, introduced is a novel particle tracking method that works with two-dimensional (2D) image data. This method uses the theory of Haar-like features for particle detection and trajectory linking is achieved using a combination of three Kalman filters within an interacting multiple models framework. The trajectory linking process utilises an extended state space variable which better describe the morphology and intensity profiles of the particles under investigation at their current position. This tracking method is validated using both 2D synthetically generated images as well as 2D experimentally collected images. It is shown that this method outperforms 14 other stateof-the-art methods. Next this method is used to analyse the dynamics of fluorescently labelled particles using a live-cell fluorescence microscopy technique, specifically a variant of the super-resolution (SR) method PALM, spt-PALM. From this application, conclusions about the organisation of the proteins under investigation at the cell membrane are drawn. Introduced next is a second particle tracking method which is highly efficient and capable of working with both 2D and 3D image data. This method uses a novel Haar-inspired feature for particle detection, drawing inspiration from the type of particles to be detected which are typically circular in 2D space and spherical in 3D image space. Trajectory linking in this method utilises a global nearest neighbour methodology incorporating both motion models to describe the motion of the particles under investigation and a further extended state space variable describing many more aspects of the particles to be linked. This method is validated using a variety of both 2D and 3D synthetic image data. The methods performance is compared with 14 other state-of-the-art methods showing it to be one of the best overall performing methods. Finally, analysis tools to study a SR image restoration method developed by our research group, referred to as Translation Microscopy (TRAM) are investigated [1]. TRAM can be implemented on any standardised microscope and deliver an improvement in resolution of up to 7-fold. However, the results from TRAM and other SR imaging methods require specialised tools to validate and analyse them. Tools have been developed to validate that TRAM performs correctly using a specially designed ground truth. Furthermore, through analysis of results on a biological sample corroborate other published results based on the size of biological structures, showing again that TRAM performs as expected.EPSC

Human localization and activity classification by machine learning on Wi-Fi channel state information

Devices communicating via Wi-Fi adjust subcarrier correction coefficients in real time. The stream of correction coefficients for all subcarriers is called channel state information (CSI). The latter can be used for human body sensing, in particular current activity and location. The thesis aims to create a robust, environment-agnostic activity classifier. In other words, a neural network (NN) trained to recognize and classify human actions in one location should not dramatically lose prediction capability if transferred to another. This purpose has been achieved in three steps. First, for a neural network to abstract from a particular environment, a diverse data has to be collected. Therefore, a dedicated laboratory equipment to automate a Wi-Fi access point (AP) physical movement and rotation has been developed and constructed. After data for training NNs has been collected, the environment-specific information has been cleaned by classic signal processing algorithms. Finally, a dedicated neural network architecture adjustments have been implemented. Altogether, the goal of environment agnostic classification for the target "sit down", "stand up", "lie down", and "unlie" activities is achieved. However, classification accuracy depends on the similarity between train and test human subjects. The work argues that activity classification and localization tasks have orthogonal goals and focus on different aspects of CSI information. In particular, activity classification NN is interested in ongoing physical movement features and should work independently of the human location. On the contrary, localization NN should ignore subject activities and infer only positioning. Therefore, these two tasks are separated into standalone NN architectures. Since environments such as apartments can be very different from each other, it is assumed that training a universal NN localizer is not possible. Since the localization NN needs to be re-trained for each particular environment, its virtue would be an inexpensive training cycle. Tho achieve this goal, localization NN is substantially reduced in size from 58.8 to 0.4 million parameters