Optimum non linear binary image restoration through linear grey-scale operations

Non-linear image processing operators give excellent results in a number of image processing tasks such as restoration and object recognition. However they are frequently excluded from use in solutions because the system designer does not wish to introduce additional hardware or algorithms and because their design can appear to be ad hoc. In practice the median filter is often used though it is rarely optimal. This paper explains how various non-linear image processing operators may be implemented on a basic linear image processing system using only convolution and thresholding operations. The paper is aimed at image processing system developers wishing to include some non-linear processing operators without introducing additional system capabilities such as extra hardware components or software toolboxes. It may also be of benefit to the interested reader wishing to learn more about non-linear operators and alternative methods of design and implementation. The non-linear tools include various components of mathematical morphology, median and weighted median operators and various order statistic filters. As well as describing novel algorithms for implementation within a linear system the paper also explains how the optimum filter parameters may be estimated for a given image processing task. This novel approach is based on the weight monotonic property and is a direct rather than iterated method

Components for oversampled signal processors

Oversampled converters trade transmission bandwidth for resolution. An idealized model gives an insight into the way in which signals are encoded and thus how they can be manipulated. Oversampling offers a form of signal processing that requires simple processing elements capable of exploiting the growing clock speeds available in integrated solutions. Simultaneously avoids the need for analog circuitry. This paper reviews common operations that can be performed on oversampled signals

ReMotENet: Efficient Relevant Motion Event Detection for Large-scale Home Surveillance Videos

This paper addresses the problem of detecting relevant motion caused by objects of interest (e.g., person and vehicles) in large scale home surveillance videos. The traditional method usually consists of two separate steps, i.e., detecting moving objects with background subtraction running on the camera, and filtering out nuisance motion events (e.g., trees, cloud, shadow, rain/snow, flag) with deep learning based object detection and tracking running on cloud. The method is extremely slow and therefore not cost effective, and does not fully leverage the spatial-temporal redundancies with a pre-trained off-the-shelf object detector. To dramatically speedup relevant motion event detection and improve its performance, we propose a novel network for relevant motion event detection, ReMotENet, which is a unified, end-to-end data-driven method using spatial-temporal attention-based 3D ConvNets to jointly model the appearance and motion of objects-of-interest in a video. ReMotENet parses an entire video clip in one forward pass of a neural network to achieve significant speedup. Meanwhile, it exploits the properties of home surveillance videos, e.g., relevant motion is sparse both spatially and temporally, and enhances 3D ConvNets with a spatial-temporal attention model and reference-frame subtraction to encourage the network to focus on the relevant moving objects. Experiments demonstrate that our method can achieve comparable or event better performance than the object detection based method but with three to four orders of magnitude speedup (up to 20k times) on GPU devices. Our network is efficient, compact and light-weight. It can detect relevant motion on a 15s surveillance video clip within 4-8 milliseconds on a GPU and a fraction of second (0.17-0.39) on a CPU with a model size of less than 1MB.Comment: WACV1

Sampling and Reconstruction of Sparse Signals on Circulant Graphs - An Introduction to Graph-FRI

With the objective of employing graphs toward a more generalized theory of signal processing, we present a novel sampling framework for (wavelet-)sparse signals defined on circulant graphs which extends basic properties of Finite Rate of Innovation (FRI) theory to the graph domain, and can be applied to arbitrary graphs via suitable approximation schemes. At its core, the introduced Graph-FRI-framework states that any K-sparse signal on the vertices of a circulant graph can be perfectly reconstructed from its dimensionality-reduced representation in the graph spectral domain, the Graph Fourier Transform (GFT), of minimum size 2K. By leveraging the recently developed theory of e-splines and e-spline wavelets on graphs, one can decompose this graph spectral transformation into the multiresolution low-pass filtering operation with a graph e-spline filter, and subsequent transformation to the spectral graph domain; this allows to infer a distinct sampling pattern, and, ultimately, the structure of an associated coarsened graph, which preserves essential properties of the original, including circularity and, where applicable, the graph generating set.Comment: To appear in Appl. Comput. Harmon. Anal. (2017

WILDFIRE DETECTION SYSTEM BASED ON PRINCIPAL COMPONENT ANALYSIS AND IMAGE PROCESSING OF REMOTE-SENSED VIDEO

Early detection and mitigation of wildfires can reduce devastating property damage, firefighting costs, pollution, and loss of life. This thesis proposes the method of Principal Component Analysis (PCA) of images in the temporal domain to identify a smoke plume in wildfires. Temporal PCA is an effective motion detector, and spatial filtering of the output Principal Component images can segment the smoke plume region. The effective use of other image processing techniques to identify smoke plumes and heat plumes are compared. The best attributes of smoke plume detectors and heat plume detectors are evaluated for combination in an improved wildfire detection system. PCA of visible blue images at an image sampling rate of 2 seconds per image effectively exploits a smoke plume signal. PCA of infrared images is the fundamental technique for exploiting a heat plume signal. A system architecture is proposed for the implementation of image processing techniques. The real-world deployment and usability are described for this system

Descriptive temporal template features for visual motion recognition

In this paper, a human action recognition system is proposed. The system is based on new, descriptive `temporal template' features in order to achieve high-speed recognition in real-time, embedded applications. The limitations of the well known `Motion History Image' (MHI) temporal template are addressed and a new `Motion History Histogram' (MHH) feature is proposed to capture more motion information in the video. MHH not only provides rich motion information, but also remains computationally inexpensive. To further improve classification performance, we combine both MHI and MHH into a low dimensional feature vector which is processed by a support vector machine (SVM). Experimental results show that our new representation can achieve a significant improvement in the performance of human action recognition over existing comparable methods, which use 2D temporal template based representations

Antenna-coupled TES bolometer arrays for CMB polarimetry

We describe the design and performance of polarization selective antenna-coupled TES arrays that will be used in several upcoming Cosmic Microwave Background (CMB) experiments: SPIDER, BICEP-2/SPUD. The fully lithographic polarimeter arrays utilize planar phased-antennas for collimation (F/4 beam) and microstrip filters for band definition (25% bandwidth). These devices demonstrate high optical efficiency, excellent beam shapes, and well-defined spectral bands. The dual-polarization antennas provide well-matched beams and low cross polarization response, both important for high-fidelity polarization measurements. These devices have so far been developed for the 100 GHz and 150 GHz bands, two premier millimeter-wave atmospheric windows for CMB observations. In the near future, the flexible microstrip-coupled architecture can provide photon noise-limited detection for the entire frequency range of the CMBPOL mission. This paper is a summary of the progress we have made since the 2006 SPIE meeting in Orlando, FL

Antenna-coupled TES bolometer arrays for CMB polarimetry

We describe the design and performance of polarization selective antenna-coupled TES arrays that will be used in several upcoming Cosmic Microwave Background (CMB) experiments: SPIDER, BICEP-2/SPUD. The fully lithographic polarimeter arrays utilize planar phased-antennas for collimation (F/4 beam) and microstrip filters for band definition (25% bandwidth). These devices demonstrate high optical efficiency, excellent beam shapes, and well-defined spectral bands. The dual-polarization antennas provide well-matched beams and low cross polarization response, both important for high-fidelity polarization measurements. These devices have so far been developed for the 100 GHz and 150 GHz bands, two premier millimeter-wave atmospheric windows for CMB observations. In the near future, the flexible microstrip-coupled architecture can provide photon noise-limited detection for the entire frequency range of the CMBPOL mission. This paper is a summary of the progress we have made since the 2006 SPIE meeting in Orlando, FL

First higher-multipole model of gravitational waves from spinning and coalescing black-hole binaries

Gravitational-wave observations of binary black holes currently rely on theoretical models that predict the dominant multipoles (l,m) of the radiation during inspiral, merger and ringdown. We introduce a simple method to include the subdominant multipoles to binary black hole gravitational waveforms, given a frequency-domain model for the dominant multipoles. The amplitude and phase of the original model are appropriately stretched and rescaled using post-Newtonian results (for the inspiral), perturbation theory (for the ringdown), and a smooth transition between the two. No additional tuning to numerical-relativity simulations is required. We apply a variant of this method to the non-precessing PhenomD model. The result, PhenomHM, constitutes the first higher-multipole model of spinning black-hole binaries, and currently includes the (l,m) = (2,2), (3,3), (4,4), (2,1), (3,2), (4,3) radiative moments. Comparisons with numerical-relativity waveforms demonstrate that PhenomHM is more accurate than dominant-multipole-only models for all binary configurations, and typically improves the measurement of binary properties.Comment: 4 pages, 4 figure