Comparison of spatial domain optimal trade-off maximum average correlation height (OT-MACH) filter with scale invariant feature transform (SIFT) using images with poor contrast and large illumination gradient

A spatial domain optimal trade-off Maximum Average Correlation Height (OT-MACH) filter has been previously developed and shown to have advantages over frequency domain implementations in that it can be made locally adaptive to spatial variations in the input image background clutter and normalised for local intensity changes. In this paper we compare the performance of the spatial domain (SPOT-MACH) filter to the widely applied data driven technique known as the Scale Invariant Feature Transform (SIFT). The SPOT-MACH filter is shown to provide more robust recognition performance than the SIFT technique for demanding images such as scenes in which there are large illumination gradients. The SIFT method depends on reliable local edge-based feature detection over large regions of the image plane which is compromised in some of the demanding images we examined for this work. The disadvantage of the SPOTMACH filter is its numerically intensive nature since it is template based and is implemented in the spatial domain. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only

Assessing cognitive dysfunction in Parkinson's disease: An online tool to detect visuo-perceptual deficits.

BackgroundPeople with Parkinson's disease (PD) who develop visuo-perceptual deficits are at higher risk of dementia, but we lack tests that detect subtle visuo-perceptual deficits and can be performed by untrained personnel. Hallucinations are associated with cognitive impairment and typically involve perception of complex objects. Changes in object perception may therefore be a sensitive marker of visuo-perceptual deficits in PD.ObjectiveWe developed an online platform to test visuo-perceptual function. We hypothesised that (1) visuo-perceptual deficits in PD could be detected using online tests, (2) object perception would be preferentially affected, and (3) these deficits would be caused by changes in perception rather than response bias.MethodsWe assessed 91 people with PD and 275 controls. Performance was compared using classical frequentist statistics. We then fitted a hierarchical Bayesian signal detection theory model to a subset of tasks.ResultsPeople with PD were worse than controls at object recognition, showing no deficits in other visuo-perceptual tests. Specifically, they were worse at identifying skewed images (P < .0001); at detecting hidden objects (P = .0039); at identifying objects in peripheral vision (P < .0001); and at detecting biological motion (P = .0065). In contrast, people with PD were not worse at mental rotation or subjective size perception. Using signal detection modelling, we found this effect was driven by change in perceptual sensitivity rather than response bias.ConclusionsOnline tests can detect visuo-perceptual deficits in people with PD, with object recognition particularly affected. Ultimately, visuo-perceptual tests may be developed to identify at-risk patients for clinical trials to slow PD dementia. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society

Invariance Violation Extends the Cosmic Ray Horizon ?

We postulate in the present paper that the energy-momentum relation is modified for very high energy particles to violate Lorentz invariance and the speed of photon is changed from the light velocity c. The violation effect is amplified, in a sensitive way to detection, through the modified kinematical constraints on the conservation of energy and momentum, in the absorption process of gamma-rays colliding against photons of longer wavelengths and converting into an electron-positron pair. For gamma-rays of energies higher than 10 TeV, the minimum energy of the soft photons for the reaction and then the absorption mean free path of gamma-rays are altered by orders of magnitude from the ones conventionally estimated. Consideration is similarly applied to high energy cosmic ray protons. The consequences may require the standard assumptions on the maximum distance that very high energy radiation can travel from to be revised.Comment: 14 pages, 1 figure, to be published in Ap J Letter

Note: An object detection method for active camera

To solve the problems caused by a changing background during object detection in active camera, this paper proposes a new method based on SURF (speeded up robust features) and data clustering. The SURF feature points of each image are extracted, and each cluster center is calculated by processing the data clustering of k adjacent frames. Templates for each class are obtained by calculating the histograms within the regions around the center points of the clustering classes. The window of the moving object can be located by finding the region that satisfies the histogram matching result between adjacent frames. Experimental results demonstrate that the proposed method can improve the effectiveness of object detection.Yong Chen, Ronghua Zhang, Lei Shang, and Eric H

Observing Air Showers from Cosmic Superluminal Particles

The Poincar\'e relativity principle has been tested at low energy with great accuracy, but its extrapolation to very high-energy phenomena is much less well established. Lorentz symmetry can be broken at Planck scale due to the renormalization of gravity or to some deeper structure of matter: we expect such a breaking to be a very high energy and very short distance phenomenon. If textbook special relativity is only an approximate property of the equations describing a sector of matter above some critical distance scale, an absolute local frame (the "vacuum rest frame", VRF) can possibly be found and superluminal sectors of matter may exist related to new degrees of freedom not yet discovered experimentally. The new superluminal particles ("superbradyons", i.e. bradyons with superluminal critical speed) would have positive mass and energy, and behave kinematically like "ordinary" particles (those with critical speed in vacuum equal to c, the speed of light) apart from the difference in critical speed (c_i >> c where c_i is the critical speed of a superluminal sector). They may be the ultimate building blocks of matter At speed v > c, they are expected to release "Cherenkov" radiation ("ordinary" particles) in vacuum. Superluminal particles could provide most of the cosmic (dark) matter and produce very high-energy cosmic rays. We discuss: a) the possible relevance of superluminal matter to the composition, sources and spectra of high-energy cosmic rays; b) signatures and experiments allowing to possibly explore such effects. Very large volume and unprecedented background rejection ability are crucial requirements for any detector devoted to the search for cosmic superbradyons. Future cosmic-ray experiments using air-shower detectors (especially from space) naturally fulfil both requirements.Comment: 10 pages, uses aipproc.sty; contribution the Workshop on "Observing Giant Cosmic Ray Air Showers for > 10E20 eV Particles from Space", Univ. of Maryland, Nov 13-15, 199

Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps

Grid maps are widely used in robotics to represent obstacles in the environment and differentiating dynamic objects from static infrastructure is essential for many practical applications. In this work, we present a methods that uses a deep convolutional neural network (CNN) to infer whether grid cells are covering a moving object or not. Compared to tracking approaches, that use e.g. a particle filter to estimate grid cell velocities and then make a decision for individual grid cells based on this estimate, our approach uses the entire grid map as input image for a CNN that inspects a larger area around each cell and thus takes the structural appearance in the grid map into account to make a decision. Compared to our reference method, our concept yields a performance increase from 83.9% to 97.2%. A runtime optimized version of our approach yields similar improvements with an execution time of just 10 milliseconds.Comment: This is a shorter version of the masters thesis of Florian Piewak and it was accapted at IV 201

A PatchMatch-based Dense-field Algorithm for Video Copy-Move Detection and Localization

We propose a new algorithm for the reliable detection and localization of video copy-move forgeries. Discovering well crafted video copy-moves may be very difficult, especially when some uniform background is copied to occlude foreground objects. To reliably detect both additive and occlusive copy-moves we use a dense-field approach, with invariant features that guarantee robustness to several post-processing operations. To limit complexity, a suitable video-oriented version of PatchMatch is used, with a multiresolution search strategy, and a focus on volumes of interest. Performance assessment relies on a new dataset, designed ad hoc, with realistic copy-moves and a wide variety of challenging situations. Experimental results show the proposed method to detect and localize video copy-moves with good accuracy even in adverse conditions