Classification of ground penetrating radar images using histogram of oriented gradients and support vector mechine

Ground Penetrating Radar or generally known as GPR is an important and popular method in subsurface imaging due to its non-destructive nature. GPR data interpretation requires expertise from human operator which is a time consuming and costly task as the data amount can be enormously large. In this study, a framework that pairs up Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) is proposed to detect subsurface targets in GPR data automatically. HOG feature descriptors are extracted by characterizing the target appearance and shape from hyperbolic signatures that appear in GPR images. Extracted feature descriptors are then sent to SVM for classification. Contribution of this research includes designing the best SVM classifier model by considering the best kernel and its optimized parameter settings. The proposed algorithm is compared to the most commonly used approach (Hough Transform) to evaluate its performance. In this research, the data sets consist of images that are collected using different GPR system models. Despite having limited sample images for training, the proposed method managed to detect hyperbolic signatures in GPR images. SVM classifier with probabilistic estimation model shows better performance for its flexibility in decision making using confidence level while SVM without probabilistic estimation model shows high false positive rate of more than 50%. Moreover, results from the experiments have also shown that the proposed method is able to produce higher detection rate with a much lower false positive rate than that of Hough Transform. The accuracy of target detection using the proposed method records an average detection rate of 89.40% and 7.38% of false positive rate for all the data sets used in this research. Apart from the improved performance, the proposed method also offers flexibility to control detection tasks through an adjustment on the probabilistic estimation model

Robust Detection of Moving Human Target in Foliage-Penetration Environment Based on Hough Transform

Attention has been focused on the robust moving human target detection in foliage-penetration environment, which presents a formidable task in a radar system because foliage is a rich scattering environment with complex multipath propagation and time-varying clutter. Generally, multiple-bounce returns and clutter are additionally superposed to direct-scatter echoes. They obscure true target echo and lead to poor visual quality time-range image, making target detection particular difficult. Consequently, an innovative approach is proposed to suppress clutter and mitigate multipath effects. In particular, a clutter suppression technique based on range alignment is firstly applied to suppress the time-varying clutter and the instable antenna coupling. Then entropy weighted coherent integration (EWCI) algorithm is adopted to mitigate the multipath effects. In consequence, the proposed method effectively reduces the clutter and ghosting artifacts considerably. Based on the high visual quality image, the target trajectory is detected robustly and the radial velocity is estimated accurately with the Hough transform (HT). Real data used in the experimental results are provided to verify the proposed method

All-sky search for periodic gravitational waves in LIGO S4 data

We report on an all-sky search with the LIGO detectors for periodic gravitational waves in the frequency range 50-1000 Hz and with the frequency's time derivative in the range -1.0E-8 Hz/s to zero. Data from the fourth LIGO science run (S4) have been used in this search. Three different semi-coherent methods of transforming and summing strain power from Short Fourier Transforms (SFTs) of the calibrated data have been used. The first, known as "StackSlide", averages normalized power from each SFT. A "weighted Hough" scheme is also developed and used, and which also allows for a multi-interferometer search. The third method, known as "PowerFlux", is a variant of the StackSlide method in which the power is weighted before summing. In both the weighted Hough and PowerFlux methods, the weights are chosen according to the noise and detector antenna-pattern to maximize the signal-to-noise ratio. The respective advantages and disadvantages of these methods are discussed. Observing no evidence of periodic gravitational radiation, we report upper limits; we interpret these as limits on this radiation from isolated rotating neutron stars. The best population-based upper limit with 95% confidence on the gravitational-wave strain amplitude, found for simulated sources distributed isotropically across the sky and with isotropically distributed spin-axes, is 4.28E-24 (near 140 Hz). Strict upper limits are also obtained for small patches on the sky for best-case and worst-case inclinations of the spin axes.Comment: 39 pages, 41 figures An error was found in the computation of the C parameter defined in equation 44 which led to its overestimate by 2^(1/4). The correct values for the multi-interferometer, H1 and L1 analyses are 9.2, 9.7, and 9.3, respectively. Figure 32 has been updated accordingly. None of the upper limits presented in the paper were affecte

A Detailed Investigation into Low-Level Feature Detection in Spectrogram Images

Being the first stage of analysis within an image, low-level feature detection is a crucial step in the image analysis process and, as such, deserves suitable attention. This paper presents a systematic investigation into low-level feature detection in spectrogram images. The result of which is the identification of frequency tracks. Analysis of the literature identifies different strategies for accomplishing low-level feature detection. Nevertheless, the advantages and disadvantages of each are not explicitly investigated. Three model-based detection strategies are outlined, each extracting an increasing amount of information from the spectrogram, and, through ROC analysis, it is shown that at increasing levels of extraction the detection rates increase. Nevertheless, further investigation suggests that model-based detection has a limitation—it is not computationally feasible to fully evaluate the model of even a simple sinusoidal track. Therefore, alternative approaches, such as dimensionality reduction, are investigated to reduce the complex search space. It is shown that, if carefully selected, these techniques can approach the detection rates of model-based strategies that perform the same level of information extraction. The implementations used to derive the results presented within this paper are available online from http://stdetect.googlecode.com

Online Object Tracking with Proposal Selection

Tracking-by-detection approaches are some of the most successful object trackers in recent years. Their success is largely determined by the detector model they learn initially and then update over time. However, under challenging conditions where an object can undergo transformations, e.g., severe rotation, these methods are found to be lacking. In this paper, we address this problem by formulating it as a proposal selection task and making two contributions. The first one is introducing novel proposals estimated from the geometric transformations undergone by the object, and building a rich candidate set for predicting the object location. The second one is devising a novel selection strategy using multiple cues, i.e., detection score and edgeness score computed from state-of-the-art object edges and motion boundaries. We extensively evaluate our approach on the visual object tracking 2014 challenge and online tracking benchmark datasets, and show the best performance.Comment: ICCV 201