Segmentation using Appearance of Mesostructure Roughness

This paper introduces mesostructure roughness as an effective cue in image segmentation. Mesostructure roughness corresponds to small-scale bumps on the macrostructure (i.e., geometry) of objects. Specifically, the focus is on the texture that is created by the projection of the mesostructure roughness on the camera plane. Three intrinsic images are derived: reflectance, smooth-surface shading and mesostructure roughness shading (meta-texture images). A constructive approach is proposed for computing a meta-texture image by preserving, equalizing and enhancing the underlying surface-roughness across color, brightness and illumination variations. We evaluate the performance on sample images and illustrate quantitatively that different patches of the same material, in an image, are normalized in their statistics despite variations in color, brightness and illumination. We develop an algorithm for segmentation of an image into patches that share salient mesostructure roughness. Finally, segmentation by line-based boundary-detection is proposed and results are provided and compared to known algorithms.     

Trajectory association across multiple airborne cameras

A camera mounted on an aerial vehicle provides an excellent means for monitoring large areas of a scene. Utilizing several such cameras on different aerial vehicles allows further flexibility, in terms of increased visual scope and in the pursuit of multiple targets. In this paper, we address the problem of associating objects across multiple airborne cameras. Since the cameras are moving and often widely separated, direct appearance-based or proximity-based constraints cannot be used. Instead, we exploit geometric constraints on the relationship between the motion of each object across cameras, to test multiple association hypotheses, without assuming any prior calibration information. Given our scene model, we propose a likelihood function for evaluating a hypothesized association between observations in multiple cameras that is geometrically motivated. Since multiple cameras exist, ensuring coherency in association is an essential requirement, e.g. that transitive closure is maintained between more than two cameras. To ensure such coherency we pose the problem of maximizing the likelihood function as a k-dimensional matching and use an approximation to find the optimal assignment of association. Using the proposed error function, canonical trajectories of each object and optimal estimates of inter-camera transformations (in a maximum likelihood sense) are computed. Finally, we show that as a result of associating objects across the cameras, a concurrent visualization of multiple aerial video streams is possible and that, under special conditions, trajectories interrupted due to occlusion or missing detections can be repaired. Results are shown on a number of real and controlled scenarios with multiple objects observed by multiple cameras, validating our qualitative models, and through simulation quantitative performance is also reported.     

Enhanced regression testing technique for agile software development and continuous integration strategies

Software Quality Journal - To survive in competitive marketplaces, most organizations have adopted agile methodologies to facilitate continuous integration and faster application delivery and rely...     

Hardware Resource Optimized Detection of LFM Signals with Unknown Start Frequency and Frequency Rate

Journal of Signal Processing Systems - Detection of very low-SNR LFM signals with unknown start frequency and frequency rate is of great interest both in electronic support measure (ESM), and radio...     

Bayesian modeling of dynamic scenes for object detection

Accurate detection of moving objects is an important precursor to stable tracking or recognition. In this paper, we present an object detection scheme that has three innovations over existing approaches. First, the model of the intensities of image pixels as independent random variables is challenged and it is asserted that useful correlation exists in intensities of spatially proximal pixels. This correlation is exploited to sustain high levels of detection accuracy in the presence of dynamic backgrounds. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, multimodal spatial uncertainties and complex dependencies between the domain (location) and range (color) are directly modeled. We propose a model of the background as a single probability density. Second, temporal persistence is proposed as a detection criterion. Unlike previous approaches to object detection which detect objects by building adaptive models of the background, the foregrounds modeled to augment the detection of objects (without explicit tracking) since objects detected in the preceding frame contain substantial evidence for detection in the current frame. Finally, the background and foreground models are used competitively in a MAP-MRF decision framework, stressing spatial context as a condition of detecting interesting objects and the posterior function is maximized efficiently by finding the minimum cut of a capacitated graph. Experimental validation of the proposed method is performed and presented on a diverse set of dynamic scenes.     

The proportionality principle in telecommunications interception and access law in an environment of heightened security and technological convergence†

As nations expand the telecommunications interception and access powers of their law enforcement agencies to address heightened threats to national security and accelerating technological convergence, the proper application of the proportionality principle is becoming an increasingly contentious issue. The ‘proportionality principle’ in telecommunications law mandates the weighing of a likely threat to public security against the potential violation of individual rights so as to ensure that the intrusive impact of a particular interception and access activity is reasonable proportionate to the potential outcome sought. The reform discourse of recent years has largely focused on expanding investigative powers, to the possible detriment of the protection of individual rights. Whilst the present environment makes such a focus wholly understandable, the present paper considers potential legislative and policy measures that could strengthen the proportionality principle in the telecommunications regulatory framework to support a more precise calibration of the relevant competing public and private interests. As Australia has recently undertaken a comprehensive review of its telecommunications access and interception laws, commencing with a 2013 referral to its Senate Committee and culminating in a 2015 law reform report, the article focuses on that nation’s experience. The analysis is, however, placed within an overarching public policy framework to ensure that the discussion is of relevance to nations around the world who are similarly engaged in telecommunications law reform.     

Photocatalytic Degradation of Dimethyl Aminoethyl Azide in Water via TiO2/Light Expanded Clay Aggregate Catalyst

The photocatalytic activity of TiO₂ over light expanded clay aggregate granules was determined for degradation of dimethyl aminoethyl azide (DMAZ) in water with mercury lamps as UV sources. First, the catalyst was synthesized and characterized, then, the effect of various parameters on DMAZ degradation was investigated. The results showed that the photodegradation improved with increasing initial concentration of DMAZ. In addition, the DMAZ conversion grew with higher pH value and catalyst dosage. Under optimum conditions, a degradation of 54.7 % for DMAZ could be achieved. A first‐order rate model presented good accordance with the experimental data.     

Surgical tool motion during conventional freehand and robot-assisted microsurgery conducted using neuroArm

Surgical tool motion during microsurgery could arguably reflect surgical performance. This paper reports on how the motion of a surgical tool correlates or differs between conventional freehand surgery and robot-assisted surgery. In this pilot study, components of the position and orientation as well as the linear and angular velocities of a surgical tool, over the same period of operation, are compared during the two scenarios. For freehand surgery, a bipolar forceps is retrofitted with a tracking system to measure translational and rotational components of the tool motion. In robot-assisted surgery, the position and orientation components are obtained using kinematics of the neuroArm image-guided robotic system. A cross correlation analysis was used to investigate correlation between each pair of displacement or velocity components from freehand and robot-assisted scenarios to indicate how strongly or weakly two sets of data are linked together. The absolute maximum value of the cross correlation coefficient is calculated for each pair of components to quantitatively investigate the correlation between two sets of data. Results showed that the positional and rotational components, reflecting the surgical workspace, in both scenarios are correlated. However, for the cases studied, surgical tool rate of motion differs between the two scenarios. Results are important as they can be utilized to design robot-assisted neurosurgical systems that reflect characteristics of freehand surgery gained by surgeons through years of training, knowledge, and experience.