Simulation of chromatic and achromatic assessments for camouflage textiles and combat background

Chromatic and achromatic (AC) assessments of camouflage textiles have been critical to the defense researchers for concealment, detection, recognition, and identification (CDRI) of target signature against multidimensional combat background (CB). AC assessment and camouflage measurement techniques are simulated and experimented for assessment of camouflage textiles against CB. This model has been demonstrated for color measurement spectrophotometer, scanning electron microscopy (SEM), digital imaging, hyperspectral imaging, and image processing software (ImageJ) for the advancement and establishment of AC camouflage textiles assessment. The chromatic variations of 48 artificial target objects (TOBs) have been synthesized by image processing; the technique can be implemented for defense CB-CDRI assessment. Microstructural variation versus optical signal of woodland, desertland and stoneland CB materials have been elucidated by SEM magnification. The achromatic variation of CB materials have been demonstrated for the replacement of optical signal against modern remote sensing device to the imaging sensor. Color difference (Δ E), microstructural variations, pixel variations to imaging signal and standard deviation of CB materials have been represented for remote sensing surveillance of defense applications against TOB-CB-CDRI. Technical simulation of color, texture, gloss, and pixel intensity has been derived for AC-CDRI assessment of camouflage textiles in TOBs-CB environment.

Detecting Plasma Detachment in the Wendelstein 7-X Stellarator Using Machine Learning

The detachment regime has a high potential to play an important role in fusion devices on the road to a fusion power plant. Complete power detachment has been observed several times during the experimental campaigns of the Wendelstein 7-X (W7-X) stellarator. Automatic observation and signaling of such events could help scientists to better understand these phenomena. With the growing discharge times in fusion devices, machine learning models and algorithms are a powerful tool to process the increasing amount of data. We investigate several classical supervised machine learning models to detect complete power detachment in the images captured by the Event Detection Intelligent Camera System (EDICAM) at the W7-X at each given image frame. In the dedicated detached state the plasma is stable despite its reduced contact with the machine walls and the radiation belt stays close to the separatrix, without exhibiting significant heat load onto the divertor. To decrease computational time and resources needed we propose certain pixel intensity profiles (or intensity values along lines) as the input to these models. After finding the profile that describes the images best in terms of detachment, we choose the best performing machine learning algorithm. It achieves an F1 score of 0.9836 on the training dataset and 0.9335 on the test set. Furthermore, we investigate its predictions in other scenarios, such as plasmas with substantially decreased minor radius and several magnetic configurations.

Optimization of the Convolution Operation to Accelerate Deep Neural Networks in FPGA

In recent years, machine learning algorithms related to images have been widely utilized by Convolution Neural Networks (CNN), and it has a high accuracy for recognition of an image. As CNN contains large number of computations, hardware accelerator like Field Programmable Gate Array is employed. Quite 90 % of operations during a CNN involves convolution. The objective of this work is to scale back the computation time to increase the peak, width and the pixel intensity levels in the input image. The execution time of a image processing program is mostly spent on loops. Loop optimization is a process of accelerating speed and reducing the overheads related to loops. It plays a crucial role in improving performance and making effective use of multiprocessing capabilities. Loop unrolling is one of the loop optimization techniques. In our work CNN with four levels of loop unrolling is used. Due to this delay is reduced compared with conventional Xilinix. With the assistance of strides and padding the 40 % of computation time has been reduced and is verified in MATLAB.

Correlations among Ultrasonographic, Physicochemical and Sensory Characteristics of Pectoralis Major Muscles in Turkeys Reared in a Sustainable Farming System

This study set out to examine associations among echotextural, physicochemical and sensory attributes of the pectoralis major muscles in 17-week-old organic turkeys (B.U.T. Big-6) varying in the amount of wheat and oat grain in daily feed rations (Group C: complete feed only; Group Exp1: 5–30% of wheat and 0–20% of oat; and Group Exp2: 5–50% of wheat and 0–50% of oat; n = 15 turkeys/group). Digital ultrasonograms of the left pectoral muscle in four different planes (longitudinal-L, transverse-T, and two oblique planes-O1 and O2) were obtained with a 5.0-MHz linear-array transducer just before slaughter. Mean numerical pixel intensity (MPI) and pixel heterogeneity (MPH) of the muscle parenchyma were computed using the ImageProPlus® analytical software. Ten significant correlations between echotextural attributes and various meat characteristics were recorded in Group C, one in Group Exp1, and eight in Group Exp2. When data were pooled for all birds studied, there were twelve significant correlations (p < 0.05); all but one correlation (between MPH and moisture) were for physical and sensory characteristics of meat samples. Computer-assisted analysis is a potential method to determine moisture as well as physical (e.g., coloration) and sensory (e.g., aroma) characteristics of pectoralis major muscles in organic turkeys.

Quantitative Imaging Parameters in the Diagnosis of Endometriomas

The classic imaging diagnosis of endometriomas encounters multiple limitations, including the subjective evaluation of medical examinations and a similar imaging appearance with other adnexal lesions, especially the functional hemorrhagic cysts. For this reason, a definite diagnosis of endometriomas can be made only by pathological analysis, which reveals particular features in terms of cellularity and biochemical components of their fluid content. It is theorized that these histopathological features can also be reflected in medical images, altering the pixel intensity and distribution, but these changes are too subtle to be assessed by the naked eye. New quantitative imaging evaluations and emerging computer-aided diagnosis techniques can provide a detailed description of image contents that can be furtherly processed by algorithms, aiming to provide a more accurate and non-invasive diagnosis for this disease.

Ice ridge density signatures in high resolution SAR images

The statistics of ice ridging signatures was studied using a high (1.25 m) and a medium (20 m) resolution SAR image over the Baltic sea ice cover, acquired in 2016 and 2011, respectively. Ice surface profiles measured by a 2011 Baltic campaign was used as ground truth data for both. The images did not delineate well individual ridges as linear features. This was assigned to the random, intermittent occurrence of ridge rubble block arrangements with bright SAR return. Instead, the ridging signature was approached in terms of the density of bright pixels and relations with the corresponding surface profile quantity, ice ridge density, were studied. In order to apply discrete statistics, these densities were quantified by counting bright pixel numbers (BPN) in pixel blocks of side length L, and by counting ridge sail numbers (RSN) in profile segments of length L. The scale L is a variable parameter of the approach. The other variable parameter is the pixel intensity threshold defining bright pixels, equivalently bright pixel percentage (BPP), or the ridge sail height threshold used to select ridges from surface profiles, respectively. As a sliding image operation the BPN count resulted in enhanced ridging signature and better applicability of SAR in ice information production. A distribution model for BPN statistics was derived by considering how the BPN values change in BPP changes. The model was found to apply over wide range of values for BPP and L. The same distribution model was found to apply to RSN statistics. This reduces the problem of correspondence between the two density concepts to connections between the parameters of the respective distribution models. The correspondence was studied for the medium resolution image for which the 2011 surface data set has close temporal match. The comparison was done by estimating ridge rubble coverage in 1 km2 squares from surface profile data and, on the other hand, assuming that the bright pixel density can be used as a proxy for ridge rubble coverage. Apart from a scaling factor, both were found to follow the presented distribution model.

An Efficient Framework for the Segmentation of Glioma Brain Tumor Using Image Fusion and Co-Active Adaptive Neuro Fuzzy Inference System Classification Method

The image segmentation of any irregular pixels in Glioma brain image can be considered as difficult. There is a smaller difference between the pixel intensity of both tumor and non-tumor images. The proposed method stated that Glioma brain tumor is detected in brain MRI image by utilizing image fusion based Co-Active Adaptive Neuro Fuzzy Inference System (CANFIS) categorization technique. The low resolution brain image pixels are improved by contrast through image fusion method. This paper uses two different wavelet transforms such as, Discrete and Stationary for fusing two brain images for enhancing the internal regions. The pixels in contrast enhanced image is transformed into multi scale, multi frequency and orientation format through Gabor transform approach. The linear features can be obtained from this Gabor transformed brain image and it is being used to distinguish the non-tumor Glioma brain image from the tumor affected brain image through CANFIS method in this paper. The feature extraction and its impacts are being assigned on the proposed Glioma detection method is also examined in terms of detection rate. Then, morphological operations are involved on the resultant of classified Glioma brain image used to address and segment the tumor portions. The proposed system performance is analyzed with respect to various segmentation approaches. The proposed work simulation results can be compared with different state-of-the art techniques with respect to various parameter metrics and detection rate.

A Comparison of Near-Infrared Imaging and Computerized Tomography Scan for Detecting Maxillary Sinusitis

Objective: To investigate the use of near-infrared (NIR) imaging as a tool for outpatient clinicians to quickly and accurately assess for maxillary sinusitis and to characterize its accuracy compared to computerized tomography (CT) scan. Methods: In a prospective investigational study, NIR and CT images from 65 patients who presented to a tertiary care rhinology clinic were compared to determine the sensitivity and specificity of NIR as an imaging modality. Results: The sensitivity and specificity of NIR imaging in distinguishing normal versus maxillary sinus disease was found to be 90% and 84%, normal versus mild maxillary sinus disease to be 76% and 91%, and mild versus severe maxillary sinus disease to be 96% and 81%, respectively. The average pixel intensity was also calculated and compared to the modified Lund-Mackay scores from CT scans to assess the ability of NIR imaging to stratify the severity of maxillary sinus disease. Average pixel intensity over a region of interest was significantly different ( P < .001) between normal, mild, and severe disease, as well as when comparing normal versus mild ( P < .001, 95% CI 42.22-105.39), normal versus severe ( P < .001, 95% CI 119.43-174.14), and mild versus severe ( P < .001, 95% CI 41.39-104.56) maxillary sinus disease. Conclusion: Based on this data, NIR shows promise as a tool for identifying patients with potential maxillary sinus disease as well as providing information on severity of disease that may guide administration of appropriate treatments.

Epistemic guidance of visual attention for robotic agents in dynamic visual scenes

<p>Humans and many animals can selectively sample important parts of their visual surroundings to carry out their daily activities like foraging or finding prey or mates. Selective attention allows them to efficiently use the limited resources of the brain by deploying sensory apparatus to collect data believed to be pertinent to the organism's current task in hand. Robots or other computational agents operating in dynamic environments are similarly exposed to a wide variety of stimuli, which they must process with limited sensory and computational resources. Developing computational models of visual attention has long been of interest as such models enable artificial systems to select necessary information from complex and cluttered visual environments, hence reducing the data-processing burden. Biologically inspired computational saliency models have previously been used in selectively sampling a visual scene, but these have limited capacity to deal with dynamic environments and have no capacity to reason about uncertainty when planning their visual scene sampling strategy. These models typically select contrast in colour, shape or orientation as salient and sample locations of a visual scene in descending order of salience. After each observation, the area around the sampled location is blocked using inhibition of return mechanism to keep it from being re-visited. This thesis generalises the traditional model of saliency by using an adaptive Kalman filter estimator to model an agent's understanding of the world and uses a utility function based approach to describe what the agent cares about in the visual scene. This allows the agents to adopt a richer set of perceptual strategies than is possible with the classical winner-take-all mechanism of the traditional saliency model. In contrast with the traditional approach, inhibition of return is achieved without implementing an extra mechanism on top of the underlying structure. This thesis demonstrates the use of five utility functions that are used to encapsulate the perceptual state that is valued by the agent. Each utility function thereby produces a distinct perceptual behaviour that is matched to particular scenarios. The resulting visual attention distribution of the five proposed utility functions is demonstrated on five real-life videos. In most of the experiments, pixel intensity has been used as the source of the saliency map. As the proposed approach is independent of the saliency map used, it can be used with other existing more complex saliency map building models. Moreover, the underlying structure of the model is sufficiently general and flexible, hence it can be used as the base of a new range of more sophisticated gaze control systems.</p>

Epistemic guidance of visual attention for robotic agents in dynamic visual scenes

<p>Humans and many animals can selectively sample important parts of their visual surroundings to carry out their daily activities like foraging or finding prey or mates. Selective attention allows them to efficiently use the limited resources of the brain by deploying sensory apparatus to collect data believed to be pertinent to the organism's current task in hand. Robots or other computational agents operating in dynamic environments are similarly exposed to a wide variety of stimuli, which they must process with limited sensory and computational resources. Developing computational models of visual attention has long been of interest as such models enable artificial systems to select necessary information from complex and cluttered visual environments, hence reducing the data-processing burden. Biologically inspired computational saliency models have previously been used in selectively sampling a visual scene, but these have limited capacity to deal with dynamic environments and have no capacity to reason about uncertainty when planning their visual scene sampling strategy. These models typically select contrast in colour, shape or orientation as salient and sample locations of a visual scene in descending order of salience. After each observation, the area around the sampled location is blocked using inhibition of return mechanism to keep it from being re-visited. This thesis generalises the traditional model of saliency by using an adaptive Kalman filter estimator to model an agent's understanding of the world and uses a utility function based approach to describe what the agent cares about in the visual scene. This allows the agents to adopt a richer set of perceptual strategies than is possible with the classical winner-take-all mechanism of the traditional saliency model. In contrast with the traditional approach, inhibition of return is achieved without implementing an extra mechanism on top of the underlying structure. This thesis demonstrates the use of five utility functions that are used to encapsulate the perceptual state that is valued by the agent. Each utility function thereby produces a distinct perceptual behaviour that is matched to particular scenarios. The resulting visual attention distribution of the five proposed utility functions is demonstrated on five real-life videos. In most of the experiments, pixel intensity has been used as the source of the saliency map. As the proposed approach is independent of the saliency map used, it can be used with other existing more complex saliency map building models. Moreover, the underlying structure of the model is sufficiently general and flexible, hence it can be used as the base of a new range of more sophisticated gaze control systems.</p>