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This article describes the development of a method to localize an athlete during sports performance on a platform, based on a static video recording. Considered sport for this method is wushu – martial art. However, any other discipline can be applied. There are specified requirements, and 2 algorithms of image processing are described. The next part presents an experiment that was held based on recordings from the Pan American Wushu Championship. Based on those recordings the steps of the algorithm are shown. Results are evaluated manually. The last part of the article concludes if the algorithm is applicable and what improvements have to be implemented to use it during sports competitions as well as for offline analysis.

The paper deals with a problem of insufficient productivity of existing computer means for large image processing, which do not meet modern requirements posed by resource-intensive computing tasks of laser beam profiling. The research concentrated on one of the profiling problems, namely, real-time processing of spot images of the laser beam profile. Development of a theory of parallel-hierarchic transformation allowed to produce models for high-performance parallel-hierarchical processes, as well as algorithms and software for their implementation based on the GPU-oriented architecture using GPGPU technologies. The analyzed performance of suggested computerized tools for processing and classification of laser beam profile images allows to perform real-time processing of dynamic images of various sizes.

In present work comparison combined image classification method of co-firing biomass and pulverized coal are proposed. The images were captured by vision monitoring system with camera and a borescope. Presented research is related with 20% weight fraction of the biomass. Defined two class of combustion: stable and unstable for nine variants with different power, secondary air value parameters and fixed amount biomass. Used combined classification of algorithm (MLP, SVM, k-NN, LDA, QDA) to classify flame image which correspond with the state of the combustion process.

The paper presents a new sharpening method that can be applied for the low-contrast medical images as well as general edge detection. The method provides more reliable diagnosis of the vascular system based on image processing. The new contouring method was compared with Sobel, low-frequency filtration and Canny edge detection methods on the example images with micro-capillaries.

Color transfer methods can alter color appearance in the input image by borrowing color statistics from the reference image. In this paper we present a novel color transfer method in which we consider both input and reference images as three-dimensional sets of data samples, where each color based component can be represented as a 3D cloud of data points. Our goal is to fit position, orientation and scale of color-component clouds from reference to input image by finding proper geometric transformation. Besides global processing approach we also present local color transfer method by applying our proposed algorithm to color segmented parts of images. We use pixel clustering for image segmentation to find groups of dominant colors pixels in each of input and reference images. Experimental results and comparisons with other methods confirm the validity and usefulness of presented method.

The visual detection technology plays an important role in the industry of precision casting. With pertinence to the features of crack images of precision casting, an automatic detection and identification software for cracks of precision casting integrating crack segmentation, identification, measurements and extractions was designed and developed. Adopting the threshold segmentation, morphological processing, feature identifications as well as feature extraction and proposing a detection method with pertinence to the precision casting crack characteristics. The software effectively eliminates the influences on the detection results by the operators due to such factors as the detection environment, fatigue degree, visual resolution and working experience. The processing of the actually collected crack images of precision casting verified the efficacy and practicality of the software in the field of casting detection.

A pre-processing step is needed to correct for the bias field signal before submitting corrupted MR images to such image-processing algorithms. This study presents a new bias field correction method. The method creates a Gaussian multi-scale space by the convolution of the inhomogeneous MR image with a two-dimensional Gaussian function. In the multi-Gaussian space, the method retrieves the image details from the differentiation of the original image and convolution image. Then, it obtains an image whose inhomogeneity is eliminated by the weighted sum of image details in each layer in the space. Next, the bias field-corrected MR image is retrieved after the Υ correction, which enhances the contrast and brightness of the inhomogeneity-eliminated MR image. We have tested the approach on T1 MRI and T2 MRI with varying bias field levels and have achieved satisfactory results. Comparison experiments with popular software have demonstrated superior performance of the proposed method in terms of quantitative indices, especially an improvement in subsequent image segmentation.

New grayscale morphological operators on hypergraph are proposed to avoid the loss of details caused by fixed structure element effectively. Hypergraph, the most general structure in discrete mathematics, is also a subset of a finite set. Being a structured representation of information, the ordinary image can be transformed into a hypergraph model, which can integrate hypergraph theory with mathematical morphology theory. Because hypergraphs have good performance in structuring information, first of all, this paper designs a reasonable method of turning grayscale images into hypergraph space. Then based on hypergraph theory, new grayscale morphological operators on hypergraph are defined. Experiments show that using the new operators can avoid the loss of image detail information, and improve the precision of image processing.

Catenary geometry measurement is one of the main inspection fields to evaluate the infrastructure quality and status of power supply system on high-speed railways. Existing measurement methods have disadvantages of complex system architecture, expensive device cost, or sensitive to environment changes. In this paper, a new measurement approach is proposed which is based on infrared image processing. The system architecture is compact in hardware, and environment changes have no effects on the measurement results. Moreover, the infrared images are shared by thermal cameras from other inspection systems, which means a low device cost of the system. The proposed method has been successfully used on the comprehensive inspection train, which shows the advantages in infrastructure inspection on high-speed railways.

The aim of the research was made the dedicated application AOTK (pol. Analiza Obrazu Trzeszczki Kopytowej) for image processing and analysis of horse navicular bone. The application was produced by using specialized software like Visual Studio 2013 and the .NET platform. To implement algorithms of image processing and analysis were used libraries of Aforge.NET. Implemented algorithms enabling accurate extraction of the characteristics of navicular bones and saving data to external files. Implemented in AOTK modules allowing the calculations of distance selected by user, preliminary assessment of conservation of structure of the examined objects. The application interface is designed in a way that ensures user the best possible view of the analyzed images.

Optical coherence tomography (OCT) is a high resolution and non-invasive imaging modality that has become one of the most prevalent techniques for ophthalmic diagnostic. However, manual segmentation is often a time-consuming and subjective process. In this work, we present a new method for retinal layer segmentation in retinal optical coherence tomography images, which uses a deep convolutional feature to train a structured random forest classifier. The experimental results show that our method achieves good results with the mean distance error of 1.45 pixels whereas that of the state-of-the-art was 1.68 pixels, and achieve a F-score of 0.86 which is also better than 0.83 that is obtained by the state-of-the-art method.

Intelligent computer image processing technology provides convenience and possibility for designers to carry out designs. Shape analysis can be achieved by extracting SURF feature. However, high dimension of SURF feature causes to lower matching speed. To solve this problem, this paper proposed a fast fabric image matching algorithm based on SURF K-means and LSH algorithm. By constructing the bag of visual words on K-Means algorithm, and forming feature histogram of each image, the dimension of SURF feature is reduced at the first step. Then with the help of LSH algorithm, the features are encoded and the dimension is further reduced. In addition, the indexes of each image and each class of image are created, and the number of matching images is decreased by LSH hash bucket. Experiments on fabric image database show that this algorithm can speed up the matching and retrieval process, the result can satisfy the requirement of dress designers with accuracy and speed.

Image restoration is a significant task in the fields of computer vision and image processing. Image restoration research consists of two aspects: kernel estimation and image restoration. A single image restoration method based on L1-regularized blur kernel estimation is proposed in this paper. First, a bilateral filter is used to remove the image noise effectively. Second, the improved shock filter is used to enhance the edge information of the image. Subsequently, L1-regularization method is used to estimate the blur kernel of the blurred image alternately, during which Split-Bregman algorithm is used to optimize the solution process. Finally, Hyper-Laplacian and sparse priors are applied to the image obtained from the non-blind deconvolution process. Experimental results show that compared to other methods, better restoration results as well as improved computational efficiency can be achieved with the proposed method.

According to features of micro-seismic signal, this paper defines the similarity between signals using the cross-correlation, and applies bilateral filtering to the domain of micro- seismic profile image processing. Relative to the general bilateral filtering and curvelet filtering, experimental results confirm that the bilateral filtering based on cross-correlation can remove interference noises a lot, enhance the signal-to-noise ration, and keep the continuity of horizontal micro-seismic events, that will ensure the accuracy of micro-seismic event identification and positioning.

In this paper, we present a new improvement technique in tolerant memoization algorithm for implementing local image processing operations. Window memoization is a method in the field of local image processing operations that uses the benefit of data redundancy in images to minimize the amount of computations performed that leads to achieve speedup. In the proposed method, the benefit of inter-pixel redundancy in images is used to improve the efficiency of tolerant window memoization where the neighboring pixels are correlated. We have developed this new optimized method on software and applied to edge detection to images of 512 × 512 pixels and compared with previous tolerant window memoization method, the results show the average speedup increasing is 34%.

Several problems of spectral photometry analysis of handwritten documents have been studied like combined reflection from the both ink and paper material impregnated by ink, the illumination spatial non-uniformity with small light intensity, flexibility and instability of paper sheets. The images acquired at different wavelengths were spatially matched using developed computer algorithm and then regions of interests, i.e. ink lines were extracted. Then reflection spectra obtained at all pixels of ink lines were composed in vector representation, and comparison of the vectors was conducted. As the result, degree of correspondence between different fragments of text has been evaluated. The optical setup and its calibration procedures are considered in detail as well as results of image processing, matching, segmentation and spectral photometry evaluation of ink. Experimental results of different text fragments matching and estimates of text fragments spectral correspondence degree are presented and discussed.

More than 80% of video surveillance systems are used for monitoring people. Old human detection algorithms, based on background and foreground modelling, could not even deal with a group of people, to say nothing of a crowd. Recent robust and highly effective pedestrian detection algorithms are a new milestone of video surveillance systems. Based on modern approaches in deep learning, these algorithms produce very discriminative features that can be used for getting robust inference in real visual scenes. They deal with such tasks as distinguishing different persons in a group, overcome problem with sufficient enclosures of human bodies by the foreground, detect various poses of people. In our work we use a new approach which enables to combine detection and classification tasks into one challenge using convolution neural networks. As a start point we choose YOLO CNN, whose authors propose a very efficient way of combining mentioned above tasks by learning a single neural network. This approach showed competitive results with state-of-the-art models such as FAST R-CNN, significantly overcoming them in speed, which allows us to apply it in real time video surveillance and other video monitoring systems. Despite all advantages it suffers from some known drawbacks, related to the fully-connected layers that obstruct applying the CNN to images with different resolution. Also it limits the ability to distinguish small close human figures in groups which is crucial for our tasks since we work with rather low quality images which often include dense small groups of people. In this work we gradually change network architecture to overcome mentioned above problems, train it on a complex pedestrian dataset and finally get the CNN detecting small pedestrians in real scenes.

For many optical systems, mainly in photography, the digital correction of distortion and lateral color aberration is a common step in the image processing pipeline. It is based on images of calibration targets and the determination of coefficients for a correction model. These coefficients are specific for the optical system which forms the aerial image for the sensor. If an exchange of the optical system is necessary, e.g. for offering optics for several applications like macro mode, wide-angle or tele-photo, the entire optical system is typically exchanged and not only parts of it. Using a technique for digital identification of the optical system, e.g. via an electronic interface, the corresponding correction coefficients can be obtained and applied. If the optical system to be exchanged is only a part of the entire optical system, the situation is more difficult. As an example, this is the case in the field of digital microscopy, where objective lenses with different numerical apertures and magnifications are available for the same microscopic body. These objective lenses are no image forming optical systems and need at least a tube lens to produce an image for capturing with a sensor. Both optical systems produce contributions to the resulting overall aberrations, e.g. distortion and lateral color aberration. For a particular pairing, coefficients for a correction model can be calibrated and applied. But in the case of an exchange of the objective, only a part of the optical system changes and a new calibration is needed. We will show how this problem can be solved using a mathematical operation and individual calibration of the distinct parts of the optical system. The advantage is, that the parts of the optical system can be arbitrarily combined and the correction-model coefficients of the combination can be computed from the corresponding coefficients of the individual parts. Hence, there’s no need to calibrate every possible combination or to manage a calibration process at the customer’s location. In addition, the manufacturing and calibration of objectives and tube lenses may take place at different locations. Furthermore, the optical design can benefit from the digital aberration correction although it is made up of interchangeable components.

Vehicle tracking technology is currently one of the most active research topics in machine vision. It is an important part of intelligent transportation system. However, in theory and technology, it still faces many challenges including real-time and robustness. In video surveillance, the targets need to be detected in real-time and to be calculated accurate position for judging the motives. The contents of video sequence images and the target motion are complex, so the objects can’t be expressed by a unified mathematical model. Object-tracking is defined as locating the interest moving target in each frame of a piece of video. The current tracking technology can achieve reliable results in simple environment over the target with easy identified characteristics. However, in more complex environment, it is easy to lose the target because of the mismatch between the target appearance and its dynamic model. Moreover, the target usually has a complex shape, but the tradition target tracking algorithm usually represents the tracking results by simple geometric such as rectangle or circle, so it cannot provide accurate information for the subsequent upper application. This paper combines a traditional object-tracking technology, Mean-Shift algorithm, with a kind of image segmentation algorithm, Active-Contour model, to get the outlines of objects while the tracking process and automatically handle topology changes. Meanwhile, the outline information is used to aid tracking algorithm to improve it.

All large infrastructures worldwide must have a suitable monitoring and maintenance plan, aiming to evaluate their behaviour and predict timely interventions. In the particular case of concrete infrastructures, the detection and characterization of crack patterns is a major indicator of their structural response. In this scope, methods based on image processing have been applied and presented. Usually, methods focus on image binarization followed by applications of mathematical morphology to identify cracks on concrete surface. In most cases, publications are focused on restricted areas of concrete surfaces and in a single crack. On-site, the methods and algorithms have to deal with several factors that interfere with the results, namely dirt and biological colonization. Thus, the automation of a procedure for on-site characterization of crack patterns is of great interest. This advance may result in an effective tool to support maintenance strategies and interventions planning. This paper presents a research based on the analysis and processing of hyper-spectral images for detection and classification of cracks on concrete structures. The objective of the study is to evaluate the applicability of several wavelengths of the electromagnetic spectrum for classification of cracks in concrete surfaces. An image survey considering highly discretized wavelengths between 425 nm and 950 nm was performed on concrete specimens, with bandwidths of 25 nm. The concrete specimens were produced with a crack pattern induced by applying a load with displacement control. The tests were conducted to simulate usual on-site drawbacks. In this context, the surface of the specimen was subjected to biological colonization (leaves and moss). To evaluate the results and enhance crack patterns a clustering method, namely k-means algorithm, is being applied. The research conducted allows to define the suitability of using clustering k-means algorithm combined with hyper-spectral images highly discretized for crack detection on concrete surfaces, considering cracking combined with the most usual concrete anomalies, namely biological colonization.

The human visual system can quickly focus on a small number of salient objects. This process was known as visual saliency analysis and these salient objects are called focus of attention (FOA). The visual saliency analysis mechanism can be used to extract the salient regions and analyze saliency of object in an image, which is time-saving and can avoid unnecessary costs of computing resources. In this paper, a novel visual saliency analysis model based on dynamic multiple feature combination strategy is introduced. In the proposed model, we first generate multi-scale feature maps of intensity, color and orientation features using Gaussian pyramids and the center-surround difference. Then, we evaluate the contribution of all feature maps to the saliency map according to the area of salient regions and their average intensity, and attach different weights to different features according to their importance. Finally, we choose the largest salient region generated by the region growing method to perform the evaluation. Experimental results show that the proposed model cannot only achieve higher accuracy in saliency map computation compared with other traditional saliency analysis models, but also extract salient regions with arbitrary shapes, which is of great value for the image analysis and understanding.

Active Appearance Model (AAM) is an accurate and robust tool and is suitable when it’s needed to estimate shape of object when its’ approximate shape is known but varies within a certain range from instance to instance. An AAM allows complex models of shape (for example human face) and appearance to be matched to new images rapidly. An AAM contains a statistical model of the shape and gray level or color appearance of an object of interest. The associated search algorithm exploits the locally linear relationship between model parameter displacements and the residual errors between model instance and image. AAM is widely used but the research of its’ accuracy and stability still remains an important and not fully learned issue. In this paper, we study landmarks stability and error estimation produced by AAM in different lightning conditions and signal-to-noise ratio (SNR).

Unstable movement in roller bearings like cage or roller slip can lead to damages or eventually even to an early break of the bearing. To prevent slip, inadequate operating states should be avoided. Therefore, it is necessary to study the dynamic behavior of the bearing. Unfortunately, there is only a limited range of measurement methods for the dynamic of bearing components. Two possible approaches are using solely a high-speed camera or the combination of an optomechanical image derotator and a high-speed camera. This work focuses on a proposal which is suitable for both. Initially, the influence of the rotational velocity in the images is eliminated. In the next step the measurement data is reduced to a region of interest which displays a particular rolling-element. A rolling element is equipped with a linear marker which, in the next stage, is segmented by a thresholding method to multiple regions. The region representing the marker is extracted from the background and the position is calculated by a Principle Component Analysis. Depending on the shift of the angular position and the lag time between two images, the rotational velocity of the rolling element is calculated. Thus, it is possible to determine whether the rolling element is operating under ideal conditions. In conclusion, it can be said that this approach enables a simple and flexible non-invasive method to depict the occurrence of roller slip in roller bearings. © 2017 SPIE.

In this work, a simulation toolset for Computer Aided Inspection Planning (CAIP) of systems for automated optical inspection (AOI) is presented along with a versatile two-robot-setup for verification of simulation and system planning results. The toolset helps to narrow down the large design space of optical inspection systems in interaction with a system expert. The image formation taking place in optical inspection systems is simulated using GPU-based real time graphics and high quality off-line-rendering. The simulation pipeline allows a stepwise optimization of the system, from fast evaluation of surface patch visibility based on real time graphics up to evaluation of image processing results based on off-line global illumination calculation. A focus of this work is on the dependency of simulation quality on measuring, modeling and parameterizing the optical surface properties of the object to be inspected. The applicability to real world problems is demonstrated by taking the example of planning a 3D laser scanner application. Qualitative and quantitative comparison results of synthetic and real images are presented.

Broken glass being the waste in many fields of production is usually used as a raw material in the production of construction materials. The purity level of collected and processed glass cullet, as a rule, is quite low. Direct usage of these materials without preliminary processing leads to the emergence of defects in the end product or sometimes even to technological downtime. That’s why purity control of cullet should be strictly verified. The study shows the method of construction and requirements for an optical-electronic system designed for cullet separation. Moreover, the author proposes a registration channel scheme and shows a scheme of control exposure area. Also the issues of image processing for the implementation of a typical system are examined.

The article is devoted to the optimization of optoelectronic systems of the spatial position of objects. Probabilistic characteristics of the detection of an active structured mark on a random noisy background are investigated. The developed computer model and the results of the study allow us to estimate the probabilistic characteristics of detection of a complex structured mark on a random gradient background, and estimate the error of spatial coordinates. The results of the study make it possible to improve the accuracy of measuring the coordinates of the object. Based on the research recommendations are given on the choice of parameters of the optimal mark structure for use in opticalelectronic systems for monitoring the spatial position of large-sized structures.

Stent quality control is a highly critical process. Cardiovascular stents have to be inspected 100% so as no defective stent is implanted in a human body. However, this visual control is currently performed manually and every stent could need tenths of minutes to be inspected. In this paper, a novel optical inspection system is presented. By the combination of a high numerical aperture (NA) optical system, a rotational stage and a line-scan camera, unrolled sections of the outer and inner surfaces of the stent are obtained and image-processed at high speed. Defects appearing in those surfaces and also in the edges are extremely contrasted due to the shadowing effect of the high NA illumination and acquisition approach. Therefore by means of morphological operations and a sensitivity parameter, defects are detected. Based on a trained defect library, a binary classifier sorts each kind of defect through a set of scoring vectors, providing the quality operator with all the required information to finally take a decision. We expect this new approach to make defect detection completely objective and to dramatically reduce the time and cost of stent quality control stage.

In modern times when technologies are developing rapidly, the high-carbon schungite rocks of Karelia are promising mineral raw material for production of active fillers for composite materials, radio shielding materials, silicon carbide, stable aqueous dispersions, sorbents, catalysts, carbon nanomaterials, and other products. An intensive evolution of radiometric separation and sorting methods based on different physical phenomena occurring in the interaction of minerals and their constituent chemical elements with different types of radiation open new enrichment opportunities for schungite materials. This is especially pertinent to optical method of enrichment, which is a part of radiometric methods. The present work is devoted to the research and development of preliminary quality assessment principles for raw schungite on the basis of image processing principles and perspectives of the optical separation for its [schungite] enrichment. Obtained results of preliminary studies allow us to describe the selective criteria for separation of mentioned raw material by optical method, as well as to propose the method of quality indicator assessing for schungite raw materials. All conceptual and theoretical fundamentals are corroborated by the results of experimental studies of schungite rock samples with breccia and vein textures with different sizes from Maksovo deposit.

The airport is one of the most crucial traffic facilities in military and civil fields. Automatic airport extraction in high spatial resolution remote sensing images has many applications such as regional planning and military reconnaissance. Traditional airport extraction strategies usually base on prior knowledge and locate the airport target by template matching and classification, which will cause high computation complexity and large costs of computing resources for high spatial resolution remote sensing images. In this paper, we propose a novel automatic airport extraction model based on saliency region detection, airport runway extraction and adaptive threshold segmentation. In saliency region detection, we choose frequency-tuned (FT) model for computing airport saliency using low level features of color and luminance that is easy and fast to implement and can provide full-resolution saliency maps. In airport runway extraction, Hough transform is adopted to count the number of parallel line segments. In adaptive threshold segmentation, the Otsu threshold segmentation algorithm is proposed to obtain more accurate airport regions. The experimental results demonstrate that the proposed model outperforms existing saliency analysis models and shows good performance in the extraction of the airport.

Mango production is highly vital in the Philippines. It is very essential in the food industry as it is being used in markets and restaurants daily. The quality of mangoes can affect the income of a mango farmer, thus incorrect time of harvesting will result to loss of quality mangoes and income. Scientific farming is much needed nowadays together with new gadgets because wastage of mangoes increase annually due to uncouth quality. This research paper focuses on profiling and sorting of Mangifera Indica using image processing techniques and pattern recognition. The image of a mango is captured on a weekly basis from its early stage. In this study, the researchers monitor the growth and color transition of a mango for profiling purposes. Actual dimensions of the mango are determined through image conversion and determination of pixel and RGB values covered through MATLAB. A program is developed to determine the range of the maximum size of a standard ripe mango. Hue, light, saturation (HSL) correction is used in the filtering process to assure the exactness of RGB values of a mango subject. By pattern recognition technique, the program can determine if a mango is standard and ready to be exported.

Image registration is the key technology of digital imaging applications, it is used widely. We researched the image registration techniques in this paper. Based on the basis of D-Nets image registration algorithms, we propose a new innovation. We turn first to process image, so we can get synthetic images of original images and enhanced images. Then we extract SIFT feature in the original image. Next, in order to reduce noise of the image, we use the Gauss filter to process the synthesized image. Then we do experiments with synthetic images in the process of image registration. In this process, we use the D-Nets algorithm to achieve. Compared to the existing method, it can greatly improve the accuracy and recall.

Pattern recognition of concrete surface crack defects is very important in determining stability of structure like building, roads or bridges. Surface crack is one of the subjects in inspection, diagnosis, and maintenance as well as life prediction for the safety of the structures. Traditionally determining defects and cracks on concrete surfaces are done manually by inspection. Moreover, any internal defects on the concrete would require destructive testing for detection. The researchers created an automated surface crack detection for concrete using image processing techniques including Hough transform, LoG weighted, Dilation, Grayscale, Canny Edge Detection and Haar Wavelet Transform. An automatic surface crack detection robot is designed to capture the concrete surface by sectoring method. Surface crack classification was done with the use of Haar trained cascade object detector that uses both positive samples and negative samples which proved that it is possible to effectively identify the surface crack defects.

Acne vulgaris, commonly called as acne, is a skin problem that occurs when oil and dead skin cells clog up in a person’s pores. This is because hormones change which makes the skin oilier. The problem is people really do not know the real assessment of sensitivity of their skin in terms of fluid development on their faces that tends to develop acne vulgaris, thus having more complications. This research aims to assess Acne Vulgaris using luminescent visualization system through optical imaging and integration of image processing algorithms. Specifically, this research aims to design a prototype for facial fluid analysis using luminescent visualization system through optical imaging and integration of fluorescent imaging system, and to classify different facial fluids present in each person. Throughout the process, some structures and layers of the face will be excluded, leaving only a mapped facial structure with acne regions. Facial fluid regions are distinguished from the acne region as they are characterized differently.

In this work, we present the framework surrounding the development of a mmW radar image-based algorithm for wire recognition and classification for rotorcraft operation in degraded visual environments. While a mmW sensor image lacks the optical resolution and perspective of an IR or LIDAR sensor, it currently presents the only true see-through mitigation under the heaviest of degraded vision conditions. Additionally, the mmW sensor produces a high-resolution, radar map that has proven to be exceedingly interpretable, especially to a familiar operator. Seizing on these clear advantages, the mmW radar image-based algorithm is trained and evaluated against independent mmW imagery data collected from a live flight test in a relevant environment. The foundation of our approach is based on image processing and machine learning techniques utilizing radar-based signal properties and sensor and platform information for added robustness. We discuss some of the requirements and practical challenges of a standalone algorithm development, and lastly, present some preliminary examples using existing development tools and discuss the path for continued advancement and evaluation.

Wolter I optics are commonly used for imaging in X-Ray spectrum. This system uses two reflections, and at higher energies, this system is not so much efficient but has a very good optical resolution. Here is another type of optics Lobster Eye, which is using also two reflections for focusing rays in Schmidt's or Angel's arrangement. Here is also possible to use Lobster eye optics as two one dimensional independent optics. This paper describes advantages of one dimensional and two dimensional Lobster Eye optics in Schmidt's arrangement and its data processing - find out a number of sources in wide field of view. Two dimensional (2D) optics are suitable to detect the number of point X-ray sources and their magnitude, but it is necessary to expose for a long time because a 2D system has much lower transitivity, due to double reflection, compared to one dimensional (1D) optics. Not only for this reason, two 1D optics are better to use for lower magnitudes of sources. In this case, additional image processing is necessary to achieve a 2D image. This article describes of approach an image reconstruction and advantages of two 1D optics without significant losses of transitivity.

This study presents a remote sensing application of using time series Landsat satellite images for monitoring the solid waste disposal site (WDS). We propose a method of detecting high-rise buildings landfills, such as municipal dumps and solid waste, according to a radar image (the height of the ground level). For disposal site detection a variety steps of image processing used (calculation image average level of the earth's surface; filtering thresholds spectral brightness coefficients, the size of the connected components, the nature of reducing the level of height with the distance of the maximum level). The spatial geometric features of waste disposal facilities are analytically expressed by linear and radial characteristics from other objects of the earth surface. As a result, the proposed method demonstrates good accuracy in detection the solid waste disposal site on real satellite images.

In the present work, a methodology for the analysis of subcellular morphology with chemical specificity for images from Stimulated Raman Scattering is suggested. In particular, a segmentation method based on a threshold algorithm and on a region growing process, to detect microstructures inside the cells, is proposed. Moreover, quantitative features for the segmented objects are extracted, in order to provide information about the possible morphological variations of microstructures in images acquired by means SRS technique.

The key to improve Hydroxyl Tagging Velocimetry (HTV) Measurement precision in the laser supersonic combustion diagnosis research is to improve the effect of image processing. In terms of strong OH background and low signal-to-noise ratio (SNR) issues, the approach for HTV signal extraction in supersonic combustion filed is proposed. Firstly, the method of compensation-and-correction-window-filter and the progressive approach characteristic filtering window approach are adopted to remove background for image preprocessing. Then the algorithm combined image segmentation and the skeleton extraction is employed for signal extraction, that improves the signal identification ability in the interferences of fierce combustion zone of a mass of hydroxyl background, solves the insufficient precision problem of extracting hydroxyl effective signal, what's more, achieves the effective information of velocity distribution in combustion flow.

With the development of a myriad of imaging sensors and associated image processing algorithms to address the DVE problem relative performance evaluations have becoming increasingly important. In this paper, we introduce image quality metrics which have been selected for DVE applications. These quality metrics are based on the human visual system and consider factors such as induced processing noise, information content, and preserved image detail. These measures are shown to be useful for the evaluation of imaging sensors and associated processing. In addition, these measures provide direction for tuning and optimizing DVE local area processing (LAP) algorithms. Results will be shown for sample test images and dust trials of a camera with various image processing algorithms.

The Open Standard for Unattended Sensors (OSUS) was developed by DIA and ARL to provide a plug-n-play platform for sensor interoperability. Our objective is to use the standardized data produced by OSUS in performing data analytics on information obtained from various sensors. Data analytics can be integrated in one of three ways: within an asset itself; as an independent plug-in designed for one type of asset (i.e. camera or seismic sensor); or as an independent plug-in designed to incorporate data from multiple assets. As a proof-of-concept, we develop a model that can be used in the second of these types – an independent component for camera images. The dataset used was collected as part of a demonstration and test of OSUS capabilities. The image data includes images of empty outdoor scenes and scenes with human or vehicle activity. We design, test, and train a convolution neural network (CNN) to analyze these images and assess the presence of activity in the image. The resulting classifier labels input images as empty or activity with 86.93% accuracy, demonstrating the promising opportunities for deep learning, machine learning, and predictive analytics as an extension of OSUS’s already robust suite of capabilities.

In recent years, the Ground Penetrating Radar (GPR) has successfully been applied to the problem of buried threat detection (BTD). A large body of research has focused on using computerized algorithms to automatically discriminate between buried threats and subsurface clutter in GPR data. For this purpose, the GPR data is frequently treated as an image of the subsurface, within which the reflections associated with targets often appear with a characteristic shape. In recent years, shape descriptors from the natural image processing literature have been applied to buried threat detection, and the histogram of oriented gradient (HOG) feature has achieved state-of-the-art performance. HOG consists of computing histograms of the image gradients in disjoint square regions, which we call pooling regions, across the GPR images. In this work we create a large body of potential pooling regions and use the group LASSO (GLASSO) to choose a subset of the pooling regions that are most appropriate for BTD on GPR data. We examined this approach on a large collection of GPR data using lane-based cross-validation, and the results indicate that GLASSO can select a subset of pooling regions that lead to superior performance to the original HOG feature, while simultaneously also reducing the total number of features needed. The selected pooling regions also provide insight about the regions in GPR images that are most important for discriminating threat and nonthreat data.

The Triangle Orientation Discrimination (TOD) methodology includes i) a widely applicable, accurate end-to-end EO/IR sensor test, ii) an image-based sensor system model and iii) a Target Acquisition (TA) range model. The method has been extensively validated against TA field performance for a wide variety of well- and under-sampled imagers, systems with advanced image processing techniques such as dynamic super resolution and local adaptive contrast enhancement, and sensors showing smear or noise drift, for both static and dynamic test stimuli and as a function of target contrast. Recently, significant progress has been made in various directions. Dedicated visual and NIR test charts for lab and field testing are available and thermal test benches are on the market. Automated sensor testing using an objective synthetic human observer is within reach. Both an analytical and an image-based TOD model have recently been developed and are being implemented in the European Target Acquisition model ECOMOS and in the EOSTAR TDA. Further, the methodology is being applied for design optimization of high-end security camera systems. Finally, results from a recent perception study suggest that DRI ranges for real targets can be predicted by replacing the relevant distinctive target features by TOD test patterns of the same characteristic size and contrast, enabling a new TA modeling approach. This paper provides an overview. © 2017 COPYRIGHT SPIE. The Society of Photo-Optical Instrumentation Engineers (SPIE)

This paper† describes a technique in which we improve upon the prior performance of the Rapid Serial Visual Presentation (RSVP) EEG paradigm for image classification though the insertion of visual attention distracters and overall sequence reordering based upon the expected ratio of rare to common “events” in the environment and operational context. Inserting distracter images maintains the ratio of common events to rare events at an ideal level, maximizing the rare event detection via P300 EEG response to the RSVP stimuli. The method has two steps: first, we compute the optimal number of distracters needed for an RSVP stimuli based on the desired sequence length and expected number of targets and insert the distracters into the RSVP sequence, and then we reorder the RSVP sequence to maximize P300 detection. We show that by reducing the ratio of target events to nontarget events using this method, we can allow RSVP sequences with more targets without sacrificing area under the ROC curve (azimuth).

Thermal-infrared cameras are used for signal/image processing and computer vision in numerous military and civilian applications. However, the cost of high quality (e.g., low noise, accurate temperature measurement, etc.) and high resolution thermal sensors is often a limiting factor. On the other hand, high resolution visual spectrum cameras are readily available and typically inexpensive. Herein, we outline a way to upsample thermal imagery with respect to a high resolution visual spectrum camera using Markov random field theory. This paper also explores the tradeoffs and impact of upsampling, both qualitatively and quantitatively. Our preliminary results demonstrate the successful use of this approach for human detection and accurate propagation of thermal measurements in an image for more general tasks like scene understanding. A tradeoff analysis of the cost-to-performance as the resolution of the thermal camera decreases is provided.

This paper summarizes the results of studies in underwater odometery using a video camera for estimating the velocity of an unmanned underwater vehicle (UUV). Underwater vehicles are usually equipped with sonar and Inertial Measurement Unit (IMU) - an integrated sensor package that combines multiple accelerometers and gyros to produce a three dimensional measurement of both specific force and angular rate with respect to an inertial reference frame for navigation. In this study, we investigate the use of odometry information obtainable from a video camera mounted on a UUV to extract vehicle velocity relative to the ocean floor. A key challenge with this process is the seemingly bland (i.e. featureless) nature of video data obtained underwater which could make conventional approaches to image-based motion estimation difficult. To address this problem, we perform image enhancement, followed by frame to frame image transformation, registration and mosaicking/stitching. With this approach the velocity components associated with the moving sensor (vehicle) are readily obtained from (i) the components of the transform matrix at each frame; (ii) information about the height of the vehicle above the seabed; and (iii) the sensor resolution. Preliminary results are presented.

Background: The majority of de novo cancers are diagnosed in low and middle-income countries, which often lack the resources to provide adequate therapeutic options. None or minimally invasive therapies such as Photodynamic Therapy (PDT) or photothermal therapies could become part of the overall treatment options in these countries. However, widespread acceptance is hindered by the current empirical training of surgeons in these optical techniques and a lack of easily usable treatment optimizing tools. Methods: Based on image processing programs, ITK-SNAP, and the publicly available FullMonte light propagation software, a work plan is proposed that allows for personalized PDT treatment planning. Starting with, contoured clinical CT or MRI images, the generation of 3D tetrahedral models in silico, execution of the Monte Carlo simulation and presentation of the 3D fluence rate, Φ, [mWcm -2 ] distribution a treatment plan optimizing photon source placement is developed. Results: Permitting 1-2 days for the installation of the required programs, novices can generate their first fluence, H [Jcm -2 ] or Φ distribution in a matter of hours. This is reduced to 10th of minutes with some training. Executing the photon simulation calculations is rapid and not the performance limiting process. Largest sources of errors are uncertainties in the contouring and unknown tissue optical properties. Conclusions: The presented FullMonte simulation is the fastest tetrahedral based photon propagation program and provides the basis for PDT treatment planning processes, enabling a faster proliferation of low cost, minimal invasive personalized cancer therapies.

The extraction of objects from advanced geospatial intelligence (AGI) products based on synthetic aperture radar (SAR) imagery is complicated by a number of factors. For example, accurate detection of temporal changes represented in two-color multiview (2CMV) AGI products can be challenging because of speckle noise susceptibility and false positives that result from small orientation differences between objects imaged at different times. These cases of apparent motion can result in 2CMV detection, but they obviously differ greatly in terms of significance. In investigating the state-of-the-art in SAR image processing, we have found that differentiating between these two general cases is a problem that has not been well addressed. We propose a framework of methods to address these problems. For the detection of the temporal changes while reducing the number of false positives, we propose using adaptive object intensity and area thresholding in conjunction with relaxed brightness optical flow algorithms that track the motion of objects across time in small regions of interest. The proposed framework for distinguishing between actual motion and misregistration can lead to more accurate and meaningful change detection and improve object extraction from a SAR AGI product. Results demonstrate the ability of our techniques to reduce false positives up to 60%.

Exercise induced muscle damage (EIMD), is usually experienced in i) humans who have been physically inactive for prolonged periods of time and then begin with sudden training trials and ii) athletes who train over their normal limits. EIMD is not so easy to be detected and quantified, by means of commonly measurement tools and methods. Thermography has been used successfully as a research detection tool in medicine for the last 6 decades but very limited work has been reported on EIMD area. The main purpose of this research is to assess and characterize EIMD, using thermography and image processing techniques. The first step towards that goal is to develop a reliable segmentation technique to isolate the region of interest (ROI). A semi-automatic image processing software was designed and regions of the left and right leg based on superpixels were segmented. The image is segmented into a number of regions and the user is able to intervene providing the regions which belong to each of the two legs. In order to validate the image processing software, an extensive experimental investigation was carried out, acquiring thermographic images of the rectus femoris muscle before, immediately post and 24, 48 and 72 hours after an acute bout of eccentric exercise (5 sets of 15 maximum repetitions), on males and females (20-30 year-old). Results indicate that the semi-automated approach provides an excellent bench-mark that can be used as a clinical reliable tool.