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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.

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%.

In this paper, we discuss a method for automatic programming of inspection image processing. In the industrial field, automatic program generators or expert systems are expected to shorten a period required for developing a new appearance inspection system. So-called “image processing expert system” have been studied for over the nearly 30 years. We are convinced of the need to adopt a new idea. Recently, a novel type of evolutionary algorithms, called genetic network programming (GNP), has been proposed. In this study, we use GNP as a method to create an inspection image processing logic. GNP develops many directed graph structures, and shows excellent ability of formulating complex problems. We have converted this network program model to Image Processing Network Programming (IPNP). IPNP selects an appropriate image processing command based on some characteristics of input image data and processing log, and generates a visual inspection software with series of image processing commands. It is verified from experiments that the proposed method is able to create some inspection image processing programs. In the basic experiment with 200 test images, the success rate of detection of target region was 93.5%.

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.

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.

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.

International audience; Visual assessment is the most common clinical investigation of skin reactions in radiotherapy. Due to the subjective nature of this method, additional noninvasive techniques are needed for more accurate evaluation. Our goal is to evaluate the effectiveness of hyperspectral image analysis for that purpose. In this pilot study, we focused on detection and grading of skin Erythema. This paper reports our proposed processing pipeline and experimental findings. Experiments have been performed to demonstrate the efficacy of the proposed approach for (1) reproducing clinical assessments, and (2) outperforming RGB imaging data.

Radiomics workflows are high-throughput disease descriptive or predictive tools that extract mineable quantitative data of pathological phenotypes from standard-of-care grayscale images using advanced image processing algorithms. The success of these workflows rely on establishing large image datasets from which diverse disease descriptors can be extracted, with the expectation that large numbers may be able to overcome some of the inherent heterogeneities inherent in standard-of-care medical imaging workflows. Here, we present such a radiomics platform which relies on a combination of existing standard-of-care imaging clinical and research software as well as custom written code. The key components of the workflow include a file organization schema for centralized data storage, deployment of image registration strategies, and frontend GUI design for ease of use by the clinical researcher, all of which increase the transparency, flexibility, and portability of our radiomics platform. Widespread establishment of such radiomics platform can greatly revolutionize radiomics research and aid in successful translation into clinical decision support systems. Presented are three preliminary studies completed using our proposed radiomics research workflow to investigate various diseases. The radiomics research workflow is modality and disease independent which allow it to serve as a general platform for medical image post-processing experimentation.

The propagation environment around airborne platforms may significantly degrade the performance of Electro-Optical (EO) self-protection systems installed onboard. To ensure the sufficient level of protection, it is necessary to understand that are the best sensors/effectors installation positions to guarantee that the aeromechanical turbulence, generated by the engine exhausts and the rotor downwash, does not interfere with the imaging systems normal operations. Since the radiation-propagation-in-turbulence is a hardly predictable process, it was proposed a high-level approach in which, instead of studying the medium under turbulence, the turbulence effects on the imaging systems processing are assessed by means of an equivalent statistical model representation, allowing a definition of a Turbulence index to classify different level of turbulence intensities. Hence, a general measurement methodology for the degradation of the imaging systems performance in turbulence conditions was developed. The analysis of the performance degradation started by evaluating the effects of turbulences with a given index on the image processing chain (i.e., thresholding, blob analysis). The processing in turbulence (PIT) index is then derived by combining the effects of the given turbulence on the different image processing primitive functions. By evaluating the corresponding PIT index for a sufficient number of testing directions, it is possible to map the performance degradation around the aircraft installation for a generic imaging system, and to identify the best installation position for sensors/effectors composing the EO self-protection suite.

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.

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.

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 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.

This paper presents a novel microscope spray droplets detecting technique based on image processing method, which is applied to an experimental measurement performed on a flat fan atomizer nozzle. The droplets images are detected by an image acquisition system. The raw images are pre-processed to enhance the images and avoid the illumination unevenness effect. An image restoration method based on iterative blind deconvolution is applied to improve the quality of the blurred image due to droplets movement and defocus. Circle Hough transform and the least square method are applied to find the droplet contour in the image, and the droplets diameters are calibrated and counted. A criterion is presented to avoid repeated statistic problem. The measurements of water spray droplet size distribution from a small flat fan nozzle atomizers are taken and the experiment results are analyzed.

The main lobe and the side lobes of strong scatterers are sometimes clearly visible in SAR images. Sidelobe reduction is of particular importance when imaging scenes contain objects such as ships and buildings having very large radar cross sections. Amplitude weighting is usually used to suppress sidelobes of the images at the expense of broadening of mainlobe, loss of resolution and degradation of SAR images. The Spatial Variant Apodization (SVA) is an Adaptive SideLobe Reduction (ASLR) technique that provides high effective suppression of sidelobes without broadening mainlobe. In this paper, we apply SVA to process COSMO-SkyMed (CSK) StripMap and Spotlight X-band data and compare the images with the standard products obtained via Hamming window processing. Different test sites have been selected in Italy, Argentina, California and Germany where corner reflectors are installed. Experimental results show clearly the resolution improvement (20%) while sidelobe kept to a low level when SVA processing is applied compared with Hamming windowing one. Then SVA technique is applied to Interferometric SAR image processing (INSAR) using a CSK StripMap interferometric tandem-like data pair acquired on East-California. The interferometric coherence of image pair obtained without sidelobe reduction (SCS_U) and with sidelobe reduction performed via Hamming window and via SVA are compared. High resolution interferometric products have been obtained with small variation of mean coherence when using ASLR products with respect to hamming windowed and no windowed one.

The Clouds and the Earth’s Radiant Energy System (CERES) has incorporated imagery from 16 individual geostationary (GEO) satellites across five contiguous domains since March 2000. In order to derive broadband fluxes uniform across satellite platforms it is important to ensure a good quality of the input raw count data. GEO data obtained by older GOES imagers (such as MTSAT-1, Meteosat-5, Meteosat-7, GMS-5, and GOES-9) are known to frequently contain various types of noise caused by transmission errors, sync errors, stray light contamination, and others. This work presents an image processing methodology designed to detect most kinds of noise and corrupt data in all bands of raw imagery from modern and historic GEO satellites. The algorithm is based on a set of different approaches to detect abnormal image patterns, including inter-line and inter-pixel differences within a scanline, correlation between scanlines, analysis of spatial variance, and also a 2D Fourier analysis of the image spatial frequencies. In spite of computational complexity, the described method is highly optimized for performance to facilitate volume processing of multi-year data and runs in fully automated mode. Reliability of this noise detection technique has been assessed by human supervision for each GEO dataset obtained during selected time periods in 2005 and 2006. This assessment has demonstrated the overall detection accuracy of over 99.5% and the false alarm rate of under 0.3%. The described noise detection routine is currently used in volume processing of historical GEO imagery for subsequent production of global gridded data products and for cross-platform calibration.

The Advance Radiographic Capability (ARC) at the National Ignition Facility (NIF) is a laser system that employs up to four petawatt (PW) lasers to produce a sequence of short-pulse kilo-Joule laser pulses with controllable delays that generate X-rays to provide backlighting for high-density internal confinement fusion (ICF) capsule targets. Multi-frame, hard-X-ray radiography of imploding NIF capsules is a capability which is critical to the success of NIF's missions. ARC is designed to employ up to eight backlighters with tens-of-picosecond temporal resolution, to record the dynamics and produce an X-ray "motion picture" of the compression and ignition of cryogenic deuterium-tritium targets. ARC will generate tens-of-picosecond temporal resolution during the critical phases of ICF shots. Additionally, ARC supports a variety of other high energy density experiments including fast ignition studies on NIF. The automated alignment image analysis algorithms use digital camera sensor images to direct ARC beams onto the tens-of-microns scale metal wires. This paper describes the ARC automatic alignment sequence throughout the laser chain from pulse initiation to target with an emphasis on the image processing algorithms that generate the crucial alignment positions for ARC. The image processing descriptions and flow diagrams detail the alignment control loops throughout the ARC laser chain beginning in the ARC high-contrast front end (HCAFE), on into the ARC main laser area, and ending in the ARC target area.

In this paper, an integral design that combines optical system with image processing is introduced to obtain high resolution images, and the performance is evaluated and demonstrated. Traditional imaging methods often separate the two technical procedures of optical system design and imaging processing, resulting in the failures in efficient cooperation between the optical and digital elements. Therefore, an innovative approach is presented to combine the merit function during optical design together with the constraint conditions of image processing algorithms. Specifically, an optical imaging system with low resolution is designed to collect the image signals which are indispensable for imaging processing, while the ultimate goal is to obtain high resolution images from the final system. In order to optimize the global performance, the optimization function of ZEMAX software is utilized and the number of optimization cycles is controlled. Then Wiener filter algorithm is adopted to process the image simulation and mean squared error (MSE) is taken as evaluation criterion. The results show that, although the optical figures of merit for the optical imaging systems is not the best, it can provide image signals that are more suitable for image processing. In conclusion. The integral design of optical system and image processing can search out the overall optimal solution which is missed by the traditional design methods. Especially, when designing some complex optical system, this integral design strategy has obvious advantages to simplify structure and reduce cost, as well as to gain high resolution images simultaneously, which has a promising perspective of industrial application.

Image processing has become ubiquitous in imaging research—so ubiquitous that it is easy to loose track of how diverse this processing has become. The Duke Center for In Vivo Microscopy has pioneered the development of Magnetic Resonance Histology (MRH), which generates large multidimensional data sets that can easily reach into the tens of gigabytes. A series of dedicated image-processing workstations and associated software have been assembled to optimize each step of acquisition, reconstruction, post-processing, registration, visualization, and dissemination. This talk will describe the image-processing pipelines from acquisition to dissemination that have become critical to our everyday work.

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.

Inspection and classification of food grains is a manual process in many of the food grain processing industries. Automation of such a process is going to be beneficial for industries facing shortage of skilled workforce. Machine Vision techniques are some of the popular approaches for developing such automations. Most of the existing works on the topic deal with identification of the rice variety by analyzing images of well separated and isolated rice grains from which a lot of geometrical features can be extracted. This paper proposes techniques to estimate geometrical parameters from the images of scattered as well as heaped rice grains where the grain boundaries are not clearly identifiable. A methodology based on convexity is proposed to separate touching rice grains in the scattered rice grain images and get their geometrical parameters. And in case of heaped arrangement a Pixel-Distance Contribution Function is defined and is used to get points inside rice grains and then to find the boundary points of rice grains. These points are fit with the equation of an ellipse to estimate their lengths and breadths. The proposed techniques are applied on images of scattered and heaped rice grains of different varieties. It is shown that each variety gives a unique set of results.

The Wide Field View (WFV), a space borne multi-spectral sensor onboard the Chinese GaoFen-1 (GF-1) satellite from the China High-resolution Earth Observation System, is operating in orbit dedicating to providing Earth observation with decametric spatial resolution, high temporal resolution and wide coverage for environment monitoring purpose. The objective of this study is to present an integrated image processing and environment monitoring platform specifically for GF-1 WFV data. The platform is developed with a multi-layer architecture and C/S structure, which primarily consists of image pre-processing, environment monitoring, data visualization, and results output modules. The client application was created by using C# whereas IDL was used to develop image processing and other relevant algorithms. This paper focuses mainly on the overall design of the platform and related key techniques. The platform has been implemented as a stand-alone application, and successfully implemented in real world environment monitoring studies.

Gemological Institute of America (GIA) has developed a diamond color measurement instrument that can provide accurate and reproducible color measurement results. The instrument uses uniform illumination by a daylight-approximating light source; observations from a high-resolution color-camera with nearly zero-distortion bi-telecentric lens, and image processing to calculate color parameters of diamonds. Experiments show the instrument can provide reproducible color measurement results and also identify subtle color differences in diamonds with high sensitivity. The experimental setup of the prototype instrument and the image processing method for calculating diamond color parameters are presented in this report.

This paper shows the results of the automatic design of a generic heat sink, through a morphogenesis algorithm done in Matlab. This algorithm is based on two-dimensional fractal images, generated by Lindenmayer systems, which create the heat sink shape. Those shapes iteratively evolve through a genetic algorithm in order to maximize their heat dissipation capability, estimating it through measurements of their surface and volume. Evaluation process was supported by image processing algorithms. Finally finite elements simulations are carried out in order to determine the real heat dissipation capability of each design and thus obtaining some valid heat sink shapes.

Dimensional measurement for hot forgings is a key factor to improve the level of forging technology in industry field. However, the high temperature, large size and hostile environment increase difficulties to guarantee the robustness and speed of the measurement. In this paper, a robust real-time image processing method based on laser-aided binocular machine vision system is proposed. Firstly, images with clear laser stipes are acquired using spectral selection method, by which the influences of thermal radiation and ambient light can be reduced. Then, to improve the speed of extraction and the robustness of matching, an extraction method based on the information consistency of the images acquired by the system and a matching method based on sequential consistency and epipolar constraints are presented. Dimensional reconstruction models for square and axial forgings are built. Finally, the image processing results are used to reconstruct the feature dimensions of a ceramic plate in the laboratory as well as forgings in a forge. Experiments show that, the root-mean-square error of the reconstructed points is 0.002mm and the relative error for width reconstruction is 0.638% in a cold state. Lengths and diameters of hot large forgings are reconstructed robustly and in real time. It is verified that the method proposed in this paper can satisfy the requirements of precision, speed and robustness for measurement of large hot forgings in industrial field.

We have developed image processing technologies for resolving issues caused by the inherent UFPA (uncooled focal plane array) sensor characteristics to spread its applications. For example, large time constant of an uncooled IR (infra-red) sensor limits its application field, because motion blur is caused in monitoring the objective moving at high speed. The developed image processing technologies can eliminate the blur and retrieve almost the equivalent image observed in still motion. This image processing is based on the idea that output of the IR sensor is construed as the convolution of radiated IR energy from the objective and impulse response of the IR sensor. With knowledge of the impulse response and moving speed of the objective, the IR energy from the objective can be de-convolved from the observed images. We have successfully retrieved the image without blur using the IR sensor of 15 ms time constant under the conditions in which the objective is moving at the speed of about 10 pixels/60 Hz. The image processing for reducing FPN (fixed pattern noise) has also been developed. UFPA having the responsivity in the narrow wavelength region, e.g., around 8 μm is appropriate for measuring the surface of glass. However, it suffers from severe FPN due to lower sensitivity compared with 8–13 μm. The developed image processing exploits the images of the shutter itself, and can reduce FPN significantly.

Image processing is proposed and experimentally demonstrated to improve the capabilities of Raman distributed optical fibre sensors. The here reported technique consists in stacking consecutive one-dimensional Raman Stokes and anti-Stokes traces in two-dimensional data arrays (one for each Raman component), which are then processed by an image denoising algorithm. Owing to the high level of correlation between consecutive measurements in conventional Raman sensing, it is experimentally demonstrated that this newly-proposed two-dimensional denoising approach provides a significant signal-to-noise ratio improvement, which in this case reaches 13.6 dB with no hardware modification to the conventional set-up. Experimental results demonstrate Raman distributed sensing with a remarkably enhanced temperature resolution of 4 mK at 9 km distance, which is obtained with 2 m spatial resolution and a short acquisition time of 35 s.

A repeater-less Brillouin optical time-domain analyzer (BOTDA) with 157.68km sensing range is demonstrated, using the combination of random fiber laser Raman pumping and low-noise laser-diode-Raman pumping. With optical pulse coding (OPC) and Non Local Means (NLM) image processing, temperature sensing with ±0.70°C uncertainty and 8m spatial resolution is experimentally demonstrated. The image processing approach has been proved to be compatible with OPC, and it further increases the figure-of-merit (FoM) of the system by 57%.

Two-photon fluorescence microscopy (TPFM) is a perfect optical imaging equipment to monitor the interaction between fast moving viruses and hosts. However, due to strong unavoidable background noises from the culture, videos obtained by this technique are too noisy to elaborate this fast infection process without video image processing. In this study, we developed a novel scheme to eliminate background noises, recover background bacteria images and improve video qualities. In our scheme, we modified and implemented the following methods for both host and virus videos: correlation method, round identification method, tree-structured nonlinear filters, Kalman filters, and cell tracking method. After these procedures, most of noises were eliminated and host images were recovered with their moving directions and speed highlighted in the videos. From the analysis of the processed videos, 93% bacteria and 98% viruses were correctly detected in each frame on average.

The impact of image processing on cancer detection is still a concern to radiologists and physicists. This work aims to evaluate the effect of two types of image processing on cancer detection in mammography. An observer study was performed in which six radiologists inspected 349 cases (a mixture of normal cases, benign lesions and cancers) processed with two types of image processing. The observers marked areas they were suspicious were cancers. JAFROC analysis was performed to determine if there was a significant difference in cancer detection between the two types of image processing. Cancer detection was significantly better with the standard setting image processing (flavor A) compared with one that provides enhanced image contrast (flavor B), p = 0.036. The image processing was applied to images of the CDMAM test object, which were then analysed using CDCOM. The threshold gold thickness measured with the CDMAM test object was thinner using flavor A than flavor B image processing. Since Flavor A was found to be superior in both the observer study and the measurements using the CDMAM phantom, this may indicate that measurements using the CDMAM correlate with change in cancer detection with different types of image processing.

In this paper we describe a Hardware Accelerator (HWA) for fast recursive approximation of separable convolution with exponential function. This filter can be used in many Image Processing (IP) applications, e.g. depth-dependent image blur, image enhancement and disparity estimation. We have adopted this filter RTL implementation to provide maximum throughput in constrains of required memory bandwidth and hardware resources to provide a power-efficient VLSI implementation.

The stereo matching image processing is studied by the characteristic area properties, the geometrical restrictions and color comparisons. The characteristic areas are the 4 directional connectivity areas bound by the edge parts. The geometrical restrictions are the size difference ratio between the characteristic areas, matching point displacement ratio between the centers of the gravity of the characteristic areas, the size distortion ratio between the added stereo matching pattern union and the reference stereo matching pattern. The proper stereo matching conditions are the state of the maximum and sufficient large pixel number on the overlap area, the satisfaction of the condition of the average row difference ratio on the overlap area, the similar state of the average color difference on the overlap area on the proper combinations of the characteristic areas. The ability of the studied stereo matching is experimented with respect to the colorful soccer ball object. The result of the stereo matching experiments provide the proper correspondence between the centers of the gravity of the characteristic areas. The geometrical restrictions is effective to select the good combination between the characteristic areas of the stereo matching patterns.

The multi-axis consistency among optical sensors is the basic guarantee to ensure the normal operation of the entire optical equipment. In the paper the new method which is image processing technology for non-cooperative target is provided to solve the axis consistency testing question. In detail, firstly an observing object is chosen as non-cooperative target in scene in front of inspected equipment. Secondly the target is observed by the optical sensor and the central location of crosshair in image is recorded. Thirdly the same target is observed again using another optical sensor and the central location of crosshair in image is record. At last, the testing result of two optical sensors can be obtained by the position deviation of crosshair central comparing two images. The axis consistency of mechanical axis and the optical axis is detected by the CCD imaging systems, which held on the mechanical axis of the measured object. Not only the proposed method is suitable to different working band, but also it is very small, not limited by field condition and convenient for carry. Furthermore experimental results show the proposed method has high inspecting accuracy, which explain the feasibility and potential applications.

Snow is a kind of complicated objects that has irregular shape. Therefore, the topic of snow three-dimensional visualization is always challenging. The real-time rendering accumulated snow is of great importance to improve reality in the scene simulation. In this paper, we put forward an algorithm based on predecessors which introduces Perlin Noise and smoothing algorithms, combining with incline coefficient and exposure coefficient. This algorithm makes snow accumulation more natural. Rendering by combining geometry and texture, complexity of large-scale scene is dropped off sharply. Using occlusion map which is processed by image processing, this algorithm could update occlusion relationship of objects in the scene and get better results. Experiment results show that the proposed algorithm accelerates the rendering and be suitable for large real-time snow accumulated rendering.

The BRITE nano-satellite mission is an international Austrian-Canadian-Polish project of six small space tele- scopes measuring photometric variability of the brightest stars in the sky. Due to the limited space onboard and the weight constraints, the CCD detectors are poorly shielded and suffer from proton impact. Shortly after the launch, various CCD defects emerged, producing various sources of impulsive noise in the images. In this paper, the methods of BRITE data-processing are described and their efficiency evaluated. The proposed algorithm, developed by the BRITE photometric team, consists of three main parts: (1) image classification, (2) image processing with aperture photometry and (3) tunable optimization of parameters. The presented pipeline allows one to achieve milli-magnitude precision in photometry. Some first scientific results of the mission have just been published.

CNES is involved in the Inter-Agency Space Debris Coordination Committee (IADC) and is observing space debris with two robotic ground based fully automated telescopes called TAROT and operated by the CNRS. An image processing algorithm devoted to debris detection in geostationary orbit is implemented in the standard pipeline. Nevertheless, this algorithm is unable to deal with debris tracking mode images, this mode being the preferred one for debris detectability. We present an algorithm improvement for this mode and give results in terms of false detection rate.

The newly developed smartphone application, named RINO, in this study allows measuring absolute dynamic displacements and processing them in real time using state-of-the-art smartphone technologies, such as high-performance graphics processing unit (GPU), in addition to already powerful CPU and memories, embedded high-speed/ resolution camera, and open-source computer vision libraries. A carefully designed color-patterned target and user-adjustable crop filter enable accurate and fast image processing, allowing up to 240fps for complete displacement calculation and real-time display. The performances of the developed smartphone application are experimentally validated, showing comparable accuracy with those of conventional laser displacement sensor.

In this paper, we introduce a fast and reasonably accurate methodology to determine patterning difficulty based on the fundamentals of optical image processing techniques to analyze the frequency content of design shapes which determines patterning difficulties via a computational patterning transfer function. In addition, with the help of Monte- Carlo random pattern generator, we use this flow to identify a set of difficult patterns that can be used to evaluate the design ease-of-manufacturability via a scoring methodology as well as to help with the optimization phases of post-tape out flows. This flow offers the combined merits of scoring-based criteria and model-based approach for early designs. The value of this approach is that it provides designers with early prediction of potential problems even before the rigorous model-based DFM kits are developed. Moreover, the flow establishes a bi-directional platform for interaction between the design and the manufacturing communities based on geometrical patterns.

Phase-space representations (PSRs) of a signal contain the signal’s spatial and local frequency information, providing a powerful tool to describe both coherent and incoherent light and its propagation in optical systems. Because of this, PSRs are widely employed in the characterization of imaging systems, phase retrieval, image processing, and, in particular, they are closely related to the fundamental theory of light field. In this manuscript, we review our recent works on phase-space optics, with particular focusing on its applications in computational imaging and optical image processing.

Most embedded image processing SoCs available on the market are highly optimized for typical consumer applications like video encoding/decoding, motion estimation or several image enhancement processes as used in DSLR or digital video cameras. For non-consumer applications, on the other hand, optimized embedded hardware is rarely available, so often PC based image processing systems are used. We show how a real time capable image processing system for a non-consumer application - namely polarization image data processing - can be efficiently implemented on an FPGA and multi-core DSP based embedded hardware platform.

Today, high-quality printing products like banknotes, stamps, or vouchers, are automatically checked by optical surface inspection systems. In a typical optical surface inspection system, several digital cameras acquire the printing products with fine resolution from different viewing angles and at multiple wavelengths of the visible and also near infrared spectrum of light. The cameras deliver data streams with a huge amount of image data that have to be processed by an image processing system in real time. Due to the printing industry’s demand for higher throughput together with the necessity to check finer details of the print and its security features, the data rates to be processed tend to explode. In this contribution, a solution is proposed, where the image processing load is distributed between FPGAs and digital signal processors (DSPs) in such a way that the strengths of both technologies can be exploited. The focus lies upon the implementation of image processing algorithms in an FPGA and its advantages. In the presented application, FPGAbased image-preprocessing enables real-time implementation of an optical color surface inspection system with a spatial resolution of 100 μm and for object speeds over 10 m/s. For the implementation of image processing algorithms in the FPGA, pipeline parallelism with clock frequencies up to 150 MHz together with spatial parallelism based on multiple instantiations of modules for parallel processing of multiple data streams are exploited for the processing of image data of two cameras and three color channels. Due to their flexibility and their fast response times, it is shown that FPGAs are ideally suited for realizing a configurable all-digital PLL for the processing of camera line-trigger signals with frequencies about 100 kHz, using pure synchronous digital circuit design.

Serial-parallel systems used to convert the image. The control of their work results with the need to solve coordination problem. The paper summarizes the model of coordination of resource allocation in relation to the task of synchronizing parallel processes; the genetic algorithm of coordination developed, its adequacy verified in relation to the process of parallel image processing.

In order to enhance the observability of multispectral image and improve the accuracy of camouflage effect evaluation based on multispectral photographic. A new method for multispectral image processing has been put forward. In the visible band, more spectral images for image fusion which are based on wavelet transformation respectively are chosen. The image information is enhanced. The visible light and near infrared band images are fused and introduced in three-channel of red, green and blue. The true color image is synthetized. While the detail of visible light image is enhanced, near infrared image information which is more interesting in camouflage evaluation is kept. Finally the fusion image are processed through histogram stretching and correlation method. The image color and luminance difference of each part is enhanced. The target recognition and camouflage effect evaluation is more advantageous. The experimental results proved that the method has a good effect.

To ensure the functionality, safe reliability and amenity of contact lens, the center thickness tc , diameter φt and base curves r0 are three key parameters to be measured. For purpose of measuring the parameters tc , φt and r0 of contact lens in a single compact instrument with high accuracy and efficiency, a new method based on digital image processing is proposed and examined. Firstly, aim at establishing appropriate measurement environment and obtaining the measuring images properly, the instrument structure is designed and implemented according to the characteristics of contact lenses. Several main environmental factors affects the accuracy has been considered, such as measuring medium and temperature. Secondly, the procedure of the geometric features location and coordinate conversion is analyzed and demonstrated. Thanks to the Canny-Zernike edge detection, the feature points in the image can be effectively positioned at sub-pixel level without increasing the hardware costs. In order to map the feature points’ pixel coordinates to world coordinates, the homography between the measuring plane and the imaging plane is estimated based on the pinhole imaging model. Lastly, with the specific obtained feature world coordinates, the distance formula and least squares curve fitting are used to calculate the object parameters. The instrument prototype and experimental analysis show that the proposed technique has advantages in terms of accuracy, volume reduction and efficiency over existing optical-mechanical techniques.

Benefit from the attractive features such as compact volume, thin and lightweight, the imaging systems based on microlens array have become an active area of research. However, current imaging systems based on microlens array have insufficient imaging quality so that it cannot meet the practical requirements in most applications. As a result, the post-digital image processing for image reconstruction from the low-resolution sub-image sequence becomes particularly important. In general, the post-digital image processing mainly includes two parts: the accurate estimation of the motion parameters between the sub-image sequence and the reconstruction of high resolution image. In this paper, given the fact that the preprocessing of the unit image can make the edge of the reconstructed high-resolution image clearer, the low-resolution images are preprocessed before the post-digital image processing. Then, after the processing of the pixel rearrange method, a high-resolution image is obtained. From the result, we find that the edge of the reconstructed high-resolution image is clearer than that without preprocessing.

We have studied the object silhouettes and surface direction through the stereo matching image processing to recognize the position, size and surface direction of the object. For this study we construct the pixel number change distribution of the HSI color component level, the binary component level image by the standard deviation threshold, the 4 directional pixels connectivity filter, the surface elements correspondence by the stereo matching and the projection rule relation. We note that the HSI color component level change tendency of the object image near the focus position is more stable than the HSI color component level change tendency of the object image over the unfocused range. We use the HSI color component level images near the fine focused position to extract the object silhouette. We extract the object silhouette properly. We find the surface direction of the object by the pixel numbers of the correspondence surface areas and the projection cosine rule after the stereo matching image processing by the characteristic areas and the synthesized colors. The epipolar geometry is used in this study because a pair of imager is arranged on the same epipolar plane. The surface direction detection results in the proper angle calculation. The construction of the object silhouettes and the surface direction detection of the object are realized.

Image processing techniques are needed to extract critical information pertinent to nano material characterization but the current processing methods are slow, expensive and labor intensive. There is a strong need to develop fast and reliable methods, enabling process control compatible automated processing of nano images. The authors believe specialized techniques are needed to address the challenges, and will discuss the recent development of nano image processing methods as well as the near- and medium-terms needs in the area of nano metrology and imaging. The authors will share their broad perspectives on this research direction.

Light efficiency uniformity is a very important parameter of medical rigid endoscope. This paper introduces a new system based on image processing to test the light efficiency uniformity of medical rigid endoscope. Employing an electric machinery to reduce the human intervention, so that the precision of measuring and automation degree are improved. We collect the image with a digital CCD camera and display it on the screen of a computer, which can avoid visual fatigue from the direct observation through the rigid endoscope. To perform the image processing on a computer, we adopt a self-developed image processing software, by which the test results can be obtained from PC itself. The processes of our self-developed image processing software include: gray-scale transformation, image pretreatment and image binarization; calculate the center and equivalent radius of the field of view (FOV); plot the curve, through which the ratio of edge and center in different field and the center axisymmetric of light efficiency can be both calculated. It concludes that the relative self-effect of illumination light luminosity is the foremost factor affecting the uniformity, and these endoscopes are all qualified with the max deviation of the center axisymmetric less than 20%. The results of our study prove that this system can test the light efficiency uniformity of medical rigid endoscope quickly, expediently and accurately, and it contains more information instead of only reflecting a particular field of the FOV, what’s more, it applies to different types, length and angles of view of medical rigid endoscope.

Optical image processing experiments can contribute to an understanding of optical diffraction and lens image formation. We are trying to discover a highly effective way of introducing lens imaging and related topics, light scattering, point sources, spatially coherent light, and image processing, in a laboratory-based holography-centered introductory optics course serving a mixture of physics, chemistry, and science education sophomores and juniors. As an early experiment in this course, a microscope slide bearing opaque stick-on letters forming a word such as PAL is back-lighted by a point source of laser light. The surround for the letter A is transparent, while the surround for the letters P and L is made translucent with Scotch MAGIC™ tape. A 20-cm focal length converging lens forms a bright image of PAL on a screen, and also an image of the laser point source in a (transform) plane between the lens and the screen. Students are startled when they see that they can choose to pass only the image of the letter “A” or only the images of “P” and “L,” by very simple manipulations in the transform plane. The interpretation of these experiments is challenging for some students, and the experiments can lead to a significant amount of discussion. Useful explanatory ray diagrams will be presented. Many demonstrations of optical image processing require long focal length lenses and precise manipulation of somewhat complex passing/blocking filters. In contrast these experiments are easy to set up and easy to perform. Students can fabricate the required objects in a matter of minutes. The use of zero-order laser light helps students discover the essential simplicity of the ideas underlying image processing. The simultaneous presence of both scattered (spatially incoherent) and not scattered (spatially coherent) laser light is thought provoking. Current explorations to further develop these and other closely-related experiments will also be described.