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For the history of the development of digital image processing and the content of the involved. Departure from the point of view of the advantages and development trends of digital image processing technology, and digital image processing technology in the specific application of the interferometer made a certain degree of analysis. In this paper, digital image processing technology in wide range of applications and significance of the interferometer, made some research, expect these research and analysis, for development of digital image processing technology and its further development in the interferometer, to have certain reference and guidance significance. [ABSTRACT FROM AUTHOR]

The article aims to introduce a gold-standard Pashto dataset and a segmentation app. The Pashto dataset consists of 300 line images and corresponding Pashto text from three selected books. A line image is simply an image consisting of one text line from a scanned page. To our knowledge, this is one of the first open access datasets which directly maps line images to their corresponding text in the Pashto language. We also introduce the development of a segmentation app using textbox expanding algorithms, a different approach to OCR segmentation. The authors discuss the steps to build a Pashto dataset and develop our unique approach to segmentation. The article starts with the nature of the Pashto alphabet and its unique diacritics which require special considerations for segmentation. Needs for datasets and a few available Pashto datasets are reviewed. Criteria of selection of data sources are discussed and three books were selected by our language specialist from the Afghan Digital Repository. The authors review previous segmentation methods and introduce a new approach to segmentation for Pashto content. The segmentation app and results are discussed to show readers how to adjust variables for different books. Our unique segmentation approach uses an expanding textbox method which performs very well given the nature of the Pashto scripts. The app can also be used for Persian and other languages using the Arabic writing system. The dataset can be used for OCR training, OCR testing, and machine learning applications related to content in Pashto. [ABSTRACT FROM AUTHOR]

Medical image can provide valuable information for preclinical research, clinical diagnosis, and treatment. As the widespread use of digital medical imaging, many researchers are currently developing medical image processing algorithms and systems in order to accommodate a better result to clinical community, including accurate clinical parameters or processed images from the original images. In this paper, we propose a web-based platform to present and process medical images. By using Internet and novel database technologies, authorized users can easily access to medical images and facilitate their workflows of processing with server-side powerful computing performance without any installation. We implement a series of algorithms of image processing and visualization in the initial version of Rayplus. Integration of our system allows much flexibility and convenience for both research and clinical communities. [ABSTRACT FROM AUTHOR]

Radiomics has been shown to have considerable potential and value in quantifying the tumor phenotype and predicting the treatment response. In most scenarios, the commercial and open-source software programs are available for quantitative analysis in medical images to streamline radiomics research. However, at this stage, most of these programs are local applications and require users to have experience in programming and software engineering, which clinicians usually do not have. Therefore, in this article, a web-based tool was proposed to flexibly support radiomics research workflow tasks. Radiomics in RayPlus requires zero installation, is easy to maintain, and accessible anywhere via any PC or MAC with an Internet connection. The system provides functions including multimodality image import and viewing, ROI definition, feature extraction, and data sharing. As a web application, it appears an effective way to multi-institution and multi-department collaborative radiomics research and moreover, its transparency, flexibility, and portability can greatly accelerate the pace of clinical data analysis. [ABSTRACT FROM AUTHOR]

The purpose was to develop a foot image capture and measurement system with web cameras (the 3-FIS) to provide reliable and valid foot anthropometric measures with efficiency comparable to that of the conventional method of using a handheld anthropometer. Eleven foot measures were obtained from 10 subjects using both methods. Reliability of each method was determined over 3 consecutive days using the intraclass correlation coefficient and root mean square error (RMSE). Reliability was excellent for both the 3-FIS and the handheld anthropometer for the same 10 variables, and good for the fifth metatarsophalangeal joint height. The RMSE values over 3 days ranged from 0.9 to 2.2 mm for the handheld anthropometer, and from 0.8 to 3.6 mm for the 3-FIS. The RMSE values between the 3-FIS and the handheld anthropometer were between 2.3 and 7.4 mm. The 3-FIS required less time to collect and obtain the final variables than the handheld anthropometer. The 3-FIS provided accurate and reproducible results for each of the foot variables and in less time than the conventional approach of a handheld anthropometer. [ABSTRACT FROM AUTHOR]

• A wrong (over or under) exposure leads to a bias in optical target pose determination. • Concentric circles targets allow detecting and compensating for any bias caused by exposure. • A rigorous sub-pixel edge detector is formulated based on a pixel-level image formation model. • A new algorithm uses geometric constraints of concentric circles to determine the exposure bias. • Experimental evaluation in a controlled environment confirms unbiased pose determination. Optical coded targets allow to determine the relative pose of a camera, on a metric scale, from one image only. Furthermore, they are easily and efficiently detected, opening to a wide range of applications in robotics and computer vision. In this work we describe the effect of pixel saturation and non-ideal lens Point Spread Function, causing the apparent position of the corners and the edges of the target to change as a function of the camera exposure time. This effect, which we call exposure bias, is frequent in over- or underexposed images and introduces a systematic error in the estimated camera pose. We propose an algorithm that is able to estimate and correct for the exposure bias exploiting specific geometric features of a common target design based on concentric circles. Through rigorous laboratory experiments carried out in a highly controlled environment, we demonstrate that the proposed algorithm is seven times more precise and three times more accurate in the target distance estimation than the algorithms available in the literature. [ABSTRACT FROM AUTHOR]

Abstract: Accurate local region description is a keypoint in many applications and has been the topic of lots of recent papers. Starting from the very accurate SIFT, most of the approaches exploit the local gradient information that suffers from several drawbacks. First it is unstable in case of severe geometry distortions, second it cannot be easily summarized in a compact way and third it is not designed to account vectorial color information. In this paper, we propose an alternative by designing compact descriptors that account both the colors present in the region and their spatial distribution. Each pixel being characterized by five coordinates, two in the image space and three in the color space, we try to evaluate affine transforms that allow to go from the spatial coordinates to the color coordinates and inversely. Obviously such kind of transform does not exist but we show that after applying it to the original coordinates, the resulted positions are both discriminative and invariant to many acquisition conditions. Hence, depending on the original space (image or color) and the destination space (color or image), we design different complementary descriptors. Their discriminative power and invariance properties are assessed and compared with the best color descriptors in the context of region matching and object classification. [Copyright &y& Elsevier]

With advanced mobile devices, the mobile applications of the high-definition display attract a lot of attentions nowadays. The existing image super-resolution methods are computationally inefficient for the high-definition display on the mobile devices. In this paper, we point out that the above critical issue deteriorates the display quality of the high-definition mobile devices. We propose an efficient and effective algorithm to reconstruct the high-resolution images for the mobile devices. Our algorithm outperforms previous approaches in not only smaller running time but also the higher quality of the super-resolution image reconstruction for the mobile devices. [ABSTRACT FROM AUTHOR]

Abstract: Image segmentation partitions an image into nonoverlapping regions, which ideally should be meaningful for a certain purpose. Thus, image segmentation plays an important role in many multimedia applications. In recent years, many image segmentation algorithms have been developed, but they are often very complex and some undesired results occur frequently. By combination of Fuzzy Support Vector Machine (FSVM) and Fuzzy C-Means (FCM), a color texture segmentation based on image pixel classification is proposed in this paper. Specifically, we first extract the pixel-level color feature and texture feature of the image via the local spatial similarity measure model and localized Fourier transform, which is used as input of FSVM model (classifier). We then train the FSVM model (classifier) by using FCM with the extracted pixel-level features. Color image segmentation can be then performed through the trained FSVM model (classifier). Compared with three other segmentation algorithms, the results show that the proposed algorithm is more effective in color image segmentation. [Copyright &y& Elsevier]

Image quality review is one of the challenging fields of digital image processing system. Measurement of visual quality is of elementary weight for abundant image and video processing applications, where the goal of quality assessment (QA) algorithms is to automatically judge the quality of images or videos in agreement with human quality judgments. The costing of image quality based on single strategy Human Vision System (HVS) may not very enough. We need some more dimensions. Full Reference method. Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Structural Content (SC)) may contribute to calculate efficient result to image quality measurements. However, it is not always possible to get the reference images to assess image quality. Human observers can easily recognize the distortion and degradation of image without referring to the original image. Therefore, there is absolutely necessary to develop objective quality assessment that correlates well with human perception without the reference image (No-Reference). [ABSTRACT FROM AUTHOR]

Changing image intensities causes problems for many computer vision applications operating in unconstrained environments. We propose generally applicable algorithms to correct for global differences in intensity between images recorded with a static or slowly moving camera, regardless of the cause of intensity variation. The proposed intensity correction is based on intensity-quotient estimation. Various intensity estimation methods are compared. Usability is evaluated with background classification as example application. For this application we introduced the PIPE error measure evaluating performance and robustness to parameter setting. Our approach retains local intensity information, is always operational and can cope with fast changes in intensity. We show that for intensity estimation, robustness to outliers is essential for dynamic scenes. For image sequences with changing intensity, the best performing algorithm (MofQ) improves foreground-background classification results up to a factor two to four on real data. [ABSTRACT FROM AUTHOR]

Rotational symmetry is important for many applications in computer graphics, vision, and image processing. However, it remains difficult to design an effective algorithm for automatic symmetry recognition. In this paper, we present a rotational symmetry detection algorithm, which is easy to use and can determine both the center and the radius of the rotational symmetry supporting region without human interaction. Our algorithm is derived from frieze-expansions approach and improved through a radius-based expansion idea. Multiresolution pyramid is used to accelerate this detection process. We also discuss a solution to deal with rotational symmetry detection under slight affine transformation. Experimental results show that the method is effective for most nature images with rotational symmetry. [ABSTRACT FROM AUTHOR]

The contours and segments of objects in digital images have many important applications. Contour extractions of gray images can be converted into contour extractions of binary images. This paper presents a novel contour-extraction algorithm for binary images and provides a deduction theory for this algorithm. First, we discuss the method used to construct convex hulls of regions of objects. The contour of an object evolves from a convex polygon until the exact boundary is obtained. Second, the projection methods from lines to objects are studied, in which, a polygon iteration method is presented using linear projection. The result of the iter-ation is the contour of the object region. Lastly, addressing the problem that direct projections probably cannot find correct projection points, an effective discrete ray-projection method is presented. Comparisons with other contour deformation algorithms show that the algorithm in the present paper is very robust with respect to the shapes of the object regions. Numerical tests show that time consumption is primarily concentrated on convex hull computation, and the implementation efficiency of the program can satisfy the requirement of interactive operations. [ABSTRACT FROM AUTHOR]

In recent times, the use of computer aided diagnosis for detection of Glaucoma from fundus images has been prevalent. In the proposed work, a cloud based system is proposed for automatic and real-time screening of Glaucoma with the use of automatic image processing techniques. The proposed system offers scalability to the developers and easy accessibility to the consumers. The proposed system is device and location independent. The input digital image is analyzed and a comprehensive diagnostic report is generated consisting of detailed analysis of indicative medical parameters like optic-cup-to-disc ratio, optic neuro-retinal rim, ISNT rules making the report informative and clinically significant. With recent advances in the field of communication technologies, the internet facilities are available that make the proposed system an efficient and economical method for initial screening and offer preventive and diagnostic steps in early disease intervention and management. The proposed system can perform an initial screening test in an average time of 6 s on high resolution fundus images. The proposed system has been tested on a fundus database and an average sensitivity of 93.7% has been achieved for Glaucoma cases. In places where there is scarcity of trained ophthalmologists and lack of awareness of such diseases, the cloud based system can be used as an effective diagnostic assistive tool. [ABSTRACT FROM AUTHOR]

Background: Image registration is to produce an entire scene by aligning all the acquired image sequences. A registration algorithm is necessary to tolerance as much as possible for intensity and geometric variation among images. However, captured image views of real scene usually produce unexpected distortions. They are generally derived from the optic characteristics of image sensors or caused by the specific scenes and objects. Methods and Findings: An analytic registration algorithm considering the deformation is proposed for scenic image applications in this study. After extracting important features by the wavelet-based edge correlation method, an analytic registration approach is then proposed to achieve deformable and accurate matching of point sets. Finally, the registration accuracy is further refined to obtain subpixel precision by a feature-based Levenberg-Marquardt (FLM) method. It converges evidently faster than most other methods because of its feature-based characteristic. Conclusions: We validate the performance of proposed method by testing with synthetic and real image sequences acquired by a hand-held digital still camera (DSC) and in comparison with an optical flow-based motion technique in terms of the squared sum of intensity differences (SSD) and correlation coefficient (CC). The results indicate that the proposed method is satisfactory in the registration accuracy and quality of DSC images. [ABSTRACT FROM AUTHOR]

Abstract: Many factors influence the measurement uncertainty when using computed tomography for dimensional metrology applications. One of the most critical steps is the surface extraction phase. An incorrect determination of the surface may significantly increase the measurement uncertainty. This paper presents an edge detection method for the surface extraction based on a 3D Canny algorithm with sub-voxel resolution. The advantages of this method are shown in comparison with the most commonly used technique nowadays, i.e. the local threshold definition. Both methods are applied to reference standards and industrial parts and the comparison of the uncertainties obtained by both methods is presented. [Copyright &y& Elsevier]

Abstract: Remote sensing image becomes a research focus on the earth observation, military reconnaissance and the missile field of image guided the end of extensive application of remote sensing image recognition. In this paper, the satellite remote sensing image data fusion techniques can detect and study the background from the remote sensing images to identify the airport runway, port and other linear goals and aircraft, as well as ships of small goals. Its purpose is for the parallel line Hough transformation detection of accelerated algorithm that is based on runways, ports and other linear large target detection location; it presents a linear phase analysis based on the port edge recognition algorithm. After the verification, the proof is the robustness of the algorithm and practice. [Copyright &y& Elsevier]

Abstract: Automatic registration of optical and IR images is a crucial step towards constructing an automated irrigation control system where plant water information is sensed via thermal imaging. The scene of the IR image is assumed to be completely included in the optical image and the alignment between the common scene in the two images may involve translation and rotation by a small angle, though a small scale difference may also be present. This automatic registration of data from two quite different, non-rigid imaging regimes presents several challenges, which cannot be overcome using common image processing techniques. In this paper, a fully automatic image registration algorithm for the alignment of optical and IR image pairs is described, where Pearson''s cross-correlation between a pair of images serves as the similarity measure. A computationally efficient algorithm is designed and packaged as a software application. This work provides an intervention free process for extracting plant water stress information which can be fed into an automated irrigation scheduling program. The proposed algorithm is justified by the comparison of its registration performance with that of other potential algorithm techniques using several experimental data collections. Our results demonstrate the effectiveness of the proposed algorithm and efficiency of its application to the registration of IR and optical images. [Copyright &y& Elsevier]