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Все статьи: 2346
Статья научная
In this paper, an automatic algorithm aimed at volumetric segmentation of acute ischemic stroke lesion in non-contrast computed tomography brain 3D images is proposed. Our deep-learning approach is based on the popular 3D U-Net convolutional neural network architecture, which was modified by adding the squeeze-and-excitation blocks and residual connections. Robust pre-processing methods were implemented to improve the segmentation accuracy. Moreover, a special patches sampling strategy was used to address the large size of medical images and class imbalance and to stabilize neural network training. All experiments were performed using five-fold cross-validation on the dataset containing non-contrast computed tomography volumetric brain scans of 81 patients diagnosed with acute ischemic stroke. Two radiology experts manually segmented images independently and then verified the labeling results for inconsistencies. The quantitative results of the proposed algorithm and obtained segmentation were measured by the Dice similarity coefficient, sensitivity, specificity and precision metrics. The suggested pipeline provides a Dice improvement of 12.0 %, sensitivity of 10.2 % and precision 10.0 % over the baseline and achieves an average Dice of 62.8 ± 3.3 %, sensitivity of 69.9 ± 3.9 %, specificity of 99.7 ± 0.2 % and precision of 61.9 ± 3.6 %, showing promising segmentation results.
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Статья научная
This paper describes the use of some color spaces in JPEG image compression algorithm and their impact in terms of image quality and compression ratio, and then proposes adaptive color space models (ACSM) to improve the performance of lossy image compression algorithm. The proposed ACSM consists of, dominant color analysis algorithm and YCoCg color space family. The YCoCg color space family is composed of three color spaces, which are YCcCr, YCpCg and YCyCb . The dominant colors analysis algorithm is developed which enables to automatically select one of the three color space models based on the suitability of the dominant colors contained in an image. The experimental results using sixty test images, which have varying colors, shapes and textures, show that the proposed adaptive color space model provides improved performance of 3 % to 10 % better than YCbCr, YDbDr, YCoCg and YCgCo-R color spaces family. In addition, the YCoCg color space family is a discrete transformation so its digital electronic implementation requires only two adders and two subtractors, both for forward and inverse conversions.
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Adjusting videoendoscopic 3D reconstruction results using tomographic data
Статья научная
Videoendoscopic and tomographic research are the two leading medical imaging solutions for detecting, classifying and characterizing a wide array of pathologies and conditions. However, source information from these types of research is very different, making it hard to cross-correlate them. The paper proposes a novel algorithm for combining results of videoendoscopic and tomographic imaging data based on 3D surface reconstruction methods. This approach allows to align separate parts of two input 3D surfaces: surface obtained by applying bundle adjustment-based 3D surface reconstruction algorithm to the endoscopic video sequence, and surface reconstructed directly from separate tomographic cross-section slice projections with regular density. Proposed alignment method is based on using local feature extractor and descriptor algorithms by applying them to the source surface normal maps. This alignment allows both surfaces to be equalized and normalized relative to each other. Results show that such an adjustment allows to reduce noise, correct reconstruction artifacts and errors, increase representative quality of the resulting model and establish correctness of the reconstruction for hyperparameter tuning.
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Advanced Hough-based method for on-device document localization
Статья научная
The demand for on-device document recognition systems increases in conjunction with the emergence of more strict privacy and security requirements. In such systems, there is no data transfer from the end device to a third-party information processing servers. The response time is vital to the user experience of on-device document recognition. Combined with the unavailability of discrete GPUs, powerful CPUs, or a large RAM capacity on consumer-grade end devices such as smartphones, the time limitations put significant constraints on the computational complexity of the applied algorithms for on-device execution. In this work, we consider document location in an image without prior knowledge of the document content or its internal structure. In accordance with the published works, at least 5 systems offer solutions for on-device document location. All these systems use a location method which can be considered Hough-based. The precision of such systems seems to be lower than that of the state-of-the-art solutions which were not designed to account for the limited computational resources. We propose an advanced Hough-based method. In contrast with other approaches, it accounts for the geometric invariants of the central projection model and combines both edge and color features for document boundary detection. The proposed method allowed for the second best result for SmartDoc dataset in terms of precision, surpassed by U-net like neural network. When evaluated on a more challenging MIDV-500 dataset, the proposed algorithm guaranteed the best precision compared to published methods. Our method retained the applicability to on-device computations.
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Aerial vehicles detection and recognition for UAV vision system
Статья научная
This article focuses on aerial vehicle detection and recognition by a wide field of view monocular vision system that can be installed on UAVs (unmanned aerial vehicles). The objects are mostly observed on the background of clouds under regular daylight conditions. The main idea is to create a multi-step approach based on a preliminary detection, regions of interest (ROI) selection, contour segmentation, object matching and localization. The described algorithm is able to detect small targets, but unlike many other approaches is designed to work with large-scale objects as well. The suggested algorithm is also intended to recognize and track the aerial vehicles of specific kind using a set of reference objects defined by their 3D models. For that purpose a computationally efficient contour descriptor for the models and the test objects is calculated. An experimental research on real video sequences is performed. The video database contains different types of aerial vehicles: airplanes, helicopters, and UAVs. The proposed approach shows good accuracy in all case studies and can be implemented in onboard vision systems.
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Agricultural plant hyperspectral imaging dataset
Статья научная
Detailed automated analysis of crop images is critical to the development of smart agriculture and can significantly improve the quantity and quality of agricultural products. A hyperspectral camera potentially allows to extract more information about the observed object than a conventional one, so its use can help in solving problems that are difficult to solve with conventional methods. Often, predictive models that solve such problems require a large dataset for training. However, sufficiently large datasets of hyperspectral images of agricultural plants are not currently publicly available. Therefore, we present a new dataset of hyperspectral images of plants in this paper. This dataset can be accessed via URL https://pypi.org/project/HSI-Dataset-API/. It contains 385 hyperspectral images with a spatial resolution of 512 by 512 pixels and spectral resolution of 237 spectral bands. The images were captured in the summer of 2021 in Samara and Novocherkassk (Russia) using Offner based Imaging Hyperspectrometer of our own production. The article demonstrates the work of some basic approaches to the analysis of hyperspectral images using the dataset and states problems for further solving.
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Статья научная
Based on the use of methods for solving the inverse problem of heat conduction, we developed an algorithm for calculating the power density distribution of the laser beam to create a desired thermal effect on technological objects. It was shown that the redistribution of power density of moving distributed surface heat sources can adjust the temperature distribution in the treated zone. The results of thermal processes calculation show the ability of the developed algorithm to create a more uniform temperature field across the width of the heat affected zone. Equalization of maximum temperature values is achieved in the center and on the periphery of the heat affected zone with an increase in the width of the regions, where required temperature is reached. The application of diffractive optical elements gives an opportunity to obtain the required properties of treated materials in the heat affected zone. The research performed has enabled parameters of the temperature field in chrome-nickel-molybdenum steel to be adjusted for laser heat treatment. In addition to achieving uniform temperature conditions across the width of the heat affected zone, the proposed approach allows the increase of the width of the isotherms of the temperature fields; this provides an opportunity to process a larger area per unit time at the same laser beam power.
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Algorithm for choosing the best frame in a video stream in the task of identity document recognition
Статья
During the process of document recognition in a video stream using a mobile device camera, the image quality of the document varies greatly from frame to frame. Sometimes recognition system is required not only to recognize all the specified attributes of the document, but also to select final document image of the best quality. This is necessary, for example, for archiving or providing various services; in some countries it can be required by law. In this case, recognition system needs to assess the quality of frames in the video stream and choose the "best" frame. In this paper we considered the solution to such a problem where the "best" frame means the presence of all specified attributes in a readable form in the document image. The method was set up on a private dataset, and then tested on documents from the open MIDV-2019 dataset. A practically applicable result was obtained for use in recognition systems.
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Статья
An algorithm for post-processing of the grayscale 3D computed tomography (CT) images of porous structures with the automatic selection of filtering parameters is proposed. The determination of parameters is carried out on a representative part of the image under analysis. A criterion for the search for optimal filtering parameters based on the count of "levitating stone" voxels is described. The stages of CT image filtering and its binarization are performed sequentially. Bilateral and anisotropic diffuse filtering is implemented; the Otsu method for unbalanced classes is chosen for binarization. Verification of the proposed algorithm was carried out on model data. To create model porous structures, we used our image generator, which implements the function of anisotropic porous structures generation. Results of the post-processing of real CT images containing noise and reconstruction artifacts by the proposed method are discussed.
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Статья научная
Image inpainting is a process of filling missing and damaged parts of image. By using the Mumford-Shah image model, the image inpainting can be formulated as a constrained optimization problem. The Mumford-Shah model is a famous and effective model to solve the image inpainting problem. In this paper, we propose an adaptive image inpainting method based on multiscale parameter estimation for the modified Mumford-Shah model. In the experiments, we will handle the comparison with other similar inpainting methods to prove that the combination of classic model such the modified Mumford-Shah model and the multiscale parameter estimation is an effective method to solve the inpainting problem.
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Статья научная
One of the problems solved by analyzing the data of long-term Video EEG monitoring is the differentiation of epileptic and artifact events. For this, not only multichannel EEG signals are used, but also video data analysis, since traditional methods based on the analysis of EEG wavelet spectrograms cannot reliably distinguish an epileptic seizure from a chewing artifact. In this paper, we propose an algorithm for detecting artifact events based on a joint analysis of the level of the optical flow and the ridges of wavelet spectrograms. The preliminary results of the analysis of real clinical data are given. The results show the possibility in principle of reliable distinguishing non-epileptic events from epileptic seizures.
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Статья научная
Many different filters can be used to reduce multiplicative speckle noise on radar images. Most of these filters have some parameters whose values influence the result of filtering. Finding optimal values of such parameters may be a non-trivial task. In this paper, a formal automated method for finding optimal parameters of speckle noise reduction filters is proposed. Using a specially designed test image, optimal parameters for the most commonly used filters were found using several image quality assessment metrics, including the Structural Similarity Index (SSIM) and Gradient Magnitude Similarity Deviation (GMSD). The use of filters with optimal parameters allows processing (detection, segmentation, etc.) of radar images with minimal influence of speckle noise.
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An efficient algorithm for non-rigid object registration
Статья научная
An efficient algorithm for registration of two non-rigid objects based on geometrical transformation of the template object to target object is proposed. The transformation is considered as warping of the template onto the target. To choose the most suitable transformation from all possible warps, a registration algorithm should satisfy deformation constraints referred to as regularization of non-rigid objects. In this work, we use variational functionals for affine transformations. With the help of computer simulation, the proposed method for searching the optimal geometrical transformation is compared with that of common algorithms.
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An efficient algorithm for overlapping bubbles segmentation
Статья научная
Image processing is an effective method for characterizing various two-phase gas/liquid flow systems. However, bubbly flows at a high void fraction impose significant challenges such as diverse bubble shapes and sizes, large overlapping bubble clusters occurrence, as well as out-of-focus bubbles. This study describes an efficient multi-level image processing algorithm for highly overlapping bubbles recognition. The proposed approach performs mainly in three steps: overlapping bubbles classification, contour segmentation and arcs grouping for bubble reconstruction. In the first step, we classify bubbles in the image into a solitary bubble and overlapping bubbles. The purpose of the second step is overlapping bubbles segmentation. This step is performed in two subsequent steps: at first, we classify bubble clusters into touching and communicating bubbles. Then, the boundaries of communicating bubbles are split into segments based on concave point extraction. The last step in our algorithm addresses segments grouping to merge all contour segments that belong to the same bubble and circle/ellipse fitting to reconstruct the missing part of each bubble. An application of the proposed technique to computer generated and high-speed real air bubble images is used to assess our algorithm. The developed method provides an accurate and computationally effective way for overlapping bubbles segmentation. The accuracy rate of well segmented bubbles we achieved is greater than 90 % in all cases. Moreover, a computation time equal to 12 seconds for a typical image (1 Mpx, 150 overlapping bubbles) is reached.
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An efficient block-based algorithm for hair removal in dermoscopic images
Статья научная
Hair occlusion in dermoscopy images affects the diagnostic operation of the skin lesion. Segmentation and classification of skin lesions are two major steps of the diagnostic operation required by dermatologists. We propose a new algorithm for hair removal in dermoscopy images that includes two main stages: hair detection and inpainting. In hair detection, a morphological bottom-hat operation is implemented on Y-channel image of YIQ color space followed by a binarization operation. In inpainting, the repaired Y-channel is partitioned into 256 non-overlapped blocks and for each block, white pixels are replaced by locating the highest peak, using a histogram function and a morphological close operation. The proposed algorithm reports a true positive rate (sensitivity) of 97.36 %, a false positive rate (fall-out) of 4.25 %, and a true negative rate (specificity) of 95.75 %. The diagnostic accuracy achieved is recorded at a high level of 95.78 %.
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An improved gray-scale transformation method for pseudo-color image enhancement
Статья научная
Image enhancement is a very important process of image preprocessing and it plays a critical role in the improvement of image quality and the follow-up image analysis, which makes the research of image enhancement algorithm a hot research field. Image enhancement not only needs to strengthen image determination and recognition, but also needs to avoid the consequential color distortion. Pseudo-color enhancement is the technique to map different gray scales of a black-and-white image into a color image. As humans have extremely strong ability in distinguishing different colors visually and relatively weak capacity in discriminating gray scales, so, color the gray-scale changes which cannot be differentiated by human eyes so that they can tell them apart. The mapping function in conventional gray-scale transform method is not working well in dark and low-contrast images. So, this paper comes up with an improved gray-scale transformation algorithm. This algorithm can achieve the enhancement, preserve the image colors, process dark and low-contrast images, reinforce the enhancement and improve the blocking effect. The experiment proves that the enhanced image obtained by the method of this paper can have improved average brightness, natural colors and more detail information and it has good application value.
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An improved video watermarking algorithm with extraction using a mobile device camera
Статья научная
The use of screen-capture-resistant digital watermarks is a promising way to store information invisibly in a video stream for later retrieval by the user using a smartphone camera. However, the development of algorithms that implement this scenario is associated with the problem of balancing between the imperceptibility of embedding and robustness. A serious problem is the extraction of watermarks using a mobile device. Most people use the vertical positioning of the smartphone when shooting, which excludes the possibility of only marked video sequences entering the frame. The extraction algorithm first finds the screen area in the image and then extracts the watermark under various distortion conditions. This study proposes an approach to improve the efficiency of the algorithm for embedding digital watermarks into video data based on rectangular patterns, which provides resistance to screen shooting. The proposed approach to increasing the embedding imperceptibility provided an increase in the PSNR and SSIM values by 17.18 % and 7.90 %, respectively. The use of a neural network at the extraction stage reduced the BER value by 64.64 %.
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Analysis of a robust edge detection system in different color spaces using color and depth images
Статья научная
Edge detection is very important technique to reveal significant areas in the digital image, which could aids the feature extraction techniques. In fact it is possible to remove un-necessary parts from image, using edge detection. A lot of edge detection techniques has been made already, but we propose a robust evolutionary based system to extract the vital parts of the image. System is based on a lot of pre and post-processing techniques such as filters and morphological operations, and applying modified Ant Colony Optimization edge detection method to the image. The main goal is to test the system on different color spaces, and calculate the system’s performance. Another novel aspect of the research is using depth images along with color ones, which depth data is acquired by Kinect V.2 in validation part, to understand edge detection concept better in depth data. System is going to be tested with 10 benchmark test images for color and 5 images for depth format, and validate using 7 Image Quality Assessment factors such as Peak Signal-to-Noise Ratio, Mean Squared Error, Structural Similarity and more (mostly related to edges) for prove, in different color spaces and compared with other famous edge detection methods in same condition...
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Analysis of logistics distribution path optimization planning based on traffic network data
Статья
With the development of economy, the distribution problem of logistics becomes more and more complex. Based on the traffic network data, this study analyzed the vehicle routing problem (VRP), designed a dynamic vehicle routing problem with time window (DVRPTW) model, and solved it with genetic algorithm (GA). In order to improve the performance of the algorithm, the genetic operation was improved, and the output solution was further optimized by hill climbing algorithm. The analysis of example showed that the improved GA algorithm had better performance in path optimization planning, the total cost of planning results was 31.44 % less than that of GA algorithm, and the total cost of planning results increased by 11.48 % considering the traffic network data. The experimental results show that the improved GA algorithm has good performance and can significantly reduce the cost of distribution and that research on VRP based on the traffic network data is more in line with the actual situation of logistics distribution, which is conducive to the further application of the improved GA algorithm in VRP.
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Статья научная
Angular characteristics of a dynamic hologram, recorded by a "fan" of plane waves in a reversible photochromatic medium, are studied under various correlations between the intensities of these waves. It is shown that the maximum increase of the relative diffraction efficiency with the increase of the angle between the reference and object waves is observed for thin holograms. There is such a PCM thickness beginning from which the relative dependence of the hologram diffraction efficiency upon the angle ceases to depend upon the PCM thickness, reaching some maximum distribution. Coincidence is shown of the angular characteristics of the holograms, recorded under the condition that the reference wave intensity is much greater than the object wave intensity, and of the holograms, recorded by a "fan" of plane waves of equal intensity. The hologram diffraction efficiency change is studied under the readout wave deviation from the Bragg angle. The reversibility of photochemical transformations in a number of photochromatic media (PCM) allows them to be used as non-linear media for a dynamic hologram recording [1-4]. In the works, accomplished up to now, on studying the dynamic hologram diffraction efficiency (HDE) in reversible PCM it has been considered that hologram recording is carried out by two plane waves, the surfaces of equal intensity of the recorded interference grating being perpendicular to the photochromatic layer faces [5-6]. In the present work the angular HDE dependence is investigated, knowing which, one can estimate the possibility of recording in such media the holograms of complex spatially modulated fields.
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