The identification of objects that blend in with their surroundings has long been a concern in fields including defence, wildlife monitoring, and surveillance due to the difficulty of detecting such objects. This find...
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The retail industry, marked by fierce competition and evolving consumer preferences, demands innovative approaches to boost customer satisfaction, drive sales, and streamline operations. This study presents a comprehe...
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In the present era enhancing the under image quality play a major role for research purpose and ocean exploration. The market for high-quality underwater images is expanding exponentially. Despite this, there are a nu...
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Increasing the speed of digital imageprocessing is an important problem in various fields. This paper proposes a way to increase the speed of 2D image filtering using the Winograd method. New algorithms for image pro...
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ISBN:
(纸本)9781665468282
Increasing the speed of digital imageprocessing is an important problem in various fields. This paper proposes a way to increase the speed of 2D image filtering using the Winograd method. New algorithms for imageprocessing based on the Winograd method have been proposed. The theoretical analysis results showed that an increase in the number of pixels in the resulting image fragment leads to a decrease in multiplications and the computational complexity of digital filtering. Winograd methods reduced multiplications to 4 times by increasing additions to 2.88 times using filter 3x3. The results obtained can be used in various imageprocessing fields that require real-time processing using modern microelectronic devices.
One of the primary problems in military surveillance systems is the safety of the visual data gathered by the sensor nodes installed in the different areas of the Visual sensor network and the remotely operated device...
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Technology is continuously changing our life. Day by day, it makes our life easy, but some challenges and issues exist. Counterfeit currency is one of them. It happens because of the production and circulation of curr...
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Deblurring high resolution remote sensing image is a very important problem in remote sensing research. In this paper, we propose a new deblurring algorithm for high-resolution remote sensing images (HSI) based on spa...
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In computer vision and imageprocessing, image deblurring is a crucial phase that attempts to restore the sharpness of the image and clarity of images that have been damaged due to motion blur, defocus, or other facto...
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Ultrasonography (US) has demonstrated many advantages in the detection, characterization, and monitoring of different diseases. Through high frequency probes, it is possible to visualize and characterize the anatomica...
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ISBN:
(纸本)9798350319439
Ultrasonography (US) has demonstrated many advantages in the detection, characterization, and monitoring of different diseases. Through high frequency probes, it is possible to visualize and characterize the anatomical layers, such as tissues, which can constitute a very helpful supporting tool in several procedures, e.g., surgeries. However, the visual identification of tissues in this type of image is still a challenge for some professionals. In this work, we evaluate deep learning (DL) segmentation algorithms for the tissue segmentation task in US. Moreover, we briefly assess whether their performance can be improved by including a crowdsourcing step. In order to perform the segmentation task, different segmentation models are trained, as posteriorly, the crowdsourcing step is included. The proposed approach is composed of the following steps: 1- automatic segmentation using a deep learning algorithm;2 - crowd evaluation and correction of the results. In order to perform step 1, different segmentation models are trained. The second step includes a visual interface where users can: a) validate the quality of the automatic segmentation;or b) correct the segmentation whether the DL result is inconsistent. All users are scored, denoting the quality of their annotations, considering the manual annotations provided by an expert for a small group of images. In order to evaluate the method and compare it to a DL algorithms alone, a total of 100 US images were used. Our experiments show that the inclusion of crowdsourcing significantly improved the performance of the tissue segmentation task compared to using the DL models alone. The performance of our method demonstrated the feasibility of applying this type of solution for the considered problem of segmenting tissues in US facial images. Moreover, the results suggest that this tool can be employed as an auxiliary tool in oral procedures.
The number of newly diagnosed cases of breast cancer exceeds 2.3 million annually worldwide. There is a severe lack of valid prognostic and predictive indicators for the clinical treatment of breast cancer patients at...
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