The Valluvan app is a language solution for native Tamil speakers. The system emphasizes the recognition of name boards, translation, and speech output to enhance communication and access to information. The app utili...
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Machine learning techniques have made significant progress in recent years in the field of healthcare by assisting clinicians in treatment interventions, identification, detection along with the classification of a va...
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The identification of anomalies (such as bone fractures or tendonitis in muscles and soft tissues) through imageprocessing and analysis techniques in Computed Tomography (CT) images is today of great importance to as...
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ISBN:
(纸本)9789893334362
The identification of anomalies (such as bone fractures or tendonitis in muscles and soft tissues) through imageprocessing and analysis techniques in Computed Tomography (CT) images is today of great importance to assist doctors and health professionals in making accurate diagnoses. The extraction of relevant information from the CT image is characterized by the calculation of gray level input image attributes. Statistical moments (SM) are calculated using the gray level distribution of an image and are therefore generally calculated from that image's histogram. These characteristics provide a statistical description of the relationship between different gray levels in the CT image. Haralick proposed a methodology for describing textures based on second order statistics, where characteristics are derived from co-occurrence matrices, which are constructed by counting different combinations of gray levels in an image according to certain directions. In this work, it is intended to automatically identify and extract regions in CT images based on textures as an aid for a quick and accurate diagnosis. CT images are first pre-processed for noise reduction and image enhancement, followed by the application of Haralick textures to segment and detect zones of interest. Classifiers trained on the Haralick invariant features showed good accuracy and performance. Despite the presence of low contrast and noise in some images, the proposed algorithms present promising results in the segmentation and automatic identification of regions of tomographic images, being an important contribution to support health professionals in the characterization of anomalies and their extension. Good results are expected for the next step of this work in the detection and segmentation of anomalies in CT images.
The predominant function of most facial analysis systems revolves around facial alignment and eye tracking, crucial for locating key facial landmarks in images or videos. While developers have access to various models...
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The image captioning is utilized to develop the explanations of the sentences describing the series of scenes captured in the image or picture forms. The practice of using image captioning is vast although it is a ted...
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Digital image restoration has become important for many image applications. Therefore, image Noise removal is an essential issue in an imageprocessing fields. In this paper, we presented a hybrid system, based on Sel...
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In this paper, we present the performance analysis of new Sub-band Improved Proportionate NLMS algorithm to improve the convergence rate of dispersive acoustical channel identification. Although the existing proportio...
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Recently, online teaching and learning have seen a notable uptrend in adoption, subsequently increasing interest in conducting online assessments. The limitation of remote online assessments lies in the challenge of s...
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ISBN:
(纸本)9783031648809;9783031648816
Recently, online teaching and learning have seen a notable uptrend in adoption, subsequently increasing interest in conducting online assessments. The limitation of remote online assessments lies in the challenge of supervising the individual being assessed. For this reason, many consider human supervision a superior method for maintaining the integrity of assessments. This paper introduces algorithm-driven techniques for the automated supervision of online assessment-takers by analysing system processes on their devices and conducting random photographic monitoring. These techniques, along with their associated algorithms, have been encapsulated into a proof of concept tool. The approach aims to deter assessment-takers from accessing unauthorised files on their devices during assessments and to instil a sense of being monitored. The system is built around two primary components: one that monitors process activity and another that analyses images captured through the assessment-taker's device webcam. Data collected through these methods are further analysed using facial recognition and additional algorithms to detect behaviours potentially indicative of cheating during the assessment. Initial testing of the proposed tool achieved a 96.3% accuracy rate in image analysis for identifying cheating behaviour. Moreover, university lecturers' evaluations strongly support the tool's potential to deter cheating, its effectiveness in detection, and its role in maintaining the integrity of online assessments. Future research is recommended to address the challenges identified with the proof of concept tool, with the objective of enhancing both the accuracy and the overall effectiveness of the proposed techniques.
In order to comply with the trend of intelligent visual communication, this study proposed an innovative visual communication scenario based on imageprocessingalgorithms. The framework aims to optimize traditional k...
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ISBN:
(数字)9798331505745
ISBN:
(纸本)9798331505752
In order to comply with the trend of intelligent visual communication, this study proposed an innovative visual communication scenario based on imageprocessingalgorithms. The framework aims to optimize traditional key technologies such as the image generation, editing, style transfer and image compression. First, as the foundation of visual communication, this study proposes a generative adversarial network model based on text semantic information for image generation and editing. The model achieves stable image generation and efficient editing from a theoretical level through paired training of text and image pairs. Secondly, for image style transfer, this study designed an improved VGG19 convolutional neural network. At the same time, the adaptive instance normalization technology was combined to optimize the effect of style transfer. Finally, in terms of image compression, the study proposed an improved generative adversarial network (REVISED-GAN) model. This model can dynamically adjust the compression error based on structured information to improve image compression efficiency. Through comparative tests, the proposed image style transfer and image compression algorithms have shown excellent performance in terms of structural similarity, image quality and compression ratio.
Spam SMS messages are a prevalent problem in today's world and have become a source of annoyance for users. This research study proposes a novel approach to detect SMS spam using Natural Language processing (NLP) ...
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