Image Processing
Human pose estimation has applications in numerous fields, including action recognition, human-robot interaction, motion capture, augmented reality, sports analytics, and healthcare. Many datasets and deep learning models are available for human pose estimation within the visible domain. However, challenges such as poor lighting and privacy issues persist. These challenges can be addressed using thermal cameras; nonetheless, only a few annotated thermal human pose datasets are available for training deep learning-based human pose estimation models.
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330 Views
Resistance training with elastic bands has been proven to effectively enhance muscle performance, making it an important component of strength and fitness training. However, assessing the intensity of resistance training typically requires large equipment such as isokinetic dynamometers or complex methods like muscle electromyography.
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37 Views
Visible and infrared DoFP images. Visible DoFP images were taken by the North Guangwei UMC4A-PU0A Micro DoFP LWIR polarization imager, which consists of an array of wire-grid micro-polarizers, with a resolution of 640 × 512 and a 14 bits depth. Infrared DoFP images were taken by Daheng Imaging MER2-503-36U3M POL DoFP visible polarization imager, which employs a monochromatic quad-polarizer array at a resolution of 2448×2048, an 8 bits depth and a frame rate of 36 frames/s.
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29 Views
This dataset consists of MRI images of brain tumors, specifically curated for tasks such as brain tumor classification and detection. The dataset includes a variety of tumor types, including gliomas, meningiomas, and glioblastomas, enabling multi-class classification. Each MRI scan is labeled with the corresponding tumor type, providing a comprehensive resource for developing and evaluating machine learning models for medical image analysis. The data can be used to train deep learning algorithms for brain tumor detection, aiding in early diagnosis and treatment planning.
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1127 Views
Accurately detecting power line defects under diverse weather conditions is crucial for ensuring power grid reliability and safety. Existing power line inspection datasets, while valuable, often lack the diversity needed for training robust machine learning models, particularly for adverse weather scenarios like fog, rain, and nighttime conditions. This paper addresses this limitation by introducing a novel framework for generating synthetic power line images under diverse weather conditions, thereby enhancing the diversity and robustness of power line inspection systems.
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205 Views
This dataset addresses the challenge of limited vocal recordings available in secondary datasets, particularly those that predominantly feature foreign accents and contexts. To enhance the accuracy of our solution tailored for Sri Lankans, we employed primary data-gathering methods.
The dataset comprises vocal recordings from a sample population of youth. Participants were instructed to read three specific sentences designed to capture a range of vocal tones:
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172 Views
The Facial Expression Dataset (Sri Lankan) is a culturally specific dataset created to enhance the accuracy of emotion recognition models in Sri Lankan contexts. Existing datasets, often based on foreign samples, fail to account for cultural differences in facial expressions, affecting model performance. This dataset bridges that gap, using high-quality data sourced from over 100 video clips of professional Sri Lankan actors to ensure expressive and clear facial imagery.
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405 Views
Image super-resolution (SR) has been an active research problem which has recently received renewed interest due to the introduction of new technologies such as deep learning. However, the lack of suitable criteria to evaluate the SR performance has hindered technology development. In this paper, we fill a gap in the literature by providing the first publicly available database as well as a new image quality assessment (IQA) method specifically designed for assessing the visual quality of super-resolved images (SRIs).
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97 Views
In recent years, the development of driver assistance technology has become a major focus in the automotive industry, particularly in enhancing road detection systems with informative features about the driving environment. These systems aim to provide navigation and improve driving safety. It is crucial for these systems to accurately recognize and understand road environments, especially marked and unmarked roads.
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669 Views
Image denoising is an important algorithm in ASIC real-time image processing. Research has found that after cascaded spatial and temporal denoising, video images still exhibit patches and structural noise. To reduce the noise of this type while considering factors such as hardware resource overhead in ASIC implementation, this paper proposes a multi-layer adaptive threshold denoising method based on Non-Local Mean algorithm and pyramid framework.
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47 Views