Image Processing
Goal
The goal of this project is to leverage Amazon Web Service's machine learning services to create a dataset that automatically adds and updates files on IEEE DataPort's S3 storage. Through this process, we sought to learn and demonstrate how an ongoing data collection script can create a shared living dataset by streaming data to our IEEE DataPort dataset storage. In the process, we also hoped to gain further insights into areas including:
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A long-standing problem in thermal imaging is the inherent assumption of a uniform and known emissivity across an entire image. Semantic segmentation of the materials in a thermal image can identify the pixel-wise emissivity, thus rectifying the spatially uniform emissivity assumption with no human intervention. We have created a multispectral thermal image dataset consisting of nine materials (acrylic, aluminum, bakelite, ceramic, cork, EVA, granite, maple, and silicone) at six different temperatures.
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— Medical image segmentation is a crucial aspect of medical image processing, and has been widely used in the detection and clinical diagnosis for brain, lung, liver, heart and other diseases. In this paper, we propose a novel multimodal mutual attention network, called MMAUNet, for medical image segmentation. MMA-UNet is divided into two parts. The first part obtains more highdimensional features by skip connection and improved network structure.
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Around from 12th century MODI script was used to write Indian languages as Marathi, Hindi, and Gujarati etc. It was used as administrative script from 17th century to mid of 19th century in Maharashtra state (India). At present, MODI script users are diminishing away, and countable persons can understand the MODI script. The preserved archaic historical MODI handwritten documents contained important and rare cultural, historic, and administrative kind of information which is usable in present-days.
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MODI script was used to write Indian languages as Marathi, Hindi, and Gujarati etc. from 12th century. From 17th century to mid of 19th century MODI was used as administrative script in Maharashtra state (India). Now a days, MODI script users are diminishing away, and countable persons can understand the MODI script. The archaic historical MODI handwritten documents contained important and rare cultural, historic, and administrative type of information which is usable in current era.
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Global Illumination (GI) is a strategy in computer graphics to add a certain degree of realism. Several approaches exist to achieve such a visual effect for computer-generated imagery. The most physically accurate approach is through conventional raytracing. It produces similar realistic results by trading-off time and computational-resource intensive, making them unsuitable for real-time usage. For more real-time usage scenarios, a set of faster algorithms exists that utilize post-processing on top of rasterization rather than performing ray-tracing.
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This experiment was implemented to collect infrared images of the coal and gangue samples at the temperature of 323.15 K. Additionally, it showed that distinguishing between coal and gangue samples is feasible, although the area, thickness, and surface conditions were changed at a constant temperature during the process of capturing the infrared images. The coal and gangue were randomly collected from the same mine. The random samples had different weights, shapes, areas, thicknesses, and surface conations.
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Visual storytelling refers to the manner of describing a set of images rather than a single image, also known as multi-image captioning. Visual Storytelling Task (VST) takes a set of images as input and aims to generate a coherent story relevant to the input images. In this dataset, we bridge the gap and present a new dataset for expressive and coherent story creation. We present the Sequential Storytelling Image Dataset (SSID), consisting of open-source video frames accompanied by story-like annotations.
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