Image Processing
Objective: Stereoelectroencephalography (SEEG) is an established invasive diagnostic technique for use in patients with drug-resistant focal epilepsy evaluated before resective epilepsy surgery. The factors that influence the accuracy of electrode implantation are not fully understood. Adequate accuracy prevents the risk of major surgery complications. Precise knowledge of the anatomical positions of individual electrode contacts is crucial for the interpretation of SEEG recordings and subsequent surgery.
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381 Views
Real-Micron dataset for real-world image super-resolution evalution
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11 Views
The dataset contains the processed snap shots from several news channels with commercial L bands and I bands that comes during the broadcasting of news and this dataset particularly focuses on the TV commercials and advertisements in L shped form. This dataset is useful for further analysis on this domain, detecting the L shaped band from news channels and further analysis on that which will be useful for news channels any other bradcastinh channels such as sports and also it will be helpful for the advertising agencies and companies.
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569 Views
Three exemplary knee bone models derived from ultrasound imaging and their respective magnetic resonance imaging reference.
Apart from the ground truth, a partial scan as accessable by ultrasound imaging as well as full bone model computed by a statistical shape model is provided.
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500 Views
zip and czip format
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52 Views
this is dataset for our paper: "Large-scale Benchmark for Uncooled Infrared Image Deblurring", submitted for IEEE SIgnal Processing Letters.
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267 Views
Accurate recognition of targets in the orchard environment is the key to vision perception for picking robots. Factors such as small, densely growing plum fruit targets and high occlusion lead to unsatisfactory recognition of plum fruit by vision algorithms. Therefore, this paper proposes an improved YOLOv5s model to detect highly occluded and dense plums in orchards. First, the backbone network of YOLOv5s is improved in this paper.
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33 Views
Automatic Modulation Classification (AMC) is a technique used to identify signal modulations in applications like cognitive radar, software-defined radio, and electronic warfare. With future communication systems like 6G operating at higher transmission frequencies than 5G, AMC algorithms need to be more complex yet suitable for embedded devices with limited resources. Although current AMC algorithms deliver high accuracy, they require substantial computing power, making them unsuitable for such devices.
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42 Views
CAPTCHA (Completely Automated Public Turing Tests to Tell Computers and Humans Apart). Only humans can successfully complete this test; current computer systems cannot. It is utilized in several applications for both human and machine identification. Text-based CAPTCHAs are the most typical type used on websites. Most of the letters in this protected CAPTCHA script are in English, it is challenging for rural residents who only speak their native tongues to pass the test.
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1712 Views
Development of the Complex-Valued (CV) deep learning architectures has enabled us to exploit the amplitude and phase components of the CV Synthetic Aperture Radar (SAR) data. However, most of the available annotated SAR datasets provide only the amplitude information (Only detected SAR data) and disregard the phase information. The lack of high-quality and large-scale annotated CV-SAR datasets is a significant challenge for developing CV deep learning algorithms in remote sensing.
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1977 Views