Computer Vision
In agriculture, the development of early treatment techniques for plant leaf diseases can be significantly enhanced by employing precise and rapid automatic detection methods. Within this realm of research, two common scenarios encountered in real field cases are the identification of different severity stages of diseases and the detection of multiple pathogens simultaneously affecting a single plant leaf. One major challenge faced in this area is the lack of publicly available datasets that contain images captured under these specific conditions.
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106 Views
Dronescape presents a dataset comprising 25 drone videos showcasing vast areas filled with trees, rivers, and mountains. The dataset includes two subsets: 25 videos with tree segmentation and 25 videos without tree segmentation, offering diverse perspectives on the presence and absence of segmented tree regions. The dataset focuses on highlighting the regions containing trees using the SAM (Segment Anything Model) and Track Anything library. Video object tracking and segmentation techniques are utilized to track the regions of trees throughout the dataset.
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613 Views
The morphological characteristics of skeletal muscles, such as fascicle orientation, fascicle length, and muscle thickness, contain valuable mechanical information that aids in understanding muscle contractility and excitation due to commands from the central nervous system. Ultrasound (US) imaging, a non-invasive measurement technique, has been employed in clinical research to provide visualized images that capture morphological characteristics. However, accurately and efficiently detecting the fascicle in US images is challenging.
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185 Views
As a hot research topic, there are many related datasets for occlusion detection. Due to the different scenarios and definitions of occlusion for different tasks, there are significant differences between different occlusion detection datasets, making existing datasets difficult to apply to the video shot occlusion detection task. To this end, we contribute the first large-scale video shot occlusion detection dataset, namely VSOD, which serves as a benchmark for evaluating the performance of shot occlusion detection methods.
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449 Views
The HQA1K dataset was developed for assessing the quality of Computer Generated Holography (CGH) image renderings based on direct human input.
HQA1K is comprised of 1,000 pairs of natural images matched to simulated CGH renderings of various quality levels. The result is a diverse set of data for evaluating image quality algorithms and models.
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723 Views
This Dataset used a non-invasive blood group prediction approach using deep learning. Rapid and meticulous prediction of blood type is a major step during medical emergency before supervising the red blood cell, platelet, and plasma transfusion. Any small mistake during transfer of blood can cause death. In conventional pathological assessment, the blood test is conducted using automated blood analyser; however, it results into time taking process.
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2104 Views
This dataset was acquired during the dissertation entitled "Optical Camera Communications and Machine Learning for Indoor Visible Light Positioning". This work was carried out in the academic year 2020/2021 at the Instituto de Telecomunicações in Aveiro in the scope of the Integrated Master in Electronics and Telecommunications Engineering at the Department of Electronics, Telecommunication and Informatics of the University of Aveiro.
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201 Views
Sign language correctness discrimination (SLCD) dataset is collected for sign language teaching. Different from general sign language recognition datasets, SLCD dataset has two kind labels of sign language category and standardization category at the same time. The standardization category is to describe action correctness of the same sign language made by students. The SLCD dataset videos in this paper are obtained by camera. 76 students are recruited to collect sign language actions.
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451 Views
N-WLASL dataset is a synthetic event-based dataset comprising 21,093 samples across 2,000 glosses. The dataset was collected using an event camera to shoot toward an LCD monitor. The monitor plays video frames from WLASL, the largest public word-level American Sign Language dataset. We use the event camera DAVIS346 with a resolution of 346x260 to record the display. The video resolution of WLASL is 256x256 and the frame rate is 25Hz. To ensure accurate recording of the display, we have implemented three video pre-processing procedures using the python-opencv and dv packages in Python.
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368 Views
Indian Rice Disease dataset (IRDD) contains rice leaf images of two classes namely BrownSpot and Healthy. The images are taken under various lightning conditions. Some images contain dew drops on the leaves. The rice leaf images are gatherd from fields in West Bengal, India. These images have been taken using smartphone camera by the project team members of IIIT Kalyani and IIT Kharagpur. The images are annotated and verified by the domain experts. This datset is a part of the project entitled "AI for Agriculture and Food Sustainability" funded by MeitY, Govt of India.
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108 Views