Image Processing
This data set contains:
- Training dataset: 271 CT-scans of inner ears used for optimization and training of the model.
- Validation dataset: 70 CT-scans of inner ears used for external validation.
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433 Views
in order to provide intelligent calligraphy evaluation assistance system to cope with the processing conditions of calligraphy word documents under poor lighting conditions, we jointly established our own data set with a calligraphy teaching company, which are all written on the grid paper, and stored in electronic devices by scanning or photographing, etc., and then split to single-word pictures by using the segmentation method [26-27].
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69 Views
Blood pressure (BP) measurement is an indispensable parameter for diagnosing many diseases, e.g., heart attack, stroke, vascular disease, and kidney disease. All these disease sometimes lead to fatal injuries due to the failure of vital human organs. The measurement of BP using BP device has several inaccuracies due to the non-availability of SI traceable calibration systems, which can also meet the criteria of International Organization of Legal Metrology (OIML) particularly OIML R 148 and OIML R 149 guidelines.
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105 Views
The Numerical Latin Letters (DNLL) dataset consists of Latin numeric letters organized into 26 distinct letter classes, corresponding to the Latin alphabet. Each class within this dataset encompasses multiple letter forms, resulting in a diverse and extensive collection. These letters vary in color, size, writing style, thickness, background, orientation, luminosity, and other attributes, making the dataset highly comprehensive and rich.
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494 Views
Our large scale alpine land cover dataset consists of 229'535 very high-resolution aerial images (50cm) and digital elevation model (50cm) with land cover annotations produced by experts in photo-interpretration . The nine land cover types in our study area include bedrock, bedrock with grass, large blocks, large blocks with grass, scree, scree with grass, water area, forest and glacier. The distribution of pixels among classes presents a typical case of a long-tailed distribution with an imbalance factor, defined as the ratio of the most frequent to the rarest class, close to 1000.
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251 Views
Quantifying performance of methods for tracking and mapping tissue in endoscopic environments is essential for enabling image guidance and automation of medical interventions and surgery. Datasets developed so far either use rigid environments, visible markers, or require annotators to label salient points in videos after collection. These are respectively: not general, visible to algorithms, or costly and error-prone. We introduce a novel labeling methodology along with a dataset that uses said methodology, Surgical Tattoos in Infrared (STIR).
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2057 Views
This dataset, presents the results of motion detection experiments conducted on five distinct datasets sourced from changedetection.net: bungalows, boats, highway, fall and pedestrians. The motion detection process was executed using two distinct algorithms: the original ViBe algorithm proposed by Barnich et al. (G-ViBe) and the CCTV-optimized ViBe algorithm known as α-ViBe.
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231 Views
This dataset, presents the results of motion detection experiments conducted on five distinct datasets sourced from changedetection.net: bungalows, boats, highway, fall and pedestrians. The motion detection process was executed using two distinct algorithms: the original ViBe algorithm proposed by Barnich et al. (G-ViBe) and the CCTV-optimized ViBe algorithm known as α-ViBe.
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162 Views
Mapping millions of buried landmines rapidly and removing them cost-effectively is supremely important to avoid their potential risks and ease this labour-intensive task. Deploying uninhabited vehicles equipped with multiple remote sensing modalities seems to be an ideal option for performing this task in a non-invasive fashion. This report provides researchers with vision-based remote sensing imagery datasets obtained from a real landmine field in Croatia that incorporated an autonomous uninhabited aerial vehicle (UAV), the so-called LMUAV.
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1219 Views
Slow moving motions are mostly tackled by using the phase information of Synthetic Aperture Radar (SAR) images through Interferometric SAR (InSAR) approaches based on machine and deep learning. Nevertheless, to the best of our knowledge, there is no dataset adapted to machine learning approaches and targeting slow ground motion detections. With this dataset, we propose a new InSAR dataset for Slow SLIding areas DEtections (ISSLIDE) with machine learning. The dataset is composed of standardly processed interferograms and manual annotations created following geomorphologist strategies.
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972 Views