Computer Vision

Photo identification (photoID) is a non-invasive technique devoted to the identification of individual animals using photos, and it is based on the hypothesis that each specimen has unique features useful for its recognition. This technique is particularly suitable to study highly mobile and hard to detect marine species, such as cetaceans. These animals play a key role in marine biodiversity conservation because they maintain the stability and health of marine ecosystems due to their apical role as top predators in food webs.

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Ovarian cancer is among the top health issues faced by women everywhere in the world . Ovarian tumours have a wide range of possible causes. Detecting and tracking down these cancers in their early stages is difficult which adds to the difficulty of treatment. In most cases, a woman finds out she has ovarian cancer after it has already spread. In addition, as technology in the field of artificial intelligence advances, detection can be done at an earlier level. Having this data will assist the gynaecologist in treating these tumours as soon as possible.

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1845 Views

Most of the existing human action datasets are common human actions in daily scenes(e.g. NTU RGB+D series, Kinetics series), not created for Human-Robot Interaction(HRI), and most of them are not collected based on the perspective of the service robot, which can not meet the needs of vision-based interactive action recognition.

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237 Views

IREYE4TASK is a dataset for wearable eye landmark detection and mental state analysis. Sensing the mental state induced by different task contexts, where cognition is a focus, is as important as sensing the affective state where emotion is induced in the foreground of consciousness, because completing tasks is part of every waking moment of life. However, few datasets are publicly available to advance mental state analysis, especially those using the eye as the sensing modality with detailed ground truth for eye behaviors.

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518 Views

Perth-WA is the localization dataset that provides 6DoF annotations in 3D point cloud maps. The data comprises a LiDAR map of 4km square region of Perth Central Business District (CBD) in Western Australia. The scenes contain commercial structures, residential areas, food streets, complex routes, and hospital building etc.

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324 Views

The ability to perceive human facial emotions is an essential feature of various multi-modal applications, especially in the intelligent human-computer interaction (HCI) area. In recent decades, considerable efforts have been put into researching automatic facial emotion recognition (FER). However, most of the existing FER methods only focus on either basic emotions such as the seven/eight categories (e.g., happiness, anger and surprise) or abstract dimensions (valence, arousal, etc.), while neglecting the fruitful nature of emotion statements.

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4514 Views

Image coordinates data of AprilTag's four edges

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Fabdepth HMI is designed for hand gesture detection for Human Machine Interaction. It contains total of 8 gestures performed by 150 different individuals. These individuals range from toddlers to senior citizens which adds diversity in this dataset. These gestures are available in 3 different formats namely resized, foreground=-background separated and depth estimated images. Additional aspect is added in terms of video format of 150 samples. Researchers may choose their combination of data modalities based on their application.

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247 Views

Solar energy production has grown significantly in recent years in the European Union (EU), accounting for 12\% of the total in 2022. The growth can be attributed to the increasing adoption of solar photovoltaic (PV) panels, which have become cost-effective and efficient means of energy production, supported by government policies and incentives. The maturity of solar technologies has also led to a decrease in the cost of solar energy, making it more competitive with other energy sources. 

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655 Views

 

 As a common dataset for change detection, its image can be divided into three parts: the image before the change, the image after the change, and the label image showing the changed area. This dataset is characterized by significant seasonal differences between bi-temporal image pairs, which makes up for some of the deficiencies in existing datasets. The labels for this dataset include some irregular changes, such as the appearance and disappearance of cars; but do not include seasonal changes, such as changes in the ground surface caused by snowfall.

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621 Views

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