Image Processing
This is the largest database of hyperspectral face images containing hyperspectral image cubes of 78 subjects imaged in multiple sessions. The data was captured with the CRI's VariSpec LCTF (Liquid Crystal Tunable Filter) integrated with a Photon Focus machine vision camera. There are 33 spectral bands comering the 400 - 720nm range with a 10nm step. The noise level in the dataset is relatively lower because we adapted the camera exposure time to the transmittance of the filter illumination intensity as well as CCD sensitivity in each band.
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The femur dataset is our internal dataset, which
was collected from the clinical data of the Affiliated Hospital
of Capital Medical University, including 41 knee joint CT
scans, with a total of 7121 axial enhanced knee joint clinical
CT images. The dataset is shown in Fig. 5, which can be
downloaded in our github.
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358 Views
Grasp intention recognition is a vital problem for controlling assistive robots to help the elderly and infirm people restore arm and hand function. This dataset contains gaze data and scene image data of healthy individuals and hemiplegic patients while performing different grasping tasks. It can be used for gaze-based grasp intention recognition studies.
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Layout planning is centrally important in the field of architecture and urban design. Among the various basic units carrying urban functions, residential community plays a vital part for supporting human life. Therefore, the layout planning of residential community has always been of concern, and has attracted particular attention since the advent of deep learning that facilitates the automated layout generation and spatial pattern recognition.
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Identification of changes in pig behavior or interaction such as playing, sniffing, chewing, lying, or aggression is important for taking the necessary action if needed. Manual identification of pig behavior by human observers is not possible because it requires continuous monitoring. It is, therefore, essential to develop an automated method that quantifies pig behavior.
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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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Deep learning has revolutionized the field of robotics. To deal with the lack of annotated training samples for learning deep models in robotics, Sim-to-Real transfer has been invented and widely used. However, such deep models trained in simulation environment typically do not transfer very well to the real world due to the challenging problem of “reality gap”. In response, this letter presents a conceptually new Digital Twin (DT)-CycleGAN framework by integrating the advantages of both DT methodology and the CycleGAN model so that the reality gap can be effectively bridged.
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The I Scan 2 scanner from Cross-Match Technologies was used to acquire all data. Iris images are captured in near-infrared wavelength band (700-900 nm) of the electromagnetic spectrum. All images were acquired in SAP laboratory of computer science and engineering department of Dr. Babasaheb Ambedkar Marathwada University, Aurangabad. The subject images were acquired during the span of 7 to 8 months in years 2017 and 2018. GMBAMU-IRIS dataset contains total 5616 images from 312 subjects.
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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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