Computer Vision

This dataset presents the recognition of handwritten hieroglyphic alphabets. In this dataset we use consisting of 18 distinct classes of hieroglyphs alphabets. The dataset is designed to facilitate research in the field of ancient script recognition, particularly focusing on handwriting variability and pattern recognition. Each class represents a unique hieroglyph, with samples collected to ensure a diverse range of writing styles. To create this dataset, 25 students each handwrote samples for all 18 classes of hieroglyphs. Afterward, we carefully photographed each image.

The SynoClip dataset is a standard dataset specifically prepared for the video synopsis task, featuring videos that represent real-world surveillance conditions. It includes six videos, with durations ranging from 8 to 45 minutes, captured from outdoor-mounted surveillance cameras. Each video comes with manually annotated tracking information, enabling direct comparisons between different video synopsis models. The dataset features videos that exhibit varying levels of crowdedness and image resolution, showcasing dynamic scenes, such as sidewalks where pedestrians frequently cross.

There is growing widespread adoption of augmented reality in tech-driven industries and sectors of society, such as medicine, gaming, flight simulation, education, interior design and modelling, entertainment, construction, tourism, repair and maintenance, public safety, agriculture, and quantum computing. However, ensuring smooth and intuitive interactions with augmented objects is challenging, requiring practical performance evaluation and optimisation models to assess and improve users' experiences as they engage with AR-enhanced devices or systems.

This dataset was collected from real-world recycling plants, primarily consisting of crushed glass from disassembled display devices. The dataset contains images of flat glass mixed with solid glass, colored glass, plastic film, and aluminum foil. The colored glass originated from frame areas, while the aluminum foil came from cable shielding materials. Additional objects, such as solid glass and plastic films, were sourced from other recycled materials like glass bottles and packaging.

The weld classification dataset contains a variety of high-resolution images that capture a wide range of weld types and conditions common in industrial environments. The dataset includes samples from a variety of welding processes, such as arc welding, laser welding, and resistance spot welding, representing a variety of materials, including steel, aluminum, and titanium alloys.

CrackAirport features images containing unique elements such as aircraft, T-hangars, vegetation, airport markings and signs, as well as evidence of previous maintenance. The dataset was captured using a Sony ILCE-7RM4A camera mounted on a drone flying at an altitude of 100 feet AGL. The imagery was sourced from various local airports in Tennessee and includes common pavement distresses and environmental patterns typical of airport surfaces. The images were annotated and then cropped into 512x512 pixel segments for training.

The ultrasound video data were collected from two sets of neck ultrasound videos of ten healthy subjects at the Ultrasound Department of Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine. Each subject included video files of two groups of LSCM, LSSCap, RSCM, and RSSCap. The video format is avi.

The MRI training data were sourced from three hospitals: Longhua Hospital, Shanghai University of Traditional Chinese Medicine; Huadong Hospital, Fudan University; and Shenzhen Traditional Chinese Medicine Hospital.

The Dataset is a large-scale, diverse collection of high-resolution RGB images containing labeled wheat heads. Assembled through a collaborative effort of nine research institutes from seven countries, the dataset encompasses a wide range of genotypes, growth stages, and pedoclimatic conditions. Its primary goal is to facilitate the development of robust and accurate wheat head detection models for applications in precision phenotyping and crop management.