Computer Vision

This dataset consists of 462 field of views of Giemsa(dye)-stained and field(dye)-stained thin blood smear images acquired using an iPhone 10 mobile phone with a 12MP camera. The phone was attached to an Olympus microscope with 1000× objective lens. Half of the acquired images are red blood cells with a normal morphology and the other half have a Rouleaux formation morphology.


This dataset comprises 1718 annotated images extracted from 29 video clips recorded during Endoscopic Third Ventriculostomy (ETV) procedures, each captured at a frame rate of 25 FPS. Out of these images, 1645 are allocated for the training set, while the remainder is designated for the testing set. The images contain a total of 4013 anatomical or intracranial structures, annotated with bounding boxes and class names for each structure. Additionally, there are at least three language descriptions of varying technicality levels provided for each structure.


This is the first multi-view, semi-indoor gait dataset captured with the DAVIS346 event camera. The dataset comprises 6,150 sequences, capturing 41 subjects from five different view angles under two lighting conditions. Specifically, for each lighting condition and view angle, there are six sequences representing normal walking (NM), three sequences representing walking with a backpack (BG), three sequences representing walking with a portable bag (PT), and three sequences representing walking while wearing a coat (CL).


Human facial data hold tremendous potential to address a variety of classification problems, including face recognition, age estimation, gender identification, emotion analysis, and race classification. However, recent privacy regulations, such as the EU General Data Protection Regulation, have restricted the ways in which human images may be collected and used for research.


One of the Dravidian language spoken majorly by 60 million people in and around Karnataka state of India is known as Kannada. It is one among 22 scheduled languages of India. Kannada langauge is written in Kannada scriptwhich has its traces back from kadamba script (325-550 AD). There are many languages which were used centuries back and aren’t being used currently whereas Kannada is one such language which is used even today for writing official documents and are being taught at schools which means it is going to be for many years.


Speech impairment constitutes a challenge to an individual's ability to communicate effectively through speech and hearing. To overcome this, affected individuals’ resort to alternative modes of communication, such as sign language. Despite the increasing prevalence of sign language, there still exists a hindrance for non-sign language speakers to effectively communicate with individuals who primarily use sign language for communication purposes. Sign languages are a class of languages that employ a specific set of hand gestures, movements, and postures to convey messages.


This paper explores the connections between traditional Large Deformation Diffeomorphic Metric Mapping methods and
unsupervised deep-learning approaches for non-rigid registration, particularly emphasizing diffeomorphic registration. The
study provides useful insights and establishes connections between the methods, thereby facilitating a profound understand-
ing of the methodological landscape. The methods considered in our study are extensively evaluated in T1w MRI images

Dataset of images of dragon fruit plants, collected from different media and taken from a dragon fruit field in Rio Branco, Brazil, with a total of 600 images
classified among 300 photos of sick plants, with fish eyes among others and 300 photos of healthy plants. For many of the photos, a simple smartphone 
camera was used to capture the images.



This dataset comprises a diverse array of image files, each captured using either a mobile phone or a camera. The primary subject of these images is experiment reports, reflecting a wide range of experimental scenarios. These images have been taken in various environments, showcasing the flexibility of the dataset in accommodating different shooting conditions. Formatted as JPG documents, the images exhibit variations in size, offering a rich diversity for analysis.


Traditional Thai medicine (TTM) is an increasingly popular treatment option. Tongue diagnosis is a highly efficient method for determining overall health, practiced by TTM practitioners. However, the diagnosis naturally varies depending on the practitioner's expertise. In this work, we propose tongue image analysis using raw pixels and artificial intelligence (AI) to support TTM diagnoses. The target classification of Tri-Dhat consists of three classes: Vata, Pitta, and Kapha. We utilize our own organized genuine datasets collected from our university's TTM hospital.