Computer Vision
This open dataset is subject to CC BY-NC-SA 4.0 License. The dataset is intended for scientific research purposes and it cannot be used for commercial purposes. The authors encourage users to use it for public research and as a testbench for private research. Please note that any promotional/marketing material built upon this dataset should be backed by publicly available description of the work leading to the promotional/marketing claims.
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Measuring the appearance time slots of characters in videos is still an unsolved problem in computer vision, and the related dataset is insufficient and unextracted. The Character Face In Video (CFIV) dataset provides the labeled appearing time slots for characters of interest for ten video clips on Youtube, two faces per character for training, and a script for downloading each video. Additionally, three videos contain around 100 images per character for evaluating the accuracy of the face recognizer.
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The dataset contains results of the paper being submitted.
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This project investigates bias in automatic facial recognition (FR). Specifically, subjects are grouped into predefined subgroups based on gender, ethnicity, and age. We propose a novel image collection called Balanced Faces in the Wild (BFW), which is balanced across eight subgroups (i.e., 800 face images of 100 subjects, each with 25 face samples).
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The experimental data in this paper comes from the bamboo sticks provided by farmers who sell bamboo in Anji. We randomly grab less than 100 bamboo sticks and bundle them together. The heights of 5cm, 10cm, 15cm, and 20cm were taken from the front and left and right inclination to take pictures, screen clear and effective experimental data, and then use labelimg software to label them. The sparse bamboo stick samples collected were 600.
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This is the data for the paper "Fusion of Human Gaze and Machine Vision for Predicting Intended Locomotion Mode" published on IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022.
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Computer vision can be used by robotic leg prostheses and exoskeletons to improve transitions between different locomotion modes (e.g., level-ground walking and stair ascent) via prediction of oncoming environmental states. We developed the StairNet dataset to support research and development in vision-based automated stair recognition.
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One of the weak points of most of denoising algoritms (deep learning based ones) is the training data. Due to no or very limited amount of groundtruth data available, these algorithms are often evaluated using synthetic noise models such as Additive Zero-Mean Gaussian noise. The downside of this approach is that these simple model do not represent noise present in natural imagery. For evaluation of denoising algorithms’ performance in poor light conditions, we need either representative models or real noisy images paired with those we can consider as groundtruth.
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Retail Gaze, a dataset for remote gaze estimation in real-world retail environments. Retail Gaze is composed of 3,922 images of individuals looking at products in a retail environment, with 12 camera capture angles.
Each image captures the third-person view of the customer and shelves. Location of the gaze point, the Bounding box of the person's head, segmentation masks of the gazed at product areas are provided as annotations.
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