Computer Vision

To treat visual attention defficiency and train pulmonary function simulataneously, we developed new Virtual reality(VR) system. Proposed VR system is consisted of three VR training games(Rocket game, Candle game and Food game).

We conducted user study on 24 ADHD children and collected Pulmonary function test(PFT), Advanced test of attention(ATA).

In this file, we coded data into the form that can be used SPSS statstic tool.

We wanted to compare date in two ways, pre-post comparision and between-subjects comparision.

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Fundus Image Myopia Development (FIMD) dataset contains 70 retinal image pairs, in which, there is obvious myopia development between each pair of images. In addition, each pair of retinal images has a large overlap area, and there is no other retinopathy. In order to perform a reliable quantitative evaluation of registration results, we follow the annotation method of Fundus Image Registration (FIRE) dataset [1] to label control points between the pair of retinal images with the help of experienced ophthalmologists. Each image pair is labeled with

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391 Views

SYPHAXAR dataset is a dataset for Arabic text detection in the wild. It was collected from Tunisia in “Sfax” city, the second largest Tunisian city after the capital. A total of 3078 images were gathered through manual collection one by one, with each image energizing text detection challenges in nature according to real existing complexity of 15 different routes along with ring roads, intersections and roundabouts. These annotated images consist of more than 31000 objects, each of which is enclosed within a bounding box.

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244 Views

It is important to accurately classify the defects in hot rolled steel strip since the detection of defects in hot rolled steel strip is closely related to the quality of the fifinal product. The lack of actual hot-rolled strip defect data sets currently limits further research on the classifification of hot-rolled strip defects to some extent. In real production, the convolutional neural network (CNN)-based algorithm has some diffificulties, for example, the algorithm is not particularly accurate in classifying some uncommon defects.

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542 Views

Blade damage inspection without stopping the normal operation of wind turbines has significant economic value. This study proposes an AI-based method AQUADA-Seg to segment the images of blades from complex backgrounds by fusing optical and thermal videos taken from normal operating wind turbines. The method follows an encoder-decoder architecture and uses both optical and thermal videos to overcome the challenges associated with field application.

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1098 Views

This dataset provides RGB and Depth images acquired by Kinect v2 of 10 cerebral palsy patients. For each subject (0001, 0002, ecc) there are 12 folders: 

- 5 folders containing 5 left full gait cycles (L_01, L_02, ecc)

- 5 folders containing 5 right full gait cycles (R_01, R_02. ecc)

- 1 folder containing one static lateral view (left side) of the subject while standing upright (L_s)

- 1 folder containing one static lateral view (right side) of the subject while standing upright  (R_s)

In each folder (dynamic and static) there are two subfolders:

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We present the RQMD dataset, a comprehensive collection of diverse material samples aimed at advancing computer vision and machine learning algorithms in terrain classification tasks. This dataset contains RGB images of 5 different terrains, such as Asphalt, Brick, Grass, Gravel, and Tiles, captured using an 8-megapixel Raspberry Pi camera from a top-view perspective. Notably, the dataset encompasses images taken at different times of the day, introducing variations in lighting conditions and environmental factors.

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487 Views

Lettuce Farm SLAM Dataset (LFSD) is a VSLAM dataset based on RGB and depth images captured by VegeBot robot in a lettuce farm. The dataset consists of RGB and depth images, IMU, and RTK-GPS sensor data. Detection and tracking of lettuce plants on images are annotated with the standard Multiple Object Tracking (MOT) format. It aims to accelerate the development of algorithms for localization and mapping in the agricultural field, and crop detection and tracking.

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565 Views

In agriculture, the development of early treatment techniques for plant leaf diseases can be significantly enhanced by employing precise and rapid automatic detection methods. Within this realm of research, two common scenarios encountered in real field cases are the identification of different severity stages of diseases and the detection of multiple pathogens simultaneously affecting a single plant leaf. One major challenge faced in this area is the lack of publicly available datasets that contain images captured under these specific conditions.

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328 Views

Dronescape presents a dataset comprising 25 drone videos showcasing vast areas filled with trees, rivers, and mountains. The dataset includes two subsets: 25 videos with tree segmentation and 25 videos without tree segmentation, offering diverse perspectives on the presence and absence of segmented tree regions. The dataset focuses on highlighting the regions containing trees using the SAM (Segment Anything Model) and Track Anything library. Video object tracking and segmentation techniques are utilized to track the regions of trees throughout the dataset.

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696 Views

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