image processing

The "ShrimpView: A Versatile Dataset for Shrimp Detection and Recognition" is a meticulously curated collection of 10,000 samples (each with 11 attributes) designed to facilitate the training of deep learning models for shrimp detection and classification. Each sample in this dataset is associated with an image and accompanied by 11 categorical attributes.


The Autofocus Projector Dataset is a collection of 555 images and 150 videos captured while projecting images and videos with varying levels of Gaussian blur. The dataset includes images and videos of different blur levels, ranging from fully focused to the maximum levels of left and right Gaussian blur as per the projector's specifications. The dataset was recorded using a Redmi Note 11T 5G mobile camera with a 50 MP, f/1.8, 26mm (wide) sensor, PDAF image camera, and 1080p@30 fps video camera.


A crowdsourcing subjective evaluation of viewport images obtained with several sphere-to-plane projections was conducted. The viewport images were rendered from eight omnidirectional images in equirectangular format. The pairwise comparison (PC) method was chosen for the subjective evaluation of projections. More details about the viewport images and subjective evaluation procedure can be found in [1].


Object detection via images has advanced quickly over the last few decades, their detection accuracy, categorization, and localization are not consistent. Achieving fast and accurate detection of fashion products in the e-commerce environment is very important for selecting the right category. This is closely related to customer satisfaction and happiness which is a critical aspect. 


LiDAR point cloud data serves as an machine vision alternative other than image. Its advantages when compared to image and video includes depth estimation and distance measruement. Low-density LiDAR point cloud data can be used to achieve navigation, obstacle detection and obstacle avoidance for mobile robots. autonomous vehicle and drones. In this metadata, we scanned over 1200 objects and classified it into 4 groups of object namely, human, cars, motorcyclist.


The LEDNet dataset consists of image data of a field area that are captured from a mobile phone camera.

Images in the dataset contain the information of an area where a PCB board is placed, containing 6 LEDs. Each state of the LEDs on the PCB board represents a binary number, with the ON state corresponding to binary 1 and the OFF state corresponding to binary 0. All the LEDs placed in sequence represent a binary sequence or encoding of an analog value.


The dataset consists of subjective evaluations of 44 naive observers judging the visual complexity of 16 images. The subjective judgments were done using a 5-point Likert-type scale with a neutral midpoint. The items in the scale were “very complex,” “complex,” “medium,” “simple,” and “very simple.” The order of the images was randomized for every participant.


This dataset collection contains eleven datasets used in Locally Linear Embedding and fMRI feature selection in psychiatric classification.

The datasets given in the Links section are reduced subsets of those contained in their respective tar files (a consequence of Mendeley Data's 10GB limitation).

The Linked datasets (not the tar files) contain just the MATLAB file and the resting state image (or block-design fMRI for the MRN dataset), where appropriate.


This database has five different linter quality classes (short cotton fibers), linter has wide applicability in the production of surgical tissue, paper money among other applications. The images available were used to classify the product in an industrial process, through the use of computer vision techniques.