Image Processing | IEEE DataPort

CoMMonS: Challenging Microscopic Material Surface Dataset

As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on recognizing textures and materials in real-world images, which plays an important role in object recognition and scene understanding. Aiming at describing objects or scenes with more detailed information, we explore how to computationally characterize apparent or latent properties (e.g. surface smoothness) of materials, i.e., computational material characterization, which moves a step further beyond material recognition.

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Database of Singular Value Decompositions of Matrix Representations of the Costas Condition

The Costas condition on a permutation matrix, expressed as row indices as elements of a vector c, can be expressed as A*c=b, where b is a vector of integers in which no element is zero. A particular formulation of the matrix A allows a singular value decomposition in which the eigenvalues are squared integers and the eigenvalues may be scaled to vectors with all integer elements. This is a database of the Costas constraint matrices A, the scaled eigenvectors, and the squared eigenvalues for orders 3 through 100.

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Automatic Segmentation of Stroke Lesions in Non-contrast Computed Tomography Datasets with Convolutional Neural Networks

This dataset contains the trained model that accompanies the publication of the same name:

Anup Tuladhar*, Serena Schimert*, Deepthi Rajashekar, Helge C. Kniep, Jens Fiehler, Nils D. Forkert, "Automatic Segmentation of Stroke Lesions in Non-Contrast Computed Tomography Datasets With Convolutional Neural Networks," in IEEE Access, vol. 8, pp. 94871-94879, 2020, doi:10.1109/ACCESS.2020.2995632. *: Co-first authors

Publication Abstract

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DATASET OF PHOTOGRAPHIC FOOD RECORDS

Objective: No data currently exist on the reproducibility of photographic food records compared to diet diaries, two commonly used methods to measure habitual dietary intake. Our aim was to examine the reproducibility of diet diaries, photographic food records, and a novel electronic sensor, consisting of counts of chews and swallows using wearable sensors and video analysis, for estimating energy intake.

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Image Processing

Adaptive Block Compressive Sensing: towards a real-time and low-complexity implementation - Supplementary Material

Supplementary material for the paper: 'Adaptive Block Compressive Imaging: towards a real-time and low complexity implementation'

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CALPA-NET

The supplementary files of our submitted TIFS paper: "CALPA-NET: Channel-pruning-assisted Deep Residual Network for Steganalysis of Digital Images".

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WHUBuilding Dataset

This aerial image dataset consists of more than 22,000 independent buildings extracted from aerial images with 0.0075 m spatial resolution and 450 km^2 covering in Christchurch, New Zealand. The most parts of aerial images are down-sampled to 0.3 m ground resolution and cropped into 8,189 non-overlapping tiles with 512* 512. These tiles make up the whole dataset. They are split into three parts: 4,736 tiles for training, 1,036 tiles for validation and 2,416 tiles for testing.

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Authentically Distorted Surveillance Videos Dataset

This Dataset contains "Pristine" and "Distorted" videos recorded in different places. The

distortions with which the videos were recorded are: "Focus", "Exposure" and "Focus + Exposure".

Those three with low (1), medium (2) and high (3) levels, forming a total of 10 conditions

(including Pristine videos). In addition, distorted videos were exported in three different

qualities according to the H.264 compression format used in the DIGIFORT software, which were:

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Mask-RCNN detection of COVID-19 pneumonia symptoms by employing Stacked Autoencoders in deep unsupervised learning on Low-Dose High Resolution CT

This paper applies AI (artificial intelligence) technology to analyze low-dose HRCT (High-resolution chest radiography) data in an attempt to detect COVID-19 pneumonia symptoms. A new model structure is proposed with segmentation of anatomical structures on DNNs-based (deep learning neural network) methods, relying on an abundance of labeled data for proper training.

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Dataset: The perils and pitfalls of block design for EEG classification experiments

Dataset asscociated with a paper in IEEE Transactions on Pattern Analysis and Machine Intelligence

"The perils and pitfalls of block design for EEG classification experiments"

DOI: 10.1109/TPAMI.2020.2973153

If you use this code or data, please cite the above paper.

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