Image Processing

This is a dataset consisting of 8 features extracted from 70,000 monochromatic still images adapted from the Genome Project Standford's database, that are labeled in two classes: LSB steganography (1) and without LSB Steganography (0). These features are Kurtosis, Skewness, Standard Deviation, Range, Median, Geometric Mean, Hjorth Mobility, and Hjorth Complexity, all extracted from the histograms of the still images, including random spatial transformations. The steganographic function embeds five types of payloads, from 0.1 to 0.5.


Accurate segmentation of test line and control line for colloidal gold immunochromatographic strip (GICS) images with image processing algorithms is essential to quantitative analysis of GICS. As common methods for GICS image segmentation, fuzzy c-means (FCM) algorithm and cellular neural network (CNN) algorithm both require presetting initial conditions (specifying initial parameters or training models) and take long running time, due to high calculation cost.


This dataset contains the comparison results on the 'Euroc' public dataset of DVIO, VINS-Mono, and ROVIO.


Smartphone has been one of the most popular digital devices in the past decades, with more than 300 million smartphones sold every quarter in the world wide. Most of the smartphone vendors, such as Apple, Huawei, Samsung, launch their new flagship smartphones every year. People use smartphone cameras to shoot selfie photos, film scenery or events, and record videos of family and friends. The specifications of smartphone camera and the quality of taken pictures are major criteria for consumer to select and buy smartphones.


Low light scenes often come with acquisition noise, which not only disturbs the viewers, but it also makes video compression harder. These type of videos are often encountered in cinema as a result of artistic perspective or the nature of a scene. Other examples include shots of wildlife (e.g. mobula rays at night in Blue Planet II), concerts and shows, surveillance camera footage and more. Inspired by all above, we are proposing a challenge on encoding low-light captured videos.

Last Updated On: 
Fri, 05/01/2020 - 09:40

This dataset comes up as a benchmark dataset for machines to automatically recognizing the handwritten assamese digists (numerals) by extracting useful features by analyzing the structure. The Assamese language comprises of a total of 10 digits from 0 to 9. We have collected a total of 516 handwritten digits from 52 native assamese people irrespective of their age (12-86 years), gender, educational background etc. The digits are captured in .jpeg format using a paint mobile application developed by us which automatically saves the images in the internal storage of the mobile.


An accurate and reliable image-based quantification system for blueberries may be useful for the automation of harvest management. It may also serve as the basis for controlling robotic harvesting systems. Quantification of blueberries from images is a challenging task due to occlusions, differences in size, illumination conditions and the irregular amount of blueberries that can be present in an image. This paper proposes the quantification per image and per batch of blueberries in the wild, using high definition images captured using a mobile device.


The sensitivity of an ultrasonic transducer is an important parameter for evaluating its effective frequency band, electroacoustic conversion efficiency, and the measurement capability of the system. Determining the sensitivity of a traditional immersion or contact piezoelectric transducer has been well investigated. However, due to the high attenuation of wave propagation in air and the large acoustic impedance mismatch between the active piezoceramic material and the load medium, there are few reports on the calibration of an air-coupled piezoelectric transducer.


The presented database contains thermal images (thermograms) of the plantar region. The database was obtained from 122 subjects with a diabetes diagnosis (DM group) and 45 non-diabetic subjects (control group). The relevance of this database consists in to study how the temperature is distributed in the plantar region of both groups and how their differences can be measured. Previous reports in the literature have established that an increase in the plantar temperature is associated with a higher ulceration risk.


The Contest: Goals and Organisation

 The 2019 Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS), the Johns Hopkins University (JHU), and the Intelligence Advanced Research Projects Activity (IARPA), aimed to promote research in semantic 3D reconstruction and stereo using machine intelligence and deep learning applied to satellite images.