Machine Learning

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.


ASNM datasets include records consisting of many features, that express various properties and characteristics of TCP communications. These features are called Advanced Security Network Metrics (ASNM) and were designed with the intention to discern legitimate and malicious connections (especially intrusions).


Dataset consists of various open GIS data from the Netherlands as Population Cores, Neighbhourhoods, Land Use, Neighbourhoods, Energy Atlas, OpenStreetMaps, openchargemap and charging stations. The data was transformed for buffers with 350m around each charging stations. The response variable is binary popularity of a charging pool.


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.


Attempts to prevent invasion of marine biofouling on marine vessels are demanding. By developing a system to detect marine fouling on vessels in an early stage of fouling is a viable solution. However, there is a  lack of database for fouling images for performing image processing and machine learning algorithm.


This is a reservoir dataset including a large number of figures. Reservoir simulation, an important part of the petroleum industry, a powerful tool helping oil companies understand the reservoir better.

In this dataset, there more than 10,000 figures are showing in different period oilfield development. From the beginning to the end, we keep some variables constant while some changes to make clear the influences of different parts.

Last Updated On: 
Wed, 10/30/2019 - 03:16

The Contest: Goals and Organization


The 2017 IEEE GRSS Data Fusion Contest, organized by the IEEE GRSS Image Analysis and Data Fusion Technical Committee, aimed at promoting progress on fusion and analysis methodologies for multisource remote sensing data.



This dataset includes the measurements of a simulated vehicle inside a Gazebo simulation using different sensors: a simulated UWB tag, a IMU and a PX4Flow. 


Iris recognition has been an interesting subject for many research studies in the last two decades and has raised many challenges for the researchers. One new and interesting challenge in the iris studies is gender recognition using iris images. Gender classification can be applied to reduce processing time of the identification process. On the other hand, it can be used in applications such as access control systems, and gender-based marketing and so on. To the best of our knowledge, only a few numbers of studies are conducted on gender recognition through analysis of iris images.


In order to increase the diversity in signal datasets, we create a new dataset called HisarMod, which includes 26 classes and 5 different modulation families passing through 5 different wireless communication channel. During the generation of the dataset, MATLAB 2017a is employed for creating random bit sequences, symbols, and wireless fading channels.