Features Extracted from BraTS 2012-2013


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


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 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.


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.





The 2017 Data Fusion Contest will consist in a classification benchmark. The task to perform is classification of land use (more precisely, Local Climate Zones or LCZ) in various urban environments. Several cities have been selected all over the world to test the ability of both LCZ prediction and domain adaptation. Input data are multi-temporal, multi-source and multi-mode (image and semantic layers). 5 cities are considered for training: Berlin, Hong Kong, Paris, Rome and Sao Paulo.


Each city folder contains:grid/        sampling gridlandsat_8/    Landsat 8 images at various dates (resampled at 100m res., split in selected bands)lcz/        Local Climate Zones as rasters (see below)osm_raster/    Rasters with areas (buildings, land-use, water) derived from OpenStreetMap layersosm_vector/    Vector data with OpenStreetMap zones and linessentinel_2/    Sentinel2 image (resampled at 100m res., split in selected bands)


Local Climate Zones

The lcz/ folder contains:`<city>_lcz_GT.tif`: The ground-truth for local climate zones, as a raster. It is single-band, in byte format. The pixel values range from 1 to 17 (maximum number of classes). Unclassified pixels have 0 value.`<city>_lcz_col.tif`: Color, georeferenced LCZ map, for visualization convenience only.Class nembers are the following:10 urban LCZs corresponding to various built types:

  • 1. Compact high-rise;
  • 2. Compact midrise;
  • 3. Compact low-rise;
  • 4. Open high-rise;
  • 5. Open midrise;
  • 6. Open low-rise;
  • 7. Lightweight low-rise;
  • 8. Large low-rise;
  • 9. Sparsely built;
  • 10. Heavy industry.

7 rural LCZs corresponding to various land cover types:

  • 11. Dense trees;
  • 12. Scattered trees;
  • 13. Bush and scrub;
  • 14. Low plants;
  • 15. Bare rock or paved;
  • 16. Bare soil or sand;
  • 17. Water



More info:http://www.grss-ieee.org/community/technical-committees/data-fusion/data-fusion-contest/




The 2017 IEEE GRSS Data Fusion Contest is organized by the Image Analysis and Data Fusion Technical Committee of IEEE GRSSLandsat 8 data available from the U.S. Geological Survey (https://www.usgs.gov/).OpenStreetMap Data © OpenStreetMap contributors, available under the Open Database Licence - http://www.openstreetmap.org/copyright. Original Copernicus Sentinel Data 2016 available from  the European Space Agency (https://sentinel.esa.int).The Contest is being organized in collaboration with the WUDAPT (http://www.wudapt.org/) and GeoWIKI (http://geo-wiki.org/) initiatives. The IADF TC chairs would like to thank the organizers and the IEEE GRSS for continuously supporting the annual Data Fusion Contest through funding and resources.


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.


The Data Fusion Contest 2016: Goals and Organization

The 2016 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.

New multi-source, multi-temporal data including Very High Resolution (VHR) multi-temporal imagery and video from space were released. First, VHR images (DEIMOS-2 standard products) acquired at two different dates, before and after orthorectification:



After unzip, each directory contains:

  • original GeoTiff for panchromatic (VHR) and multispectral (4bands) images,

  • quick-view image for both in png format,

  • capture parameters (RPC file).



Wide varieties of scripts are used in writing languages throughout the world. In a multiscript and multi-language environment, it is necessary to know the different scripts used in every part of a document to apply the appropriate document analysis algorithm. Consequently, several approaches for automatic script identification have been proposed in the literature, and can be broadly classified under two categories of techniques: those that are structure and visual appearance-based and those that are deep learning-based.



The database consists of printed and handwritten documents. We realized that the documents from each script contain some sort of watermark owing to the fact that each script’s documents came from a different original native location. Therefore, the sheets and some layouts were different, depending on their origins. This poses a risk of the document watermark, rather than the script, being recognized, which could be the case with a deep learning-based classifier.

Segmenting text from the backgrounds of some documents was challenging. Even with state-of-the art segmentation techniques used, the result was not satisfactory, and included a lot of salt and pepper noise or black patches, or was missing some parts of the text.

To avoid these drawbacks and provide a dataset for script recognition, all the documents were preprocessed and converted to a white background, while the foreground text ink was equalized. Furthermore, all documents were manually revised. Both original and processed documents are included in the database.

To allow for script recognition at different levels (i.e., document, line and word), each document was divided into lines and each line into words. In the division, a line is defined as an image with 2 or more words, and a word is defined as an image with 2 or more characters.


The printed part of the database was recorded from a wide range of local newspapers and magazines to ensure that the samples would be as realistic as possible. The newspaper samples were collected mainly from India (as a wide verity of scripts are used there), Thailand, Japan, the United Arab Emirates and Europe. The database includes 13 different scripts: Arabic, Bengali, Gujarati, Gurmukhi, Devanagari, Japanese, Kannada, Malayalam, Oriya, Roman, Tamil, Telugu and Thai.

The newspapers were scanned at a 300 dpi resolution. Paragraphs with only one script were selected for the database (paragraph here means the headline and body text). Thus, different text sizes, fonts, and styles are included in the database. Further, we tried to ensure that all the text lines were not skewed horizontally. All images were saved in png format, and using the script_xxx.png naming convention, with script being an abbreviation or memo for each script, and xxx, the file number starting at 001 for each script.


Similar to the printed part in the handwritten database, we also included 13 different scripts: Persian as Arabic, Bengali, Gujarati, Punjabi, Gurmukhi, Devanagari, Japanese, Kannada, Malayalam, Oriya, Roman, Tamil, Telugu and Thai.

Most of the documents were provided by native volunteers capable of writing documents in their respective scripts. Each volunteer wrote a document, scanned it at 300 dpi, and then sent it to us by email. Consequently, the documents had large ink, sheet and scanner quality variations. Some of the Roman sheets came from the IAM handwritten database.


Due to the broad quality range of the documents, a two-step preprocessing was performed. In the first step, images are binarized by transforming the background into white, while in the second step, an ink equalization is performed.

Because the background texture, noise and illumination condition are primary factors degrading document image binarization performance, we used an iterative refinement framework in this paper to support robust binarization, In the process, the input image is initially transformed into a Bhattacharyya similarity matrix with a Gaussian kernel, which is subsequently converted into a binary image using a maximum entropy classifier. Then, the run-length histogram estimates the character stroke width. After noise elimination, the output image is used for the next round of refinement, and the process terminates when the estimated stroke width is stable. However, some documents are not correctly binarized, and in such cases, a manual binarization is performed using local thresholds. All the documents were revised and some noise was removed manually.

For ink equalization, we used an ink deposition model.  All the black pixels on the binarized images were considered as ink spots and correlated with a Gaussian of width 0.2 mm.  Finally, the image was equalized to duplicate fluid ink.


For the lines from a document to be segmented, they must be horizontal, otherwise a skew correction algorithm must be used ADDIN CSL_CITATION
of Pattern Recognition and Computer
SCIENTIFIC","title":"Texture Analysis with Local Binary

For the line segmentation, each connected object/component of the image is detected, and its convex hull obtained. The result is dilated horizontally in order to connect the objects belonging to the same line  and each connected object is labeled. The next step is a line-by-line extraction, performed as follows:

1.     Select the top object of the dilated lines and determine its horizontal histogram.

2.     If its histogram has a single maximum, then it should be a single line, and the object is used as a mask to segment the line (see Figure 4).

3.     If the object has several peaks, we assume that there are several lines. To separate them, we follow the next steps:

a.     The object is horizontally eroded until the top object contains a single peak.

b.     The new top object is dilated to recover the original shape and is used as a mask to segment the top line.

4.     The top line is deleted, and the process is repeated from step 1 to the end.


The segmentation results were manually reviewed, and lines that had been wrongly segmented were manually repaired. The lines were saved as image files and named using the script_xxx_yyy.png format, where yyy is the line number, xxx isthe document number and script is the abbreviation for the script, as previously mentioned. Figure 3 presents an example of a segmented line for handwriting. These images are saved in grayscale format.


The words were segmented from the lines in two steps, with the first step being completely automatic. Each line was converted to a black and white component, a vertical histogram was obtained, and points where the value of the histogram was found to be zero were identified as the gaps or the intersection. Gaps wider than one-third of the line height were labeled as word separations.

In the second step, failed word segmentations were manually corrected. Each word was saved individually as a black and white image. The files were named using the script_xxx_yyy_zzz.png format, with zzz being the word number of the line script_xxx_yyy. For instance, a file named roma_004_012_004.png contains the black and white image of the fourth word on the 12th line of the 4th document in Roman script.

In Thai and Japanese, word segmentation is done heuristically because their lines consist of two or three long sequences of characters separated by a greater space. This is because in these scripts, there is generally no gap between two words, and contextual meaning is generally used to decide which characters comprise a word. Since we do not use contextual meaning in the present database, we used the following approach for pseudo-segmentation of Thai and Japanese scripts: for each sequence of characters, the first two characters are the first pseudo-word; the third to the fifth characters are the second pseudo-word; the sixth to the ninth character are the third pseudo-word, and so on, up to the end of the sequence.


It should be noted that in this work, our intention is not to develop a new line/word segmentation system. We only use this simple procedure to segment lines and words in a bid to build our database. We thus use a semi-automatic approach, with human verification and correction in case of erroneous segmentation.