Segmentation of TC clouds in 2016. The segmentation task was accomplished by an algorithm which takes a time series of brightness temperature images of TCs and uses image processing techniques to acquire segmentation for each image in a semi-supervised manner. 


2016 TC cloud segmentation animation



1. Movie "movie_S1.avi" demonstrates the normalized absolute element values of dynamic influence matrices and its scaled matrices by comb drive torque along the amplitude. The dynamic influence matrices are plotted according to the comb drive frequency component, index n, and the input frequency component, index m. The absolute values of the elements of the matrix are normalized by its maximum element. The maximum element is drawn in white and the normalized elements less than 10e−6 of the maximum element are depicted in black.


Results(including reported and extra results) for LSstab. Please refer to our paper "Efficient real-time video stabilization with a novel least squares formulation and parallel AC-RANSAC".


Stabilization results for LSstab. Please refer to our paper"Efficient real-time video stabilization with a novel

least squares formulation and parallel AC-RANSAC"


Stabilization results include:

(1) stabilized videos reported in the paper

(2) extra stabilized videos

(3) Challenging videos that LStab fails to stabilize. 


[Now uploading... Total size is 300GB.]


Please see the Information Hiding Criteria Ver. 6 document.


This dataset includes the IHC standard movies, which are high quality raw movies. The types of size are 2K and 4K.

The original movies are sampled by 16-bit-depth.



  • 4K-size 16-bit raw movies:



  • 2K-size 16-bit raw movies:
    1. Basketball_00000001.WAV
    2.  Lego_00000001.WAV
    3.  Library_00000001.WAV
    4.  Walk1_00000001.WAV
    5.  Walk2_00000001.WAV



  • 2K-size 8-bit raw movies: [5.3GB each]

A 16-bit raw image file is quantized to 8-bit-depth uncompressed AVI files.

  1.  Basketball.avi
  2.  Lego.avi
  3.  Library.avi
  4.  Walk1.avi
  5.  Walk2.avi



[ Acknowledgments ]

The 2K raw video clips were taken with a Canon Cinema EOS C500 system with support from Canon Inc. The IHC Committee would like to thank this company for its valuable contributions.




The videos demonstrate 2D thermal gradient mappings based on two pairs of 50 µm x- an y- thin film thermocouple (TFTC) sensors. We investigate thin film thermocouples (TFTC) asthermal gradient sensors at the micro-scale and demonstrate two-dimensional dynamic thermal gradient mapping for features as small as 20 μm. Pairs of x-direction and y-direction thermocouples sense the thermal gradient while another calibrates the Seebeck coefficient as S = 20.33±0.01μV/K. The smallest detectable temperature difference is 10 mK, and the sensitivity is 0.5 mK/μm.