This is the code related to the paper Atrous cGAN for SAR to Optical Image Translation:
This is a new face dataset based on the TB-100 visual tracking benchmark dataset (Wu, Y, Lim, J, Yang, M. Object tracking benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence 2015;37(9):1834–1848 - available on http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html).
As a result of the project “Multimodal Biometric Recognition using Face and Speech”, we present hereafter a prototype and a Matlab library, intended to evaluate the algorithms used in the multimodal classification of voice and face, assessing their functionality in real operational scenarios.
The main objective of this project is to provide a tool to evaluate automatic facial estimation algorithms. Different methods and configurations setting are made available.
The main objective of 3D Liver is to provide automatic methods to detect and visualize in three dimensions liver structures using monophasic sequences of computer tomography images.
InterSeg is an open-source image segmentation tool, which provides distributed processing on computer clusters or on cloud computing infrastructures. It can also be considered as an independent module of InterCloud, a distributed image interpretation platform for handling large remote sensing datasets.
InterCloud Data Mining Package is an open source software tool implemented in Java, able to perform supervised classification procedures on huge amounts of data, usually referred as big data, on a distributed infrastructure using Hadoop MapReduce. The package can be used as a stand alone or as part of InterCloud – an open-source, distributed framework for automatic interpretation of remote sensing and medical image data built on top of Hadoop.
InterCloud is an open-source, distributed framework for automatic interpretation of remote sensing and medical image data built on top of Hadoop. This new system can be understood as a functional and architectural redesign of the first version of InterIMAGE. InterCloud delivers some important features for automatically interpreting big data using expert knowledge. It:
- Enables the user to embed expert knowledge into the system intuitively through the definition of semantic networks and rule sets defined in a high-level programming language;
- Allows programmers to extend the system by adding their own algorithms straight forwardly;
- Delivers the robustness of MapReduce for cluster computing without the complexity of handling the Hadoop programming language.
Segmentation Parameter Tuning (SPT), is an open source software designed for automatic tuning of segmentation parameters based on a number of optimization algorithms using different quality metrics as fitness functions.
InterImage is an open source knowledge based framework for automatic image interpretation.
The Region Growing Segmenation on GPU is a parallel segmentation program based on the algorithm proposed by Baatz and Schäpe (2000). Two variants of the parallel algorithm with distinct heuristics for the selection of adjacent segments to be merged are avaiable. In addition, both sequential versions are also avaiable.
XMRS (eXtended MultiResolution Segmentation) is a segmentation program based on the algorithm proposed by Baatz and Schäpe (2000). Instead of using only the two morphological attributes proposed in the original paper, this program extends the original algorithm and implements 10 different morphological attributes. By changing the source code you can add more attributes to the program.