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Open Source Software

 
A reference-based metric to evaluate the accuracy of pronoun translation (APT)
The APT software is a reference-based metric to evaluate the accuracy of pronoun translation.
HOOSC
Histogram of Orientation Shape Context
warca
WARCA is a simple and fast algorithm for metric learning.
Torch3vision
Common software library for computer vision with machine learning algorithms. Written in simple C++, this library is based on Torch and distributed under a BSD license.
Torch
Statistical machine learning library containing most of the state-of-the-art algorithms. Written in Lua and C, the library is distributed under a BSD license.
Juicer
Juicer is a Weighted Finite State Transducer (WFST) based decoder for Automatic Speech Recognition (ASR).
Tracter
Tracter is a data flow framework.
HTS-VTLN
This software is a patch to HMM based statistical parametric speech synthesis toolkit (HTS 2.2).
Face Color Model
This page contains the source code and data needed to train and use a model for skin, hair, clothing and background color modelling and segmentation.
HEAT Image Retrieval System
HEAT is an image retrieval web-application that is intended for large unstructured collections of images without semantic annotations. The system implements a novel searching paradigm that does not require any explicit query. At each iteration, the system displays a small set of images and the user chooses the image that best matches what she is looking for. After a few iterations, the sets of displayed images are gradually concentrated on images that satisfy the user.
Tasting Families of Features for Image Classification
Please find below the code necessary to reproduce the experiments of the paper "Tasting Families of Features for Image Classification" under the GPL v2 license.
Speaker Diarization Toolkit
The toolkit is intended to facilitate research in multistream speaker diarization providing a platform for research in novel audio, video or location features. It is based on the Information Bottleneck principle and is explicitely designed to use of several hetergenous feature streams.
BOB
Bob is a free signal-processing and machine learning toolbox developed by the Biometrics group at Idiap Research Institute, Switzerland. The toolbox is written in a mix of Python and C++ and is designed to be both efficient and reduce development time.
The Multi-Tracked Paths
This is an implementation of the variant of KSP for tracking presented in (Berclaz et al. 2011). You can get more information and the reference implementation from the CVLab's web page about multi-camera tracking.
Exact Acceleration of Linear Object Detectors
We describe a general and exact method to considerably speed up linear object detection systems operating in a sliding, multi-scale window fashion, such as the individual part detectors of part-based models.
MSER
Linear time Maximally Stable Extremal Regions (MSER) implementation as described in D. Nistér and H. Stewénius, "Linear Time Maximally Stable Extremal Regions", ECCV 2008.
ACT
ACT for Accuracy of Connective Translation is a reference-based metric to measure the accuracy of discourse connective translation, mainly for statistical machine translation systems.
SSP
SSP stands for Speech Signal Processing. It is a fairly small package written in python. Its functionality is similar to tracter, with some overlap and some additional capabilities. In particular, SSP contains a parametric vocoder, a pitch extractor and feature extraction for ASR.
ISS
The Idiap Speech Scripts (ISS) is a collection of speech databases and dictionaries, and for training and testing of models for ASR. The scripts in turn are reliant on many other packages including HTK/HTS, Juicer and the ICSI speech tools.
Probabilistic Models: temporal topic models and more
Topic models such as Latent Dirichlet Allocation (LDA) have been used successfully in many domains for data mining. Originally designed for text documents, these methods find some hidden “topics” considering that each document is a weighted mixture of topics. Each topic expresses itself in a document by generating some specific words with more probability than others.
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