Large Scale Image Search Benchmark
This work performs a thorough benchmark of the two leading approaches for large
scale image search: Bag of Words (BoW) vs Full Representation (FR). It includes
methods such as: Inverted File, Min-Hash, Kd-Trees, Hierarchical K-Means,
Locality Sensitive Hashing (LSH), among others.
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Online Parameter Selection for Large Scale Image Search
This work explores using online learning for selecting the best parameters
of Bag of Words systems when searching large scale image collections.
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Automatic Discovery of Image Families
This work investigates the problem of how to automatically discover image families
in unorganized image collections. Image families are groups of images with significant
similarity. The problem has applications to content-based image search, automatic
visualization and organization of image collections, or computer vision research
datasets, among others.
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Real time Lane Detection in Urban Streets
This project was part of Team Caltech,
Caltech's entry in the DARPA Urban Challenge
in November 2007. This project's main aim was to detect and localize lane lines in
urban streets, which will help Alice, Team Caltech's autonomous vehicle, find its way
in traffic.
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Fast Face Detection
This project was done as a requirement for the
Computer Vision
class EE148 of Spring 2006. The project was mainly to provide an open source
Matlab implementation of a realtime face detection system developed by
François Fleuret.
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Face Recognition using SIFT Features
This project was part of the requirements of the
CNS/Bi/EE 186:
Vision: From Computational Theory to Neuronal Mechanisms class for Winter 2006.
It implemented a simple face recognition system in Matlab exploiting the
power of SIFT features to discriminate between faces of different individuals.
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