Research

MindX: Mixed Impulse Poisson-Gaussian Denoising

This work aims at developing denoising algorithms for images corrupted by mixed impulse noise and Poisson-Gaussian noise.

MindX: Mixed Impulse Poisson-Gaussian Denoising


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X-Ray Computed Tomography

This works aims at developing high quality X-ray CT Reconstruciton algorithms based on proximal operators.

X-Ray Computed Tomography


Arabic Sentiment Analysis

This work explores applying Sentiment Analysis techniques to the Arabic language, especially in the field of social media.


Street View Goes Indoors

This work explores processing a set of unordered spherical panoramas of an indoor place by estimating their relative poses to extract a scaled map of the place suitable for indoor exploration and navigation.


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Distributed Kd-Trees for Large Scale Image Search

This work explores using Distributed Kd-Trees for implementing fast, accurate, and scalable large scale image search.


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Compact Kd-Trees for Large Scale Image Search

This work explores using Distributed Kd-Trees for implementing fast, accurate, and scalable large scale image search.


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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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Survey on Multi-Class Classification

Description

This project aimed at compiling a survey for the various techniques used for multi-class classification. This is an important problem, specially in computer vision, where we would like to recognize hundreds of different object categories. The survey report introduces the several techniques employed to solve this problem, with a discussion of their advantages and disadvantages.

References

Mohamed Aly, Survey on Multi-Class Classification Methods. [pdf]