Fwd: Wednesday seminar at Sharif

[vahid asaeedi]

---------- Forwarded message ----------
From: Soraya Panahi <sor...@gmail.com>
Date: Tue, 30 Apr 2013 08:48:19 +0330
Subject: Wednesday seminar at Sharif
To:
Cc: Mehrdad Mirshams Shahshahani <mshahs...@gmail.com>

Hello everybody,

The speaker for this week is Mostafa Kamali from university of Tokyo. He
will give a lecture about his current researches in computer vision with
focus on *video processing* with applications in *intelligent
transportation systems*. He may have a review on his works on *cloth
fitting *as well.


The seminar will be held at 3 p.m. at *room 403* of the *mathematical
science *department.


Bests,
Soraya Panahi

[vahid asaeedi]

---------- Forwarded message ----------
From: Soraya Panahi <sor...@gmail.com>
Date: Mon, 6 May 2013 09:50:44 +0330
Subject: Wednesday seminar at Sharif
To:
Cc: Mehrdad Mirshams Shahshahani <mshahs...@gmail.com>

Hello everybody,

The speaker for this week is Mehdi Noroozi. He will give a lecture
about "*RGB-D
Object Recognition: Features and Algorithms*". You can find the abstract of
this lecture in the following.

The seminar will be held at 3 p.m. at room 204 at the computer engineering
department.
*

*
Bests,
Soraya Panahi

**


*Abstract*: Over the last decade, the availability of public image
repositories and recognition benchmarks has enabled rapid progress in
visual object category and
instance detection. Today we are witnessing the birth of a new generation
of sensing technologies capable of providing high quality synchronized
videos of both color
and depth, the RGB-D (Kinect-style) camera. With its advanced sensing
capabilities and the potential for mass adoption, this technology
represents an opportunity
to dramatically increase robotic object recognition, manipulation,
navigation, and interaction capabilities. We introduce a large-scale,
hierarchical multi-view object
dataset collected using an RGB-D camera. The dataset consists of two parts:
The RGB-D Object Dataset containing views of 300 objects organized into 51
categories,cand the RGB-D Scenes Dataset containing 8 video sequences of
office and kitchen environments. The dataset has been made publicly
available to the research community so as to enable rapid progress based on
this promising technology. We describe the dataset collection procedure and
present techniques for RGB-D object recognition and detection of objects in
scenes recorded using RGB-D videos, demonstrating that combining color and
depth information substantially improves quality of results.