Computer Science Department
University of California, Berkeley
Updated 06 Mar 2013
Our method for timely multi-class detection aims to give the best possible performance at any single point between a start time and a deadline. We formulate a dynamic, closed-loop policy that infers the contents of the image in order to decide which detector to deploy next. We evaluate our method with a novel timeliness measure, computed as the area under an Average Precision vs. Time curve.
Using the Microsoft Kinect, we gather a large dataset of indoor crowded scenes. We investigate ways to unify state-of-the-art object detection systems and improve them with depth information.
Our method for additively decomposing local image patches, LDA-SIFT, shows best performance on a novel transparent object recognition dataset. We recursively extend the model to multiple layers and successfully apply it to general object classification.
We present an open-source system for quickly searching large image collections by multiple colors given as a palette, or by color similarity to a query image.
We present a mobile web app to match users who request similar trips and would like to share a cab. The application is hosted on Amazon’s EC2 service and combines several open-source frameworks (Django, PostgresQL, Redis, Node.js) with social networking and mapping APIs. The modularity of our design allows the service to easily scale in the cloud as the user base grows. The service is live.
Setting up a development environment on Mac OS X 10.8 Mountain Lion
CabFriendly – a cloud-based mobile web app.
Attentional Object Detection – introductory slides.
Review of Kanan and Cottrell, Robust Classification of Objects, Faces, and Flowers Using Natural Image Statistics, CVPR 2010.
Review of Itti and Koch, Computational Modeling of Visual Attention, Nature Neuroscience 2001.