Current projects
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Wayfinding for blind persons using camera cell phones
We have developed a system that allows blind persons to find their way in an unfamiliar environment using a regular camera cell phone. Locations of interest are labeled with specialized color marker that are detected quickly and robustly by the cell phone. A blind user can thus be guided through these landmarks to destination.
Bagherinia, Bin, Gray, Manduchi. Work in collaboration with J. Coughlan of SKERI. Research funded by NIH and NSF.
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Reading difficult bar codes with cell phones
There is a growing interest in cell phone apps that can read bar codes printed on products. Unfortunately, a number of factors (low resolution, motion blur, poor lighting) make bar code reading by cell phones a challenging problem. We have developed an algorithm for maximum likelihood bar code reading that outperforms all other published state-of-the-art techniques.
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Viewpoint Invariant Pedestrian Recognition
Recognizing people in images and video is one of the most fundamental problems in computer vision. We focus on matching images of pedestrians from single image frames of different pose and viewpoint. Our approach focuses on finding methods of comparing pedestrian images which are invariant to elements not associated with the persons identity. Our dataset (VIPeR) is freely available for use by the community.
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Environment exploration using a virtual white cane
The long cane is the most widely used mobility tool for blind people. It allows one to extend touch and to "preview" the lower portion of the space in front of oneself. We are designing laser-based hand-held devices that enable environment exploration without the need for physical contact. Using active triangulation, our devices can identify obstacles and other features that are important for safe ambulation (such as steps and drop-offs).
Ilstrup, Yuan, Manduchi. Research funded by NSF.
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Efficient image representation using Haar-like features
The efficient and compact representation of images is a fundamental problem in computer vision. In this project, we propose methods that use Haar-like binary box functions to represent a single image or a set of images. A desirable property of these box functions is that their inner product operation with an image can be computed very efficiently. We show that using this efficient representation, many vision appliations can be significantly accelerated, for example: template matching, image filtering, PCA project, image reconstruction.
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Co-tracking using semi-supervised support vector machines
We treat tracking as a foreground/background classification problem and propose an online semi-supervised learning framework. Classification of new data and updating of the classifier are achieved simultaneously in a co-training framework. Experiments show that this framework performs better than state-of-the-art tracking algorithms on challenging sequences.