Gary Bradski

NeurIPS Conference 2009 Conference Paper

An Additive Latent Feature Model for Transparent Object Recognition

• Mario Fritz
• Gary Bradski
• Sergey Karayev
• Trevor Darrell
• Michael Black

Existing methods for recognition of object instances and categories based on quantized local features can perform poorly when local features exist on transparent surfaces, such as glass or plastic objects. There are characteristic patterns to the local appearance of transparent objects, but they may not be well captured by distances to individual examples or by a local pattern codebook obtained by vector quantization. The appearance of a transparent patch is determined in part by the refraction of a background pattern through a transparent medium: the energy from the background usually dominates the patch appearance. We model transparent local patch appearance using an additive model of latent factors: background factors due to scene content, and factors which capture a local edge energy distribution characteristic of the refraction. We implement our method using a novel LDA-SIFT formulation which performs LDA prior to any vector quantization step; we discover latent topics which are characteristic of particular transparent patches and quantize the SIFT space into transparent visual words according to the latent topic dimensions. No knowledge of the background scene is required at test time; we show examples recognizing transparent glasses in a domestic environment.

IJCAI Conference 2007 Conference Paper

• Stephen Gould
• Joakim Arfvidsson
• Adrian Kaehler
• Benjamin Sapp
• Marius Messner
• Gary Bradski
• Paul Baumstarck
• Sukwon Chung

Human object recognition in a physical 3-d environment is still far superior to that of any robotic vision system. We believe that one reason (out of many) for this - one that has not heretofore been significantly exploited in the artificial vision literature - is that humans use a fovea to fixate on, or near an object, thus obtaining a very high resolution image of the bject and rendering it easy to recognize. In this paper, we present a novel method for identifying and tracking objects in multi-resolution digital video of partially cluttered environments. Our method is motivated by biological vision systems and uses a learned "attentive" interest map on a low resolution data stream to direct a high resolution "fovea. " Objects that are recognized in the fovea can then be tracked using peripheral vision. Because object recognition is run only on a small foveal image, our system achieves performance in real-time object recognition and tracking that is well beyond simpler systems.

NeurIPS Conference 2006 Conference Paper

Map-Reduce for Machine Learning on Multicore

• Cheng-tao Chu
• Sang Kim
• Yi-An Lin
• Yuanyuan Yu
• Gary Bradski
• Kunle Olukotun
• Andrew Ng

We are at the beginning of the multicore era. Computers will have increasingly many cores (processors), but there is still no good programming framework for these architectures, and thus no simple and unified way for machine learning to take advantage of the potential speed up. In this paper, we develop a broadly applicable parallel programming method, one that is easily applied to many different learning algorithms. Our work is in distinct contrast to the tradition in machine learning of designing (often ingenious) ways to speed up a single algorithm at a time. Specifically, we show that algorithms that fit the Statistical Query model [15] can be written in a certain "summation form, " which allows them to be easily parallelized on multicore computers. We adapt Google's map-reduce [7] paradigm to demonstrate this parallel speed up technique on a variety of learning algorithms including locally weighted linear regression (LWLR), k-means, logistic regression (LR), naive Bayes (NB), SVM, ICA, PCA, gaussian discriminant analysis (GDA), EM, and backpropagation (NN). Our experimental results show basically linear speedup with an increasing number of processors.

NeurIPS Conference 2005 Conference Paper

The Information-Form Data Association Filter

• Brad Schumitsch
• Sebastian Thrun
• Gary Bradski
• Kunle Olukotun

This paper presents a new filter for online data association problems in high-dimensional spaces. The key innovation is a representation of the data association posterior in information form, in which the “proxim- ity” of objects and tracks are expressed by numerical links. Updating these links requires linear time, compared to exponential time required for computing the exact posterior probabilities. The paper derives the algorithm formally and provides comparative results using data obtained by a real-world camera array and by a large-scale sensor network simu- lation.