Feature-based methods have found increasing use in many applications such as object recognition, 3D reconstruction and mosaicing. In this paper, we focus on the problem of matching such features. While a histogram-of-gradients type methods such as SIFT, GLOH and Shape Context are currently popular, several papers have suggested using orders of pixels rather than raw intensities and shown improved results for some applications. The papers suggest two different techniques for doing so: (1) A Histogram of Relative Orders in the Patch and (2) A Histogram of LBP codes. While these methods have shown good performance, they neglect the fact that the orders can be quite noisy in the presence of Gaussian noise. In this paper, we propose changes to these approaches to make them robust to Gaussian noise. We also show how the descriptors can be matched using recently developed more advanced techniques to obtain better matching performance. Finally, we show that the two methods have complimentary strengths and that by combining the two descriptors, one obtains much better results than either of them considered separately. The results are shown on the standard 2D Oxford and the 3D Caltech datasets.

Collaborative filtering (CF) based recommender systems have gained wide popularity in Internet companies like Amazon, Netflix, Google News, and others. These systems make automatic predictions about the interests of a user by inferring from information about like-minded users. Realtime CF on highly sparse massive datasets, while achieving a high prediction accuracy, is a computationally challenging problem. In this paper, we present the design of a soft real-time (around 1 min.) parallel CF algorithm based on the Concept Decomposition technique. Our parallel algorithm has been optimized for multicore/many-core architectures while maintaining the prediction accuracy of 0.84 RMSE. Using the Netflix dataset, we demonstrate the performance and scalability of our algorithm (in both batch mode and online mode) on a 32-core Power6 based SMP system. Our parallel algorithm delivered training time of 64s on the full Netflix dataset and prediction time of 4.5s on 1.4M ratings (3.2/μs per rating prediction). This is 12.6× better than the best known sequential training time and around 33 × better than the best known sequential prediction time, along with high accuracy (0.84 RMSE). To the best of our knowledge, this is also the best known parallel performance at such high accuracy.

We present an approach for illumination and affine-invariant point matching using ordinal features. Ordinal measures for matching only consider the order between pixels and not the absolute intensity values which enables them to be invariant to a monotonic change in intensities. The utility of such measures has been demonstrated in the past for point matching for some applications such as background subtraction and stereo matching. However, invariance of such methods to geometric transformations has been limited leading to their inapplicability for more generic matching applications such as object recognition, wide-baseline stereo matching, mosaicing or tracking points on moving objects. In this paper, we extend such methods for use in such applications. The method is invariant to an affine transformation in the patch, which makes it applicable to a variety of applications. At the same time, our method is robust to different types of noise processes possible in a real scene. Experiments indicate favorable performance when compared to other state-of-the-art methods for affine-invariant point matching.