Stereo Matching in the Presence of Sub-Pixel Calibration Errors

Stereo matching commonly requires rectified images that are computed from calibrated cameras. Since all under-lying parametric camera models are only approximations, calibration and rectification will never be perfect. Additionally, it is very hard to keep the calibration perfectly stable in application scenarios with large temperature changes and vibrations. We show that even small calibration errors of a quarter of a pixel are severely amplified on certain structures. We discuss a robotics and a driver assistance example where sub-pixel calibration errors cause severe problems. We propose a filter solution based on signal theory that removes critical structures and makes stereo algorithms less sensitive to calibration errors. Our approach does not aim to correct decalibration, but rather to avoid amplifications and mismatches. Experiments on ten stereo pairs with ground truth and simulated decalibrations as well as images from robotics and driver assistance scenarios demonstrate the success and limitations of our solution that can be combined with any stereo method

Medical Image Classification via SVM using LBP Features from Saliency-Based Folded Data

Good results on image classification and retrieval using support vector machines (SVM) with local binary patterns (LBPs) as features have been extensively reported in the literature where an entire image is retrieved or classified. In contrast, in medical imaging, not all parts of the image may be equally significant or relevant to the image retrieval application at hand. For instance, in lung x-ray image, the lung region may contain a tumour, hence being highly significant whereas the surrounding area does not contain significant information from medical diagnosis perspective. In this paper, we propose to detect salient regions of images during training and fold the data to reduce the effect of irrelevant regions. As a result, smaller image areas will be used for LBP features calculation and consequently classification by SVM. We use IRMA 2009 dataset with 14,410 x-ray images to verify the performance of the proposed approach. The results demonstrate the benefits of saliency-based folding approach that delivers comparable classification accuracies with state-of-the-art but exhibits lower computational cost and storage requirements, factors highly important for big data analytics.Comment: To appear in proceedings of The 14th International Conference on Machine Learning and Applications (IEEE ICMLA 2015), Miami, Florida, USA, 201