An Outlier Detection Algorithm Based on Cross-Correlation Analysis for Time Series Dataset

Outlier detection is a very essential problem in a variety of application areas. Many detection methods are deficient for high-dimensional time series data sets containing both isolated and assembled outliers. In this paper, we propose an Outlier Detection method based on Cross-correlation Analysis (ODCA). ODCA consists of three key parts. They are data preprocessing, outlier analysis, and outlier rank. First, we investigate a linear interpolation method to convert assembled outliers into isolated ones. Second, a detection mechanism based on the cross-correlation analysis is proposed for translating the high-dimensional data sets into 1-D cross-correlation function, according to which the isolated outlier is determined. Finally, a multilevel Otsu\u27s method is adopted to help us select the rank thresholds adaptively and output the abnormal samples at different levels. To illustrate the effectiveness of the ODCA algorithm, four experiments are performed using several high-dimensional time series data sets, which include two smallscale sets and two large-scale sets. Furthermore, we compare the proposed algorithm with the detection methods based on wavelet analysis, bilateral filtering, particle swarm optimization, auto-regression, and extreme learning machine. In addition, we discuss the robustness of the ODCA algorithm. The statistical results show that the ODCA algorithm is much better than existing mainstream methods in both effectiveness and time complexity

Outlier modeling in image matching

We address the question of how to characterize the outliers that may appear when matching two views of the same scene. The match is performed by comparing the difference of the two views at a pixel level, aiming at a better registration of the images. When using digital photographs as input, we notice that an outlier is often a region that has been occluded, an object that suddenly appears in one of the images, or a region that undergoes an unexpected motion. By assuming that the error in pixel intensity levels generated by the outlier is similar to an error generated by comparing two randomly picked regions in the scene, we can build a model for the outliers based on the content of two views. We illustrate our model by solving a pose estimation problem: the goal is to compute the camera motion between two views. The matching is expressed as a mixture of inliers versus outliers an defines a function to minimise for improving the pose estimation. Our model has two benefits: First it delivers a probability for each pixel to belong to the outliers. Second our tests show that the method is substantially more robust than traditional robust estimators (M-estimators) used in image stitching applications, with only a slightly higher computational complexity

Perspectives on panoramic photography

Digital imaging brings a new set of possibilities to photography. For example, little pictures can be assembled to form a large panorama, and digital cameras are trying to mimic the human visual system to produce better pictures. This manuscript aims at developing the algorithms required to stitch a set of pictures together to obtain a bigger and better image. This thesis explores three important topics of panoramic photography: The alignment of images, the matching of the colours, and the rendering of the resulting panorama. In addition, one chapter is devoted to 3D and constrained estimation. Aligning pictures can be difficult when the scene changes while taking the photographs. A method is proposed to model these changes —or outliers— that appear in image pairs, by computing the outlier distribution from the image histograms and handling the image-to-image correspondence problem as a mixture of inliers versus outliers. Compared to the standard methods, this approach uses the information contained in the image in a better way, and leads to a more reliable result. Digital cameras aim at reproducing the adaptation capabilities of the human eye in capturing the colours of a scene. As a consequence, there is often a large colour mismatch between two pictures. This work exposes a novel way of correcting for colour mismatches by modelling the transformation introduced by the camera, and reversing it to get consistent colours. Finally, this manuscript proposes a method to render high dynamic range images that contain very bright as well as very dark regions. To reproduce this kind of pictures the contrast has to be reduced in order to match the maximum contrast displayable on a screen or on paper. This last method, which is based on a complex model of the human visual system, reduces the contrast of the image while maintaining the little details visible the scene