Visualization of Skewed Data: A Tool in R

In this work we present a visualization tool specifically tailored to deal with skewed data. The technique is based upon the use of two types of notched boxplots (the usual one, and one which is tuned for the skewness of the data), the violin plot, the histogram and a nonparametric estimate of the density. The data is assumed to lie on the same line, so the plots are compatible. We show that a good deal of information can be extracted from the inspection of this tool; in particular, we apply the technique to analyze data from synthetic aperture radar images. We provide the implementation in R.Comment: Submitted to the Revista Colombiana de Estad\'istic

Point Cloud Processing Algorithms for Environment Understanding in Intelligent Vehicle Applications

Understanding the surrounding environment including both still and moving objects is crucial to the design and optimization of intelligent vehicles. In particular, acquiring the knowledge about the vehicle environment could facilitate reliable detection of moving objects for the purpose of avoiding collisions. In this thesis, we focus on developing point cloud processing algorithms to support intelligent vehicle applications. The contributions of this thesis are three-fold.;First, inspired by the analogy between point cloud and video data, we propose to formulate a problem of reconstructing the vehicle environment (e.g., terrains and buildings) from a sequence of point cloud sets. Built upon existing point cloud registration tool such as iterated closest point (ICP), we have developed an expectation-maximization (EM)-like technique that can automatically mosaic multiple point cloud sets into a larger one characterizing the still environment surrounding the vehicle.;Second, we propose to utilize the color information (from color images captured by the RGB camera) as a supplementary source to the three-dimensional point cloud data. Such joint color and depth representation has the potential of better characterizing the surrounding environment of a vehicle. Based on the novel joint RGBD representation, we propose training a convolution neural network on color images and depth maps generated from the point cloud data.;Finally, we explore a sensor fusion method that combines the results given by a Lidar based detection algorithm and vehicle to everything (V2X) communicated data. Since Lidar and V2X respectively characterize the environmental information from complementary sources, we propose to get a better localization of the surrounding vehicles by a linear sensor fusion method. The effectiveness of the proposed sensor fusion method is verified by comparing detection error profiles

2013 Summer Research Symposium Abstract Book

2013 Summer volume of abstracts for science research projects conducted by students at Trinity College

Information extraction from multimedia web documents: an open-source platform and testbed

The LivingKnowledge project aimed to enhance the current state of the art in search, retrieval and knowledge management on the web by advancing the use of sentiment and opinion analysis within multimedia applications. To achieve this aim, a diverse set of novel and complementary analysis techniques have been integrated into a single, but extensible software platform on which such applications can be built. The platform combines state-of-the-art techniques for extracting facts, opinions and sentiment from multimedia documents, and unlike earlier platforms, it exploits both visual and textual techniques to support multimedia information retrieval. Foreseeing the usefulness of this software in the wider community, the platform has been made generally available as an open-source project. This paper describes the platform design, gives an overview of the analysis algorithms integrated into the system and describes two applications that utilise the system for multimedia information retrieval

Image Restoration by Matching Gradient Distributions

The restoration of a blurry or noisy image is commonly performed with a MAP estimator, which maximizes a posterior probability to reconstruct a clean image from a degraded image. A MAP estimator, when used with a sparse gradient image prior, reconstructs piecewise smooth images and typically removes textures that are important for visual realism. We present an alternative deconvolution method called iterative distribution reweighting (IDR) which imposes a global constraint on gradients so that a reconstructed image should have a gradient distribution similar to a reference distribution. In natural images, a reference distribution not only varies from one image to another, but also within an image depending on texture. We estimate a reference distribution directly from an input image for each texture segment. Our algorithm is able to restore rich mid-frequency textures. A large-scale user study supports the conclusion that our algorithm improves the visual realism of reconstructed images compared to those of MAP estimators

Unsupervised detection and localization of structural textures using projection profiles

Cataloged from PDF version of article.The main goal of existing approaches for structural texture analysis has been the identification of repeating texture primitives and their placement patterns in images containing a single type of texture. We describe a novel unsupervised method for simultaneous detection and localization of multiple structural texture areas along with estimates of their orientations and scales in real images. First, multi-scale isotropic filters are used to enhance the potential texton locations. Then, regularity of the textons is quantified in terms of the periodicity of projection profiles of filter responses within sliding windows at multiple orientations. Next, a regularity index is computed for each pixel as the maximum regularity score together with its orientation and scale. Finally, thresholding of this regularity index produces accurate localization of structural textures in images containing different kinds of textures as well as non-textured areas. Experiments using three different data sets show the effectiveness of the proposed method in complex scenes.(C)2010 Elsevier Ltd. All rights reserved