Simple Fusion Technique Research Articles

A comparative review: Medical image fusion using SWT and DWT

Image Fusion is the methodology to combining multiple images resulting in a single image keeping all the necessary aspects and features of original image with extended information. In this paper, we perform the comparative study between Stationary Wavelet Transform (SWT) and Discrete Wavelet Transform (DWT). We will discover various sectors where SWT performs better than DWT. Stationary Wavelet Transform (SWT) is primarily applied on the original image for obtaining the edge information of image, in Level 1 and Level 2 respectively. Next, both edge images are fused to get the finalised absolute edge image using spatial frequency measurement. Therefore, simple fusion techniques are used for comparing images.

Multi-Sensor Orientation Tracking for a Façade-Cleaning Robot.

Glass-façade-cleaning robots are an emerging class of service robots. This kind of cleaning robot is designed to operate on vertical surfaces, for which tracking the position and orientation becomes more challenging. In this article, we have presented a glass-façade-cleaning robot, Mantis v2, who can shift from one window panel to another like any other in the market. Due to the complexity of the panel shifting, we proposed and evaluated different methods for estimating its orientation using different kinds of sensors working together on the Robot Operating System (ROS). For this application, we used an onboard Inertial Measurement Unit (IMU), wheel encoders, a beacon-based system, Time-of-Flight (ToF) range sensors, and an external vision sensor (camera) for angular position estimation of the Mantis v2 robot. The external camera is used to monitor the robot’s operation and to track the coordinates of two colored markers attached along the longitudinal axis of the robot to estimate its orientation angle. ToF lidar sensors are attached on both sides of the robot to detect the window frame. ToF sensors are used for calculating the distance to the window frame; differences between beam readings are used to calculate the orientation angle of the robot. Differential drive wheel encoder data are used to estimate the robot’s heading angle on a 2D façade surface. An integrated heading angle estimation is also provided by using simple fusion techniques, i.e., a complementary filter (CF) and 1D Kalman filter (KF) utilizing the IMU sensor’s raw data. The heading angle information provided by different sensory systems is then evaluated in static and dynamic tests against an off-the-shelf attitude and heading reference system (AHRS). It is observed that ToF sensors work effectively from 0 to 30 degrees, beacons have a delay up to five seconds, and the odometry error increases according to the navigation distance due to slippage and/or sliding on the glass. Among all tested orientation sensors and methods, the vision sensor scheme proved to be better, with an orientation angle error of less than 0.8 degrees for this application. The experimental results demonstrate the efficacy of our proposed techniques in this orientation tracking, which has never applied in this specific application of cleaning robots.

Bring Your Own Learner: A Cloud-Based, Data-Parallel Commons for Machine Learning

Abstract-We introduce FCUBE, a cloud-based framework that enables machine learning researchers to contribute their learners to its community-shared repository. FCUBE exploits data parallelism in lieu of algorithmic parallelization to allow its users to efficiently tackle large data problems automatically. It passes random subsets of data generated via resampling to multiple learners that it executes simultaneously and then it combines their model predictions with a simple fusion technique. It is an example of what we have named a Bring Your Own Learner model. It allows multiple machine learning researchers to contribute algorithms in a plug-and-play style. We contend that the Bring Your Own Learner model signals a design shift in cloud-based machine learning infrastructure because it is capable of executing anyone's supervised machine learning algorithm. We demonstrate FCUBE executing five different learners contributed by three different machine learning groups on a 100 node deployment on Amazon EC2. They collectively solve a publicly available classification problem trained with 11 million exemplars from the Higgs dataset.

Dynamic comparison of headlights

Continuous innovations in automotive lighting technology pose the problem of how to assess new headlights systems. For car manufacturers, assessment is mostly relative: given a headlights system to be tested, how does it compare with another, maybe from a different supplier, in terms of features such as light intensity, homogeneity or reach? This comparison is best performed dynamically, asking experts actually to drive along a certain testing track to write down later the visual impressions that they remember. However, this procedure suffers from several drawbacks: comparisons cannot be repeated, are not retrospective, and cannot be properly shared with other people since the only record is a paper form. To overcome these, it is proposed to record, for each headlights system, a video sequence of what the driver sees with a camera attached to the windshield screen. The problem becomes now how to compare a pair of such sequences. Two issues must be addressed: the temporal alignment or synchronization of the two sequences, and then the spatial alignment or registration of all the corresponding frames. In this paper a semiautomatic but fast procedure for the former, and an automatic method for the later are proposed. In addition, an alternative to the joint visualization of corresponding frames called the bird's-eye view transform is explored, and a simple fusion technique for better visualization of the headlights differences in two sequences is proposed. Results are provided for a number of headlights with different light sources and from several vehicle brands, in the form of both still images and video sequences.

Segmentation of X-ray and C-scan images of fiber reinforced composite materials

This paper is concerned with the segmentation of images corrupted by non-uniform illumination and sensor noise. Four segmentation techniques, i.e. simple thresholding, the adaptive thresholding scheme of Chow and Kaneko, the iterated conditional modes method of Besag, and the adaptive thresholding scheme of Yanowitz and Bruckstein, are presented here. The results of these four methods applied to three sets of artificial and real images are compared. Since these segmentation results are not satisfactory, an algorithm based on the adaptive thresholding method proposed by Yanowitz and Bruckstein and the Canny edge detector is proposed for the segmentation of real X-ray and C-scan images of composite materials. This method has been applied to several images of composite specimens containing a variety of defects. The segmentation results of X-ray and C-scan images are satisfactory which are further improved by a simple fusion technique.