A^2Net: Adjacent Aggregation Networks for Image Raindrop Removal

Existing methods for single images raindrop removal either have poor robustness or suffer from parameter burdens. In this paper, we propose a new Adjacent Aggregation Network (A^2Net) with lightweight architectures to remove raindrops from single images. Instead of directly cascading convolutional layers, we design an adjacent aggregation architecture to better fuse features for rich representations generation, which can lead to high quality images reconstruction. To further simplify the learning process, we utilize a problem-specific knowledge to force the network focus on the luminance channel in the YUV color space instead of all RGB channels. By combining adjacent aggregating operation with color space transformation, the proposed A^2Net can achieve state-of-the-art performances on raindrop removal with significant parameters reduction

Influence of Rain on Vision-Based Algorithms in the Automotive Domain

The Automotive domain is a highly regulated domain with stringent requirements that characterize automotive systems’ performance and safety. Automotive applications are required to operate under all driving conditions and meet high levels of safety standards. Vision-based systems in the automotive domain are accordingly required to operate at all weather conditions, favorable or adverse. Rain is one of the most common types of adverse weather conditions that reduce quality images used in vision-based algorithms. Rain can be observed in an image in two forms, falling rain streaks or adherent raindrops. Both forms corrupt the input images and degrade the performance of vision-based algorithms. This dissertation describes the work we did to study the effect of rain on the quality images and the target vision systems that use them as the main input. To study falling rain, we developed a framework for simulating failing rain streaks. We also developed a de-raining algorithm that detects and removes rain streaks from the images. We studied the relation between image degradation due to adherent raindrops and the performance of the target vision algorithm and provided quantitive metrics to describe such a relation. We developed an adherent raindrop simulator that generates synthetic rained images, by adding generated raindrops to rain-free images. We used this simulator to generate rained image datasets, which we used to train some vision algorithms and evaluate the feasibility of using transfer-learning to improve DNN-based vision algorithms to improve performance under rainy conditions.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/170924/1/Yazan Hamzeh final dissertation.pdfDescription of Yazan Hamzeh final dissertation.pdf : Dissertatio

Conference on Charge-Coupled Device Technology and Applications

Papers were presented from the conference on charge coupled device technology and applications. The following topics were investigated: data processing; infrared; devices and testing; electron-in, x-ray, radiation; and applications. The emphasis was on the advances of mutual relevance and potential significance both to industry and NASA's current and future requirements in all fields of imaging, signal processing and memory

New advances in vehicular technology and automotive engineering

An automobile was seen as a simple accessory of luxury in the early years of the past century. Therefore, it was an expensive asset which none of the common citizen could afford. It was necessary to pass a long period and waiting for Henry Ford to establish the first plants with the series fabrication. This new industrial paradigm makes easy to the common American to acquire an automobile, either for running away or for working purposes. Since that date, the automotive research grown exponentially to the levels observed in the actuality. Now, the automobiles are indispensable goods; saying with other words, the automobile is a first necessity article in a wide number of aspects of living: for workers to allow them to move from their homes into their workplaces, for transportation of students, for allowing the domestic women in their home tasks, for ambulances to carry people with decease to the hospitals, for transportation of materials, and so on, the list don’t ends. The new goal pursued by the automotive industry is to provide electric vehicles at low cost and with high reliability. This commitment is justified by the oil’s peak extraction on 50s of this century and also by the necessity to reduce the emissions of CO2 to the atmosphere, as well as to reduce the needs of this even more valuable natural resource. In order to achieve this task and to improve the regular cars based on oil, the automotive industry is even more concerned on doing applied research on technology and on fundamental research of new materials. The most important idea to retain from the previous introduction is to clarify the minds of the potential readers for the direct and indirect penetration of the vehicles and the vehicular industry in the today’s life. In this sequence of ideas, this book tries not only to fill a gap by presenting fresh subjects related to the vehicular technology and to the automotive engineering but to provide guidelines for future research. This book account with valuable contributions from worldwide experts of automotive’s field. The amount and type of contributions were judiciously selected to cover a broad range of research. The reader can found the most recent and cutting-edge sources of information divided in four major groups: electronics (power, communications, optics, batteries, alternators and sensors), mechanics (suspension control, torque converters, deformation analysis, structural monitoring), materials (nanotechnology, nanocomposites, lubrificants, biodegradable, composites, structural monitoring) and manufacturing (supply chains). We are sure that you will enjoy this book and will profit with the technical and scientific contents. To finish, we are thankful to all of those who contributed to this book and who made it possible.info:eu-repo/semantics/publishedVersio