Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robots

Safety is paramount for mobile robotic platforms such as self-driving cars and unmanned aerial vehicles. This work is devoted to a task that is indispensable for safety yet was largely overlooked in the past -- detecting obstacles that are of very thin structures, such as wires, cables and tree branches. This is a challenging problem, as thin objects can be problematic for active sensors such as lidar and sonar and even for stereo cameras. In this work, we propose to use video sequences for thin obstacle detection. We represent obstacles with edges in the video frames, and reconstruct them in 3D using efficient edge-based visual odometry techniques. We provide both a monocular camera solution and a stereo camera solution. The former incorporates Inertial Measurement Unit (IMU) data to solve scale ambiguity, while the latter enjoys a novel, purely vision-based solution. Experiments demonstrated that the proposed methods are fast and able to detect thin obstacles robustly and accurately under various conditions.Comment: Appeared at IEEE CVPR 2017 Workshop on Embedded Visio

Analysis of Edge Detection Technique for Hardware Realization

Edge detection plays an important role in image processing and computer vision applications. Different edge detection technique with distinct criteria have been proposed in various literatures. Thus an evaluation of different edge detection techniques is essential to measure their effectiveness over a wide range of natural images with varying applications. Several performance indices for quantitative evaluation of edge detectors may be found in the literature among which Edge Mis-Match error (EMM), F-Measure (FM), Figure of Merit (FOM) and Precision and Recall (PR) curve are most effective. Several experiments on different database containing a wide range of natural and synthetic images illustrate the effectiveness of Canny edge detector over other detectors for varying conditions. Moreover, due to the ever increasing demand for high speed and time critical tasks in many image processing application, we have implemented an efficient hardware architecture for Canny edge detector in VHDL. The studied implementation technique adopts parallel architecture of Field Programmable Gate Array (FPGA) to accelerate the process of edge detection via. Canny’s algorithm. In this dissertation, we have simulated the considered architecture in Modelsim 10.4a student edition to demonstrate the potential of parallel processing for edge detection. This analysis and implementation may encourage and serve as a basis building block for several complex computer vision applications. With the advent of Field Programmable Gate Arrays (FPGA), massively parallel architectures can be developed to accelerate the execution speed of several image processing algorithms. In this work, such a parallel architecture is proposed to accelerate the Canny edge detection algorithm. The architecture is simulated in Modelsim 10.4a student edition platform

Mobofoto: a mobile platform for concentration measurement through colorimetric analysis

Traditional colorimetric measurement is widely used in chemical concentration estimation. However, compared to the laboratory solutions for accurate measurements with professional measuring equipment, colorimetric measurement is often more qualitative than quantitative. It would be most productive if we were only looking for a range of readings to draw a qualitative analysis conclusion. This means the traditional colorimetric measurement would only be an appropriate implementation for pre-medical self-diagnoses at home. Since it can only provide limited information about the tested chemical solutions, we are now featuring MoboFoto, an integrated mobile platform applying image analysis based on traditional colorimetric method to provide a more quantitative measurement, which can be used clinically. The mobile integration enables high-accuracy measurement and data visualization with a more affordable cost in a user-friendly setup environment. And the power of mobile computing also provides interfaces to capture, extract and aggregate measurement data for trend analysis from a medical perspective. Specifically, this research focuses on the exploration of the feasibility of high-accuracy colorimetric measurement as well as hardware-software implementation for the entire mobile platform

A study of smart device-based mobile imaging and implementation for engineering applications

Title from PDF of title page, viewed on June 12, 2013Thesis advisor: ZhiQiang ChenVitaIncludes bibliographic references (pages 76-82)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2013Mobile imaging has become a very active research topic in recent years thanks to the rapid development of computing and sensing capabilities of mobile devices. This area features multi-disciplinary studies of mobile hardware, imaging sensors, imaging and vision algorithms, wireless network and human-machine interface problems. Due to the limitation of computing capacity that early mobile devices have, researchers proposed client-server module, which push the data to more powerful computing platforms through wireless network, and let the cloud or standalone servers carry out all the computing and processing work. This thesis reviewed the development of mobile hardware and software platform, and the related research done on mobile imaging for the past 20 years. There are several researches on mobile imaging, but few people aim at building a framework which helps engineers solving problems by using mobile imaging. With higher-resolution imaging and high-performance computing power built into smart mobile devices, more and more imaging processing tasks can be achieved on the device rather than the client-server module. Based on this fact, a framework of collaborative mobile imaging is introduced for civil infrastructure condition assessment to help engineers solving technical challenges. Another contribution in this thesis is applying mobile imaging application into home automation. E-SAVE is a research project focusing on extensive use of automation in conserving and using energy wisely in home automation. Mobile users can view critical information such as energy data of the appliances with the help of mobile imaging. OpenCV is an image processing and computer vision library. The applications in this thesis use functions in OpenCV including camera calibration, template matching, image stitching and Canny edge detection. The application aims to help field engineers is interactive crack detection. The other one uses template matching to recognize appliances in the home automation system.Introduction -- Background and related work -- Basic imaging processing methods for mobile applications -- Collaborative and interactive mobile imaging -- Mobile imaging for smart energy -- Conclusion and recommendation