Modern vehicles are very dependent on sensors and especially cameras to analyze different objects and conditions. Single camera systems are frequently used for lane detection and identifying objects in the distance. These systems depend on good conditions to work properly and are easily disrupted by environmental interferences. This project is targeted in developing an image processing algorithm that can detect disturbances applied upon the camera lens. Several focus measure operators are evaluated and compared by expected outcome while still maintaining a satisfying computational time, low resource usage and accuracy with an ERR between 0% - 1%.

This thesis explores a novel approach to the computer vision eld in the form of lowcost computer vision intended for industrial use. The system proposed in this thesis, calledSimpleEye, is implemented and tested against an existing system. Dierent approachesto object detection and data extraction from a scene, as well as common applications ofcomputer vision in the industry, are examined. Three algorithms are implemented, aimedat dierent industrial applications. These are two types of object recognition, using CannyEdge detection and connected-component labeling, as well as barcode scanning. The tests,each targeting one of the implemented approaches, show promising results for low costcomputer vision. While the system is expectedly lacking in speed, it has no diculties inachieving good result in applications which are not highly time critical. SimpleEye yieldedaccuracy and precision comparable to commercial systems, with parts costing approximately100 USD. The tests show that the system is able to function in several computer visionapplications used today, including visual servoing, blob detection, blob tracking, and barcodescanning.

Plants are vital to the health of our biosphere, and effectively sustaining their growth is fundamental to the existence of life on this planet. A critical aspect, which decides the sustainability of plant growth is the quality of soil. All other things being fixed, the quality of soil greatly impacts the plant stress, which in turn impacts overall health. Although plant stress manifests in many ways, one of the clearest indicators are colors of the leaves. In this thesis, we conducted an experimental study in a greenhouse for detecting plant stress caused by nutrient deficienceies in soil using smartphone cameras, coupled with image processing and machine learning algorithms. The greenhouse experiment was conducted by growing two plant species; willows (Salix Pentandra) and poplars (Populus deltoides x nigra, DN34), in two treatments. These treatments included: unamended tailings (collected from a lead mine tailings pond and characterized by nutrient deficiency), and biosolids amended tailings. Biosolids are very rich in nutrients and were added to the tailings in one of the two treatments to supply plants with nutrients. Subsequently, we captured various images of plant leaves grown in both soils. Each image taken was pre-processed via filteration to remove associated noise, and was segmented into pixels to facilitate scalability of analysis. Subsequently, we designed random forests based algorithms to detect the stress of leaves as indicated by their coloring. In a dataset consisting of 34 leaves, our technique yields classifications with a high degree of prediction, recall and F1 score. Our work in this thesis, while restricted to two types of plants and soils, can be generalized. We see applications in the emerging area of urban farming in terms of empowering citizens with tools and technologies for enhancing quality of farming practices.

Vision-based positioning systems are founded primarily on a simple image processing technique of identifying various visually significant key-points in an image and relating them to a known coordinate system in a scene. Fiducial markers are used as a means of providing the scene with a number of specific key-points, or features, such that computer vision algorithms can quickly identify them within a captured image. This thesis proposes a reliable vision-based positioning system which utilizes a unique pseudo-random fiducial marker. The marker itself offers 49 distinct feature points to be used in position estimation. Detection of the designed marker occurs after an integrated process of adaptive thresholding, k-means clustering, color classification, and data verification. The ultimate goal behind such a system would be for indoor localization implementation in low cost autonomous mobile platforms.

This master’s thesis deals with conveyor belt control design. Control system is created in LabVIEW environment. Application uses machine vision for monitoring objects on the conveyor belt. In the first part there is theoretical background. In the next parts there are described design, implementation and verification of created control system.

This master’s thesis deals with the fundamentals of machine vision application and its practical implementation. The research part is focused on the basic possibilities of image acquisition and image processing in different dimensions. The practical part describes the design of the dimension control algorithm using a 3D camera. This algorithm is then implemented in a single-purpose machine for optical dimension control.

With the rapid development of deep learning techniques, the application of Convolutional Neural Network (CNN) has benefited the task of target object recognition. Several state-of-the-art object detectors have achieved excellent performance on the precision for object recognition. When it comes to applying the detection results for the real world application of collaborative robots, the reliability and robustness of the target object detection stage is essential to support efficient task allocation. In this work, collaborative robots task allocation is based on the assumption that each individual robotic agent possesses specialized capabilities to be matched with detected targets representing tasks to be performed in the surrounding environment which impose specific requirements. The goal is to reach a specialized labor distribution among the individual robots based on best matching their specialized capabilities with the corresponding requirements imposed by the tasks. In order to further improve task recognition with convolutional neural networks in the context of robotic task allocation, this thesis proposes an innovative approach for progressively refining the target detection process by taking advantage of the fact that additional images can be collected by mobile cameras installed on robotic vehicles. The proposed methodology combines a CNN-based object detection module with a refinement module. For the detection module, a two-stage object detector, Mask RCNN, for which some adaptations on region proposal generation are introduced, and a one-stage object detector, YOLO, are experimentally investigated in the context considered. The generated recognition scores serve as input for the refinement module. In the latter, the current detection result is considered as the a priori evidence to enhance the next detection for the same target with the goal to iteratively improve the target recognition scores. Both the Bayesian method and the Dempster-Shafer theory are experimentally investigated to achieve the data fusion process involved in the refinement process. The experimental validation is conducted on indoor search-and-rescue (SAR) scenarios and the results presented in this work demonstrate the feasibility and reliability of the proposed progressive refinement framework, especially when the combination of adapted Mask RCNN and D-S theory data fusion is exploited.

Unmanned ground vehicles are increasingly employing a combination of active sensors such as LIDAR with passive sensors like cameras to perform at all levels of perception, which includes detection, recognition and classification. Typical cameras measure the intensity of light at a variety of different wavelengths to classify objects in different areas of an image. A polarimetric camera not only measures intensity of light, but can also determine its state of polarization. The polarization of light is the angle the electric field of the wave of light takes as it travels. A polarimetric camera can identify the state of polarization of the light, which can be used to segment highly polarizing areas in a natural environment, such the surface of water. The polarimetric camera designed and built for this thesis was created with low cost in mind, as commercial polarimetric cameras are very expensive. It uses multiple beam splitters to split incoming light into four machine vision cameras. In front of each machine vision camera is a linear polarizing filter that is set to a specific orientation. Using the data from each camera, the Stokes vector can be calculated on a pixel by pixel basis to determine what areas of the image are more polarized. Test images of various scenes that included running water, standing water, mud, and vehicles showed promise in using polarization data to highlight and identify areas of interest. This data could be used by a UGV to make more informed decisions in an autonomous navigation mode. / Master of Science

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A study of the soil characteristics, weather conditions, and effect of management skills on the yield of the agricultural crop requires site-specific details, which involves large amount of labor and resources, compared to the traditional whole field based analysis. This thesis discusses the design and implemention of yield monitor for sweetpotatoes grown in heavy clay soil. A data acquisition system is built and image segmentation algorithms are implemented. The system performed with an R-Square value of 0.80 in estimating the yield. The other main contribution of this thesis is to investigate the effectiveness of statistical methods and neural networks to correlate image-based size and shape to the grade and weight of the sweetpotatoes. An R-Square value of 0.88 and 0.63 are obtained for weight and grade estimations respectively using neural networks. This performance is better compared to statistical methods with an R-Square value of 0.84 weight analysis and 0.61 in grade estimation.