A middleware for a large array of cameras

Large arrays of cameras are increasingly being employed for producing high quality image sequences needed for motion analysis research. This leads to the logistical problem with coordination and control of a large number of cameras. In this paper, we used a lightweight multi-agent system for coordinating such camera arrays. The agent framework provides more than a remote sensor access API. It allows reconfigurable and transparent access to cameras, as well as software agents capable of intelligent processing. Furthermore, it eases maintenance by encouraging code reuse. Additionally, our agent system includes an automatic discovery mechanism at startup, and multiple language bindings. Performance tests showed the lightweight nature of the framework while validating its correctness and scalability. Two different camera agents were implemented to provide access to a large array of distributed cameras. Correct operation of these camera agents was confirmed via several image processing agents

Image quality based x-ray dose control in cardiac imaging

An automated closed-loop dose control system balances the radiation dose delivered to patients and the quality of images produced in cardiac x-ray imaging systems. Using computer simulations, this study compared two designs of automatic x-ray dose control in terms of the radiation dose and quality of images produced. The first design, commonly in x-ray systems today, maintained a constant dose rate at the image receptor. The second design maintained a constant image quality in the output images. A computer model represented patients as a polymethylmetacrylate phantom (which has similar x-ray attenuation to soft tissue), containing a detail representative of an artery filled with contrast medium. The model predicted the entrance surface dose to the phantom and contrast to noise ratio of the detail as an index of image quality. Results showed that for the constant dose control system, phantom dose increased substantially with phantom size (x5 increase between 20cm and 30 cm thick phantom), yet the image quality decreased by 43% for the same thicknesses. For the constant quality control, phantom dose increased at a greater rate with phantom thickness (>x10 increase between 20 cm and 30 cm phantom). Image quality based dose control could tailor the x-ray output to just achieve the quality required, which would reduce dose to patients where the current dose control produces images of too high quality. However, maintaining higher levels of image quality for large patients would result in a significant dose increase over current practice

GRAPHOS – An open-source software for photogrammetric applications

19 p.This paper reports the latest developments for the photogrammetric open‐source tool called GRAPHOS (inteGRAted PHOtogrammetric Suite). GRAPHOS includes some recent innovations in the image‐based 3D reconstruction pipeline, from automatic feature detection/description and network orientation to dense image matching and quality control. GRAPHOS also has a strong educational component beyond its automated processing functions, reinforced with tutorials and didactic explanations about algorithms and performance. The paper highlights recent developments carried out at different levels: graphical user interface (GUI), didactic simulators for image processing, photogrammetric processing with weight parameters, dataset creation and system evaluationS

A system-theoretic approach for image-based infectious plant disease severity estimation

The demand for high level of safety and superior quality in agricultural products is of prime concern. The introduction of new technologies for supporting crop management allows the efficiency and quality of production to be improved and, at the same time, reduces the environmental impact. Common strategies to disease control are mainly oriented on spraying pesticides uniformly over cropping areas at different times during the growth cycle. Even though these methodologies can be effective, they present a negative impact in ecological and economic terms, introducing new pests and elevating resistance of the pathogens. Therefore, consideration for new automatic and accurate along with inexpensive and efficient techniques for the detection and severity estimation of pathogenic diseases before proper control measures can be suggested is of great realistic significance and may reduce the likelihood of an infection spreading. In this work, we present a novel system-theoretic approach for leaf image-based automatic quantitative assessment of pathogenic disease severity regardless of disease type. The proposed method is based on a highly efficient and noise-rejecting positive non-linear dynamical system that recursively transforms the leaf image until only the symptomatic disease patterns are left. The proposed system does not require any training to automatically discover the discriminative features. The experimental setup allowed to assess the system ability to generalise symptoms detection beyond any previously seen conditions achieving excellent results. The main advantage of the approach relies in the robustness when dealing with low-resolution and noisy images. Indeed, an essential issue related to digital image processing is to effectively reduce noise from an image whilst keeping its features intact. The impact of noise is effectively reduced and does not affect the final result allowing the proposed system to ensure a high accuracy and reliability