The development of lab-on-a-chip technology and its applications in biochemical and biomedical analyses has, during the last two decades, led to the potential realisation of portable and on-site detection devices, the so-called point-of-care (PoC) detection systems. These are essentially cheap, easy-to-handle systems, offering rapid sample-to-answer results to non-technical operators. In this perspective, we do not review all the current advances of Lab-on-a-chip techniques for the realisation of PoC. Instead, we aim to provide insight into what we foresee as the three most important factors to play the essential roles for succeeding in making commercially viable PoC pathogen-detection devices. The three insights are namely: the utilizations of (i) disposable polymer (microfluidic) chips, (ii) the implementation of surface-bound (or solid-phase) nucleic-acid amplification techniques and (iii) relying (more) on open-source hardware and software.

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Point-of-care devices for pathogen detections: The three most important factors to realise towards commercialization

If you want a basic understanding of computer vision's underlying theory and algorithms, this hands-on introduction is the ideal place to start. As a student, researcher, hacker, or enthusiast, you'll learn as you follow examples written in Python - the easy-to-learn language that has modules for handling images and mathematical computing and data mining on a par with commercial alternatives. Programming Computer Vision with Python teaches computer vision in broad terms that won't bog you down in theory. Instead, you'll find this book to be inspiring and motivating. You'll get all the code you need, with clear explanations on how to reproduce the book's examples and build upon them directly.

Programming Computer Vision With Python

Unmanned aerial systems (UAS) are becoming a common tool for aerial sensing applications. Nevertheless, sensed data need further processing before becoming useful information. This processing requires large computing power and time before delivery. In this paper, we present a parallel architecture that includes an unmanned aerial vehicle (UAV), a small embedded computer on board, a communication link to the Internet, and a cloud service with the aim to provide useful real-time information directly to the end-users. The potential of parallelism as a solution in remote sensing has not been addressed for a distributed architecture that includes the UAV processors. The architecture is demonstrated for a specific problem: the counting of olive trees in a crop field where the trees are regularly spaced from each other. During the flight, the embedded computer is able to process individual images on board the UAV and provide the total count. The tree counting algorithm obtains an F 1 score of 99.09 % for a sequence of ten images with 332 olive trees. The detected trees are geolocated and can be visualized on the Internet seconds after the take-off of the flight, with no further processing required. This is a use case to demonstrate near real-time results obtained from UAS usage. Other more complex UAS applications, such as tree inventories, search and rescue, fire detection, or stock breeding, can potentially benefit from this architecture and obtain faster outcomes, accessible while the UAV is still on flight.

On-the-Fly Olive Tree Counting Using a UAS and Cloud Services

This study focuses on building a portable device capable of plant identification by image processing of leaf veins using Raspberry pi. The devise that the study will develop can help professionals in the field of Botany and Biology. It can be used by Pathologist, plant breeders and Doctors that specializes on medicinal plants. This study will concentrate on identifying the plant using its leaf veins. Its concentration is on simple leaf type. Twenty different kinds of plant leaves will be tested with 10 trials per leaf. Each leaf image will have its own labeled folder in the database that is created automatically after registration of leaf image. Series of algorithms were used for the leaf recognition method. Scale-Invariant Feature Transform (SIFT) extraction will be used to describe and detect the local features of the recognized leaf vein image. With the use of Support Vectors Machines (SVM), the matrix produced by the SIFT will be used to classify the correct plant to be shown on the Liquid Crystal Display (LCD) screen as the output containing the plant name, description and image. Initial results showed accuracy rate of 84.29% while the error rate was 15.71%.

Plant Identification by Image Processing of Leaf Veins

Fire is something that burns giving out bright light, heat, and smoke. Fire can be dangerous and disastrous at times causing loss of people and property. In case of such fires, there is a need to detect it at the right time and act immediately. So, we are using computer vision technology to detect fire and send an alert to a remote fire station. This alert is not only sent as an emergency message but also a video is sent along with it, considering the room is under constant surveillance. Through this video, we can know how many people are inside the room and the fire station might send sufficient rescuers based on the video to rescue people. The advantage is that we avoid using flame and smoke sensors that might give false alarms. In case there is a false alarm by using our system, we can verify it by checking the video. In this paper, we propose a fire detection method using OpenCV and Raspberry Pi to detect fire and send an alert as an alarm in the same building and a short video is sent to the remote fire alarm control unit.

Computer Vision Based Fire Detection with a Video Alert System

Today image processing are used in various techniques, this paper presents the implementation of image processing operations on Raspberry Pi. The Raspberry Pi is a basic embedded system and being a low cost a singleboard
computer used to reduce the complexity of systems in real time applications. This platform is mainly based on
python. Raspberry pi consist of Camera slot Interface (CSI) to interface the raspberry pi camera. Here, the Dark and
Low contrast images captured by using the Raspberry Pi camera module are enhanced in order to identify the particular region of image. This concept is used in the real time application of MAV, The MAVs are basically used to capture images and videos through the Raspberry pi camera module. Because of its credit card sized (small) and less weight in the design. However, the image captured by MAVs will consist of unwanted things due to atmospheric conditions; hence it is necessary to remove noise present in the MAVs images.

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KS Shilpashree

Papers

Implementation of Image Processing on Raspberry Pi