Overview
Internet of Things and Image processing have been so far been applied for various applications independently. Their individual application in the field of industries exists and has achieved a certain degree of success. However, the combination of both these techniques so far is non-existent. This article describes an approach to combine IoT and image processing; in order to determine the environmental factor or man-made factor (pesticides/fertilizers). Which is specifically hindering the growth of the plant. Using an IoT sensing network; which takes the readings of the crucial environmental factors and the image of the leaf lattice. It processed under MATLAB software by the help of histogram analysis to arrive at conclusive results.
Internet of Things(IoT)
The internet of things, or IoT; a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that provide with unique identifiers (UIDs). The ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. An IoT system consists of sensors/devices which “talk” to the cloud through some kind of connectivity. Once the data gets to the cloud; software processes it and then might decide to perform an action, such as sending an alert or automatically adjusting the sensors/devices without the need for the user. IoT essentially a platform where image processors connected to the internet; so they can collect and exchange data with each other. It enables devices to interact, collaborate and, learn from each other’s experiences just like humans do.
Image Processing
The image processing based on interfacing of Field Program Gate Array (FPGA); and Raspberry Pi using the Internet of Things which a recently introduced technique and a hot topic in the present scenario. The unimaginable interconnection of smart devices, smart cities, smart vehicles, and smart people throughout the globe made possible by the Internet of things.
The hardware implementation of various filters; use in image processing using the Internet of things on an FPGA platform present in this dissertation. The Raspberry Pi and FPGA interfacing-based implementation of image processing; filters using the Internet of Things have got huge consideration from the exploration group in a previous couple of years. In this paper, we highlight how one can access the design resources based on FPGA from any place. The primary point of this research is to highlight how the clients can get the FPGA-based outline resources from anyplace. In this manner, we exhibit an idea that abbreviates the utilization of immediately unused resources for executing different assignments automatically.
Importance of Image Processing in IoT
Digital image processing consists of the manipulation of images using digital computers. Its use has been increasing exponentially in the last decades. Its applications range from medicine to entertainment, passing by geological processing and remote sensing. Multimedia System, one of the pillars of the modern information society, rely heavily on digital image processing.
The discipline of digital image processing is a vast one; encompassing digital signal processing techniques as well as techniques that are specific to images. An image can be regarded as a function f (x, y) of two continuous variables x and y.
To be processed digitally, it sampled and transformed into a matrix of numbers. Since a computer represents the numbers using finite precision, these numbers have to be quantized to be represented digitally. Digital image processing consists of the manipulation of those finite precision numbers. The processing of digital images divided into several classes: image enhancement, image restoration, image analysis, and image compression. In image enhancement; an image manipulated, mostly by heuristic techniques, so that a human viewer can extract useful information from it. Image restoration; techniques aim at processing corrupted images from which there is a statistical or mathematical description of the degradation so that it can be reverted. Image analysis techniques permit that an image is processed so that information can be automatically extracted from it.
Examples
Examples of image analysis are; image segmentation, edge extraction, and texture and motion analysis. An important characteristic of images is the huge amount of information required to represent them. Even a greyscale image of moderate resolution; say 512 × 512, needs 512 × 512 × 8 ≈ 2 × 106 bits for its representation. Therefore, to be practical to store and transmit digital images, one needs to perform some sort of image compression; whereby the redundancy of the images is exploited for reducing the number of bits needed in their representation.
Applications of Image Processing in IoT
Power utilization and conservation in smart homes using IoT and Image Processing
Overuse of energy has caused many environmental and economic crises. Home appliances consume high energy. Energy consumption by home appliances is considered as one of the most critical areas for the attention to the researchers. Energy-saving is a bit challenging. Energy can be saved effectively by proper management of electricity distribution for home appliances based on the activities of the users. Recognizing human activities and providing energy supply for those appliances that are related to that activity can provide effective power utilization and conservation.
The existing system uses multiple sensors and servers which monitors the human activities, causing discomfort to users. Thus, a simple technique, based on Internet of Things (IoT), for recognizing human activity through image processing proposed in this article. It is a real-time approach for energy management in which a machine to machine communication takes place.
AI Surveillance Robot using IoT and Image Processing
An embedded system; a computer system designed for specific control functions within a larger system, often with real-time computing constraints. It is embedded as part of a complete device often including hardware and mechanical parts. By contrast; a general-purpose computer, such as a personal computer, is designed to be flexible and to meet a wide range of end-user needs. Embedded systems control many devices in common use today. A processor is an important unit in the embedded system hardware. It is the heart of the embedded system. Real-time image/Video processing becomes a challenging task because it is highly environment-dependent. The illumination of light highly affects the processing.
Lack of proper lighting condition; focusing on mobile subjects; interferences in signals causes; the presence of various type of noise in the image which makes processing not only difficult but also slower. Presence of complex background makes segmentation, a tiresome task for computation. A Computer having high processing speed is preferable for this purpose. However, nowadays many stand-alone development boards like Beagle Board, ARM9, ARM11; are available which is compatible for porting Image processing projects on it and make portable Image Processing projects. These boards are much cheaper than our personal computer, but a lot of hands-on exercises needed to be performed to have a deep knowledge about its architecture and functioning before switching to build Image Processing applications on it.
Smart Monitoring System Using IoT and Image Processing
Current global statistics show that an increasing number of elderly people live alone. Considering this unavoidable situation, a smart IoT system that can ease young family members to monitor their elderly family member from anywhere has been proposed. In this paper, the system uses a low-cost single board computer, named Raspberry Pi, with embedded webcam to perform 24 hours monitoring is demonstrated. A fall incident can detect by a captured video that will process using an image processing technique. This fall detection is done by several basic activities; separating moving objects from the background, calculating the parameters for these areas and finally, fall detection itself.
The fall detector is essential for elderly person monitoring since most of them suffer from chronic diseases and thus need more attention from their young family members. The system can also send a notification to the user using social media application when detecting fall incidents in the monitoring area. Video captured by the system stored in the cloud server so that it can be used for any incident investigation in the future. By using the system, incidents such as the death of elderly family members can be avoided by notifying fall incidents to family members that might be away from home.
Analogue signal and image processing with large memristor crossbars
Memristor crossbars offer reconfigurable non-volatile resistance states and could remove the speed and energy efficiency bottleneck in vector-matrix multiplication, a core computing task in signal and image processing. Using such systems to multiply an analogue-voltage-amplitude-vector by an analogue-conductance-matrix at a reasonably large scale has, however, proved challenging due to difficulties in device engineering and array integration.
The reconfigurable memristor crossbars composed of hafnium oxide memristors on top of metal-oxide-semiconductor transistors are capable of analogue vector-matrix multiplication with array sizes of up to 128 × 64 cells. Our output precision (5–8 bits, depending on the array size) is the result of high device yield (99.8%) and the multilevel, stable states of the memristors, while the linear device current-voltage characteristics and low wire resistance between cells leads to high accuracy. With the large memristor crossbars, we demonstrate signal processing, image compression and constitutional filtering, which expected to be important applications in the development of the Internet of Things (IoT) and edge computing.
Image Processing in Wildlife
We investigate the design and implementation of Where’s The Animal, an end-to-end, distributed, IoT system for wildlife monitoring. It implements a multi-tier (cloud, edge, sensing) system that integrates recent advances in machine learning-based image processing to automatically classify animals in images from remote, motion-triggered camera traps. We use non-local, resource-rich, public/private cloud systems to train the machine learning models, and “in-the-field,” resource-constrained edge systems to perform classification near the IoT sensing devices (cameras).
We deploy it at the UCSB Sedgwick Reserve, a 6000-acre site for environmental research and use it to aggregate, manage, and analyze over 1.12M images integrates Google TensorFlow and Open-CV applications to perform automatic classification and tagging for a subset of these images. To avoid transferring large numbers of training images for TensorFlow over a low-bandwidth network linking Sedgwick to the public/private clouds, we devise a technique that uses stock Google Images to construct a synthetic training set using only a small number of empty, background images from Sedgwick. Our system is able to accurately identify bears, deer, coyotes, and empty images and significantly reduces the time and bandwidth requirements for image transfer, as well as end-user analysis time since it automatically filters the images on-site.
Image Processing in Smart Agriculture System
At the present era, the farmers have been using various pesticides for crop at regular intervals. Presence of pests and disease affect the rate of crop cultivation. It reduces crop yield in a significant amount and as a result, there will be an increase in poverty, food insecurity and mortality rate. So, The current system relies on visual observation which is a time-consuming process. This problem can completely be resolved if we use automatic control of using pesticides in which the pesticides will be used based on the growth of the crop. With the advancement in image processing technology, it is feasible to create an automated mechanism for the detection of pests.
Image processing is the processing of images using mathematical operations by using any form of signal processing for which the input is an image, a series of images, or a video, such as a photograph or video frame; the output of image processing may be either an image or a set of characteristics or parameters related to the image. It usually refers to the digital image. Digital image processing makes use of various computer algorithms to perform image processing on digital images. It widely used for classification (identifies to which class does a newly found observation belong), pattern recognition (recognize known and discover unknown patterns), feature extraction (initial information which is used to make further derivations), multiscale signal analysis (signal processing) and projection (three-dimensional object is converted into a planar surface).
Image Processing in Traffic Control System
With the popularity of automobiles, road traffic accidents and congestion have become increasingly serious. Therefore, technologies needed to solve problems such as speeding and congestion. So, The detection and tracking of vehicles based on computer vision and Internet of Things monitoring are an important part of the intelligent traffic monitoring system. The angle between the camera and the vehicle will cause the gradually moving vehicles to have a connection during image segmentation. Moving areas extracted by inter-frame differences and vehicle areas formed from the areas. If more than one vehicle area partially overlaps as one area, it is necessary to separate the area. So, The existing method extracts a place to separate from an outline of the area.
However, it is impossible for the method to separate vehicles using the extracted shape. Therefore, a new method proposed that makes the place to be separated defined by the reshaping of the area with the use of the Fourier descriptor. Thus, The method tries to detect the place from the area. As a result, this method makes it possible to separate the area that the existing method cannot separate and it has obtained a high accuracy of separation in the experimental data of the Internet of Things monitoring.
Image Processing in Bio Medical Field
Biomedical imaging concentrates on the capture of images for both diagnostic and therapeutic purposes. So, Snapshots of in physiology and physiological processes can be garnered through advanced sensors and computer technology. Biomedical imaging technologies utilize either x-ray (CT scans), sound (ultrasound), magnetism (MRI), radioactive pharmaceuticals (nuclear medicine: SPECT, PET) or light (endoscopy, OCT) to assess the current condition of an organ or tissue and can monitor a patient over time over time for diagnostic and treatment evaluation.
The science and engineering behind the sensors; instrumentation and software used to obtain biomedical imaging has been evolving continuously since the x-ray was first invented in 1895. Modern x-rays using solid-state electronics require just milliseconds of exposure time; drastically reducing the x-ray dose originally needed for recording to film cassettes. The image quality has also improved, with enhanced resolution and contrast detail providing more reliable and accurate diagnoses.
The limitations of what x-rays can reveal the partially addressed through the introduction of a contrast medium to help visualize organs and blood vessels. First introduced as early as 1906, contrast agents, too, have evolved over the years.
Today, digital x-rays enable images to more easily shared and compared. Digital imaging gave rise to the CT scanner and allows physicians to watch real-time x-rays on a monitor—a technique known as x-ray fluoroscopy—to help guide invasive procedures such as angiograms and biopsies. No longer limited to simple anatomical imaging, current research is focusing on what can be gleaned through functional imaging. Biomedical engineers are using CT and MRI to measure the blood profusion of tissue; especially important after a heart attack or suspected heart attack. Researchers are also using functional MRI (fMRI) to measure different types of brain activity following strokes and traumatic head injuries.
Summary
A major challenge for automatic image analysis is that the sheer complexity of the visual task which has been mostly ignored by the current approaches. A new technological breakthrough in the areas of digital computation and telecommunication has relevance for future applications of image processing. The satellite imaging and remote sensing applications programs of the future will feature a variety of sensors orbiting the earth. This technology required for military and other types of surveillance, statistical data collection in the fields of forestry, agriculture, medical field, smart homes. In order to extract scientifically useful information; it will be necessary to develop techniques to register real-time data recorded by a variety of sensors for various applications.
Future Scope of IoT in Image Processing
The future of image processing will involve scanning the heavens for other intelligent life out in space. Also new intelligent; digital species created entirely by research scientists in various nations of the world will include advances in image processing applications. Due to advances in image processing; and related technologies there will be millions and millions of robots in the world in a few decades time; transforming the way the world is managing itself.
Advances in image processing and artificial intelligence; will involve spoken commands, anticipating the information requirements of governments; translating languages, recognizing and tracking people and things; diagnosing medical conditions, performing surgery, reprogramming defects in human DNA, and automatic driving all forms of transport. So, with increasing power and sophistication of modern computing; the concept of computation can go beyond the present limits and in future, image processing technology will advance and the visual system of man can be replicated. So, The future trend in remote sensing will be towards improved sensors that record the same scene in many spectral channels. Graphics data is becoming increasingly important in image processing applications. Thus, The future image processing applications of satellite-based imaging range from planetary exploration to surveillance applications.
