This subset of strong and spatially well distributed point correspondences are used to ro- bustly calculate a homography transformation H between the two images using a RANSAC  approach. The process of using RANSAC to estimate a model is explained in Section 2.2 and shown in Algorithm 2. At a very high level, the algorithm randomly picks pairs of four point correspondence to calculate a homography and considers point correspondences from the re- maining points to be inliers (or the consensus set) if they are within a certain threshold t of the Symmetric Transfer Error (STE) as per Equation 6.15 based on the computed homography. Samples are picked at random repeatedly until a maximum number of trails N is exceeded or a critical number of T inlier correspondences are found. If the maximum number trials are exceeded the model with the largest consensus set obtained so far is considered. The critical number of T inlier correspondences can be computed from the desired probability p of choos- ing at least one sample free from outliers as shown by Hartley and Zisserman . We use the implementation of Kovesi  for robust homography estimation which is based on the RANSAC based robust homography estimation method described by Hartley and Zisserman . Once the homography H is computed, it is common practice (e.g., Hartley and Zisserman ) to perform guided matching to obtain further interest point correspondences by using H to defined search regions about the transfered point position. However, in our scenario the re- flection edge points which we wish to detect, should not in general satisfy H unlike the surface edge points. Hence, we do not perform a subsequent guided matching.
damaging the government property, severe traveler injuries and on-road fatalities. Since 1988, around 5,000,000 automobile crashes occurred based on the reports published by the national road traffic safety administration (NHTSA), most of the reports end with traveler injuries and on-road fatalities. NHTSA 2015 reports say that accidents have been decreased through effective traffic maintenance with several detection strategies and their corresponding responses. Transportation management can only be possible by detecting traffic accidents and congestions.
This research is based on image recognition technology, taking sports as the research object, identifying the sports process of sports athletes, and performing image processing on sports videos. At the same time, this paper obtains effective information through image processing, and on this basis, further improves the efficiency of sports training and sports competition. In this study, the image is segmented from the video. Segmentation in space is the detection and segmentation of the moving target. Specifically, it separates the independent regions of interest or meaning in the video sequence from the background. After the skin color detection, a series of connected regions are obtained, and the image only con- tains the black pixels of these connected regions and other excluded white regions, and a new binary image is obtained. The main research goal of this paper is to identify the types of poses that athletes make in the game in a complex environment, and use the idea of fea- ture fusion to characterize the poses of people with mul- tiple features. Therefore, in the representation of human motion state, this paper extracts the key features of the overall shape and motion of the human body. The ex- perimental research shows that the technology proposed in this study has certain practical effects and can be ap- plied to the actual competition.
Technological advances in telecommunications and information technology, coupled with ultramodern/state-of- the-art microchip, RFID (Radio Frequency Identification), and inexpensive intelligent beacon sensing technolo- gies, have enhanced the technical capabilities that will facilitate motorist safety benefits for intelligent transpor- tation systems globally. Sensing systems for ITS are vehicle- and infrastructure-based networked systems, i.e., intelligent vehicle technologies. Infrastructure sensors are indestructible (such as in-road reflectors) devices that are installed or embedded in the road or surrounding the road (e.g., on buildings, posts, and signs), as re- quired, and may be manually disseminated during preventive road construction maintenance or by sensor injec- tion machinery for rapid deployment. Vehicle-sensing systems include deployment of infrastructure-to-vehicle and vehicle-to-infrastructure electronic beacons for identification communications and may also employ vid- eo automatic number plate recognition or vehicle magnetic signature detection technologies at desired intervals to increase sustained monitoring of vehicles operating in critical zones.
As per stated in the paper, We can implement the image processing by using mat lab further that data is given to the ATMEGA8 microcontroller through USART module (Universal Synchronous Asynchronous Receiver Transmitter) module for sending the information from software to microcontroller. The use of this microcontroller, because it's a low power CMOS 8- bit microcontroller based on the AVR RISC architecture. By executing powerful instructions in a single clock cycle, its achieves throughput approaching 1 MIPS per MHZ, allows the system designed to optimize the power consumption vs. processing speed. All the necessary information is processed and sends by mat lab to the microcontroller for a particular signal to be lighted. So there is longer traffic cycle for more traffic density vice versa accordingly the traffic light is controlled. It's obtained by using model weight and weight time allocation.
Vehicle tracking using the canny edge detector algorithm is used for detecting the edges. A Dynamic Bayesian Network (DBN) is constructed for classification purpose. A well trained DBN can estimate the probability of a pixel belonging to a vehicle or not. It also relates among neighboring pixels in a region. There is a fast growth in computer technology and increasing needs in security and studies of target vehicledetection in aerial surveillance using image processing techniques and based IPs and Location also it will work effectively in vehicle tracking using HMA VPN.
ARM7 is one of the widely used micro-controller family in embedded system application. This section is humble effort for explaining basic features of ARM7 is a family of instruction set architectures for computer processor based on a reduced instruction set computer developed by British company ARM holdings. A RISC-based computer design approach means ARM processor require significantly fewer transistor than typical processor in average computer.
Pavlopoulos, et al.  proposed a CAD system based on texture features estimated from Gray Level Difference Statistics (GLDS), SGLDM, Fractal Dimension (FD) and a novel fuzzy neural network classifier to classify a liver ultrasound images into normal, fatty and cirrhosis with accuracy in the order of 82.7%.
To overcome the words in the news video size and frame between rapid transformation leads to the problem of the large text jumps, this paper proposes a multi-scale image fusion based on news video text area detection localization algorithm. First of all the artificial collection of positive and negative samples wavelet high-frequency characteristics and local binary pattern such as feature extraction, the two features can reflect the similarities and differences of background of video and text image block; Then use support vector machine (SVM) training samples, obtained classifier; Last video frame of the test for multi-scale image sub-block traversal classification, the final test results to fusion, and connected domain analysis and regional constraints, test results in the end to image. Compared with the method based on edge detection, because of video frames for the multi-scale detection, this paper algorithm in addition to text size problem can be overcome, and raised the recall text detection, has certain practical significance.
barcode with scanner machine only. In order to improve the practical application property of the two-dimensional barcode Quick Response (QR) code, we investigate the coding and decoding process of the QR code image. Run-length coding is applied to binary QR code image so as to accelerate the identification of QR code image. The QR code is transformed into many runs of data in alternate pixels of black and white. The related runs of data among adjacent rows are formed a unit module. After the whole image has been scanned, all of such modules in binary QR code image can be generated accordingly. With a noisy QR image captured by an industrial camera as an example, the experiments of image binarization, image seeking and localization adjustment are accomplished in sequence. Also the error correction algorithm is discussed in detail. A decoding system of QR code is designed and the online detection experiments are carried out.
DOI: 10.4236/jcc.2019.76001 2 Journal of Computer and Communications ment. Various methods based on RADAR (Radio Detection and Ranging) or LIDAR (Laser Infrared Detection and Ranging) or camera have been developed, but none of these techniques are perfect.
Diabetic retinopathy, also known as diabetic in eye disease, is a medical condition which damage occurs to the retina due to diabetes and is a leading cause of blindness. It affects up to 80 percent of people who have had diabetes for 20 years or more. At least 90% of new cases could be reduced if there were proper treatment and monitoring of the eyes. The longer a person has diabetes, the higher his or her chances of developing diabetic retinopathy. Each year in the United States, diabetic retinopathy accounts for 12% of all new cases of blindness. It is also the leading cause of blindness for people aged 20 to 64 years. Retinopathy is any damage to the retina of the eyes, which may cause vision impairment. Retinopathy often refers to retinal vascular disease, or damage to the retina caused by abnormal blood flow.
If someone has an MI, a coronary artery or one of it is smaller branches is suddenly blocked. The part of the heart muscle supplied by this artery loses it's blood (and oxygen) supply. This part of the heart muscle is then at risk of damage unless the blockage is quickly undone. (Strictly speaking, 'infarction' means death of some tissue due to a blocked artery which stops blood from getting past.)
connected on each pixel, the likelihood of the pixel gets to be a portion of the LP region. Every column checked individually. If the blackness ratio of columns becomes greater than 50 percent, then the present column related the LP region. Consequently, the column will be recovered by a vertical dark line within the yield image. The condition for checking each column is given as, on the off chance that blackPix 0.5 x columnHght, then the show column is has a place the LP locale in which the blackPix speaks to the number of dark pixels for each one column within the display candidate locale. A few of candidate districts pixels are not recognized when the darkness to the entire length proportion of the locale is more prominent than 50 percent. Then, the condition is changed to ended up less than 50 percent, concurring to the blurry level ratio. Hence the condition will get to be as follows: blackPix PRS x columnHght, where PRS speaks to the PRS factor. PRS esteem is diminished when the blurry level is greatest to highlight vital details. It is expanded when the foggy level is least. Subsequently, the scientific representation for the LP locale determination can be given as follows:
In year 2010, Ehab F. Badran, Esraa Galal Mahmoud and Nadder Hamdy performed a work, “An Algorithm for Detecting Brain Tumors in MRI Images” .In this paper for defining the tumor region and classification of tissues, a computer based method has been applied on MRI images of brain. For automaticdetection of brain tumor, a method is used that include various steps of image processing like image preprocessing, image segmentation, feature extraction and classification using neural network.
to implement at a negligible cost for automated toll system.  Researches are there on developing image processing based toll system like Vehicle Number plate Recognition System for Automatic Toll Tax Collection by Shoaib Rehman  but it‟s based upon software computer dependent image processing system. In this project raspberry pi basedimage processing system depending automated toll system been proposed. Raspberry Pi can receive picture through Wi-Fi camera and then process the image of license plate. It will connect with database, search the user account and then subtract the toll from user account. As soon as the toll is received the barrier will be moved automatically and after the car has passed the barrier will be placed again automatically.
Abstract: The NPR (Number Plate Recognition) using is a system designed to help in recognition of number plates of vehicles. This system is designed for the purpose of the security system. This system is based on the image processing system. This system helps in the functions like detection of the number plates of the vehicles, processing them and using processed data for further processes like storing, allowing vehicle to pass or to reject vehicle. NPR is an image processing technology which uses number (license) plate to identify the vehicle. The objective is to design an efficient automatic authorized vehicle identification system by using the vehicle number plate. The system is implemented on the entrance for security control of a highly restricted area like military zones or area around top government offices e.g. Parliament, Supreme Court etc. The developed system first captures the vehicleimage. Vehicle number plate region is extracted using the image segmentation in an image. Optical character recognition technique is used for the character recognition. The resulting data is then used to compare with the records on a database. The system is implemented and simulated in Matlab, and it performance is tested on real image. It is observed from the experiment that the developed system successfully detects and recognize the vehicle number plate on real images.
Main purpose of this project is to prevent accidents. Accident preventions are taken care with the help of automatic breaking system using ultrasonic sensor. Using ultrasonic as a ranging sensor, its function based on ultrasonic wave. After transmit by transmitter, the wave can reflect when obstacle detected and receive by receiver. The main target for this project is cars can automatically braking due to obstacles when the sensor senses the obstacles. The braking circuit function is to brake the car automatically after received signal from the sensor. To prevent these accidents of vehicles from taking place we are using Automated Emergency Brake Systems and Ultrasonic Sensors.
An image extraction algorithm in Licence Plate Detection and recognition System was proposed by Salah Al-Shami et al . Here, the optical character recognition is being implemented by assigning weights to characters. The tests were performed on real world licence plate images. Character recognition is undoubtedly a difficult task, and many researchers have worked with full authenticity to sort out this problem. This process depends upon manual selection of each line of character and also each character individually. This manual selection of characters is crucial to this technique as it produces ideal conditions to get accurate character recognition. Different stages are incorporated into this recognition plan of a character. Characterization depends on definitive features of that character which are stored earlier to compare. These characters are matched to the manually selected characters to get results. In KSA (Kingdom Of Saudi Arabia) this technique was utilized on a few datasets in License Plates. Consequently, the generated results showed the precise and the productive way of the proposed technique in contrast with the traditional proposed plans.
Shyang-Lih Chang et al.  proposed a license plate image technique consisting of two main models: a license plate locating model and a license number identification module. Specifically, the license plate candidates extracted from the first model are examined in the identification model to reduce the error rate. In the first model, several features such as color are taken into consideration to determine the license plate region. Initially, they use color edge detection to compute edge map E which contains three types edge (i.e., black-white, red-white and green-white edges) due to the fact that there are just four kinds of color(white, black, red and green) for the plate and character in Taiwan. To detect the color edges, these three kinds of edges are taken into consideration. The RGB color differences (4r, 4g, 4b) can be calculated to find the edge. Next, with unique formulas, the program can transform RGB space into HSI space that denote (red, green, blue) and (hue, saturation, intensity) values of an image pixel, respectively. The transform formula is as below.