Real time sobel square edge detector for night vision analysis

Vision analysis with low or no illumination is gaining more and more attention recently, especially in the fields of security surveillance and medical diagnosis. In this paper, a real time sobel square edge detector is developed as a vision enhancer in order to render clear shapes of object in targeting scenes, allowing further analysis such as object or human detection, object or human tracking, human behavior recognition, and identification on abnormal scenes or activities. The method is optimized for real time applications and compared with existing edge detectors. Program codes are illustrated in the content and the results show that the proposed algorithm is promising to generate clear vision data with low noise

Lane Line Detection and Object Scene Segmentation Using Otsu Thresholding and the Fast Hough Transform for Intelligent Vehicles in Complex Road Conditions

An Otsu-threshold- and Canny-edge-detection-based fast Hough transform (FHT) approach to lane detection was proposed to improve the accuracy of lane detection for autonomous vehicle driving. During the last two decades, autonomous vehicles have become very popular, and it is constructive to avoid traffic accidents due to human mistakes. The new generation needs automatic vehicle intelligence. One of the essential functions of a cutting-edge automobile system is lane detection. This study recommended the idea of lane detection through improved (extended) Canny edge detection using a fast Hough transform. The Gaussian blur filter was used to smooth out the image and reduce noise, which could help to improve the edge detection accuracy. An edge detection operator known as the Sobel operator calculated the gradient of the image intensity to identify edges in an image using a convolutional kernel. These techniques were applied in the initial lane detection module to enhance the characteristics of the road lanes, making it easier to detect them in the image. The Hough transform was then used to identify the routes based on the mathematical relationship between the lanes and the vehicle. It did this by converting the image into a polar coordinate system and looking for lines within a specific range of contrasting points. This allowed the algorithm to distinguish between the lanes and other features in the image. After this, the Hough transform was used for lane detection, making it possible to distinguish between left and right lane marking detection extraction; the region of interest (ROI) must be extracted for traditional approaches to work effectively and easily. The proposed methodology was tested on several image sequences. The least-squares fitting in this region was then used to track the lane. The proposed system demonstrated high lane detection in experiments, demonstrating that the identification method performed well regarding reasoning speed and identification accuracy, which considered both accuracy and real-time processing and could satisfy the requirements of lane recognition for lightweight automatic driving systems

Multiscale Astronomical Image Processing Based on Nonlinear Partial Differential Equations

Astronomical applications of recent advances in the field of nonastronomical image processing are presented. These innovative methods, applied to multiscale astronomical images, increase signal-to-noise ratio, do not smear point sources or extended diffuse structures, and are thus a highly useful preliminary step for detection of different features including point sources, smoothing of clumpy data, and removal of contaminants from background maps. We show how the new methods, combined with other algorithms of image processing, unveil fine diffuse structures while at the same time enhance detection of localized objects, thus facilitating interactive morphology studies and paving the way for the automated recognition and classification of different features. We have also developed a new application framework for astronomical image processing that implements some recent advances made in computer vision and modern image processing, along with original algorithms based on nonlinear partial differential equations. The framework enables the user to easily set up and customize an image-processing pipeline interactively; it has various common and new visualization features and provides access to many astronomy data archives. Altogether, the results presented here demonstrate the first implementation of a novel synergistic approach based on integration of image processing, image visualization, and image quality assessment

NIGHT-TIME ANIMAL RECOGNITION SYSTEM USING DIGITAL IMAGE PROCESSING

Animal-related accidents always haunted the road users in Malaysia. Victims and animals involved in animal-related accidents are either injured or lost their lives. This project proposes to develop a buffalo detection system to alert drivers by using the image processing technique during night-time. With this system, drivers will be able to have sufficient time to avoid collision with the animals. Various MATLAB image processing techniques are used to perform the buffalo recognition in this project

License Plate Detection based on Genetic Neural Networks, Morphology, and Active Contours

This paper describes a new method for License Plate Detection based on Genetic Neural Networks, Morphology, and Active Contours. Given an image is divided into several virtual regions sized 10×10 pixels, applying several performance algorithms within each virtual region, algorithms such as edge detection, histograms, and binary thresholding, etc. These results are used as inputs for a Genetic Neural Network, which provides the initial selection for the probable situation of the license plate. Further refinement is applied using active contours to fit the output tightly to the license plate. With a small and well–chosen subset of images, the system is able to deal with a large variety of images with real–world characteristics obtaining great precision in the detection. The effectiveness for the proposed method is very high (97%). This method will be the first stage of a surveillance system which takes into account not only the actual license plate but also the model of the car to determine if a car should be taken as a threat

Application of Fuzzy Logic on Image Edge Detection

In this paper a novel method for an application of digital image processing, Edge Detection is developed. The contemporary Fuzzy logic, a key concept of artificial intelligence helps to implement the fuzzy relative pixel value algorithms and helps to find and highlight all the edges associated with an image by checking the relative pixel values and thus provides an algorithm to abridge the concepts of digital image processing and artificial intelligence. Exhaustive scanning of an image using the windowing technique takes place which is subjected to a set of fuzzy conditions for the comparison of pixel values with adjacent pixels to check the pixel magnitude gradient in the window. After the testing of fuzzy conditions the appropriate values are allocated to the pixels in the window under testing to provide an image highlighted with all the associated edges

Image-based traffic monitoring system.

Lau Wai Hung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 63-65).Abstracts in English and Chinese.abstract --- p.I摘要 --- p.IIacknowledgement --- p.IIItable of contents --- p.IVlist of figures --- p.VIChapter CHAPTER 1 --- introduction --- p.1Chapter CHAPTER 2 --- literature review --- p.4Chapter 2.1 --- Traffic data collection methods --- p.4Chapter 2.2 --- Vision-based traffic monitoring techniques --- p.6Chapter 2.2.1 --- Vehicle tracking approaches --- p.7Chapter 2.2.2 --- Image processing techniques --- p.10Chapter CHAPTER 3 --- methodology --- p.15Chapter 3.1 --- Solution Concept --- p.16Chapter 3.2 --- System Framework --- p.18Chapter 3.2.1 --- Edge Detection Module --- p.20Chapter 3.2.2 --- Background Update Module --- p.22Chapter 3.2.3 --- Feature Extraction Modules --- p.25Chapter CHAPTER 4 --- experiments and evaluation --- p.41Chapter 4.1 --- Setup and Data Collection --- p.41Chapter 4.2 --- Evaluation Criteria --- p.42Chapter 4.3 --- Experimental Results --- p.44Chapter 4.3.1 --- Comparing overall accuracies --- p.44Chapter 4.3.2 --- Accuracies for different traffic conditions --- p.46Chapter 4.3.3 --- Comparing balanced sampling and random sampling --- p.48Chapter 4.3.4 --- Comparing day and night conditions --- p.50Chapter 4.3.5 --- Testing on time-series of images --- p.52Chapter CHAPTER 5 --- analysis --- p.54Chapter 5.1 --- Strengths and Weaknesses --- p.54Chapter 5.1.1 --- Sobel Edge Histogram --- p.54Chapter 5.1.2 --- Horizontal Line Detection --- p.55Chapter 5.1.3 --- Block Detection --- p.56Chapter 5.1.4 --- Combined Learning --- p.57Chapter 5.1.5 --- Overall Framework --- p.58Chapter 5.2 --- Future Research --- p.59Chapter 5.2.1 --- Static image based monitoring combined with other traffic monitoring approaches --- p.59Chapter 5.2.2 --- Horizontal Line Detection as tracked features of vehicles --- p.60Chapter 5.2.3 --- Application in aerial image-based system --- p.60Chapter CHAPTER 6 --- conclusion --- p.62bibliography --- p.63appendix a sobel edge detection --- p.66appendix b neural network setup --- p.67appendix c numerical results --- p.6

Principal Component Analysis based Image Fusion Routine with Application to Stamping Split Detection

This dissertation presents a novel thermal and visible image fusion system with application in online automotive stamping split detection. The thermal vision system scans temperature maps of high reflective steel panels to locate abnormal temperature readings indicative of high local wrinkling pressure that causes metal splitting. The visible vision system offsets the blurring effect of thermal vision system caused by heat diffusion across the surface through conduction and heat losses to the surroundings through convection. The fusion of thermal and visible images combines two separate physical channels and provides more informative result image than the original ones. Principal Component Analysis (PCA) is employed for image fusion to transform original image to its eigenspace. By retaining the principal components with influencing eigenvalues, PCA keeps the key features in the original image and reduces noise level. Then a pixel level image fusion algorithm is developed to fuse images from the thermal and visible channels, enhance the result image from low level and increase the signal to noise ratio. Finally, an automatic split detection algorithm is designed and implemented to perform online objective automotive stamping split detection. The integrated PCA based image fusion system for stamping split detection is developed and tested on an automotive press line. It is also assessed by online thermal and visible acquisitions and illustrates performance and success. Different splits with variant shape, size and amount are detected under actual operating conditions