Infrared Image Superpixel Segmentation Based on Seed Strategy of Contour Encoding

Superpixel segmentation has become a crucial pre-processing tool to reduce computation in many computer vision applications. In this paper, a superpixel extraction algorithm based on a seed strategy of contour encoding (SSCE) for infrared images is presented, which can generate superpixels with high boundary adherence and compactness. Specifically, SSCE can solve the problem of superpixels being unable to self-adapt to the image content. First, a contour encoding map is obtained by ray scanning the binary edge map, which ensures that each connected domain belongs to the same homogeneous region. Second, according to the seed sampling strategy, each seed point can be extracted from the contour encoding map. The initial seed set, which is adaptively scattered based on the local structure, is capable of improving the capability of boundary adherence, especially for small regions. Finally, the initial superpixels limited by the image contour are generated by clustering and refined by merging similar adjacent superpixels in the region adjacency graph (RAG) to reduce redundant superpixels. Experimental results on a self-built infrared dataset and the public datasets BSD500 and 3Dircadb demonstrate the generalization ability in grayscale and medical images, and the superiority of the proposed method over several state-of-the-art methods in terms of accuracy and compactness.

Autonomous Gateway Architecture for Security Using OpenCV

Today as we can see security for anything is considered to be a very important part of our livelihood and we need to seek more and more security every day in this fast growing world. As the security of public parking lots increases day by day and to ensure safety, many people are required in this job that increases the cost of security So we have looked into the process and came up with a plan to use computer vision for the security purpose which will reduce the manpower required for work instead with machine intelligence. We are going to use Computer Vision to mask the license plate and save it with the entry and exit time. This research paper will enhance the security provided by a CCTV camera in any public parking and will also keep the record of every car entering and exiting the parking area. Keywords: OpenCV, Machine Learning, EasyOCR, SQLite, Image Contour Processing

Devising an image processing method for transport infrastructure monitoring systems

Identifying and categorizing contours in images is important in many areas of computer vision. Examples include such operational tasks solved by using unmanned aerial vehicles as dynamic monitoring of the condition of transport infrastructure, in particular road markings. This study has established that current methods of image contour analysis do not produce clear and reliable results when solving the task of monitoring the state of road markings. Therefore, it is a relevant scientific and applied task to improve the methods and models of filtration, processing of binary images, and qualitative and meaningful separation of the boundaries of objects of interest. To solve the task of highlighting road marking contours on images acquired from an unmanned aerial vehicle, a method has been devised that includes an operational tool for image preprocessing – a combined filter. The method has several advantages and eliminates the limitations of known methods in determining the boundaries of the location of the object of interest, by highlighting the contours of a cluster of points using histograms. The method and procedures reported here make it possible to successfully solve problems that are largely similar to those that an expert person can face when solving intelligent tasks of processing and filtering information. The proposed method was verified at an enterprise producing the Ukrainian unmanned aerial vehicle "Spectator" during tests of information technology of dynamic monitoring of the state of transport infrastructure. The results could be implemented in promising intelligent control systems in the field of modeling human conscious behavior when sorting data required for the perception of environmental features

The Accurate Repair of Image Contour of Human Motion Tracking Based on Improved Snake Model

With the rapid development of sports science, human motion recognition technology, as a new biometric recognition technology, has many advantages, such as noncontact target, long recognition distance, secret recognition process, and so on. Traditional human motion recognition technology is affected by environmental factors such as motion background, which is prone to rough edges of the recognized objects and loss of motion tracking information, thus further reducing the recognition accuracy. In this paper, the traditional snake model will be improved and optimized to improve the defect of human motion model contour extraction, so as to realize the accurate repair of image contour; in terms of algorithm running time, this paper innovatively improves the construction process of the snake model, further improves the running time of model evaluation, and solves the concave contour problem of corresponding moving objects in the snake model. In order to solve the problem of accurate convergence, this paper improves the snake model of the average moving algorithm and sets the corresponding weight coefficient to distinguish the corresponding moving target background, so as to achieve the convergence of the differential concave contour. In order to verify the superiority of the improved optimized snake model, experiments are carried out in the corresponding database. The experimental results show that the contour of the moving object extracted by the improved snake model algorithm is complete and the segmentation effect is obvious. At the same time, the running speed of the whole algorithm has been significantly improved.

A Hybrid Landmark and Contour-Matching Image Registration Model

In this manuscript, we propose a novel hybrid Landmark and Contour-Matching (LCM) image registration model to align image pairs. The proposed model uses image contour information to supplement missing edge information in between exact landmarks. We demonstrate that the model circumvent the drawbacks associated with an straightforward application of the Thin Plate Spline (TPS) registration technique.The proposed model provides higher post-registration Dice similarity between the reference and registered template images by improving the image overlap away from major landmarks and visually reduces the appearance of the ''unnatural bending'' typically present in TPS-registered images. We also show that naively increasing the number of landmarks in a TPS model does not always guarantee an accurate registration result. We indicate how the proposed model using even less number of exact landmarks along with additional approximate contour information provided suitable results, as opposed to the TPS model. Lastly, the proposed model produces physically relevant registration results with improved Dice similarity indices even when landmark localization errors are present in data.Overall, the proposed Landmark and Contour-Matching (LCM) model increases the flexibility of the TPS approach especially when only a few landmarks can be defined, when defining too many landmarks leads to high oscillations in the registration transformations, or when the identification of exact landmarks is susceptible to human error.

Improving the quality of identification and filtering of micro-object images based on neural networks

Constructive approaches, principles, and models for optimizing the identification of micro-objects have been developed based on the use of combined statistical, dynamic models and neural networks with mechanisms for filtering noise and foreign particles of images of medical objects and pollen grains. Algorithms for learning neural networks under conditions of a priori insufficiency, uncertainty of parameters, and low accuracy of data processing are investigated. The mechanisms of contour selection, segmentation, obtaining the boundaries of segments with hard and soft thresholds, filtering using the morphological features of the image have been developed [1]. Mechanisms for recognition and classification of images, adaptation of parameter values, tuning of the network structure, approximation and smoothing of random emissions, bursts in the image contour are proposed. A mechanism for suppressing impulse noise and noise is implemented based on various filtering methods, preserving the boundaries of objects and small-sized parts. Mathematical expressions are obtained for estimating the identification errors caused by nonstationarity, inadequacy of approximation, interpolation, and extrapolation of the image contour. A software package for the recognition and classification of micro-objects has been developed. The results were obtained for correct, incorrect recognition, as well as rejected pollen samples, which were synthesized with cubic, biquadratic, interpolation spline-functions and wavelet transforms.

Models and Paradigms of Cellular Automata With an Organized Set of Active Cells

The chapter presents the principles of functioning of asynchronous cellular automata with a group of cells united in a colony. The rules of the formation of colonies of active cells and methods to move them along the field of a cellular automaton are considered. Each formed colony of active cells has a main cell that controls the movement of the entire colony. If several colonies of identical cells meet and combine, then the main cell is selected according to the priority, which is evaluated by the state of the cells of their neighborhoods. Colonies with different active cells can interact, destroying each other. The methods of interaction of colonies with different active states are described. An example of colony formation for solving the problem of describing contour images is presented. The image is described by moving the colony through the cells belonging to the image contour and fixing the cell sectors of the colony, which include the cells of the contour at each time step.