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1989, International Journal of Computer Vision
P,*kt rbool~l b d4tM4• t, t c o Il•. 0 .ntotmatima estmnated t Image Understanding Architecture, Knowledge-Based Vision, AI Real-Time Computer Vision, Software Simulator, Parallel Processor IL PRICE CODE 17. SECURITY CLASSIFICATION 11. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20. UMITATION OF ABSTRACT
Mwe findings In this report are not to be comstrued as an official Department of the Army position unless so designated by other authorized docummts. The citation In this report of trade names of commrdaly available products does not comstitute official endorsement or approval of the use of such products.
Iee Proceedings-vision Image and Signal Processing, 2005
Video security is becoming more and more important today, as the number of installed cameras can attest. There are many challenging commercial applications to monitor people or vehicle traffic. The work reported here has both research and commercial motivations. Our goals are first to obtain an efficient intelligent system that can meet strong industrial surveillance system requirements and therefore be real-time, distributed, generic and robust. Our second goal is to have a development platform that allows researchers to conceive and easily test new vision algorithms thanks to its modularity and easy set-up.
Lecture Notes in Computer Science, 2008
In the paper, the main paradigm of image understanding as well as possible way for practical machine realization in relatively simple situations is presented. The notion ’simple situations’ reflects more our humility with respect to the complication of human perception process than the form of objects to be recognized and interpreted. Crucial for our approach are formalization of human knowledge
Artificial Intelligence and Soft Computing, 2004
Paper presents absolutely new ideas about needs and possibilities of automatic understanding of the image semantic content. The idea under consideration can be found as next step on the way starting from capturing of the images in digital form as two–dimensional data structures, next going throw images processing as a tool for enhancement of the images visibility and readability, applying images analysis algorithms for extracting selected features of the images (or parts of images e.g. objects), and ending on the algorithms devoted to images classification and recognition. In the paper we try to explain, why all procedures mentioned above can not give us full satisfaction, when we do need understand image semantic sense, not only describe the image in terms of selected features and/or classes. The general idea of automatic images understanding is presented as well as some remarks about the successful applications of such ides for increasing potential possibilities and performance of computer vision systems dedicated to advanced medical images analysis.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2020
Machines can learn to elucidate images the same way our brains do and analyse those images much more thoroughly than we can. When applied to Image Processing, Artificial Intelligence (AI) can propel face recognition and security functionality in public places, detecting and recognizing intruders, objects, and patterns in live images and videos, etc. Image processing technology focuses on the development of data extraction methods applied to the statistical classification of visual imagery. In classical image processing systems, an image is pre-processed to remove noise (denoising), segmented to produce close object boundaries, analysed to extract a representative feature, and compared to the ideal object feature vectors by a classifier to decide the nearest object classification and its associated level. In this paper, we discuss about digital image processing and the role of AI in it.
Intelligent Systems Design and Applications, 2005
In this paper there will be presented the new opportunities for applying linguistic algorithms of pattern recognition for computer understanding of image semantic content in intelligent information systems. A successful obtaining of the crucial semantic information of the image - especially medical - may contribute considerably to the creation of new intelligent cognitive information systems. Thanks to the new algorithms of cognitive resonance between stream of the data extracted from the image and expectations taken from the representation of the medical knowledge, we can understand the merit content of the image even if the form of the image is very different from any known pattern. It seems that in the near future the technique of automatic understanding of images may become one of the effective tools for semantic interpreting, and intelligent storing of the visual data in scattered databases. In this article we will try proving that structural techniques may be applied in the case of tasks related to automatic classification and machine perception of the semantic meaning of selected classes of medical patterns.
Recent Developments in Artificial Intelligence Methods, 2004
Short article presenting general idea of new approach to automatic image interpretation - so called "Automatic Understanding". Localization of such form of image interpretation is presented as a next step in process starting from image acquisition, next image processing, image analysis, pattern recognition - and last but not least - image understanding. The difference between automatic understanding and automatic recognition is also presented and discussed.
2005
This paper presents distributed, automated, scene surveillance architecture. Object detection and tracking is performed by a set of Region and Object Agent. The Area under surveillance is divided in several sub-areas. One camera is assigned to each sub-area. A Region Agent is responsible for monitoring a given sub-area. First a background subtraction is performed to the scene taken by the camera. Based on the foreground mask, Region Agent segments the incoming frame and creates Object Agents dedicated to tracking detected objects. Tracking information and segments are sent to a Scene Processing Unit that analyzed this information and determined if a threat pattern is present at the scene and performed appropriate action Index Terms-cooperative agents, sensor network, image understanding I. INTRODUCTION AND RELATED WORK HERE is an increasing demand for surveillance system in today's daily life. From the technological-solution perspective, video surveillance has been widely employed for this purpose. However, the advances in this area have mainly aimed the video sensors. The human operator still has analyzed the images. In other words, despite the technological advances individually made for networking and computing capabilities, there are challenges to overcome before a reliable automated surveillance system is realized [1]. These technical challenges include system design and configuration, architecture design, object identification, tracking and analysis, restrictions on network bandwidth, physical placement of cameras, installation cost, privacy concerns, and robustness to change of weather and lighting conditions.
Lecture Notes in Computer Science, 2011
This article describes a proposition and first examples of using inductive learning methods in building of the image understanding system with the hierarchical structure of knowledge. This system may be utilized in various task of automatic image interpretation, classification and image enhancement. The paper points to the essential problems of the whole method: the constructing an effective algorithm of conceptual clustering and creation of the method of knowledge evaluation. Some possible solutions are discussed and first practical results (image filtering) are presented.
This paper deals with an original image interpretation device, called CAATI. This knowledge-based system relies on a dynamic construction of the sequential ordering of the image processing operators. The sequential ordering, represented by a graph at each node of which is found a task, can be called into question at any moment, as a function of evaluations performed at each level of the graph. These evaluations permit to guarantee the reliability of the information or, at least, to localize easily the operator in default in the processing scheme. In this paper, we describe the global structure of the system, as well as the evaluation management of each task.
International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020
we all know safety is the major thing that everyone is concerned about. In this project, we provide security by using face recognition system. Face detection is the initial step for face recognition. So, this system is going to provide smart security by detecting images in the dataset which consists of a collection of images where some are trained and stored in trained-set and some are tested, stored in test-set. This Security system predicts accuracy and leads to get the appropriate result by detects a particular person by recognizing an image of that person which ensures security. The techniques used for the whole process of face recognition are machine learning-based because of their high accuracy as compared with the other techniques. This system uses one of the deep learning technique i.e. Convolution Neural Network (CNN). CNN is a neural network that has one or more convolution layers trained to perform a specific task using classification without any human supervision.
2002
In this paper we present projects developed in the Computer Vision Laboratory, which address the issue of safety. First, we present the Internet Video Server (IVS) monitoring system [5] that sends live video stream over the Internet and enables remote camera control. Its extension GlobalView [1,6], which incorporates intuitive user interface for remote camera control, is based on panoramic image. Then we describe our method for automatic face detection [3] based on color segmentation and feature extraction. Finally, we introduce our SecurityAgent system [4] for automatic surveillance of observed location.
Studies in Computational Intelligence, 2011
Presented article was printed as introduction to the book "Innovations in Intelligent Image Analysis". Content of this book was based on many chapters given by many authors, who presented different methods of improvement computer vison technology by means of artificial intelligence (AI) methods. In presented here article the general overview of relations between image analysis and AI was presented. One of the most important conclusions presented in the article was connected with the limitations of AI applications in particular steps of image analysis.
1989
This paper describes the development of a knowledgebased system which will be used to automate the interpretation of an alarm event resulting from a perimeter intrusion detection system. The knowledge-based system analyses a sequence of digital images captured before, during and after the alarm is generated. Additional data, pertaining to the alarm sensor, prevailing weather conditions and time-of-day are also available to assist the interpretation. In order to cope with the diverse nature of the different data sources, a knowledge-based approach is used to perform the interpretation. Models are maintained for a variety of possible alarm causes (human, animal, environmental, false etc.) and each model characterises a number of properties associated with that particular alarm source. The event data is interrogated by the KBS following the selection of a particular model.
Now a days, security systems are meant only for the purpose of recording the images like cc cameras or for giving some alerts to the security officers about the theft. But, they won't take any action on the thief during the theft. This problem may be overcome by employing the enhanced security provided by the " INTELLIGENT SECURITY SYSTEM " , without any manual assistance. It will take the action directly on the thief during the theft in a fraction of seconds. Intelligent Security Systems are employed mainly for the MILLITARY ROBOTS to fight with the enemy person by automatically turning to the direction of the enemy and firing at him. They are also applicable in the MUSEUMS for high security.
Journal of Applied Computer Science, 2010
In the paper, the roles of intelligence, knowledge, learning and wisdom are discussed in the context of image content understanding. The known model of automatic image understanding is extended by the role of learning. References to example implementations are also given.
Envisaging a future where the interaction between human and a computer has become advanced enough so as to be able to understand the motion of an arm, blink of an eye or even the emotions of a human. With the interaction between humans and computers becoming decidedly easy for us, now is the right time to use such an advanced technology in order to secure our information in computers and private networks. We are well aware of the importance and the money spent in order to either collect information or to protect information. Therefore, in this paper we are proposing the architecture of a security system that uses the gesture and human body action recognition techniques in the efforts of creating a safe, secure and spoof free system for access to critically important information
ABSTRACT
Computer, 2000
The three overviews that follow are short reports of ongoing research in image understanding architecture, SIMD parallelism in computer vision, and software environments for parallel computer vision.
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