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    Percept: Fundamentals and Applications
    Percept: Fundamentals and Applications
    Percept: Fundamentals and Applications
    Ebook109 pages1 hourArtificial Intelligence

    Percept: Fundamentals and Applications

    By Fouad Sabry

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    About this ebook

    What Is Percept


    Input that is being perceived by an intelligent agent at any given moment is referred to as a percept at that moment. It is essentially the same idea as what is known as a percept in the field of psychology, with the exception that the agent, rather than the brain, is the one who is experiencing it. A percept is picked up by a sensor, which is typically a camera, then processed in the appropriate manner, and finally acted upon by an actuator. A "percept sequence" is a comprehensive history of all percepts that have ever been identified, and each percept that is recognized is added to this sequence. The action taken by the agent at any one instant point may be contingent on the entirety of the percept sequence experienced up to that specific instant point. Not only does a smart agent make decisions about how to act based on the present perception, but also on the sequence of previous perceptions. The agent function, which associates each perception with a certain action, decides what the next action will be.


    How You Will Benefit


    (I) Insights, and validations about the following topics:


    Chapter 1: Percept (artificial intelligence)


    Chapter 2: Computer vision


    Chapter 3: Intelligent agent


    Chapter 4: Cognitive robotics


    Chapter 5: Machine perception


    Chapter 6: Active perception


    Chapter 7: Subsumption architecture


    Chapter 8: Automated planning and scheduling


    Chapter 9: Gesture recognition


    Chapter 10: Affective computing


    (II) Answering the public top questions about percept.


    (III) Real world examples for the usage of percept in many fields.


    (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of percept' technologies.


    Who This Book Is For


    Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of percept.

    LanguageEnglish
    PublisherOne Billion Knowledgeable
    Release dateJul 6, 2023
    Percept: Fundamentals and Applications

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      Book preview

      Percept - Fouad Sabry

      Chapter 1: Percept (artificial intelligence)

      Input that is being perceived by an intelligent agent at any given instant is referred to as a percept at that moment. It is basically the same idea as what is known as a percept in the field of psychology, with the exception that the agent, rather than the brain, is the one who is experiencing it. A percept is picked up by a sensor, which is often a camera, then processed in the appropriate manner, and finally acted upon by an actuator. A percept sequence is a comprehensive history of all percepts that have ever been identified, and each percept that is recognized is added to this series. The action taken by the agent at any one instant point may be contingent on the whole of the percept sequence experienced up to that specific instant point. Not only does a smart agent make decisions about how to respond based on the present perception, but also on the sequence of previous perceptions. The agent function, which converts each percept into an action, makes the decision on the next action to take.

      For instance, if a camera were to capture a gesture, the agent would analyse the percepts, compute the relevant spatial vectors, investigate its own perceptual history, and then utilize the agent program (the application of the agent function) to behave in accordance with what it found.

      Inputs from many types of sensors, such as touch sensors, cameras, infrared sensors, sonar, microphones, mice, and keyboards are all examples of percepts. A higher-level element of the data may also be considered a percept; examples of this include lines, depth, objects, faces, and movements.

      {End Chapter 1}

      Chapter 2: Computer vision

      The study of how computers can derive high-level knowledge from digital pictures or videos is the focus of the multidisciplinary scientific area of computer vision. From a technological point of view, it investigates and attempts to automate activities that are within the capabilities of the human visual system.

      Tasks associated with computer vision include techniques for obtaining, processing, analyzing, and comprehending digital pictures, as well as the extraction of high-dimensional data from the physical environment in order to create numeric or symbolic information, such as judgments.

      Computer vision is a subfield of computer science that investigates the theoretical underpinnings of artificial systems designed to derive information from pictures. The visual data may be presented in a variety of formats, including video sequences, images obtained from several cameras, multi-dimensional data obtained from a 3D scanner or medical scanning equipment, and so on. The goal of the technical field known as computer vision is to implement the ideas and models it has developed in the process of building computer vision systems.

      The fields of scene reconstruction, object detection, event detection, video tracking, object recognition, 3D pose estimation, learning, indexing, motion estimation, visual servoing, 3D scene modeling, and image restoration are all sub-domains of computer vision. Other sub-domains of computer vision include 3D scene modeling.

      Computer vision is a multidisciplinary study that examines how computers can be programmed to extract high-level knowledge from digital pictures or movies. This area focuses on how computers can be taught to comprehend what is being shown to them. From the point of view of engineering, the goal is to find ways to automate operations that can already be done by the human visual system. Computer vision is a field of study in the field of information technology that focuses on applying existing theories and models to the process of building computer vision systems.

      In the late 1960s, colleges that were on the cutting edge of artificial intelligence were the first to experiment with computer vision. Its purpose was to function in a manner similar to that of the human visual system, with the ultimate goal of imbuing robots with intelligent behavior. In the 1990s, several of the study areas that had been studied before became more active than the others. The study of projective three-dimensional reconstructions led to a deeper understanding of how to calibrate a camera. It became clear, with the introduction of optimization techniques for camera calibration, that a significant number of the concepts had previously been investigated by the discipline of photogrammetry's bundle adjustment theory. This came to light as a result of this development. This resulted in the development of techniques for doing sparse three-dimensional reconstructions of scenes using several photographs. Both the dense stereo correspondence issue and the development of further multi-view stereo approaches saw some degree of forward movement. Concurrently, many variants of graph cut were used in order to address the picture segmentation problem. This decade was especially significant since it was the first time statistical learning methods were used in practice to the task of recognizing faces in photographs (see Eigenface). The areas of computer graphics and computer vision have become more intertwined in recent years, which has led to a large rise in the amount of collaboration that has taken place between the two. This featured early forms of light-field rendering, panoramic picture stitching, image morphing, view interpolation, and image-based rendering. The area of computer vision has been given a new lease of life thanks to the development of algorithms based on deep learning. The accuracy of deep learning algorithms on numerous benchmark computer vision data sets for tasks ranging from classification to optical flow has exceeded that of earlier approaches. These tasks include segmentation of images and optical flow.

      Solid-state Computer vision is strongly connected to a number of other disciplines, including physics. The vast majority of computer vision systems are based on image sensors, which are devices that are able to detect electromagnetic radiation. This radiation is commonly manifested as either visible or infrared light. Quantum physics was used in the development of the sensors. The scientific discipline of physics provides an explanation for the method through which light interacts with surfaces. The behavior of optics, which is a fundamental component of the majority of imaging systems, may be explained by physics. In order to offer a comprehensive knowledge of the process by which a picture is formed, sophisticated image sensors need the use of quantum mechanics. Computer vision may also be used to solve a variety of measurement issues that arise in physics, such as those involving the motion of fluids.

      The scientific discipline of neurobiology, more especially the investigation of the biological

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