Applications and Implications of Artificial Intelligence and Deep Learning in Computer Vision
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R. Ranjana
Abstract
Recent advancements in machine learning have enhanced the features several applications. One such field is the computer vision (CV). The recent times have seen the deployment of robots in many industries and household. Vision for robots or robotic vision is also an emerging field of research. CV can be characterized as a set of errands that incorporate styles for obtaining, deciding, taking apart and grasping computerized pictures, and parentage of highlayered information from this present reality to deliver mathematical or authentic information, for example, in the types of liberations. Understanding in this contexture implies the transformation of visual pictures (the contribution of the retina) into portrayals of the world that appear to be legit to concentrate on processes and can inspire relevant activity. This picture understanding should be visible as the unraveling of significant data from picture information utilizing models developed with the guide of figure, medications, insights, and learning recommendation. The logical discipline of CV is worried about the speculation behind counterfeit frameworks that wine tool data from pictures. The picture information can take innumerable structures, looking like live video or video recording arrangements, sees from different cameras, multilayered information from a 3D scanner, or clinical examining gadget. The innovative discipline of CV looks to apply its recommendations and models to the development of vision frameworks. Deep learning and other artificial intelligence techniques can be used to enhance CV. This chapter aims to bring out the various methods and algorithms that are used in enabling a computer to see.
Abstract
Recent advancements in machine learning have enhanced the features several applications. One such field is the computer vision (CV). The recent times have seen the deployment of robots in many industries and household. Vision for robots or robotic vision is also an emerging field of research. CV can be characterized as a set of errands that incorporate styles for obtaining, deciding, taking apart and grasping computerized pictures, and parentage of highlayered information from this present reality to deliver mathematical or authentic information, for example, in the types of liberations. Understanding in this contexture implies the transformation of visual pictures (the contribution of the retina) into portrayals of the world that appear to be legit to concentrate on processes and can inspire relevant activity. This picture understanding should be visible as the unraveling of significant data from picture information utilizing models developed with the guide of figure, medications, insights, and learning recommendation. The logical discipline of CV is worried about the speculation behind counterfeit frameworks that wine tool data from pictures. The picture information can take innumerable structures, looking like live video or video recording arrangements, sees from different cameras, multilayered information from a 3D scanner, or clinical examining gadget. The innovative discipline of CV looks to apply its recommendations and models to the development of vision frameworks. Deep learning and other artificial intelligence techniques can be used to enhance CV. This chapter aims to bring out the various methods and algorithms that are used in enabling a computer to see.
Chapters in this book
- Frontmatter I
- Contents V
- List of Authors VII
- The Learning of Deep Learning: Overview, Methods, and Applications 1
- Foundation of Cognitive Computing 19
- Applications and Implications of Artificial Intelligence and Deep Learning in Computer Vision 35
- A Study of Voice Recognition System Using Deep Learning Techniques 53
- Building Machine Learning–Based Prediction System for Critical Diseases 75
- An Overview of Internet of Things and Machine Learning for Smart Healthcare 97
- Nutrition Food Recognition Using Deep Learning Algorithm for Physically Challenged Human Being 113
- Healthcare Data Analysis Using Deep Learning Paradigm 129
- Cognitive Authentication for Smart Healthcare System 149
- Cognitive-Inspired Computer Vision Assist System for Diabetic Retinopathy Detection from Fundus Images 165
- A Novel Deep Belief Neural Network Model for Abstractive Text Summarization 179
- Index 201
Chapters in this book
- Frontmatter I
- Contents V
- List of Authors VII
- The Learning of Deep Learning: Overview, Methods, and Applications 1
- Foundation of Cognitive Computing 19
- Applications and Implications of Artificial Intelligence and Deep Learning in Computer Vision 35
- A Study of Voice Recognition System Using Deep Learning Techniques 53
- Building Machine Learning–Based Prediction System for Critical Diseases 75
- An Overview of Internet of Things and Machine Learning for Smart Healthcare 97
- Nutrition Food Recognition Using Deep Learning Algorithm for Physically Challenged Human Being 113
- Healthcare Data Analysis Using Deep Learning Paradigm 129
- Cognitive Authentication for Smart Healthcare System 149
- Cognitive-Inspired Computer Vision Assist System for Diabetic Retinopathy Detection from Fundus Images 165
- A Novel Deep Belief Neural Network Model for Abstractive Text Summarization 179
- Index 201
