標題: Titlebook: Building Computer Vision Applications Using Artificial Neural Networks; With Step-by-Step Ex Shamshad Ansari Book 20201st edition Shamshad [打印本頁] 作者: 補給線 時間: 2025-3-21 18:18
書目名稱Building Computer Vision Applications Using Artificial Neural Networks影響因子(影響力)
書目名稱Building Computer Vision Applications Using Artificial Neural Networks影響因子(影響力)學科排名
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書目名稱Building Computer Vision Applications Using Artificial Neural Networks網(wǎng)絡(luò)公開度學科排名
書目名稱Building Computer Vision Applications Using Artificial Neural Networks被引頻次
書目名稱Building Computer Vision Applications Using Artificial Neural Networks被引頻次學科排名
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書目名稱Building Computer Vision Applications Using Artificial Neural Networks年度引用學科排名
書目名稱Building Computer Vision Applications Using Artificial Neural Networks讀者反饋
書目名稱Building Computer Vision Applications Using Artificial Neural Networks讀者反饋學科排名
作者: Calibrate 時間: 2025-3-21 23:16 作者: facetious 時間: 2025-3-22 00:57
Building Computer Vision Applications Using Artificial Neural Networks978-1-4842-5887-3作者: Carbon-Monoxide 時間: 2025-3-22 08:03
ial applications, such as facial recognition in video surveillance and surface defect detection in manufacturing.?..The final section is about training neural networks involving a large number of images on clou978-1-4842-5887-3作者: 窒息 時間: 2025-3-22 09:33
Techniques of Image Processing,other application. In most cases, these input images are converted from one form into another. For example, we may need to resize, rotate, or change their colors. In some cases, we may need to remove the background pixels or merge two images. In other cases, we may need to find the boundaries around作者: 無表情 時間: 2025-3-22 13:52
Deep Learning in Object Detection,During classification tasks, we predict the class of the entire image and do not care what kind of objects are in the image. In this chapter, we will detect objects and their locations within the image.作者: 帳單 時間: 2025-3-22 17:54
Practical Example: Object Tracking in Videos, set of images, object detection provides the ability to identify one or more objects in an image, and object tracking provides the ability to track a detected object across a set of images. In previous chapters, we explored the technical aspects of training deep learning models to detect objects. I作者: 出汗 時間: 2025-3-22 22:16
Practical Example: Face Recognition,ct and locate the position of the face in the input image. This is a typical object detection task like we learned about in the previous chapters. After the face is detected, a feature set, also called a . or ., is created from various key points on the face. A human face has 80 nodal points or dist作者: 改變 時間: 2025-3-23 02:03
Computer Vision Modeling on the Cloud,rk depending on the number of training samples, network configuration, and available hardware resources. A single GPU may not be feasible to train a complex network involving large numbers of training images. The models need to be trained on multiple GPUs. Only a limited number of GPUs can be instal作者: 刺耳 時間: 2025-3-23 08:06 作者: 其他 時間: 2025-3-23 10:19 作者: Talkative 時間: 2025-3-23 17:54
Integral calculus on curves and surfaces,led on a single machine. A single machine with multiple GPUs may not be sufficient for training on a large number of images. It will be faster if the model is trained on multiple machines with each machine having multiple GPUs.作者: Conflagration 時間: 2025-3-23 18:39
vision working code examples.Explains training neural netwo.Apply computer vision and machine learning concepts in developing business and industrial applications ?using a practical, step-by-step approach.?..The book comprises four main sections starting with setting up your programming environment作者: hemoglobin 時間: 2025-3-23 22:27 作者: exclamation 時間: 2025-3-24 03:34 作者: CARE 時間: 2025-3-24 09:24
Practical Example: Object Tracking in Videos, detected object across a set of images. In previous chapters, we explored the technical aspects of training deep learning models to detect objects. In this chapter, we will explore a simple example of putting that knowledge to practice in the context of videos.作者: Barrister 時間: 2025-3-24 12:55
Practical Example: Face Recognition,er the face is detected, a feature set, also called a . or ., is created from various key points on the face. A human face has 80 nodal points or distinguishing landmarks that are used to create the feature set (USPTO Patent Number US7634662B2, .). The face embedding is then compared against a database to establish the identity of the face.作者: 指派 時間: 2025-3-24 16:13 作者: 熔巖 時間: 2025-3-24 21:23 作者: Enliven 時間: 2025-3-25 00:04 作者: Jargon 時間: 2025-3-25 06:31 作者: 開始發(fā)作 時間: 2025-3-25 09:07 作者: engender 時間: 2025-3-25 13:56 作者: 口訣法 時間: 2025-3-25 17:02 作者: Parley 時間: 2025-3-25 22:01
Ordinary differential equations,other application. In most cases, these input images are converted from one form into another. For example, we may need to resize, rotate, or change their colors. In some cases, we may need to remove the background pixels or merge two images. In other cases, we may need to find the boundaries around作者: Notify 時間: 2025-3-26 02:11 作者: maintenance 時間: 2025-3-26 08:11
Integral calculus on curves and surfaces, set of images, object detection provides the ability to identify one or more objects in an image, and object tracking provides the ability to track a detected object across a set of images. In previous chapters, we explored the technical aspects of training deep learning models to detect objects. I作者: Obloquy 時間: 2025-3-26 11:52 作者: Thymus 時間: 2025-3-26 16:00 作者: freight 時間: 2025-3-26 20:11
Ordinary differential equations,This is a hands-on book that describes how to develop computer vision applications in the Python programming language. In this book, you will learn how to work with OpenCV to manipulate images and build machine learning models using TensorFlow.作者: prosthesis 時間: 2025-3-26 21:11 作者: 威脅你 時間: 2025-3-27 02:06
Alexandru-Darius Filip,Voichi?a Adriana RaduYou learned about various image processing techniques in the previous chapter. In this chapter, we will discuss the steps to develop machine learning computer vision systems. This chapter is a primer for the next chapter, which will provide details on various deep learning algorithms and how to write code with Python to execute on TensorFlow.作者: fidelity 時間: 2025-3-27 05:30 作者: 下垂 時間: 2025-3-27 10:37
https://doi.org/10.1007/978-88-470-1784-9Computer vision has many applications in industrial manufacturing. One such application is in the automation of visual inspection for quality control and assurance.作者: 下邊深陷 時間: 2025-3-27 17:27 作者: 傳授知識 時間: 2025-3-27 19:38
Core Concepts of Image and Video Processing,This chapter introduces the building blocks of an image and describes various methods to manipulate them. Our learning objectives in this chapter are as follows:作者: 能夠支付 時間: 2025-3-27 23:20
,Building a Machine Learning–Based Computer Vision System,You learned about various image processing techniques in the previous chapter. In this chapter, we will discuss the steps to develop machine learning computer vision systems. This chapter is a primer for the next chapter, which will provide details on various deep learning algorithms and how to write code with Python to execute on TensorFlow.作者: 不給啤 時間: 2025-3-28 06:00
Deep Learning and Artificial Neural Networks,This chapter will cover deep learning and artificial neural networks. The chapter will explore this topic with working code examples to show how to apply deep learning concepts in computer vision. Our learning objectives of this chapter are as follows:作者: 宮殿般 時間: 2025-3-28 07:40
Industrial Application: Real-Time Defect Detection in Industrial Manufacturing,Computer vision has many applications in industrial manufacturing. One such application is in the automation of visual inspection for quality control and assurance.作者: 群居男女 時間: 2025-3-28 11:19 作者: 腐爛 時間: 2025-3-28 14:50
Conference proceedings 2022ernational Conference on Human-Computer Interaction, HCII 2022, which took place virtually in June-July 2022...The 132 papers included in this HCI 2022 proceedings were organized in topical sections as follows:..Part I: Theoretical and Multidisciplinary Approaches in HCI; Design and Evaluation Metho作者: 事與愿違 時間: 2025-3-28 20:52
Natural Halophytes as a Potential Resource for New Salt-Tolerant Crops: Some Progress and Prospects,eet his needs for food and fiber. Historically the production of these staples has been dependent upon a supply of fresh water. Recently the supply of this resource has become ever more critical as populations and technologies continue to expand. The problem with respect to crop production was stated succintly by Flowers et al. (1977):作者: 冒失 時間: 2025-3-29 00:05 作者: Jogging 時間: 2025-3-29 04:09
Ethnisierung und M?nnlichkeitsinszenierungen Symbolische K?mpfe von Jungen mit türkischem Migrationsgen mit türkischem Migrationshintergrund werden dabei Wechselwirkungen von Prozessen der Fremd- und Selbstethnisierung beleuchtet sowie die Relevanz sozialer Randst?ndigkeit als Basis für die Realisierung bestimmter Biografien für diese Ethnisierungsprozesse untersucht.作者: 火光在搖曳 時間: 2025-3-29 08:58
Zhong-Hua Pang,Guo-Ping Liu,Donghua Zhou,Dehui Sund conventional approaches for OSA detection. In this chapter, the diagnostic procedures for OSA diagnosis in children and adults are summarized. The usefulness of history and physical examination as well as OSA questionnaires for the detection of patients with high risk for the disease is analyzed. 作者: Silent-Ischemia 時間: 2025-3-29 15:08 作者: 很像弓] 時間: 2025-3-29 16:19
Book 2014mbedded features, investment strategies, commodity markets, energy, high-frequency trading, credit risk, numerical algorithms, financial econometrics and operational risk..Hidden Markov Models in Finance: Further Developments and Applications, Volume II. presents recent applications and case studies作者: 拒絕 時間: 2025-3-29 21:59 作者: fleeting 時間: 2025-3-30 02:53 作者: 先兆 時間: 2025-3-30 04:23
https://doi.org/10.1007/978-3-540-39606-2r Thema, sondern auch Vollzug dieses Denkens pr?gt? Die naheliegende Wahl, den Leitfaden der Darstellung in der chronologischen Abfolge der Werke zu verankern, entpuppt sich als dem philosophischen Problem des radikalen Anfangens ?u?erlich. Wo scheinbar ursprüngliche Einf?lle und Ideen unmittelbar a
