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===위키데이터===
 
===위키데이터===
 
* ID :  [https://www.wikidata.org/wiki/Q1971661 Q1971661]
 
* ID :  [https://www.wikidata.org/wiki/Q1971661 Q1971661]
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===Spacy 패턴 목록===
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* [{'LOWER': 'object'}, {'LEMMA': 'recognition'}]

2021년 2월 17일 (수) 00:35 기준 최신판

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말뭉치

  1. Object recognition systems constitute a deeply entrenched and omnipresent component of modern intelligent systems.[1]
  2. In this paper we discuss the evolution of computer-based object recognition systems over the last fifty years, and overview the successes and failures of proposed solutions to the problem.[1]
  3. Deep learning techniques have become a popular method for doing object recognition.[2]
  4. At the time of writing, this Faster R-CNN architecture is the pinnacle of the family of models and continues to achieve near state-of-the-art results on object recognition tasks.[3]
  5. Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence.[4]
  6. In this article, we have seen that image and object recognition are the same concept.[5]
  7. Although it may sound rather theoretical and abstract, object recognition has a lot of interesting use cases in business.[5]
  8. For example, through object recognition, we developed an automated checkout system for a major player in the foodservice industry.[5]
  9. , H. Is human object recognition better described by geon structural descriptions or by multiple views?[6]
  10. 21 Tarr, M. & Bülthoff, H. Image-based object recognition in man, monkey and machine.[6]
  11. 41 Mel, B. SEEMORE: combining color, shape, and texture histogramming in a neurally inspired approach to visual object recognition.[6]
  12. In recent years, deep learning methods have emerged as powerful machine learning methods for object recognition and detection.[7]
  13. We prefer this network, since it was applied successfully on an ImageNet dataset for object recognition tasks.[7]
  14. Object recognition is the ability to recognize an object.[8]
  15. Object recognition allows robots and AI programs to pick out and identify objects from inputs like video and still camera images.[9]
  16. Gathered visual data from cloud robotics can allow multiple robots to learn tasks associated with object recognition faster.[9]
  17. Scientists at Brigham Young University have developed an object recognition algorithm that can learn to identify objects on its own.[9]
  18. I have a slight confusion differentiating between object recognition and object detection.[10]
  19. Some people say object detection is a sub-topic of object recognition?[10]
  20. Object Recognition allows you to detect and track intricate 3D objects, in particular toys (such as action figures and vehicles) and other smaller consumer products.[11]
  21. Using Object Recognition Object Recognition can be used to build rich and interactive experiences with 3D objects.[11]
  22. For Object Recognition to work well, the physical object should be: Opaque, rigid and contain none or only very few moving parts.[11]
  23. Creating Object Targets Object Scanner To enable Object Recognition in your app you will need to create an Object Target.[11]
  24. A popular approach to tackle this problem is to utilize a deep neural network for object recognition.[12]
  25. In this study, our objective was the development and validation of a deep object recognition framework using a synthetic depth image dataset.[12]
  26. The object detection framework can be trained on synthetically generated depth data, and then employed for object recognition on the real depth data in a cluttered environment.[12]
  27. A review of codebook models in patch-based visual object recognition.[13]
  28. “Object recognition with features inspired by visual cortex,” in CVPR (2) (San Diego, CA: IEEE Computer Society), 994–1000.[13]
  29. A novel object recognition test can be done in any open field or home cage.[14]
  30. This paper details the procedure and parameters used for the training of convolutional neural networks (CNNs) on a set of aerial images for efficient and automated object recognition.[15]
  31. The object recognition results show that by selecting a proper set of parameters, a CNN can detect and classify objects with a high level of accuracy (97.5%) and computational efficiency.[15]
  32. Object recognition technologies are a powerful tool to do just that, by giving manufacturers the ability to scan and track every item of their inventory that is added or subtracted.[16]
  33. The 4 object recognition startups showcased above are promising examples out of 552 we analyzed for this article.[16]
  34. Object recognition is the task of recognizing the object and labeling the object in an image.[17]
  35. The main goal of this survey is to present a comprehensive study in the field of 2D object recognition.[17]
  36. In this paper, various feature extraction techniques and classification algorithms are discussed which are required for object recognition.[17]
  37. As the deep learning has made a tremendous improvement in object recognition process, so the paper also presents the recognition results achieved with various deep learning methods.[17]
  38. Object recognition is the technique of identifying the object present in images and videos.[18]
  39. It is widely used and most state-of-the-art neural networks used this method for various object recognition related tasks such as image classification.[18]
  40. Object Recognition and Image Processing techniques can help detect disease more accurately.[18]
  41. The accuracy of object recognition is affected by the quality of both the training images and also the target images in which to search for the objects.[19]
  42. You can use IDOL Admin to perform training for object recognition.[19]
  43. There is currently no unique method to perform object recognition.[20]
  44. For this reason, the Object Recognition Kitchen was designed to easily develop and run simultaneously several object recognition techniques.[20]
  45. Several object recognition pipelines have been implemented for this framework.[20]
  46. Introduction There are fascinating problems with computer vision, such as image classification and object detection, both of which are part of an area called object recognition.[21]
  47. Object recognition using local invariant features for robotic applications: A survey.[22]
  48. Modified Dendrite Morphological Neural Network Applied to 3D Object Recognition.[22]
  49. Object recognition is a pervasive process that fascinates and puzzles in equal measure.[23]

소스

  1. ↑ 1.0 1.1 50 Years of object recognition: Directions forward ☆
  2. ↑ Object Recognition
  3. ↑ A Gentle Introduction to Object Recognition With Deep Learning
  4. ↑ Outline of object recognition
  5. ↑ 5.0 5.1 5.2 Object recognition definition and use cases
  6. ↑ 6.0 6.1 6.2 Models of object recognition
  7. ↑ 7.0 7.1 Object recognition and detection with deep learning for autonomous driving applications
  8. ↑ Object recognition
  9. ↑ 9.0 9.1 9.2 What is object recognition?
  10. ↑ 10.0 10.1 Object detection versus object recognition
  11. ↑ 11.0 11.1 11.2 11.3 Object Recognition
  12. ↑ 12.0 12.1 12.2 Deep Learning Based Object Recognition Using Physically-Realistic Synthetic Depth Scenes
  13. ↑ 13.0 13.1 Object Detection: Current and Future Directions
  14. ↑ Novel object recognition – Automate your test
  15. ↑ 15.0 15.1 Object Recognition in Aerial Images Using Convolutional Neural Networks
  16. ↑ 16.0 16.1 4 Top Object Recognition Startups In Industry 4.0 Out Of 552
  17. ↑ 17.0 17.1 17.2 17.3 2D Object Recognition Techniques: State-of-the-Art Work
  18. ↑ 18.0 18.1 18.2 Object Detection vs Object Recognition vs Image Segmentation
  19. ↑ 19.0 19.1 Object Recognition
  20. ↑ 20.0 20.1 20.2 Object Recognition Kitchen — object
  21. ↑ Object Recognition Using Convolutional Neural Networks
  22. ↑ 22.0 22.1 Visual Object Recognition
  23. ↑ Object Recognition: Complexity of Recognition Strategies

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