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위키데이터

• ID : Q1971661

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1. Object recognition systems constitute a deeply entrenched and omnipresent component of modern intelligent systems.
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.
3. Deep learning techniques have become a popular method for doing object recognition.
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.
5. Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence.
6. In this article, we have seen that image and object recognition are the same concept.
7. Although it may sound rather theoretical and abstract, object recognition has a lot of interesting use cases in business.
8. For example, through object recognition, we developed an automated checkout system for a major player in the foodservice industry.
9. , H. Is human object recognition better described by geon structural descriptions or by multiple views?
10. 21 Tarr, M. & Bülthoff, H. Image-based object recognition in man, monkey and machine.
11. 41 Mel, B. SEEMORE: combining color, shape, and texture histogramming in a neurally inspired approach to visual object recognition.
12. In recent years, deep learning methods have emerged as powerful machine learning methods for object recognition and detection.
13. We prefer this network, since it was applied successfully on an ImageNet dataset for object recognition tasks.
14. Object recognition is the ability to recognize an object.
15. Object recognition allows robots and AI programs to pick out and identify objects from inputs like video and still camera images.
16. Gathered visual data from cloud robotics can allow multiple robots to learn tasks associated with object recognition faster.
17. Scientists at Brigham Young University have developed an object recognition algorithm that can learn to identify objects on its own.
18. I have a slight confusion differentiating between object recognition and object detection.
19. Some people say object detection is a sub-topic of object recognition?
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.
21. Using Object Recognition Object Recognition can be used to build rich and interactive experiences with 3D objects.
22. For Object Recognition to work well, the physical object should be: Opaque, rigid and contain none or only very few moving parts.
23. Creating Object Targets Object Scanner To enable Object Recognition in your app you will need to create an Object Target.
24. A popular approach to tackle this problem is to utilize a deep neural network for object recognition.
25. In this study, our objective was the development and validation of a deep object recognition framework using a synthetic depth image dataset.
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.
27. A review of codebook models in patch-based visual object recognition.
28. “Object recognition with features inspired by visual cortex,” in CVPR (2) (San Diego, CA: IEEE Computer Society), 994–1000.
29. A novel object recognition test can be done in any open field or home cage.
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.
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.
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.
33. The 4 object recognition startups showcased above are promising examples out of 552 we analyzed for this article.
34. Object recognition is the task of recognizing the object and labeling the object in an image.
35. The main goal of this survey is to present a comprehensive study in the field of 2D object recognition.
36. In this paper, various feature extraction techniques and classification algorithms are discussed which are required for object recognition.
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.
38. Object recognition is the technique of identifying the object present in images and videos.
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.
40. Object Recognition and Image Processing techniques can help detect disease more accurately.
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.
42. You can use IDOL Admin to perform training for object recognition.
43. There is currently no unique method to perform object recognition.
44. For this reason, the Object Recognition Kitchen was designed to easily develop and run simultaneously several object recognition techniques.
45. Several object recognition pipelines have been implemented for this framework.
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.
47. Object recognition using local invariant features for robotic applications: A survey.
48. Modified Dendrite Morphological Neural Network Applied to 3D Object Recognition.
49. Object recognition is a pervasive process that fascinates and puzzles in equal measure.