Template matching

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  1. A basic method of template matching uses an image patch (template), tailored to a specific feature of the search image, which we want to detect.[1]
  2. Template matching has various applications and is used in such fields as face recognition (see facial recognition system) and medical image processing.[1]
  3. How to implement custom object detection with template matching.[2]
  4. Template matching is a technique in digital image processing for finding small parts of an image that matches a template image.[2]
  5. When you use OpenCV template matching, your template slides pixel by pixel on your image.[2]
  6. Then, we loop over templates to perform object detection with template matching for each template.[2]
  7. In this effort we develop, analyze and compare template matching and deep learning algorithms for use in the task of classifying occluded targets in light detection and ranging (LiDAR) data.[3]
  8. Template matching is part of OpenCV and it takes our grayscale source image and the template image with the statistical metrics we need to use.[4]
  9. Template matching is an important topic in the field of Artificial Intelligence (AI) as it is one of the approaches to the basic problem of image processing which is locating the region of interest.[5]
  10. Simple template matching involves comparing the template image against the source image by sliding it.[5]
  11. Feature-based template matching includes four main steps.[5]
  12. Template matching by normalized cross correlation (NCC) is widely used for finding image correspondences.[6]
  13. Comparisons are made between the traditional template matching method and other approaches incorporating neural networks.[7]
  14. The best methods based on Neural Networks outperformed the template matching method as expected.[7]
  15. Errors were detected manually with a tool that allowed human annotators to inspect the template matching inputs and outputs.[8]
  16. After a few minutes of browsing Facebook, I came across a template matching tutorial I did over at Machine Learning Mastery.[9]
  17. But template matching is not ideal if you are trying to match rotated objects or objects that exhibit non-affine transformations.[9]
  18. On Line 41 we make a check to ensure that the input image is larger than our template matching.[9]
  19. We then apply template matching using cv2.matchTemplate on Line 47.[9]
  20. But, as I’ve said in the past, in the end, kernel machines are shallow networks that perform “glorified template matching”.[10]
  21. SVM solves an optimization problem with a well defined objective function, how is it doing template matching?[10]
  22. It's been weeks since I am looking for an “advanced” template matching.[11]
  23. This paper d iscusses the algorithms for tracking a m oving object through video data using template matching.[12]
  24. However, template matching is limited to searching for a single intensity pattern, rendering it sensitive to changes in orientation or perspective of objects.[13]
  25. Template matching can also be used for supervised classification, e.g. for nearest-neighbour search based on a set of annotated templates.[13]
  26. Template matching uses a small image, or template, to find matching regions in a larger image.[14]
  27. These missing events could be detected by a template matching method, which uses waveforms of existing events as templates to scan through continuous data for new events with high similarities.[15]
  28. Template matching is a method for searching and finding the location of a template in a larger image.[16]
  29. A typical pair of sequences for template matching is shown in Figure 1.[17]
  30. In this chapter, based on the point of template matching, we proposed an improved subsequence Dynamic Time Wrapping (IsDTW) method, to realize the real-time and high precision FOG detection and alarm.[17]
  31. The outline of this article is arranged as follows: The second section discusses the related work on low-texture objects detection based on template matching.[18]
  32. To solve these problems, we use the template matching method, because it has less computational complexity and it does not need to spend much time on training.[18]
  33. 19 proposed a fast template matching method for affine cases.[18]
  34. Acharya 20 proposed a histograms of gradients (HOG)-based template matching method.[18]

소스

  1. ↑ 1.0 1.1 Template matching
  2. ↑ 2.0 2.1 2.2 2.3 Object Detection with No Data Thanks to Template Matching
  3. ↑ A comparison of template matching and deep learning for classification of occluded targets in LiDAR data
  4. ↑ Object Detection on Python Using Template Matching
  5. ↑ 5.0 5.1 5.2 Template-based versus Feature-based Template Matching
  6. ↑ [PDF Deep Learning Improves Template Matching by Normalized Cross Correlation]
  7. ↑ 7.0 7.1 A Comparison of Template Matching with Neural Network Approaches in the Recognition of Numeric Characters Hand-Stamped in Aluminium
  8. ↑ Weakly Supervised Deep Metric Learning for Template Matching
  9. ↑ 9.0 9.1 9.2 9.3 Multi-scale Template Matching using Python and OpenCV
  10. ↑ 10.0 10.1 How are SVMs = Template Matching?
  11. ↑ Advanced template matching
  12. ↑ (PDF) Machine learning using template matching applied to object tracking in video data
  13. ↑ 13.0 13.1 Multi-template matching: a versatile tool for object-localization in microscopy images
  14. ↑ Deep Learning, Semantic Segmentation, and Detection
  15. ↑ Department of Earth and Environmental Sciences Seminar - Microearthquake Detection from Template Matching to Machine Learning
  16. ↑ Image Processing Techniques That You Can Use in Machine Learning Projects
  17. ↑ 17.0 17.1 Detection of Freezing of Gait Using Template-Matching-Based Approaches
  18. ↑ 18.0 18.1 18.2 18.3 A fast template matching algorithm based on principal orientation difference

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