Object Matching in Digital Video Using Descriptors with Python and Tkinter
Idioma: inglés
Editorial: Independently Published, 2024
- Tapa blanda
- Nuevo
Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK
Vendedor de IberLibro desde 11 de junio de 1999
Condición: Nuevo
EUR 23,09
Cantidad disponible: Más de 20 disponibles
Añadir al carritoDescripción del artículo del vendedor
New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
N° de ref. del artículo L0-9798328535519
- Título
- Object Matching in Digital Video Using Descriptors with Python and Tkinter
- Autor
- Siahaan, Vivian; Sianipar, Rismon Hasiholan
- Editorial
- Independently Published
- Año de publicación
- 2024
- Estado
- New
- Encuadernación
- PAP
- Idioma
- inglés
- ISBN 13
- 9798328535519
- Peso del artículo
- 372 gramos
The second project is a Python-based GUI application designed for image matching using the ORB (Oriented FAST and Rotated BRIEF) algorithm, leveraging OpenCV for image processing, Tkinter for GUI development, and PIL for image format handling. Users can load and match two images, adjusting parameters such as number of features, scale factor, and edge threshold directly through sliders and options provided in the interface. The application computes keypoints and descriptors using ORB, matches them using a BFMatcher based on Hamming distance, and visualizes the top matches by drawing lines between corresponding keypoints on a combined image. ORBMacher.py offers a user-friendly platform for experimenting with ORB's capabilities in feature detection and image matching, suitable for educational and practical applications in computer vision and image processing.
The third project is a Python application designed for visualizing keypoint matches between images using the FAST (Features from Accelerated Segment Test) detector and SIFT (Scale-Invariant Feature Transform) descriptor. Built with Tkinter for the GUI, it allows users to load two images, adjust detector parameters like threshold and non-maximum suppression, and visualize matches in real-time. The interface includes controls for image loading, parameter adjustment, and features a scrollable canvas for exploring matched results. The core functionality employs OpenCV for image processing tasks such as keypoint detection, descriptor computation, and matching using a Brute Force Matcher with L2 norm. This tool is aimed at enhancing user interaction and analysis in computer vision applications.
The fourth project creates a GUI for matching keypoints between images using the AGAST (Adaptive and Generic Accelerated Segment Test) algorithm with BRIEF descriptors. Utilizing OpenCV for image processing and Tkinter for the interface, it initializes a window titled "AGAST Image Matcher" with a control_frame for buttons and sliders. Users can load two images using load_button1 and load_button2, which trigger file dialogs and display images on a scrollable canvas via load_image1(), load_image2(), and show_image(). Adjustable parameters include AGAST threshold and BRIEF descriptor bytes. Clicking match_button invokes match_images(), checking image loading, detecting keypoints with AGAST, computing BRIEF descriptors, and using BFMatcher for matching and visualization. The matched image, enhanced with color-coded lines, replaces previous images on the canvas, ensuring clear, interactive results presentation.
The fifth project is a Python-based application that utilizes the AKAZE feature detection algorithm from OpenCV for matching keypoints between images. Implemented with Tkinter for the GUI, it features a "AKAZE Image Matcher" window with buttons for loading images and adjusting AKAZE parameters like detection threshold, octaves, and octave layers. Upon loading images via file dialog, the app reads and displays them ...
“Sinopsis” puede pertenecer a otra edición de este título.
PBShop.store UK
Fairford, GLOS, Reino Unido
Vendedor de IberLibro desde 11 de junio de 1999
Tarifas de envío de Reino Unido a Estados Unidos de America
| Artículo | De 10 a 20 días hábiles | De 10 a 20 días hábiles |
|---|---|---|
| Primer artículo | EUR 4,97 | EUR 4,97 |
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