Name: Hierarchical approach for object detection using shape descriptors (en Inglés)
Price: 57.93 USD
Availability: InStock
Author: Arshad, Bassam Syed
ISBN: 9783330353060

portada Hierarchical approach for object detection using shape descriptors (en Inglés)

Hierarchical approach for object detection using shape descriptors (en Inglés)

Formato

Libro Físico

Autor

Bassam Syed Arshad

Editorial

LAP Lambert Academic Publishing

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

Dimensiones

22.9 x 15.2 x 0.3 cm

Peso

0.10 kg.

ISBN13

9783330353060

Categorías

Empresa y gestión

Hierarchical approach for object detection using shape descriptors (en Inglés)

Bassam Syed Arshad (Autor) · LAP Lambert Academic Publishing · Tapa Blanda

Libro Nuevo Importado

Envío: 25 a 32 días háb.

$ 105.33$ 57.93

-45%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan 10 unidades

$ 57.93

Llega entre el 16 Sep y el 29 Sep a Quito, Pichincha. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "Hierarchical approach for object detection using shape descriptors (en Inglés)"

Automatic object recognition is a fundamental problem in the fields of computer vision and machine learning, that has received a lot of research attention lately. While there are different methods, that build upon various low level features to construct object models, this work explores and implements the use of closed-contours as formidable object features. A hierarchical technique is employed to extract the contours, exploiting the inherent spatial relationships between the parent and child contours of an object. Fourier Descriptors are used to effectively and invariantly describe the extracted contours. A simple hierarchical, shape label and spatial descriptor matching method is implemented, to determine the nearest object-model. Multi-threaded architecture and GPU efficient image-processing functions are adopted making the technique efficient for use in real world applications. The technique is successfully tested on common traffic signs in real world images, with overall good performance and robustness being obtained as an end result.