| dc.contributor | Ramírez Torres, Marco Tulio | |
| dc.contributor | Mejía Carlos, Marcela | |
| dc.contributor | Marco Tulio Ramírez Torres;0000-0002-7457-7318 | |
| dc.contributor | Marcela Mejia Carlos;0000-0003-2872-9461 | |
| dc.creator | Juan Manuel Fortuna-Cervantes;0000-0002-9229-3159 | |
| dc.creator | Fortuna Cervantes, Juan Manuel | |
| dc.date | 2022-09-08T20:28:39Z | |
| dc.date | 2022-09-08T20:28:39Z | |
| dc.date | 2022-08 | |
| dc.date.accessioned | 2023-07-17T20:32:54Z | |
| dc.date.available | 2023-07-17T20:32:54Z | |
| dc.identifier | https://repositorioinstitucional.uaslp.mx/xmlui/handle/i/7954 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/7517580 | |
| dc.description | Texture characterization in digital images has become an analysis tool in computer
vision. Texture in visual perception is a very important physical property since
it provides information about the structural composition of surfaces and objects
in the image. This research involves two areas of knowledge, wavelet analysis
and deep learning, both of which functioned as feature extraction methods for
image processing with textures and materials. This work aimed to study the
adaptability of deep learning with wavelet analysis and implement a detection
and classification system for aerial navigation. The first approach analyzes the
extracted information (spatial domain vs. wavelet domain) in object detection
and aerial navigation. In addition, to evaluate the learning performance of a
binary classifier. In the second approach, a multi-class classifier is proposed for the
following databases: KTH-TIPS-2B (KT2B), Describable Textures Dataset (DTD),
and Flickr Material Database (FMD). The possibility of merging both domains
is evaluated since Convolutional Neural Networks (CNNs) do not learn spectral
information, important information for texture recognition. In the third approach,
a classification system for textured objects in aerial navigation tasks is implemented,
where texture is involved as a physical property of the object. A classification model
is developed using the knowledge transfer method and wavelet features. In the
fourth approach, it is shown that internal pooling layers often lead to information
loss. A classification system with a new pooling method called Discrete Wavelet
Transform Pooling (DWTP) is proposed to solve this problem. The combination
of these methods achieves acceptable classification performance. The learning
plots reflect that all three datasets show learning generalization. In addition, the
images obtained from the virtual environment show learning generalization for
some classes in the DTD database. Moreover, the fusion of deep learning with
wavelet analysis is recommended for small datasets of images with textures. Due
to the limitation of learning about spectral information that is lost in conventional
CNNs. Furthermore, it is argued that this helps to eliminate overfitting. The
results show that it is possible to integrate this methodology into the technological
development of applications, such as image classification or restoration tasks and
object detection. | |
| dc.description | CONACYT, Beca 776118 | |
| dc.description | Investigadores | |
| dc.description | Estudiantes | |
| dc.format | application/pdf | |
| dc.language | Inglés | |
| dc.relation | REPOSITORIO NACIONAL CONACYT | |
| dc.rights | Acceso Abierto | |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/4.0 | |
| dc.subject | CIENCIAS FÍSICO MATEMATICAS Y CIENCIAS DE LA TIERRA | |
| dc.subject | INGENIERÍA Y TECNOLOGÍA | |
| dc.title | Implementation and application of the wavelet transform into deep learning techniques for texture classification and object detection | |
| dc.type | Tesis de doctorado | |
| dc.coverage | México.San Luis Potosí. San Luis Potosí. | |
