Procesamiento de Señales e Imágenes Digitales.

Permanent URI for this collectionhttp://98.81.228.127/handle/20.500.12404/5040

Browse

Now showing 1 - 2 of 2

Corn crops identification using multispectral images from unmanned aircraft systems
(Pontificia Universidad Católica del Perú, 2019-04-08) Trujillano Asato, Fedra Catherine; Racoceanu, Daniel
Climate change and migration of population from rural to urban areas are affecting the agricultural production around the world. This study was based in the particular department of Ancash - Peru where corn is one of the most important crops of the region. Authorities in this region are concerned in finding a method, different from census; that can constantly monitor corn crops areas. This data is important to evaluate how these two causes will impact on food security in Ancash. The first part of the present thesis reviews the current techniques in the recognition of crop areas using remote sensing and multispectral images. The second part explains the methodology developed for this study, considering the data acquisition using Unmanned Aircraft Systems, the preparation of the acquired data and two deep learning model approaches. The first approach is based on binary classification of corn patches using Le Net model with near infrared images. The second one describes the segmentation of corn areas in different stages using the U-net model, in this case five band images were considered. The third part shows the results of both approaches. From these results it is concluded that training a model with data from different stages and scenarios of two campaigns (2016 and 2017) can achieve a 95% of accuracy in corn segmentation.
Deep Learning for Semantic Segmentation versus Classification in Computational Pathology: Application to mitosis analysis in Breast Cancer grading
(Pontificia Universidad Católica del Perú, 2019-04-12) Jiménez Garay, Gabriel Alexandro; Racoceanu, Daniel
Existing computational pathology approaches did not allow, yet, the emergence of effective/efficient computer-aided tools used as a second opinion for pathologists in the daily practice. Focusing on the case of computer-based qualification for breast cancer diagnosis, the present article proposes two deep learning architectures to efficiently and effectively detect and classify mitosis in a histopathological tissue sample. The first method consisted of two parts, entailing a preprocessing of the digital histological image and a free-handcrafted-feature Convolutional Neural Network (CNN) used for binary classification. Results show that the methodology proposed can achieve 95% accuracy in testing with an F1-score of 94.35%, which is higher than the results from the literature using classical image processing techniques and also higher than the approaches using handcrafted features combined with CNNs. The second approach was an end-to-end methodology using semantic segmentation. Results showed that this algorithm can achieve an accuracy higher than 95% in testing and an average Dice index of 0.6 which is higher than the results from the literature using CNNs (0.9 F1-score). Additionally, due to the semantic properties of the deep learning approach, an end-to-end deep learning framework is viable to perform both tasks: detection and classification of mitosis. The results showed the potential of deep learning in the analysis of Whole Slide Images (WSI) and its integration to computer-aided systems. The extension of this work to whole slide images is also addressed in the last two chapters; as well as, some computational key points that are useful when constructing a computer-aided-system inspired by the described technology.