Enhancing Core Image Classification Using Generative Adversarial Networks (GANs)
In the thrilling world of oil exploration, drill core samples are key to unlocking geological information critical to finding lucrative oil deposits. Despite the importance of these samples, traditional core logging techniques are known to be laborious and, worse still, subjective. Thankfully, the industry has embraced an innovative solution core imaging that allows for nondestructive and noninvasive rapid characterization of large quantities of drill cores. Our preeminent research paper aims to tackle the pressing problem of core detection and classification. Using state-of-the-art techniques, we present a groundbreaking solution that will transform the industry. Our first challenge is detecting the cores and segmenting the holes in images, which we will achieve using the Faster RCNN and Mask RCNN models, respectively. Then, we will address the problem of filling the hole in the core image, utilizing the powerful Generative Adversarial Networks (GANs) and employing Contextual Residual Aggregation (CRA) to create high-frequency residuals for missing contents in images. Finally, we will apply sophisticated texture recognition models for the classification of core images, revealing crucial information to oil companies in their quest to uncover valuable oil deposits. Our research paper presents an innovative and groundbreaking approach to tackling the complex issues surrounding core detection and classification. By harnessing cutting-edge techniques and technologies, we are poised to revolutionize the industry and make significant contributions to the field of oil exploration.
Code (0)
등록된 구현이 없습니다.
Tasks
Classificationimage-classificationImage ClassificationSimilar Papers 제목 키워드 기반
Assessing Robustness via Score-Based Adversarial Image Generation
Most adversarial attacks and defenses focus on perturbations within small $\ell_p$-norm constraints. However, $\ell_p$ threat models cannot capture all relevant semantic-preserving perturbations, and hence, the scope of …
Image GenerationScore-Based Generative Classifiers
The tremendous success of generative models in recent years raises the question whether they can also be used to perform classification. Generative models have been used as adversarially robust classifiers on simple data…
ClassificationAdversarial Augmentation for Enhancing Classification of Mammography Images
Supervised deep learning relies on the assumption that enough training data is available, which presents a problem for its application to several fields, like medical imaging. On the example of a binary image classificat…
Cancer ClassificationClassificationGeneral ClassificationImage Augmentation+2Generative Adversarial Models for People Attribute Recognition in Surveillance
In this paper we propose a deep architecture for detecting people attributes (e.g. gender, race, clothing ...) in surveillance contexts. Our proposal explicitly deal with poor resolution and occlusion issues that often o…
AttributeGeneral ClassificationAddressing Small and Imbalanced Medical Image Datasets Using Generative Models: A Comparative Study of DDPM and PGGANs with Random and Greedy K Sampling
The development of accurate medical image classification models is often constrained by privacy concerns and data scarcity for certain conditions, leading to small and imbalanced datasets. To address these limitations, t…
Denoisingimage-classificationImage ClassificationMedical Image Classification