With the recent developments in machine learning and big data analysis, this study proposes an innovative method for efficient seismic fault detection based on semi-supervised classification of multiple attribute patches through the popular multi-layer perceptron (MLP) technique. Such method consists with...
Support vector machines image classification We used support vector machines (SVMs), a supervised, non-parametric classifier to classify the sub-centimeter 3-band RGB imagery into the following classes: dark biocrust, light biocrust, bare soil, dark rock, light rock, green vegetation, non-photosynth...
[29] further advanced this field by developing GAN architectures capable of producing high-fidelity images of multiple plant species, improving multiclass classification performance. Building on these foundations, advances in generative techniques have focused on addressing more complex challenges in ...
At step 2, our contour processing step produces contours and then filters them with a second classification step, helping to improve overall accuracy. We show that our final level set operation, which is automatically seeded with output from previous steps, helps to smooth the results. Overall, ...
Implementation of Transformer in Transformer, pixel level attention paired with patch level attention for image classification, in Pytorch - lucidrains/transformer-in-transformer
Finally, Decision tree classifier is utilized for classification of lung nodules. The system has been tested with a number of real Computed Tomography lung images and has achieved satisfactory results in classifying the lung diseases. The results show that the proposed method achieves higher ...
Convolutional natural networksMid-level discriminative patchScene classificationDeep learningScene classification is an important task for computer vision, and Convolutional Neural Networks, a model of deep learning, is widely used for object classification. However, they rely on pooling and......
Patch-level classification is done under a decision flow method and a normal multi-class neural network method. The decision flow iteratively filters out areas from benign to most severe using 4 separate models. The generated mask images contains regional information regarding cancer severity for each...
Respiratory sound classificationAdventitious soundContrastive learningPatch-level methodAn effective approach for the automatic identification of respiratory sounds is presented in this paper, which is helpful to assist in the preliminary diagnosis for respiratory diseases.Differently from most methods that ...
Because of the strong variability of the cortical sulci, their automatic recognition is still a challenging problem. The last algorithm developed in our laboratory for 125 sulci reaches an average recognition rate around 86%. It has been applied to thous