This is the second episode of a miniseries in the vast world of Deep Neural Networks, where I start from Intel® Open Image Denoise open-source library (OIDN) and create a CUDA/CUDNN-based denoiser that produces high-quality results at real-time frame rates. In this series, I narrate the story in first person. Photo by Miguel u00c1. Padriu00f1u00e1n…
Latent fingerprint segmentation is a complex process of separating relevant areas called fingerprints from an irrelevant background in the latent fingerprint image which is of poor quality. A breakthrough in the field can be used to segment fingerprints accurately from the background by using optimal resources. Processing of unwanted background of the entire image can lead to false and missed detection of fingerprints. An early fingerprint distinction technique based on colour and saliency masks is proposed to detect potentially relevant areas out of the entire image area for further processing, using a non-learning approach. Later, the patches of early detected fingermarks are fed to a stacked convolutional autoencoder for separating imposters of fingerprint(s) region from relevant fingerprint(s) regions, using a deep learning approach. The inspiration to use the convolutional neural network in this hybrid approach is to effectively capture feature distinction from potential features
With the rapid development of the economy， financial services such as bills are also increasing day by day. Among them， the important information in the bill business， such as personal vouchers， checks， and other bills， requires manual reading and input of a large amount of digital information. In order to avoid the waste of human and financial resources， related researchers classify and recognize handwritten fonts based on the neural network classification idea of deep learning. When the method based on deep learning is used for feature extraction of handwritten fonts， there is often a lack of detailed information such as edges and textures， which leads to the problem of low recognition accuracy. Aiming at the problem that the features of handwritten digits or letters are difficult to effectively extract， the recognition efficiency was not high and even caused recognition errors， a new automatic recognition method of handwritten digits or letters was propos
The focal point of this work is to automatically detect metaphor instances in short texts. It is the study of extricating the most optimal features for the task by using a deep learning architecture and carefully hand-crafted contextual features. The first feature set is created using a Convolutional Neural Network (CNN) architecture. Then, three other feature sets are manually hand-crafted using contextual justifications. Next, all of the feature sets are combined. Finally, the combined feature sets undergo the classification process using Support Vector Machine, Logistic Regression, Decision Tree, K-Nearest Neighbour and Discriminatory Analysis. These well-known ma-chine learning classification algorithms are used at the same time for the purpose of comparison. The best algorithm for this task is found to be Support Vector Machine (SVM). The outcome of all the experiments using SVM are good in all the metrics used, with F1-measure of 0.83. Finally, comparison to existing works and pe