The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
SVM (Support Vector Machine), a state of the art classifier model is implemented on a computational mobile platform and its performances are evaluated against a low complexity classifier such as SFSVC (Super Fast Vector Support Classifier) on the same platform. For a better comparison, similar implementation for the two architectures are considered, such as using the same basic linear algebra library...
In this work, we adopt the use of deep learning method for no-reference image quality assessment. With the development of deep neural networks technology, foundational and deep features of images could be captured without much prior knowledge. So a sparse autoencoder (SAE) was trained to express a 32 × 32 pixels image into a feature vector. Then the original images were cut into serial sub-images...
The SFSVC (Super Fast Support Vector Classifier) architecture is implemented to a computational mobile platform and its performances are evaluated against its implementation on a classic machine (personal computer). The aim of this article is to prove that the SFSVC architecture can have good performances on an environment with very limited resources by taking advantages of its compact structure and...
The accuracy of object recognition has been greatly improved due to the rapid development of deep learning, but the deep learning generally requires a lot of training data and the training process is very slow and complex. We propose an incremental object recognition system based on deep learning techniques and speech recognition technology with high learning speed and wide applicability. The system...
Nowadays, deep learning is a technique that takes place in many computer vision related applications and studies. While it is put in the practice mostly on content based image retrieval, there is still room for improvement by employing it in diverse computer vision applications. In this study, we aimed to build a Convolutional Neural Network (CNN) based Facial Expression Recognition System (FER),...
Machine learning has been a detection technique used by many security vendors for some time now. With the enhancement brought by GPUs, many security products can now use different deep learning methods and forms of neural networks for malware classification. However, these new methods, as powerful as they are, are also limited by the amount of memory a GPU has or by the constant need of transferring...
This paper presents the design of a convolutional neural network architecture using the MatConvNet library for MATLAB in order to achieve the recognition of 2 classes of hand gestures: ”open” and ”closed”. Six architectures were implemented to which their hyperparameters and depth were varied to observe their behavior through the validation error in the training and accuracy in the estimation of each...
Children with autism often experience sudden meltdowns which not only makes the moment tough for the caretakers/parents but also make the children hurt themselves physically. Studies have discovered that children with autistic spectrum disorder exhibit certain actions through which we can anticipate mutilating meltdowns in them. The objective of our project is to build a system that can recognize...
Due to its low storage cost and fast query speed, cross-modal hashing (CMH) has been widely used for similarity search in multimedia retrieval applications. However, most existing CMH methods are based on hand-crafted features which might not be optimally compatible with the hash-code learning procedure. As a result, existing CMH methods with hand-crafted features may not achieve satisfactory performance...
Abundance and availability of video capture devices, such as mobile phones and surveillance cameras, have instigated research in video face recognition, which is highly pertinent in law enforcement applications. While the current approaches have reported high accuracies at equal error rates, performance at lower false accept rates requires significant improvement. In this paper, we propose a novel...
The function of each protein in the body is determined by its 3D structure, which can be predicted by computational methods. These methods generate an exceptional quantity of candidate models (decoys). similarity and machine learning methods are used to assess their quality. When measuring the distance from the decoy to its native structure (RMSD, TM-Score, Z-Score), similarity methods may be applied...
Convolutional Neural Networks (CNNs) trained on large scale RGB databases have become the secret sauce in the majority of recent approaches for object categorization from RGB-D data. Thanks to colorization techniques, these methods exploit the filters learned from 2D images to extract meaningful representations in 2.5D. Still, the perceptual signature of these two kind of images is very different,...
In this paper, we present a new face recognition algorithm based on weighted deep face learning. Our proposed method composes of two steps: face detection and face feature extraction. The aim of face detection is to find an accurate face position. The face alignment is then applied by finding the facial landmarks in the face rectangle. With the help of face alignment the error rate of face recognition...
Fingerprint classification is an effective technique for reducing the candidate numbers of fingerprints in the stage of matching in automatic fingerprint identification system (AFIS). In recent years, deep learning is an emerging technology which has achieved great success in many fields, such as image processing, computer vision. In this paper, we have a preliminary attempt on the traditional fingerprint...
A deep neural networks is proposed for the classification of premature ventricular contraction (PVC) beat, which is an irregular heartbeat initiated by Purkinje fibers rather than by sinoatrial node. Several machine learning approaches were proposed for the detection of PVC beats although they resulted in either achieving low accuracy of classification or using limited portion of data from existing...
Facial micro-expression refers to split-second muscle changes in the face, indicating that a person is either consciously or unconsciously suppressing their true emotions. Although these expressions are constantly occurring on people faces, they were easily ignored by people with the eye blinking. That is to say, most people don't notice them and it is the true representation of people emotions and...
This paper presents a novel method for fingerprint ROI (region of interest) segmentation using Deep learning technique — Convolutional Neural Networks. Experimental results, obtained using a publicly available test database of 200 fingerprint images in two variations — with and without Gaussian noise, demonstrate that this method is competitive with Fourier coefficients and NN based method for fingerprint...
Automatic prediction of photo aesthetic quality is useful for many practical purposes. Current computational approaches typically solved this problem by assigning a categorical label (good or bad) to a photo. However, due to the subjectivity and complexity of humans aesthetic judgments, only a categorical label is insufficient to represent humans perceived aesthetic quality of a photo. This paper...
Proposed algorithm is a face recognition algorithm from video using Generalized mean Deep Learning Neural Network. Generalized mean provides fast convergence of the feature set and Deep learning neural network is enhanced using wavelet transform as it improves the classification efficiency of the neural network. The performance of the proposed algorithm is evaluated on PaSC and Youtube dataset. The...
In big data era, digital information is growing rapidly. False and unlawful images influence our normal work and life, especially the exaggerated or fake propaganda of electronic commerce merchants. In this article, our purpose is to help people find out fake qualification certificate information automatically. Base on collecting and classifying web images, we apply Convolutional Neural Network (CNN)...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.