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For a practical intracranial brain computer interface (BCI), minimizing the invasiveness of the electrode implantation is crucial. In this study, we used only one intracranial electrode to implement an online BCI for fast typing. When the subject attended the virtual button containing visual motion stimuli, prominent responses were elicited at the stereo-EEG (SEEG) electrodes within the fMRI defined...
Human action recognition is an imperative research area in the field of computer vision due to its numerous applications. Recently, with the emergence and successful deployment of deep learning techniques for image classification, object recognition, and speech recognition, more research is directed from traditional handcrafted to deep learning techniques. This paper presents a novel method for human...
Laughter detection is an essential aspect towards effective human-computer interaction. This work primarily addresses the problem of laughter detection in a real-time environment. We utilize annotated audio and visual data collected from a Kinect sensor to identify discriminative features for audio and video, separately. We show how the features can be used with classifiers such as support vector...
Images on the Internet and in multimedia systems are rising successively. There are different research works on visual information and automatic analysis of images. Image memorability is a new task in computer vision. Actually, the human brain processes simultaneously millions of images and other information from multiple sources. Among these various images and information some of them are more memorable...
To collect various data, millions of probes and sensors mounted on a Google and Baidu street view vehicles, 360-degree view and GPS location. From millions of street sensors, image data of GB level is transmitted back per second. It is an important research topic to classify and identify images quickly. The experimental results yielded a 23% error rate of recognition on images collected by probes...
The volume of academic paper submissions and publications is growing at an ever increasing rate. While this flood of research promises progress in various fields, the sheer volume of output inherently increases the amount of noise. We present a system to automatically separate papers with a high from those with a low likelihood of gaining citations as a means to quickly find high impact, high quality...
Dermoscopy image is usually used in early diagnosis of malignant melanoma. The diagnosis accuracy by visual inspection is highly relied on the dermatologist's clinical experience. Due to the inaccuracy, subjectivity, and poor reproducibility of human judgement, an automatic recognition algorithm of dermoscopy image is highly desired. In this work, we present a hybrid classification framework for dermoscopy...
Kernel function implicitly maps data from its original space to a higher dimensional feature space. Kernel based machine learning algorithms are typically applied to data that is not linearly separable in its original space. Although kernel methods are among the most elegant part of machine learning, it is challenging for users to define or select a proper kernel function with optimized parameter...
Human Activity detection is an imperative area of research in computer vision. This paper focuses on activity recognition by construction personnel at the construction sites. The method uses bag of features (BOF) approach to detect an activity. Here we have considered five types of activities done at construction sites namely ladder climbing, brick laying, carpentry work, painting and plastering work...
Hand-engineered local image features have been proven to be intended representation for a variety of high-level visual recognition tasks. But as the visual recognition tasks such as scene classification and object detection become more challenging, the semantic gap between low-level feature and the concept descriptor of the scene images increases. In this paper, we present novel semantic multinomial...
Compressed domain human action recognition algorithms are extremely efficient, because they only require a partial decoding of the video bit stream. However, the question what exactly makes these algorithms decide for a particular action is still a mystery. In this paper, we present a general method, Layer-wise Relevance Propagation (LRP), to understand and interpret action recognition algorithms...
Image cropping is a fundamental task in image editing to enhance the aesthetic quality of images. In this paper, we propose an automatic image cropping technique based on aesthetic map and gradient energy map. Instead of utilizing aesthetic rules in previous methods, we learn the aesthetic map by a deep convolutional neural network with a large-scale dataset for aesthetic quality assessment. The aesthetic...
In this paper, we advocate the use of Grassmann manifolds for discovering object images in different states (e.g., unripe, peeled, etc.). We propose a novel dictionary learning algorithm, which derives the subspaces on a Grassmann manifold for describing each object state. By our introduced geodesic-flow constraint, our Grassmann manifold exhibits excellent capabilities in relating objects in distinct...
Most approaches for scene parsing, recognition or retrieval use detectors that are either (i) independently trained or (ii) jointly trained for conjunctions of object-object or object-attribute phrases. We posit that neither of these two extremes is uniformly optimal, in terms of performance, across all categories and conjunctions. The choice of whether one should train an independent or composite...
Machine-learning algorithms have shown outstanding image recognition performance for computer vision applications. While these algorithms are modeled to mimic brain-like cognitive abilities, they lack the remarkable energy-efficient processing capability of the brain. Recent studies in neuroscience reveal that the brain resolves the competition among multiple visual stimuli presented simultaneously...
Zero-shot Learning (ZSL) can leverage attributes to recognise unseen instances. However, the training data is limited and cannot adequately discriminate fine-grained classes with similar attributes. In this paper, we propose a complementary procedure that inversely makes use of attributes to infer discriminative visual features for unseen classes. In this way, ZSL is fully converted into conventional...
Automated Visual Inspection (AVI) systems for metal surface inspection is increasingly used in industries to aid human visual inspectors for classification of possible anomalies. For classification, the challenge lies in having a small and specific dataset that may easily result in over-fitting. As a solution, we propose to use deep Convolutional Neural Networks (ConvNets) learnt from the large ImageNet...
In the multilingual country like India most of the official documents are prepared by using official regional language of that particular state and Hindi or English language. In present situation country is moving towards digitization of these documents by introducing e-governance concept. In this paper we proposed here a Local Binary Pattern(LBP) based feature extraction approach for recognition...
This paper presents a novel technique of image classification using BOVW model. The entire process first involves feature detection of images using FAST, the choice made in order to speed up the process of detection. Then comes the stage of feature extraction for which FREAK, a binary feature descriptor is employed. K-means clustering is then applied in order to make the bag of visual words. Every...
Face recognition system is used for the identification and verification of a face from a video or digital image. In the first phase, Viola Jones algorithm is used to detect and crop face region automatically from image/video frame. The second phase is to recognize the face of a person, in our proposed method Bag of Word technique used to extract features from an image which uses SURF for interest...
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