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Convolutional Neural Network (CNN) has received remarkable achievements in hyperspectral image (HSI) classification. However, how to effectively implement spatial context that has been demonstrated to be crucial for classification of HSI is still an open issue. Current CNNs for hyperspectral classification are restricted into a small scale due to small-scale input and limited training samples. Therefore,...
Chronic Kidney Disease (CKD) is an increasingly prevalent condition affecting 13% of the US population. The disease is often a silent condition, making its diagnosis challenging. Identifying CKD stages from standard office visit records can help in early detection of the disease and lead to timely intervention. The dataset we use is highly imbalanced. We propose a hierarchical meta-classification...
Data mapping among different data standards in health institutes is often a necessity when data exchanges occur among different institutes. However, no matter rule-based approaches or traditional machine learning methods, none of these methods have achieved satisfactory results yet. In this work, we propose a deep learning method, mixture feature embedding convolutional neural network (MfeCNN), to...
In recent years, most breakthroughs in fields such as image and video processing were based on machine learning technologies that allow computers to recognize objects in images with nearly human precision. In some application domains, computers even surpassed human level performance. These breakthroughs result from an exponential increase of computational resources and digitization of society (massive...
Infants communicate their physiological state and emotions mostly by crying. Identifying the cause of crying is natural and easy for human beings, but challenging for machines. Automatic identification of cry-cause factors has vast applications in assistive healthcare and timely remedial measures in critical situations. In this paper, Infant cry signals analysis is carried out to identify four cry-causes,...
We propose a new variant of the Correlation-based Feature Selection (CFS) method for coping with longitudinal data – where variables are repeatedly measured across different time points. The proposed CFS variant is evaluated on ten datasets created using data from the English Longitudinal Study of Ageing (ELSA), with different age-related diseases used as the class variables to be predicted. The results...
The success of deep neural networks usually relies on a large number of labeled training samples, which unfortunately are not easy to obtain in practice. Unsupervised domain adaptation focuses on the problem where there is no labeled data in the target domain. In this paper, we propose a novel deep unsupervised domain adaptation method that learns transferable features. Different from most existing...
The increased popularity of Massive Open Online Courses (MOOC) and e-learning has constantly increased video-based online education platforms. There are also many video lectures for software engineering education in online education platforms. Although online lectures have many advantages, there are also limitations. We performed a verification research to see if high frequency words can detect mind...
The paper considers the problem of feature selection in learning using privileged information (LUPI), where some of the features (referred to as privileged ones) are only available for training, while being absent for test data. In the latest implementation of LUPI, these privileged features are approximated using regressions constructed on standard data features, but this approach could lead to polluting...
Color and texture have been proven to be very discriminant attributes in image analysis across many works. This paper proposes a color texture analysis method based on the graph theory, in which we convert the texture in question into an undirected weighted graph and explore the shortest paths between four pairs of pixels according to different scales and orientations of the image. Basically, we extend...
We propose the Anchored Regression Network (ARN), a nonlinear regression network which can be seamlessly integrated into various networks or can be used stand-alone when the features have already been fixed. Our ARN is a smoothed relaxation of a piecewise linear regressor through the combination of multiple linear regressors over soft assignments to anchor points. When the anchor points are fixed...
Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in their building modules. In this work, we introduce two new modules to enhance the transformation modeling capability of CNNs, namely, deformable convolution and deformable RoI pooling. Both are based on the idea of augmenting the spatial sampling locations in the...
Gender recognition from facial images has become one of challenging research problem in computer vision, security, verbal-nonverbal communication and human computer interaction applications nowadays. Because facial images include many information such as gender, facial expressions, age, ethnic origin in computer-aided applications, the success rate of the gender recognition depends on quality of facial...
Mid-level representations are used to map sets of local features into one global representation for a given media descriptor. In visual pattern recognition tasks, Bag-of-Words (BoW) is one popular strategy, among many methods available in literature, due mainly by the simplicity in concept and implementation. Despite the overall good results achieved by BoW in many tasks, the method is unstable in...
We propose a framework to extend corner feature detection in standard rectangular images with less distortion to distorted circular images captured with fisheye lenses. To solve two problems of nonuniformity of spatial resolution and spherical polar coordinates singularity, our approach makes use of a modification in the Yin-Yang grid, which is an overset grid consisting of two latitude/longitude...
Encoding spatio-temporally varying textures is challenging for standardised video encoders, with significantly more bits required for textured blocks compared to non-textured blocks. It is therefore beneficial to understand video textures in terms of both their spatio-temporal characteristics and their encoding statistics in order to optimize coding modes and performance. To this end, we examine the...
This paper aims to classify a peripheral pulmonary lesion whether it is malignant or benign by proposing the new method to select a window of interest (WOI) using window slicing and the new feature called the "weight-sum of upper and lower gray level co-occurrence matrix (GLCM)" of an endobronchial ultrasound (EBUS) image. The proposed feature can be used to determine the heterogeneity of...
Insulator is an important guarantee for the safe operation of electrified railway, but because of working conditions, the insulator would face long-term effects of wind, snow and rain. Insulator surface would easily accumulate filth and pollution flashover occurs, which often leads to blackouts and threatens the safe operation of the railway. Therefore, detection of the insulator working state is...
A social robot has to ascertain the personality of the human interacting with it through verbal or non-verbal communication. While existing literature focuses on facial feature recognition for non-verbal communication, we propose using handshake patterns to classify the personality of the concerned human. Due to the largely random nature of the gestures in handshakes, general pattern recognition methods...
Robust semantic knowledge of the environment is one of the building blocks for autonomous driving. If different sensor types are employed for the same task independently, the overall accuracy and safety of the system can increase. Therefore, it is desirable to maximize each sensor's capabilities and to build up redundancies, as it is often required by functional safety. To this end, this paper demonstrates...
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