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We propose a highly structured neural network architecture for semantic segmentation with an extremely small model size, suitable for low-power embedded and mobile platforms. Specifically, our architecture combines i) a Haar wavelet-based tree-like convolutional neural network (CNN), ii) a random layer realizing a radial basis function kernel approximation, and iii) a linear classifier. While stages...
In this work, we have proposed a novel filtering approach based on empirical mode decomposition (EMD) and tunable-Q wavelet transform (TQWT) for the detection of epileptic seizure electroencephalogram (EEG) signals, which is termed as EMD-TQWT method. In this EMD-TQWT method, the intrinsic mode functions (IMFs) obtained from EEG signals using EMD method are considered as a set of amplitude modulated...
Activity recognition from first-person (ego-centric) videos has recently gained attention due to the increasing ubiquity of the wearable cameras. There has been a surge of efforts adapting existing feature descriptors and designing new descriptors for the first-person videos. An effective activity recognition system requires selection and use of complementary features and appropriate kernels for each...
Crowd density estimation is an effective automated video surveillance technique to ensure crowd safety. In spite of various efforts being taken to estimate crowd density, it remains a challenging task. This paper proposes a new texture feature-based approach for the estimation of crowd density where two efficient texture features namely Local Binary Pattern (LBP) and Gabor Filter are used. The LBP...
In this paper, we propose a novel kernel learning scheme for acoustic scene classification using multiple short-term observations. The method takes inspiration from the recent result of psychological research — "Humans use summary statistics to perceive auditory sequences" we endeavor to devise computational framework imitating such important auditory mechanism for acoustic scene parsing...
This work applies the Gaussian Mixture Probability Hypothesis Density (GMPHD) Filter to multi-object tracking in video data. In order to take advantage of additional visual information, Kernelized Correlation Filters (KCF) are evaluated as a possible extension of the GMPHD tracking-by-detection scheme to enhance its performance. The baseline GMPHD filter and its extension are evaluated on the UA-DETRAC...
We proposed a novel method of feature extraction for multi-modal images called modality-convolution. It extracts both the intra- and inter-modality information. Whats more, it completes the data fusion at pixel-level so that the complementarity of information contained in multi-modal data is fully utilized. Based on the modality-convolution, we describe a modality-CNN for multi-modal gesture recognition...
In this work, we propose a deep learning approach for the detection of the activities of daily living (ADL) in a home environment starting from the skeleton data of an RGB-D camera. In this context, the combination of ad hoc features extraction/selection algorithms with supervised classification approaches has reached an excellent classification performance in the literature. Since the recurrent neural...
Image retrieval has attracted increasing interests in recent years. This paper proposes a coarse-to-fine method for fast indexing with marching probability model. We first use a vector quantized Deep Convolutional Neural Network(DCNN) feature descriptors and exploit enhanced Locality-sensitive hashing(LSH) techniques for fast coarse-grained retrieval. Then, we focus on obtaining high-precision preserved...
Timely and accurate traffic classification and application characterization are becoming increasingly important with many applications in wired and wireless networks, e.g., traffic engineering, security monitoring, and quality of service (QoS). In particular, Software Defined Networking (SDN) is a new networking paradigm that has great impact on future IP networks and 5G wireless networks. In SDN...
A fault diagnosis method was proposed based on Semi-supervised manifold learning and Transductive support vector machine (TSVM), to overcome scarcity of labeled training samples. Firstly, wavelet packet decomposition (WPD) was used to decompose vibration signals into several sub-bands. The fault features were extracted from the sub-bands to construct a high-dimensional fault feature set, and the improved...
This research proposes a reliable machine learning based computational solution for human detection. The proposed model is specifically applicable for illumination-variant natural scenes in big data video frames. In order to solve the illumination variation problem, a new feature set is formed by extracting features using histogram of gradients (HoG) and linear phase quantization (LPQ) techniques,...
Today, Convolutional Neural Network (CNN) is adopted in a lot of areas such as computer vision and natural language processing. By employing hardware accelerators such as graphic processing unit (GPU), a significant amount of speedup can be achieved in CNN and many studies have proposed such acceleration methods. However, it is not straightforward to parallelize the CNN on a hardware accelerator because...
Visual inspection process for weld defects still manually operated by human vision, so the result of the test still highly subjective. In this research, the visual inspection process will be done through image processing on the image sequence to make data accuracy more better. CNN as one of the image processing technique can determine the feature automatically which is suitable for this problem in...
Ultrasound image is one of the modalities that is widely used to examine the abnormality of thyroid gland since it is relatively low-cost and safety. Fine needle aspiration biopsy (FNAB) is usually used by radiologists to determine the thyroid nodule whether malignant or benign. Commonly, malignancy of thyroid nodule determined based on shape feature. This research proposes a scheme for classifying...
In this paper, we present a novel spectrum mapping method — Continuous Frequency Warping and Magnitude Scaling (CFWMS) for voice conversion under the Joint Density Gaussian Mixture Model (JDGMM) framework. JDGMM is a mature clustering technique that models the joint probability density of speech signals from paired speakers. The conventional JDGMM-based approaches morph the spectral features via least...
This paper focuses on a novel approach for handling radical overhaul of anomalous behavior in a visual surveillance network. The initial objective is online detection of anomalies using a Kernel-based online anomaly detection algorithm. The algorithm will operate onimages collected from a moving camera over a span of space and time. The proposed algorithm established based upon machine learning principles...
In this paper, we explored the development of an anxiety detection (AnD) system using the respiratory signal as its input. Time and frequency domain statistical features derived from breath-to-breath (BB) interval series of respiratory signal is input to a support vector machine (SVM) backend classifier. We used data from normative population, individuals with anxiety disorders and regular meditators...
Essays in different text genres have different ideas and writing method. Prediction the text genres firstly will help get a better accuracy when predicting the success of literary or finding the beautiful words and sentences in the essay. And it will help set a different standard for different text genres when scoring the writing by computer. Words and structure can be effective in discriminating...
The time-varying Received Signal Strength (RSS) drastically reduces the correlation between signals and location information, which leads to degrade the indoor positioning accuracy in WiFi. And the kernel selection of Support Vector Regression (SVR) is limited by the Mercer theorem, it has a negative influence on the regressive result. In this paper, a new positioning algorithm based on Kernel Direct...
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