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The earliest research on emotion recognition starts with simulated/acted stereotypical emotional corpus, and then extends to elicited corpus. Recently, the demanding for real application forces the research shift to natural and spontaneous corpus. Previous research shows that accuracies of emotion recognition are gradual decline from simulated speech, to elicited and totally natural speech. This paper...
Traditional Support Vector Regression (SVR) Machine acts as approximating a regression function. This paper, however, proposes a novel multi-class classification approach based on the SVR framework, called Support Vector Regression Machine with Consistency (SVRC). The contributions of this paper are: (1) To implement multi-class classification task, were place the margin term with its l1 norm in the...
The music listened by human is sometimes exposed to noise. For example, background noise usually exists when listening to music in broadcasts or lives. The noise will worsen the performance in various music emotion recognition systems. To solve the problem, this work constructs a robust system for music emotion classification in a noisy environment. Furthermore, the genre is considered when determining...
Online detection and tracking of a variable number of faces in video is a crucial component in many real-world applications ranging from video-surveillance to online gaming. In this paper we propose FAST-DT, a fully automated system capable of detecting and tracking a variable number of faces online without relying on any scene-specific cues. FAST-DT integrates a generic face detector with an adaptive...
Recognizing the face of target individuals in a watch-list is among the most challenging applications in video surveillance, especially when enrollment is based on one reference still facial image. Besides the limited representativeness of facial models used for matching, the appearance of faces captured in videos varies due to changes in illumination, pose, scales, etc., and to camera inter-operability...
An object recognition method based on Gabor wavelet and SVM is proposed in this paper. First features of the object are extracted by using Gabor wavelet, and then the dimensions of the Gabor features are reduced with Principal Component Analysis, and finally classification is performed with Support Vector Machine. And this method is applied to the Columbia image library COIL-20 for experiments. Compared...
In this paper, the effect of low, middle and high frequency DCT coefficients are investigated onto gray scale image watermarking in terms of imperceptibility and robustness. The performance of Lagrangian twin support vector regression (LTSVR), which was successfully applied on synthetic datasets obtained from UCI repository for various kinds of regression problems by Balasundaram et al. [9], onto...
This paper investigates the use of the Oriented Fast, Rotated Brief (ORB) method to automatically detect the most significant broadcast view associated with cricket broadcasts: the Bowler Run-up Sequence (BRS) for cricket highlight generation. This method is computationally less expensive than other methods proposed for BRS detection. It is shown here that only a single frame is required for training...
We propose a new method of recognizing daily human activities based on a Deep Neural Network (DNN), using multimodal signals such as environmental sound and subject acceleration. We conduct recognition experiments to compare the proposed method to other methods such as a Support Vector Machine (SVM), using real-world data recorded continuously over 72 hours. Our proposed method achieved a frame accuracy...
The identification of predictive biomarkers of complex disease with robustness and specificity is an ongoing challenge. Gene expressions provide information on how the cell reacts to a particular state and the relationship of genes may lead to novel information. A network-based approach integrating expression data with protein-protein interaction network can be used to identify gene-subnetwork biomarkers...
Biometric gait analysis using wearable sensors offers an objective and quantitative method for gait parameter extraction. However, current techniques are constrained to specific platform parameters, and hence significantly lack generality, scalability and sustainability. In this paper, we propose a platform-independent and self-adaptive approach for gait cycle detection and cadence estimation. Our...
This paper proposes a superpixel tracking method via a graph-based hybrid discriminative-generative appearance model. By utilizing a superpixel-based graph structure as the visual representation, spatial information between superpixels is considered. For constructing the discriminative appearance model, we propose a graph-based semi-supervised support vector machine (SVM) approach by taking superpixels...
In the future robotic applications, robot requires the ability not only to recognize human actions but also to learn incrementally and quickly. Therefore, we proposed an incremental action learning system for this future requirement. The proposed system can continuously learn new actions quickly with robust performance and less effort.
A novel and robust vision-based human-machine interface system to naturally interact with computers/smart devices is proposed. The key contribution is the introduction of a Compressive Sensing technique to largely reduce the dimensionality of highly discriminative feature descriptors (computed from depth imagery), which originally have an excessive and inoperative high dimension to be applied to a...
Hand gesture recognition plays a vital role in human computer interaction. It is a difficult task to classify a real time video. This paper presents a dynamic hand gesture recognition method from a real time video of hand gesture. The colour space ‘YCbCr’ is used for correctly determining the skin colour. Features from real time video are then extracted using Histogram of Oriented Gradient (HOG) descriptors...
Hand posture recognition is an extremely active research topic in Computer Vision and Robotics, with many applications ranging from automatic sign language recognition to human-system interaction. Recently, a new descriptor for object representation based on the kernel method (KDES) has been proposed. While this descriptor has been shown to be efficient for hand posture representation, across-the-board...
In this paper we examine efficacy of occlusion-free appearance learning for part based model. Appearance modeling with less accurate appearance data is problematic because it adversely affects entire learning process. We evaluate the effectiveness of excluding occluded body parts to be modeled for better appearance modeling process. To meet this end, We employ a simple but effective occlusion detection...
Automatic classification of packaging cartons according to their contents is an industrial need. In this paper we present an Optical Character Recognition (OCR) system to segment and recognize the sparse dot matrix text printed on the cartons in order to classify them based on the contents. Proposed solution is robust to non-uniformities in background illumination, shadow artifacts, inclined text,...
This paper reports on a method to perform robust visual relocalization between temporally separated sets of underwater images gathered by a robot. The place recognition and relocalization problem is more challenging in the underwater environment mainly due to three factors: 1) changes in illumination; 2) long-term changes in the visual appearance of features because of phenomena like biofouling on...
Presence of neurobiological disorder, like epilepsy, causes abnormalities in brain functionality. This is why connectivity and activity patterns of brain regions in epileptic patients are very different as compared with healthy subjects. These asymmetries can be used to distinguish epileptic patients from healthy subjects. Robust features are extracted to capture asymmetries in connectivity patterns...
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