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In human action classification task, a video must be classified into a pre-determined class. To cope with this problem, we propose a mid-level representation, in which information about quantization errors is embedded together with the aggregated data on low level features. The main contributions of this article are twofold: (i) assembly of low-level features (dense trajectories) by a mid-level representation...
The scanning speed of atomic force microscopes continues to advance with some current commercial microscopes achieving on the order of one frame per second with at least one reaching 10 frames per second. Despite the success of these instruments, even higher frame rates are needed with scan ranges larger than is currently achievable. Moreover, there is a significant installed base of slower instruments...
Action recognition is considered a promising field in computer vision and can be used in many applications such as video indexing and retrieval. In this paper, we present a novel technique for action recognition based on traditional three stages of feature extraction, action learning, and action recognition. The proposed technique builds a foreground snippet from the input video file, then uses integral...
The success of online recognition systems leads to improving the performance of offline recognition systems using the recovery of dynamic information. Therefore, it appears the techniques that extract dynamic information from static text. Several techniques have been proposed in the field of trajectory recovery. The principle of these techniques is to find an oriented path similar to that used by...
With the acceleration of urbanization and modern civilization, more and more complex regions are formed in urban area. Although understanding these regions could provide huge insights to facilitate valuable applications for urban planning and business intelligence, few methods have been developed to effectively capture the rapid transformation of urban regions. In recent years, the widely applied...
Mouse models are broadly used to study the mechanisms of neuropsychiatric disorders and to test potential treatments. In these models, automation to monitor behavioural differences during social interactions is currently limited. We propose in the present study a new method to conduct automatic behavioural classification, using an original unsupervised machine learning. We applied the proposed method...
A solution for precise indoor localization is sought by the robot navigation scientific community. There are many approaches to solve this problem, including sensor fusion: inertial sensors, cameras, lasers and signal strength-based methods (Wi-Fi). This paper presents an approach using a single camera embedded on the robot, pointing to the floor. To validate this method, an application using an electric...
we propose two new methods to accelerate the learning of a task using Q-learning algorithm. We focus specifically on learning of a task, which has the Credit Assignment (CA) problem. A Reinforcement Algorithm (RL) agent is performing this task in high dimensional state-space. The main idea of this paper is to use latent variables that deep autoencoders provide, to make a better rewarding system. We...
In this paper, we proposed new framework for human action representation, which leverages the strengths of convolutional neural networks (CNNs) and the linear dynamical system (LDS) to represent both spatial and temporal structures of actions in videos. We make two principal contributions: first, we incorporate image-trained CNNs to detect action clip concepts, which takes advantage of different levels...
Dense trajectories are widely used in human action recognition. However, the relationships among trajectories are rarely exploited and a large mount of useful information is missing. In this paper, we propose a novel approach to employ the space-time relationships between different trajectories for action recognition. In our approach, each trajectory is paired up with several neighbors which are spatially...
Seeking effective measures to characterize the chaotic patterns of EEG signals for seizure diagnosis is a long-term endeavor in the literature. We propose to count the number of zero-crossing (ZC) points on Poincaré surface as a feature when the time series of interest is embedded into the reconstructed state space. The experiments show that Poincaré surface can act as a platform to observe the chaotic...
In this work, we decompose a first-person action into verb and noun. We then study how the coupling of an action's constituent verb and noun affects the learners' ability to learn them separately and to combine them to perform recognition. We compare different information fusion methods on conventional action recognition and zero-shot learning, of which the latter is a strong indication of the feature's...
We propose a new approach to action classification in video, which uses deep appearance and motion features extracted from spatio-temporal volumes defined along body part trajectories to learn mid-level classifiers called deep moving poselets. A deep moving poselet is a classifier that captures a characteristic body part configuration, with a specific appearance and undergoing a specific movement...
The advancement of smartphones with various type of sensors enabled us to harness diverse information with crowd sensing mobile application. However, traditional approaches have suffered drawbacks such as high battery consumption as a trade off to obtain high accuracy data using high sampling rate. To mitigate the battery consumption, we proposed low sampling point of interest (POI) extraction framework,...
In today's world data represented in the form of a video are prolific and has increased the requisite of storage devices unconditionally. These video sets takes up a huge space for amassing data and takes a long time to ascertain the content that requires a higher cognitive process for content search and retrieval. The efficient method for storing video data is to remove high-degree redundancies and...
In this paper an algorithm used for video matching process is discussed. This algorithm basically works on the principle of tie-point extraction. This algorithm is conceptualized to extract information from different video frames in the similar locations. This extracted information is called tie-points. Without the video matching process, most of the video processing applications cannot exist. A few...
Accurate prediction of clinical changes of Mild Cognitive Impairment (MCI) patients at future time points is important for early diagnosis and possible prevention of Alzheimer's disease (AD). In this paper, future clinical changes in Neuropsychological Measures (NM) of MCI patients are estimated via three different predictive models employing linear regression and extrapolation. The completed time...
This work introduces a set of tools for motion pattern analysis in video surveillance. For a given video stream, first the motion trajectories are extracted and an affinity matrix is constructed. Then, motion pattern analysis is conducted based on Normalized Spectral Clustering. An Eigengap based methodology is proposed for determining the number of clusters. It was observed that in real life scenarios,...
Recently studies have been performed on spectral features such as Mel Frequency Cepstral Coefficients (MFCC) and Linear Predictor Cepstral Coefficients (LPCC) for speech emotion recognition. It was found in our study that the Fourier Transform of MFCC time trajectories also play an important role in speech emotion recognition. And also a new hierarchical classification method was proposed based on...
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