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Nonverbal cues constitute a significant part of human communication. Traditionally the object of psychology, nonverbal communication studies now permeate fields such as social signal processing and human computer interaction. The ubiquity of digital recordings of human social interactions and of free sharing platforms offers many opportunities for the automated analysis of group interaction dynamics;...
Several conventional methods have been implemented in pattern recognition, but few of them have biological plausibility. This paper mimics the hierarchical visual system and uses the precise-spike-driven (PSD) synaptic plasticity rule to learn. The well-known HMAX model imitates the visual cortex and uses Gabor filter and max pooling to extract features. Compared with the traditional HMAX model, our...
In order to recognize faults of the high voltage circuit breaker (HVCB) in the whole fault state space precisely and minimize the impact of the lack of fault data on the accuracy of fault recognition, a method of fault recognition was proposed based on the incremental learning algorithm for SVM. Firstly, the incremental learning algorithm for SVM was analyzed theoretically, and the state monitoring...
Computational Auditory Scene Analysis (CASA) is typically achieved by statistical models trained offline on available data. Their performance relies heavily on the assumption that the process generating the data along with the recording conditions are stationary over time. Nowadays, there is a high demand for methodologies and tools dealing with a series of problems tightly coupled with non-stationary...
Many crowd abnormal motion detection methods in video surveillance have been proposed in resent years. However, most of them are based on low semantic features, such gray value, velocity and gradient. Usually, low semantic features contain weak discriminative information of the scene. In addition, these methods often ignore important information in time and space dimension. In this work, a high semantic...
In this paper, using a general convolutional neural network (CNN) model, which was developed for object recognition, a successful system has been introduced for the person re-identification problem. To use this CNN model for the person re-identification problem properly, it is individually fine-tuned using different body parts of person images. For feature extraction, we used the seventh layer of...
There are several current systems developed to identify common skin lesions such as eczema that utilize image processing and most of these apply feature extraction techniques and machine learning algorithms. These systems extract the features from pre-processed images and use them for identifying the skin lesions through machine learning as the core. This paper presents the design and evaluation of...
One main challenge in developing a system for visual surveillance event detection is the annotation of target events in the training data. By making use of the assumption that events with security interest are often rare compared to regular behaviours, this paper presents a novel approach by using Kullback-Leibler (KL) divergence for rare event detection in a weakly supervised learning setting, where...
An experimental analysis of two-dimensional (2D) shape classification method based on moment invariants is presented. Various types of translation, scale and rotation invariants are used to construct feature vectors for classification. The performance is evaluated using five different objects picked up from real scenes with a TV camera. Silhouettes and contours are extracted from nonoccluded 2D objects...
Depth maps based action recognition has been received much research attention in recent years due to its robustness to environmental elements in capturing and its relatively well performance in protecting user's privacy. Taking the captured sequential depth maps as inputs, we propose a framework in this paper to recognize actions from such data. We first recover multi-view 2D observations in each...
Group abnormal behaviors often occur abruptly under video surveillance, thus bringing serious consequences. How to recognize these behaviors correctly has always been the difficulty in research on intelligence video surveillance. This paper is based on the basic theory of Markov Random Fields to extract the features of those in video images, so as to recognize the group abnormal behaviors under video...
In this paper, we address the problem of recognizing group activities that include interactions between human objects based on their motion trajectory analysis. In order to resolve the complexity and ambiguity problems caused by a large number of human objects, we propose a Group Interaction Zone (GIZ) to detect meaningful groups in a scene so as to be robust against noisy information. Two novel features,...
Inspired by the recent success of hierarchical representation, we propose a new hierarchical variant of latent Dirichlet allocation (h-LDA) for action recognition. The model consists of an appearance group and a motion group, and we introduce a new hierarchical structure including two-layer topics in each group to learn the spatial temporal patterns (STPs) of human actions. The basic idea is that...
This paper presents comparative study of palmprint matching algorithms for low resolution and noisy images. The principal lines and wrinkles are the only features easily extractable under low resolution. Therefore widely used Radon based approach has been studied and presented in this paper. Based on the computational complexity and fast matching, four different approaches are studied.
A large number of filters has been proposed to compute local gradients in grayscale images, usually having as goal the adequate characterization of edges. A significant portion of such filters are antisymmetric with respect to the origin. In this work we propose to generalize those filters by incorporating an explicit evaluation of the tonal difference. More specifically, we propose to apply restricted...
In this paper, we present an action recognition framework based on binary stochastic latent variables model, Hidden unit Conditional Random Fields(HuCRF). It is a chain structured undirected graphs model with nonlinear dependencies at each frame/segment, contrast to standard log-linear models like CRF. So it is more powerful in sequence modeling tasks like action recognition. The observations of actions...
Studies on the Human Visual System (HVS) have demonstrated that human eyes are more attentive to spatial or spectral components with irregularities on the scene. This fact was modeled differently in many computational methods such as saliency residual (SR) approach, which tries to find the irregularity of frequency components by subtracting average filtered and original amplitude spectra. However,...
Typically, the saliency map of an image is usually inferred by only using the information within this image. While efficient, such single-image-based methods may fail to obtain reliable results, because the information within a single image may be insufficient for defining saliency. In this paper, we propose a novel idea of learning with labeled images and adopt a new paradigm called sample specific...
Human action recognition is a challenging filed in computer vision. In this paper, a novel probabilistic graphical model, called topic-relative conditional random field(TCRF), is firstly proposed. The model is constructed by adding a topic node and using a triangular-chain structure in the top layer of the linear-chain conditional random field(LCRF) to overcome the drawback of independent and identical...
RANSAC and RANSAC-like algorithm are most employed for the robust computation of relations from a number of potential matches in the field of computer vision, such as stereo matching, image retrieval, mosaic and elsewhere. There have been a number of recent efforts that aim to increase the efficiency of the standard RANSAC algorithm. This paper presents a novel optimal solution of the RANSAC algorithm...
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