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Spatio-Temporal interest points are the most popular feature representation in the field of action recognition. A variety of methods have been proposed to detect and describe local patches in video with several techniques reporting state of the art performance for action recognition. However, the reported results are obtained under different experimental settings with different datasets, making it...
A system for humanoid robot arm movement concerns about generating a human-like point-to-point (p2p) trajectory. For more than a decade, numerous systems have been devised and many of them were based on complex dynamical systems. In this paper, we introduce a simpler system, integrating support vector machine (SVM) learning model, which can achieve same p2p objective. In our experiment, we compare...
Human activity recognition finds many applications in areas such as surveillance, and sports. Such a system classifies a spatio-temporal feature descriptor of a human figure in a video, based on training examples. However many classifiers face the constraints of the long training time, and the large size of the feature vector. Our method, due to the use of an Support Vector Machine (SVM) classifier,...
We address the problem of predicting the miRNA: miRNA∗ duplex stemming from a microRNA (miRNA) hairpin precursor and we present a SVM-based methodology to address it. Predicting the miRNA: miRNA∗ duplex is a first step towards identifying the mature miRNA, suggesting possible miRNA targets and ultimately, reducing experimentation effort, time, and cost. We measure the error in terms of the absolute...
In this paper, we present a monocular, texture-based method for person detection and upper-body orientation classification. We build on a commonly used approach for person recognition that uses a Support Vector Machine (SVM) on Histograms of Oriented Gradients (HOG) [1] but replace the SVM by a decision tree with SVMs as binary decision makers. Thereby, in addition to the pure detection of persons,...
This work investigates a semantic-driven human detection algorithm, which employs global human template matching to inspire the local features based Adaboosting algorithm. We use distance transform to analyze distances between training samples and human contour template to obtain a classifier based on human outline features. At the training stage, the global outline feature will be coordinated into...
Pedestrian detection is an important part of intelligent transportation systems. In the literature, Histogram of Oriented Gradients (HOG) detector for pedestrian detection is known for its good performance, but there are still some false detections appearing in the cases with flat area or clustered background. To deal with these problems, in this research work we develop a new feature which is based...
Electrocardiogram (ECG) as a biological information, it has some special feature. Different people will have different ECG information, even one person has different ECG when he is under different body state. In this paper we use the Electrocardiogram (ECG) to identify disease or to detect different person. Firstly, we collect the ECG information form different body state of the different people....
The paper describes a new method of detecting human figures in the video scene in real time. This problem can be found, for example, in the protection of buildings where unauthorized persons have access, surveillance of persons in common areas such as shopping centers, airport lounges, etc. For the detection of the contour of a human figure the HOG algorithm is often used which detects the human figure...
Pedestrian detection is of much importance for its practical applications. This paper develops a novel pedestrian detection system which consists of three stages: motion region detection based on background modeling, feature extraction in the guidance of prior information, and map-based classification applying support vector machine (SVM) and Adaboost. First of all, an adaptive Gaussian Mixture Model...
To detect human sex from complex background, illumination variations and objects by machine is very difficult but important for adaptive information service. In this research, we present a preliminary design and experimental results of gender recognition from walking movements that utilizes gait energy image(GEI) with denoised energy image(DEI) pre-processing as support vector machine(SVM) classifier...
The paper presents a new online incremental zero-shot learning method for applications in robotics and mobile communications where attribute labeling is obtained via online interaction with users, and where the potential for inconsistency exists. Unique to most previous offline batch learning methods, the proposed method is based on the indirect-attribute-prediction (IAP) model instead of the direct-attribute-prediction...
In many visual classification tasks the spatial distribution of discriminative information is (i) non uniform e.g. person ‘reading’ can be distinguished from ‘taking a photo’ based on the area around the arms i.e. ignoring the legs and (ii) has intra class variations e.g. different readers may hold the books differently. Motivated by these observations, we propose to learn the discriminative spatial...
Since high-level events in images (e.g. “dinner”, “motorcycle stunt”, etc.) may not be directly correlated with their visual appearance, low-level visual features do not carry enough semantics to classify such events satisfactorily. This paper explores a fully compositional approach for event based image retrieval which is able to overcome this shortcoming. Furthermore, the approach is fully scalable...
The human vision tends to recognize more variants of a distinctive exemplar. This observation suggests that discriminative power of training exemplars could be utilized for shaping a desirable global classifier that generalizes maximally from a few exemplars. We propose to derive classification uncertainty for each exemplar, using a local classification task to separate the exemplar from those in...
Reaching and grasping of objects in an everyday-life environment seems so simple for humans, though so complicated from an engineering point of view. Humans use a variety of strategies for reaching and grasping anything from the simplest to the most complicated objects, achieving high dexterity and efficiency. This seemingly simple process of reach-to-grasp relies on the complex coordination of the...
The ability to recognize human activities from sensed information becomes more attractive to computer science researchers due to a demand on a high quality and low cost of health care services at anytime and anywhere. This work compares C-Support Vector Machine (C-SVM), Conditional Random Fields (CRF) and Linear Discriminant Analysis (LDA) for imbalanced dataset to perform automatic recognition of...
In some sport training application, it is necessary to search the key frames of training video for carefully analysis. In this paper, we take the key frame searching issue as a pose estimation problem. First, a set of various pose detectors are collected trough the twice SVM training process, each of which can be interpreted as a learned pose-specific HOG weight classifier. Then we run each linear...
The problem of detection of label-noise in large datasets is investigated. We consider applications where data are susceptible to label error and a human expert is available to verify a limited number of such labels in order to cleanse the data. We show the support vectors of a Support Vector Machine (SVM) contain almost all of these noisy labels. Therefore, the verification of support vectors allows...
Vision-based object detectors are crucial for different applications. They rely on learnt object models. Ideally, we would like to deploy our vision system in the scenario where it must operate. Then, the system should self-learn how to distinguish the objects of interest, i.e., without human intervention. However, the learning of each object model requires labelled samples collected through a tiresome...
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