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We advocate the inference of qualitative information about 3D human pose, called posebits, from images. Posebits represent boolean geometric relationships between body parts (e.g., left-leg in front of right-leg or hands close to each other). The advantages of posebits as a mid-level representation are 1) for many tasks of interest, such qualitative pose information may be sufficient (e.g., semantic...
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...
This paper is focused on comparing corpus-based methods for estimating word sentiment. Evaluated algorithms represent varying degrees of supervision and range from regression alike approaches to more heavily supervised classifications. The main idea is to explore the opportunities arising from mining medium sized, balanced corpora -- as opposed to web as a corpus paradigm. The comparisons have been...
Convolutional neural network models have covered a broad scope of computer vision applications, achieving competitive performance with minimal domain knowledge. In this work, we apply such a model to a task designed to deter automated systems. We trained a convolutional neural network to distinguish between images of human faces from computer generated avatars as part of the ICMLA 2012 Face Recognition...
An important task of aging research is to find genes that regulate lifespan. Wet-lab identification of aging genes is tedious and labor-intensive activity. Developing an algorithm to predict aging genes will be greatly helpful. In this paper, we systematically analyzed topological features of proteins encoded by Drosophila melanogaster aging genes versus those encoded by non-aging genes in protein-protein...
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...
Due to the maturing of digital image processing techniques, there are many tools, which can edit an image easily without leaving obvious traces to the human eyes. So the authentication of digital images is an important issue in our life. In this paper, multi-resolution Weber law descriptors (WLD) based method that detects copy-move image forgery is introduced. The proposed multi-resolution WLD extracts...
Using BCI technologies in neural rehabilitation, we can substantially improve hundreds of lives of people after stroke with more effective rehabilitation in restoration of their motor control. In this study, we demonstrate a neuro-rehabilitation framework which integrates BCI and a robot device to provide an active upper limb physical therapy. Our target population for neuro-rehabilitation are patients...
As human action is uncertain and illegible, a human action recognition method basing on fuzzy support vector machine is presented. Fuzzy support vector machine employs the membership function to solve the unclassifiable areas which happens the traditional SVMs' two-class problems extend to the multi-class problems. the method is evaluated on the Weizmann action dataset and received comparative high...
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...
This paper focuses on monocular-video-based stationary detection of the pedestrian's intention to enter the traffic lane. We propose a motion contour image based HOG-like descriptor, MCHOG, and a machine learning algorithm that reaches the decision at an accuracy of 99 % within the initial step at the curb of smart infrastructure. MCHOG implicitly comprises the body language of gait initiation, especially...
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...
A story is defined as "an actor(s) taking action(s) that culminates in a resolution(s)." In this paper, we investigate the utility of standard keyword based features, statistical features based on shallow-parsing (such as density of POS tags and named entities), and a new set of semantic features to develop a story classifier. This classifier is trained to identify a paragraph as a "story,"...
Recognizing Textual Entailment, as one of the branches of Nature Language Processing, has been widely used in Human Computer Interaction, Question Answering System, etc. RTE is trying to build an intelligent system which can analyze the content of an input text (T), and then raises a hypothesis (H) based on that. My self-design RTE system which is called SNRTE combines lexical, syntax, and semantic...
Given a remarkable recent progress in robotics research, we can envision the day when robots and humans coexist and robots become closely integrated into our daily lives. This means endowing robots with the ability to communicate so they perceive human emotion, adapt their behavior to humans, and sense situations even without explicit instructions. Meanwhile, affective computing, that interprets emotion...
In this paper, we present a model for learning atomic actions for complex activities classification. A video sequence is first represented by a collection of visual interest points. The model automatically clusters visual words into atomic actions based on their co-occurrence and temporal proximity using an extension of Hierarchical Dirichlet Process (HDP) mixture model. Our approach is robust to...
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....
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