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Many cues have been proposed for contour detection or image segmentation. These include low-level image gradients to high-level information such as the identity of the objects in the scene or 3D depth understanding. While state-of-the-art approaches have been incorporating more cues, the relative importance of the cues is unclear. In this paper, we examine the relative importance of low-, mid- and...
In this paper we present the first large-scale scene attribute database. First, we perform crowd-sourced human studies to find a taxonomy of 102 discriminative attributes. Next, we build the “SUN attribute database” on top of the diverse SUN categorical database. Our attribute database spans more than 700 categories and 14,000 images and has potential for use in high-level scene understanding and...
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...
Action classification is an important component of human-computer interaction. Trajectory classification is an effective way of performing action recognition with significant success reported in the literature. We compare two different representation schemes, raw multivariate time-series data and the covariance descriptors of the trajectories, and apply sparse representation techniques for classifying...
We had proposed a general model for WSD which is novel in some interesting ways and we have customized the model in form of a method (algorithm group) which is further customized in form of a number of algorithms to study the model's capability. We have introduced an optimization which has resulted in an unsupervised system which is the second best when evaluated against the renowned Senseval-3 international...
Ensuring that an interaction is initiated with a particular and unsuspecting member of a group is a complex task. As a first step the robot must effectively, expediently and reliably recognise the humans as they carry on with their typical behaviours (in situ). A method for constructing a scale and viewing angle robust feature vector (from analysing a 3D pointcloud) designed to encapsulate the inter-person...
Natural body gesture, as well as speech dialog, is crucial for human-robot interaction and human-robot symbiosis. We have already proposed a real-time gesture planning method. In this paper, we afford this method more flexibility by adding motion parameterization function. Especially in multi-person HRI, this function becomes more important because of its adaptation to changes of a speaker's and/or...
This paper presents an analysis of the effect of thirteen different kinds of sound on visual gaze when looking freely at videos to help to predict eye positions. First, an audio-visual experiment was designed with two groups of participants, with audio-visual (AV) and visual (V) conditions, to test the sound effect. Then, an audio experiment was designed to validate the classification of sound we...
Problem of fake image in ID card spreads all over the world e.g. false Thai national ID card in Thailand. This problem is quite difficult to detect because appearances of human face always change. Digital image watermarking for photo authentication in Thai national ID card is therefore proposed in this paper to overcome such a problem. Basically, a watermark signal consisting of ID code is embedded...
The goal of this study is to equip a mobile service robot with an ability to navigate to the human who commands the robot by speech. This paper proposes a Bayesian approach to sound source localization for speaker tracking module. Based on time delay of arrival (TDOA) features, sound source direction is classified using a naïve Bayesian network. The method requires low computational complexity and...
Voice based call centers enable customers to query for information by speaking to agents in the call center. Most often these call conversations are recorded for analysis with the intent of trying to identify things that can help improve the performance of the call center to serve the customer better. Today the recorded conversations are analyzed by humans by listening to call conversations, which...
In this paper, we propose a comparison of human gesture recognition using data mining classification methods in video streaming. In particular, we are interested in a specific stream of vector of twenty body-joint positions which are representative of the human body captured by Kinect camera. The recognized gesture patterns of the study are stand, sit down, and lie down. Classification methods chosen...
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...
Human activity recognition using wearable body sensors is playing a significant role in ubiquitous and mobile computing. One of the issues related to this wearable technology is that the captured activity signals are highly dependent on the location where the sensors are worn on the human body. Existing research work either extracts location information from certain activity signals or takes advantage...
Human activity monitoring, through the use of body-wearable sensors, allows for many exciting possibilities, from gaming, to exercise, to preventative health care, where childhood obesity is a growing epidemic. The rapidly increasing nature of this trend requires serious thought at targeting its causes and finding solutions. One major influence is video gaming and the hours of sedentary behavior associated...
The future of tele-conferencing is towards multi-party 3D Tele-Immersion (TI) and TI environments that can support realistic inter-personal communications and virtual interaction among participants. In this paper, we address two important issues, pertinent to TI environments. The paper focuses on techniques for the real-time, 3D reconstruction of moving humans from multiple Kinect devices. The off-line...
We investigate body soft biometrics capabilities to perform pruning of a hard biometrics database improving both retrieval speed and accuracy. Our pre-classification step based on anthropometric measures is elaborated on a large scale medical dataset to guarantee statistical meaning of the results, and tested in conjunction with a face recognition algorithm. Our assumptions are verified by testing...
In this paper, we present an unsupervised learning method, based on the finite Dirichlet mixture model and the bag-of-visual words representation, for categorizing human action videos. The proposed Bayesian model is learned through a principled variational framework. A variational form of the Deviance Information Criterion (DIC) is incorporated within the proposed statistical framework for evaluating...
Aimed at contextual mapping of environments by exploration, this paper proposes a method that recognises human activity observed from a moving camera and references this information to a previously mapped environment. We first introduce a novel method that uses sparse features and dense optical flow, to perform dense background subtraction for an agile camera. With the ego-motion disambiguated, we...
The ability to automatically discover error conditions with little human input is a feature lacking in most modern computer systems and networks. However, with the ever increasing size and complexity of modern systems, such a feature will become a necessity in the not too distant future. Our work proposes a hybrid framework that allows High Performance Clusters (HPC) to detect error conditions in...
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