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In this paper, a robust regression method is proposed for human age estimation, in which, outlier samples are corrected by their neighbors, through asymptotically increasing the correlation coefficients between the desired distances and the distances of sample labels. As another extension, we adopt a nonlinear distance function and approximate it by neural network. For fair comparison, we also experiment...
Human pose estimation from monocular image sequences is attracting increasing attention, and 2D (image-based) as well as 3D (joint-motion based) approaches have been proposed. The former is computationally fast, and works also for less frequent poses, but reliability is low. The latter is computationally expensive owing to the high-dimensionality of the problem, despite attempts at dimensionality-reduction...
We propose a method for learning shape models enabling accurate articulated human pose estimation from a single image. Where previous work has typically employed simple geometric models of human limbs e.g. cylinders which lead to rectangular projections, we propose to learn a generative model of limb shape which can capture the wide variation in shape due to varying anatomy and pose. The model is...
This project deals with overcoming the limitations of the SIFT matching algorithm. We propose a novel way of taking the strengths of the SIFT algorithm and removing the underlying weaknesses to improve SIFT matching in pose variation. This is done by increasing the size of the search database to a certain degree of acceptance. In our bid to improve SIFT matching we have come up with an algorithm to...
In 3G long term evolution (LTE) systems, the random access channel (RACH) is used for initial access, resource request, and handover. Since the random access delay is determined by the arrival rate of the random access preambles and the number of RACH subframes, we should configure the number of RACH subframes given the arrival rate in order to guarantee the delay performance. In this work, by carefully...
This paper presents a remote sensing monitoring system for maritime search and rescue (RS-MSR). This work is a part of our project—Vision Enhancement System for Maritime Search and Rescue. In this work, satellite remote sensing imageries of large scale water area are acquired to detect and locate for guiding the search operation. Some important data such as current and sea state are retrieved to help...
A monitoring system based on multiple microelectromechanical systems (MEMS) has been developed to maintain human healthcare. Using such a MEMS based monitoring system, several kinds of numerical data from several types of sensors can be measured. Our goal is to develop a intelligent monitoring system with small size. In order to microminiaturize the monitoring system, we need to minimize the power...
The challenge of markerless human motion tracking is the high dimensionality of the search space. Thus, efficient exploration in the search space is of great significance. In this paper, a motion capturing algorithm is proposed for upper body motion tracking. The proposed system tracks human motion based on monocular silhouette-matching, and it is built on the top of a hierarchical particle filter,...
In this study, a delay between the start of electromyogram (EMG) signals and the start of actual human motions which is called Electro Mechanical Delay (EMD) is used in order to eliminate a delay such as a mechanical delay and a communication delay in manipulating systems. The man-machine interface to accomplish zero-phase tracking eliminating some delay is proposed in this paper. This interface consists...
This paper proposes a simple yet novel method for recognition of certain sorts of moving entities incorporating their shape and motion patterns. Although shape features have been commonly employed in object recognition, motion characteristics are in general not integrated to geometric models. In the interest of utilizing the motion attributes, the trajectories are investigated to extract the ‘coherence...
A new computational scheme for visual attention modeling is proposed. It adopts both low-level and high-level features to predict visual attention from a video signal and fuses the features by using machine learning. We show that such a scheme is more robust than those using purely single level features. Unlike conventional techniques, our scheme is able to avoid perceptual mismatch between the estimated...
In this paper we address robot-human interactions in a multi-robot and people group framework. The objective is to develop and evaluate techniques for missions in which several robots cooperate among themselves, interacting with a group of people. The robots detect people behaviors and act consequently adopting different strategies. Probabilistic techniques for robust cooperative detection of group...
This paper describes a human shape reconstruction method from multiple cameras in daily living environment, which leads to robust markerless motion capture. Due to continual illumination changes in daily space, it had been difficult to get human shape by background subtraction methods. Recent statistical foreground segmentation techniques based on graph-cuts, which combine background subtraction information...
This article presents a new method to index document images. This work is done in an industrial context where thousands of document images are daily digitized, these images have to be sorted in different classes like payroll, various bills, information letters. We propose a software method which aims to accelerate this task. Usually, the number of document classes is a priori unknown. In this paper,...
As the desire to see robots ubiquitous in society grows, so does the need for providing the robots with the means of building awareness of any humans with which it may be sharing the environment. This paper presents a real-world suitable system which enables robots to robustly perceive the presence of people acoustically. The proposed binaural system first identifies voiced signal by means of a novel...
Removing the safety fences that separate humans and robots, to allow for an effective human-robot interaction, requires innovative safety control systems. An advanced functionality of a safety controller might be to detect the presence of humans entering the robotic cell and to estimate their intention, in order to enforce an effective safety reaction. This paper proposes advanced algorithms for cognitive...
The communicative importance of gestures in teaching environments have been widely studied. Two classes of gestures — point and spread gestures — have been identified to indicate pedagogical importance in teaching discourse [1]. In this work, we propose a system for the identification of the poses of point and spread gestures as a preliminary step toward their identification in low-quality unstructured...
Human analysis in images and video is a hard problem due to the large variation in human pose, clothing, camera view-points, lighting and other factors. While the explicit modeling of this variability is difficult, the huge amount of available person images motivates for the implicit, data-driven approach to human analysis. In this work we aim to explore this approach using the large amount of images...
In this study the perception of crowds was investigated in urban environment. The images of crowds were viewed non-stereoscopically and stereoscopically with HMD (head-mounted display). The task of the participants was to count the number of persons in the crowds. The results clearly indicate that stereoscopic viewing enhances perception of crowds. The counting task was determined to be easiest with...
In this paper, we propose an approach that fuses information from a network of visual sensors for the analysis of human social behavior. A discriminative interaction classifier is trained based on the relative head orientation and distance between a pair of people. Specifically, we explore human interaction detection at different levels of feature fusion and decision fusion. While feature fusion mitigates...
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