Human-computer interaction based on visual hand-gesture recognition using volumetric spatiograms of local binary patterns

A more natural, intuitive, user-friendly, and less intrusive Human–Computer interface for controlling an application by executing hand gestures is presented. For this purpose, a robust vision-based hand-gesture recognition system has been developed, and a new database has been created to test it. The system is divided into three stages: detection, tracking, and recognition. The detection stage searches in every frame of a video sequence potential hand poses using a binary Support Vector Machine classifier and Local Binary Patterns as feature vectors. These detections are employed as input of a tracker to generate a spatio-temporal trajectory of hand poses. Finally, the recognition stage segments a spatio-temporal volume of data using the obtained trajectories, and compute a video descriptor called Volumetric Spatiograms of Local Binary Patterns (VS-LBP), which is delivered to a bank of SVM classifiers to perform the gesture recognition. The VS-LBP is a novel video descriptor that constitutes one of the most important contributions of the paper, which is able to provide much richer spatio-temporal information than other existing approaches in the state of the art with a manageable computational cost. Excellent results have been obtained outperforming other approaches of the state of the art.

Development of a hand-gesture recognition system for human-computer interaction

The aim of this Master Thesis is the analysis, design and development of a robust and reliable Human-Computer Interaction interface, based on visual hand-gesture recognition. The implementation of the required functions is oriented to the simulation of a classical hardware interaction device: the mouse, by recognizing a specific hand-gesture vocabulary in color video sequences. For this purpose, a prototype of a hand-gesture recognition system has been designed and implemented, which is composed of three stages: detection, tracking and recognition. This system is based on machine learning methods and pattern recognition techniques, which have been integrated together with other image processing approaches to get a high recognition accuracy and a low computational cost. Regarding pattern recongition techniques, several algorithms and strategies have been designed and implemented, which are applicable to color images and video sequences. The design of these algorithms has the purpose of extracting spatial and spatio-temporal features from static and dynamic hand gestures, in order to identify them in a robust and reliable way. Finally, a visual database containing the necessary vocabulary of gestures for interacting with the computer has been created.

The EF-hand Ca2+-binding Protein p22 Associates with Microtubules in an N-Myristoylation–dependent Manner

Proteins containing the EF-hand Ca2+-binding motif, such as calmodulin and calcineurin B, function as regulators of various cellular processes. Here we focus on p22, an N-myristoylated, widely expressed EF-hand Ca2+-binding protein conserved throughout evolution, which was shown previously to be required for membrane traffic. Immunofluorescence studies show that p22 distributes along microtubules during interphase and mitosis in various cell lines. Moreover, we report that p22 associates with the microtubule cytoskeleton indirectly via a cytosolic microtubule-binding factor. Gel filtration studies indicate that the p22–microtubule-binding activity behaves as a 70- to 30-kDa globular protein. Our results indicate that p22 associates with microtubules via a novel N-myristoylation–dependent mechanism that does not involve classic microtubule-associated proteins and motor proteins. The association of p22 with microtubules requires the N-myristoylation of p22 but does not involve p22’s Ca2+-binding activity, suggesting that the p22–microtubule association and the role of p22 in membrane traffic are functionally related, because N-myristoylation is required for both events. Therefore, p22 is an excellent candidate for a protein that can mediate interactions between the microtubule cytoskeleton and membrane traffic.

Role of essential light chain EF hand domains in calcium binding and regulation of scallop myosin.

The specific Ca2+ binding site that triggers contraction of molluscan muscle requires the presence of an essential light chain (ELC) from a Ca2+ binding myosin. Of the four EF hand-like domains in molluscan ELCs, only domain III has an amino acid sequence predicted to be capable of binding Ca2+. In this report, we have used mutant ELCs to locate the Ca2+ binding site in scallop myosin and to probe the role of the ELC in regulation. Point mutations in domain III of scallop ELC have no effect on Ca2+ binding. Interestingly, scallop and rat cardiac ELC chimeras support Ca2+ binding only if domain I is scallop. These results are nevertheless in agreement with structural studies on a proteolytic fragment of scallop myosin, the regulatory domain. Furthermore, Ca2+ sensitivity of the scallop myosin ATPase requires scallop ELC domain I: ELCs containing cardiac domain I convert scallop myosin to an unregulated molecule whose activity is no longer repressed in the absence of Ca2+. Despite its unusual EF hand domain sequence, our data indicate that the unique and required contribution of molluscan ELCs to Ca2+ binding and regulation of molluscan myosins resides exclusively in domain I.

The Impact of Sexual Boundary Violations in Supervision and Therapy: A Qualitative Study of a Trainee's First-Hand Account

This study involves a qualitative analysis of a doctoral-level psychology trainee's first-hand account of sexual attraction and boundary violations that occurred in her clinical supervision and psychotherapy. Concepts of power, gender, social performance theory, and relational framing are applied to two case examples, illustrating the differing demands on a trainee when her relationships were sexualized in two distinct professional contexts. Ramifications of supervisory exploitation and the impact of such an experience on a trainee's professional development are discussed. Recommendations are provided for improving psychology training programs' prevention and response efforts.

Hand gesture recognition based on morphologic features

Comunicación presentada en el IX Simposium Nacional de Reconocimiento de Formas y Análisis de Imágenes, Benicàssim, Mayo, 2001.

3D visual sensing of human hand for remote operation of a robotic hand

New low cost sensors and open free libraries for 3D image processing are making important advances in robot vision applications possible, such as three-dimensional object recognition, semantic mapping, navigation and localization of robots, human detection and/or gesture recognition for human-machine interaction. In this paper, a novel method for recognizing and tracking the fingers of a human hand is presented. This method is based on point clouds from range images captured by a RGBD sensor. It works in real time and it does not require visual marks, camera calibration or previous knowledge of the environment. Moreover, it works successfully even when multiple objects appear in the scene or when the ambient light is changed. Furthermore, this method was designed to develop a human interface to control domestic or industrial devices, remotely. In this paper, the method was tested by operating a robotic hand. Firstly, the human hand was recognized and the fingers were detected. Secondly, the movement of the fingers was analysed and mapped to be imitated by a robotic hand.

3D Hand Pose Estimation with Neural Networks

We propose the design of a real-time system to recognize and interprethand gestures. The acquisition devices are low cost 3D sensors. 3D hand pose will be segmented, characterized and track using growing neural gas (GNG) structure. The capacity of the system to obtain information with a high degree of freedom allows the encoding of many gestures and a very accurate motion capture. The use of hand pose models combined with motion information provide with GNG permits to deal with the problem of the hand motion representation. A natural interface applied to a virtual mirrorwriting system and to a system to estimate hand pose will be designed to demonstrate the validity of the system.

RGBD Human-Hand recognition for the Interaction with Robot-Hand

New low cost sensors and the new open free libraries for 3D image processing are permitting to achieve important advances for robot vision applications such as tridimensional object recognition, semantic mapping, navigation and localization of robots, human detection and/or gesture recognition for human-machine interaction. In this paper, a method to recognize the human hand and to track the fingers is proposed. This new method is based on point clouds from range images, RGBD. It does not require visual marks, camera calibration, environment knowledge and complex expensive acquisition systems. Furthermore, this method has been implemented to create a human interface in order to move a robot hand. The human hand is recognized and the movement of the fingers is analyzed. Afterwards, it is imitated from a Barret hand, using communication events programmed from ROS.

Stability in Linear Optimization Under Perturbations of the Left-Hand Side Coefficients

This paper studies stability properties of linear optimization problems with finitely many variables and an arbitrary number of constraints, when only left hand side coefficients can be perturbed. The coefficients of the constraints are assumed to be continuous functions with respect to an index which ranges on certain compact Hausdorff topological space, and these properties are preserved by the admissible perturbations. More in detail, the paper analyzes the continuity properties of the feasible set, the optimal set and the optimal value, as well as the preservation of desirable properties (boundedness, uniqueness) of the feasible and of the optimal sets, under sufficiently small perturbations.