justin

Today I read a paper titled “3D Hand Pose Detection in Egocentric RGB-D Images”

The abstract is:
We focus on the task of everyday hand pose estimation from egocentric viewpoints

For this task, we show that depth sensors are particularly informative for extracting near-field interactions of the camera wearer with his/her environment

Despite the recent advances in full-body pose estimation using Kinect-like sensors, reliable monocular hand pose estimation in RGB-D images is still an unsolved problem

The problem is considerably exacerbated when analyzing hands performing daily activities from a first-person viewpoint, due to severe occlusions arising from object manipulations and a limited field-of-view

Our system addresses these difficulties by exploiting strong priors over viewpoint and pose in a discriminative tracking-by-detection framework

Our priors are operationalized through a photorealistic synthetic model of egocentric scenes, which is used to generate training data for learning depth-based pose classifiers

We evaluate our approach on an annotated dataset of real egocentric object manipulation scenes and compare to both commercial and academic approaches

Our method provides state-of-the-art performance for both hand detection and pose estimation in egocentric RGB-D images

Today I read a paper titled “Computer- and robot-assisted Medical Intervention”

The abstract is:
Medical robotics includes assistive devices used by the physician in order to make his/her diagnostic or therapeutic practices easier and more efficient

This chapter focuses on such systems

It introduces the general field of Computer-Assisted Medical Interventions, its aims, its different components and describes the place of robots in that context

The evolutions in terms of general design and control paradigms in the development of medical robots are presented and issues specific to that application domain are discussed

A view of existing systems, on-going developments and future trends is given

A case-study is detailed

Other types of robotic help in the medical environment (such as for assisting a handicapped person, for rehabilitation of a patient or for replacement of some damaged/suppressed limbs or organs) are out of the scope of this chapter

Today I read a paper titled “A novel approach for determining fatigue resistances of different muscle groups in static cases”

The abstract is:
In ergonomics and biomechanics, muscle fatigue models based on maximum endurance time (MET) models are often used to integrate fatigue effect into ergonomic and biomechanical application

However, due to the empirical principle of those MET models, the disadvantages of this method are: 1) the MET models cannot reveal the muscle physiology background very well; 2) there is no general formation for those MET models to predict MET

In this paper, a theoretical MET model is extended from a simple muscle fatigue model with consideration of the external load and maximum voluntary contraction in passive static exertion cases

The universal availability of the extended MET model is analyzed in comparison to 24 existing empirical MET models

Using mathematical regression method, 21 of the 24 MET models have intraclass correlations over 0.9, which means the extended MET model could replace the existing MET models in a general and computationally efficient way

In addition, an important parameter, fatigability (or fatigue resistance) of different muscle groups, could be calculated via the mathematical regression approach

Its mean value and its standard deviation are useful for predicting MET values of a given population during static operations

The possible reasons influencing the fatigue resistance were classified and discussed, and it is still a very challenging work to find out the quantitative relationship between the fatigue resistance and the influencing factors

Today I read a paper titled “3-Phase Recognition Approach to Pseudo 3D Building Generation from 2D Floor Plan”

The abstract is:
Nowadays three dimension (3D) architectural visualisation has become a powerful tool in the conceptualisation, design and presentation of architectural products in the construction industry, providing realistic interaction and walkthrough on engineering products

Traditional ways of implementing 3D models involves the use of specialised 3D authoring tools along with skilled 3D designers with blueprints of the model and this is a slow and laborious process

The aim of this paper is to automate this process by simply analyzing the blueprint document and generating the 3D scene automatically

For this purpose we have devised a 3-Phase recognition approach to pseudo 3D building generation from 2D floor plan and developed a software accordingly

Our 3-phased 3D building system has been implemented using C, C++ and OpenCV library [24] for the Image Processing module; The Save Module generated an XML file for storing the processed floor plan objects attributes; while the Irrlitch [14] game engine was used to implement the Interactive 3D module

Though still at its infancy, our proposed system gave commendable results

We tested our system on 6 floor plans with complexities ranging from low to high and the results seems to be very promising with an average processing time of around 3s and a 3D generation in 4s

In addition the system provides an interactive walk-though and allows users to modify components

Today I read a paper titled “Animation of 3D Human Model Using Markerless Motion Capture Applied To Sports”

The abstract is:
Markerless motion capture is an active research in 3D virtualization

In proposed work we presented a system for markerless motion capture for 3D human character animation, paper presents a survey on motion and skeleton tracking techniques which are developed or are under development

The paper proposed a method to transform the motion of a performer to a 3D human character (model), the 3D human character performs similar movements as that of a performer in real time

In the proposed work, human model data will be captured by Kinect camera, processed data will be applied on 3D human model for animation

3D human model is created using open source software (MakeHuman)

Anticipated dataset for sport activity is considered as input which can be applied to any HCI application

Today I read a paper titled “Implementation of Hand Detection based Techniques for Human Computer Interaction”

The abstract is:
The computer industry is developing at a fast pace

With this development almost all of the fields under computers have advanced in the past couple of decades

But the same technology is being used for human computer interaction that was used in 1970s

Even today the same type of keyboard and mouse is used for interacting with computer systems

With the recent boom in the mobile segment touchscreens have become popular for interaction with cell phones

But these touchscreens are rarely used on traditional systems

This paper tries to introduce methods for human computer interaction using the users hand which can be used both on traditional computer platforms as well as cell phones

The methods explain how the users detected hand can be used as input for applications and also explain applications that can take advantage of this type of interaction mechanism

Today I read a paper titled “A Unified Framework for Multi-Sensor HDR Video Reconstruction”

The abstract is:
One of the most successful approaches to modern high quality HDR-video capture is to use camera setups with multiple sensors imaging the scene through a common optical system

However, such systems pose several challenges for HDR reconstruction algorithms

Previous reconstruction techniques have considered debayering, denoising, resampling (align- ment) and exposure fusion as separate problems

In contrast, in this paper we present a unifying approach, performing HDR assembly directly from raw sensor data

Our framework includes a camera noise model adapted to HDR video and an algorithm for spatially adaptive HDR reconstruction based on fitting of local polynomial approximations to observed sensor data

The method is easy to implement and allows reconstruction to an arbitrary resolution and output mapping

We present an implementation in CUDA and show real-time performance for an experimental 4 Mpixel multi-sensor HDR video system

We further show that our algorithm has clear advantages over existing methods, both in terms of flexibility and reconstruction quality

Today I read a paper titled “Social Data Mining through Distributed Mobile Sensing”

The abstract is:
In this article, we present a distributed framework for collecting and analyzing environmental and location data recorded by human users (carriers) with the use of portable sensors

We demonstrate the data mining analysis potential among the recorded environmental and location variables, as well as the potential for classification analysis of human activities

We recognize that the success of such an experimental framework relies on the adoption rate by its candidate user network; thus, we have built our experimental prototype on top of hardware equipment already embedded within the potential users’ everyday routine – i.e. hardware sensors installed on modern mobile phones

Finally, we present preliminary analysis results on our collected data sample, as well as potential further work directions and proposed use case scenarios

Today I read a paper titled “Reversible Circuit Optimization via Leaving the Boolean Domain”

The abstract is:
For years, the quantum/reversible circuit community has been convinced that: a) the addition of auxiliary qubits is instrumental in constructing a smaller quantum circuit; and, b) the introduction of quantum gates inside reversible circuits may result in more efficient designs

This paper presents a systematic approach to optimizing reversible (and quantum) circuits via the introduction of auxiliary qubits and quantum gates inside circuit designs

This advances our understanding of what may be achieved with a) and b)

Today I read a paper titled “Finding Convex Hulls Using Quickhull on the GPU”

The abstract is:
We present a convex hull algorithm that is accelerated on commodity graphics hardware

We analyze and identify the hurdles of writing a recursive divide and conquer algorithm on the GPU and divise a framework for representing this class of problems

Our framework transforms the recursive splitting step into a permutation step that is well-suited for graphics hardware

Our convex hull algorithm of choice is Quickhull

Our parallel Quickhull implementation (for both 2D and 3D cases) achieves an order of magnitude speedup over standard computational geometry libraries

Today I read a paper titled “Reinforcement Learning and Nonparametric Detection of Game-Theoretic Equilibrium Play in Social Networks”

The abstract is:
This paper studies two important signal processing aspects of equilibrium behavior in non-cooperative games arising in social networks, namely, reinforcement learning and detection of equilibrium play

The first part of the paper presents a reinforcement learning (adaptive filtering) algorithm that facilitates learning an equilibrium by resorting to diffusion cooperation strategies in a social network

Agents form homophilic social groups, within which they exchange past experiences over an undirected graph

It is shown that, if all agents follow the proposed algorithm, their global behavior is attracted to the correlated equilibria set of the game

The second part of the paper provides a test to detect if the actions of agents are consistent with play from the equilibrium of a concave potential game

The theory of revealed preference from microeconomics is used to construct a non-parametric decision test and statistical test which only require the probe and associated actions of agents

A stochastic gradient algorithm is given to optimize the probe in real time to minimize the Type-II error probabilities of the detection test subject to specified Type-I error probability

We provide a real-world example using the energy market, and a numerical example to detect malicious agents in an online social network.

Today I read a paper titled “Human Muscle Fatigue Model in Dynamic Motions”

The abstract is:
Human muscle fatigue is considered to be one of the main reasons for Musculoskeletal Disorder (MSD)

Recent models have been introduced to define muscle fatigue for static postures

However, the main drawbacks of these models are that the dynamic effect of the human and the external load are not taken into account

In this paper, each human joint is assumed to be controlled by two muscle groups to generate motions such as push/pull

The joint torques are computed using Lagrange’s formulation to evaluate the dynamic factors of the muscle fatigue model

An experiment is defined to validate this assumption and the result for one person confirms its feasibility

The evaluation of this model can predict the fatigue and MSD risk in industry production quickly

Today I read a paper titled “An Approximation Algorithm for Computing Shortest Paths in Weighted 3-d Domains”

The abstract is:
We present the first polynomial time approximation algorithm for computing shortest paths in weighted three-dimensional domains

The efficiency of the proposed algorithm is based on an in-depth study of the local behavior of geodesic paths and additive Voronoi diagrams in weighted three-dimensional domains, which are of independent interest

The paper extends the results of Aleksandrov, Maheshwari and Sack [JACM 2005] to three dimensions

Today I read a paper titled “Depth-Optimized Reversible Circuit Synthesis”

The abstract is:
In this paper, simultaneous reduction of circuit depth and synthesis cost of reversible circuits in quantum technologies with limited interaction is addressed

We developed a cycle-based synthesis algorithm which uses negative controls and limited distance between gate lines

To improve circuit depth, a new parallel structure is introduced in which before synthesis a set of disjoint cycles are extracted from the input specification and distributed into some subsets

The cycles of each subset are synthesized independently on different sets of ancillae

Accordingly, each disjoint set can be synthesized by ?different synthesis methods

Our analysis shows that the best worst-case synthesis cost of reversible circuits in the linear nearest neighbor architecture is improved by the proposed approach

Our experimental results reveal the effectiveness of the proposed approach to reduce cost and circuit depth for several benchmarks

Today I read a paper titled “A Very Simple Approach for 3-D to 2-D Mapping”

The abstract is:
Many times we need to plot 3-D functions e.g., in many scientificc experiments

To plot this 3-D functions on 2-D screen it requires some kind of mapping

Though OpenGL, DirectX etc 3-D rendering libraries have made this job very simple, still these libraries come with many complex pre- operations that are simply not intended, also to integrate these libraries with any kind of system is often a tough trial

This article presents a very simple method of mapping from 3D to 2D, that is free from any complex pre-operation, also it will work with any graphics system where we have some primitive 2-D graphics function

Also we discuss the inverse transform and how to do basic computer graphics transformations using our coordinate mapping system