justin

Today I read a paper titled “Indoor 3D Video Monitoring Using Multiple Kinect Depth-Cameras”

The abstract is:

This article describes the design and development of a system for remote indoor 3D monitoring using an undetermined number of Microsoft(R) Kinect sensors.

In the proposed client-server system, the Kinect cameras can be connected to different computers, addressing this way the hardware limitation of one sensor per USB controller.

The reason behind this limitation is the high bandwidth needed by the sensor, which becomes also an issue for the distributed system TCP/IP communications.

Since traffic volume is too high, 3D data has to be compressed before it can be sent over the network.

The solution consists in selfcoding the Kinect data into RGB images and then using a standard multimedia codec to compress color maps.

Information from different sources is collected into a central client computer, where point clouds are transformed to reconstruct the scene in 3D.

An algorithm is proposed to merge the skeletons detected locally by each Kinect conveniently, so that monitoring of people is robust to self and inter-user occlusions.

Final skeletons are labeled and trajectories of every joint can be saved for event reconstruction or further analysis.

Today I read a paper titled “An end-to-end machine learning system for harmonic analysis of music”

The abstract is:

We present a new system for simultaneous estimation of keys, chords, and bass notes from music audio.

It makes use of a novel chromagram representation of audio that takes perception of loudness into account.

Furthermore, it is fully based on machine learning (instead of expert knowledge), such that it is potentially applicable to a wider range of genres as long as training data is available.

As compared to other models, the proposed system is fast and memory efficient, while achieving state-of-the-art performance.

Today I read a paper titled “Modeling the Rise in Internet-based Petitions”

The abstract is:

Contemporary collective action, much of which involves social media and other Internet-based platforms, leaves a digital imprint which may be harvested to better understand the dynamics of mobilization.

Petition signing is an example of collective action which has gained in popularity with rising use of social media and provides such data for the whole population of petition signatories for a given platform. This paper tracks the growth curves of all 20,000 petitions to the UK government over 18 months, analyzing the rate of growth and outreach mechanism.

Previous research has suggested the importance of the first day to the ultimate success of a petition, but has not examined early growth within that day, made possible here through hourly resolution in the data.

The analysis shows that the vast majority of petitions do not achieve any measure of success; over 99 percent fail to get the 10,000 signatures required for an official response and only 0.1 percent attain the 100,000 required for a parliamentary debate.

We analyze the data through a multiplicative process model framework to explain the heterogeneous growth of signatures at the population level.

We define and measure an average outreach factor for petitions and show that it decays very fast (reducing to 0.1% after 10 hours).

After 24 hours, a petition’s fate is virtually set.

The findings seem to challenge conventional analyses of collective action from economics and political science, where the production function has been assumed to follow an S-shaped curve.

Today I read a paper titled “Dynamic Facial Expression of Emotion Made Easy”

The abstract is:

Facial emotion expression for virtual characters is used in a wide variety of areas.

Often, the primary reason to use emotion expression is not to study emotion expression generation per se, but to use emotion expression in an application or research project.

What is then needed is an easy to use and flexible, but also validated mechanism to do so.

In this report we present such a mechanism.

It enables developers to build virtual characters with dynamic affective facial expressions.

The mechanism is based on Facial Action Coding.

It is easy to implement, and code is available for download.

To show the validity of the expressions generated with the mechanism we tested the recognition accuracy for 6 basic emotions (joy, anger, sadness, surprise, disgust, fear) and 4 blend emotions (enthusiastic, furious, frustrated, and evil).

Additionally we investigated the effect of VC distance (z-coordinate), the effect of the VC’s face morphology (male vs. female), the effect of a lateral versus a frontal presentation of the expression, and the effect of intensity of the expression.

Participants (n=19, Western and Asian subjects) rated the intensity of each expression for each condition (within subject setup) in a non forced choice manner.

All of the basic emotions were uniquely perceived as such.

Further, the blends and confusion details of basic emotions are compatible with findings in psychology.

Today I read a paper titled “Mobile augmented reality survey: a bottom-up approach”

The abstract is:

Augmented Reality (AR) is becoming mobile.

Mobile devices have many constraints but also rich new features that traditional desktop computers do not have.

There are several survey papers on AR, but none is dedicated to Mobile Augmented Reality (MAR).

Our work serves the purpose of closing this gap.

The contents are organized with a bottom-up approach.

We first present the state-of-the-art in system components including hardware platforms, software frameworks and display devices, follows with enabling technologies such as tracking and data management.

We then survey the latest technologies and methods to improve run-time performance and energy efficiency for practical implementation.

On top of these, we further introduce the application fields and several typical MAR applications.

Finally we conclude the survey with several challenge problems, which are under exploration and require great research efforts in the future.

Today I read a paper titled “Learning discriminative trajectorylet detector sets for accurate skeleton-based action recognition”

The abstract is:

The introduction of low-cost RGB-D sensors has promoted the research in skeleton-based human action recognition.

Devising a representation suitable for characterising actions on the basis of noisy skeleton sequences remains a challenge, however.

We here provide two insights into this challenge.

First, we show that the discriminative information of a skeleton sequence usually resides in a short temporal interval and we propose a simple-but-effective local descriptor called trajectorylet to capture the static and kinematic information within this interval.

Second, we further propose to encode each trajectorylet with a discriminative trajectorylet detector set which is selected from a large number of candidate detectors trained through exemplar-SVMs.

The action-level representation is obtained by pooling trajectorylet encodings.

Evaluating on standard datasets acquired from the Kinect sensor, it is demonstrated that our method obtains superior results over existing approaches under various experimental setups.

Today I read a paper titled “Algorithms to automatically quantify the geometric similarity of anatomical surfaces”

The abstract is:

We describe new approaches for distances between pairs of 2-dimensional surfaces (embedded in 3-dimensional space) that use local structures and global information contained in inter-structure geometric relationships.

We present algorithms to automatically determine these distances as well as geometric correspondences.

This is motivated by the aspiration of students of natural science to understand the continuity of form that unites the diversity of life.

At present, scientists using physical traits to study evolutionary relationships among living and extinct animals analyze data extracted from carefully defined anatomical correspondence points (landmarks).

Identifying and recording these landmarks is time consuming and can be done accurately only by trained morphologists.

This renders these studies inaccessible to non-morphologists, and causes phenomics to lag behind genomics in elucidating evolutionary patterns.

Unlike other algorithms presented for morphological correspondences our approach does not require any preliminary marking of special features or landmarks by the user.

It also differs from other seminal work in computational geometry in that our algorithms are polynomial in nature and thus faster, making pairwise comparisons feasible for significantly larger numbers of digitized surfaces.

We illustrate our approach using three datasets representing teeth and different bones of primates and humans, and show that it leads to highly accurate results.

Today I read a paper titled “Face Prediction Model for an Automatic Age-invariant Face Recognition System”

The abstract is:

Automated face recognition and identification softwares are becoming part of our daily life; it finds its abode not only with Facebook’s auto photo tagging, Apple’s iPhoto, Google’s Picasa, Microsoft’s Kinect, but also in Homeland Security Department’s dedicated biometric face detection systems.

Most of these automatic face identification systems fail where the effects of aging come into the picture.

Little work exists in the literature on the subject of face prediction that accounts for aging, which is a vital part of the computer face recognition systems.

In recent years, individual face components’ (e.g. eyes, nose, mouth) features based matching algorithms have emerged, but these approaches are still not efficient.

Therefore, in this work we describe a Face Prediction Model (FPM), which predicts human face aging or growth related image variation using Principle Component Analysis (PCA) and Artificial Neural Network (ANN) learning techniques.

The FPM captures the facial changes, which occur with human aging and predicts the facial image with a few years of gap with an acceptable accuracy of face matching from 76 to 86%.

Today I read a paper titled “Robust and Real Time Detection of Curvy Lanes (Curves) with Desired Slopes for Driving Assistance and Autonomous Vehicles”

The abstract is:

One of the biggest reasons for road accidents is curvy lanes and blind turns.

Even one of the biggest hurdles for new autonomous vehicles is to detect curvy lanes, multiple lanes and lanes with a lot of discontinuity and noise.

This paper presents very efficient and advanced algorithm for detecting curves having desired slopes (especially for detecting curvy lanes in real time) and detection of curves (lanes) with a lot of noise, discontinuity and disturbances.

Overall aim is to develop robust method for this task which is applicable even in adverse conditions.

Even in some of most famous and useful libraries like OpenCV and Matlab, there is no function available for detecting curves having desired slopes , shapes, discontinuities.

Only few predefined shapes like circle, ellipse, etc, can be detected using presently available functions.

Proposed algorithm can not only detect curves with discontinuity, noise, desired slope but also it can perform shadow and illumination correction and detect/ differentiate between different curves.

Today I read a paper titled “Estimating 3D Human Shapes from Measurements”

The abstract is:

The recent advances in 3-D imaging technologies give rise to databases of human shapes, from which statistical shape models can be built.

These statistical models represent prior knowledge of the human shape and enable us to solve shape reconstruction problems from partial information.

Generating human shape from traditional anthropometric measurements is such a problem, since these 1-D measurements encode 3-D shape information.

Combined with a statistical shape model, these easy-to-obtain measurements can be leveraged to create 3D human shapes.

However, existing methods limit the creation of the shapes to the space spanned by the database and thus require a large amount of training data.

In this paper, we introduce a technique that extrapolates the statistically inferred shape to fit the measurement data using nonlinear optimization.

This method ensures that the generated shape is both human-like and satisfies the measurement conditions.

We demonstrate the effectiveness of the method and compare it to existing approaches through extensive experiments, using both synthetic data and real human measurements.

Today I read a paper titled “Virtual Texturing”

The abstract is:

In this thesis a rendering system and an accompanying tool chain for Virtual Texturing is presented.

Our tools allow to automatically retexture existing geometry in order to apply unique texturing on each face.

Furthermore we investigate several techniques that try to minimize visual artifacts in the case that only a small amount of pages can be streamed per frame.

We analyze the influence of different heuristics that are responsible for the page selection.

Alongside these results we present a measurement method to allow the comparison of our heuristics.

Today I read a paper titled “A Behavior-based Approach for Multi-agent Q-learning for Autonomous Exploration”

The abstract is:

The use of mobile robots is being popular over the world mainly for autonomous explorations in hazardous/ toxic or unknown environments.

This exploration will be more effective and efficient if the explorations in unknown environment can be aided with the learning from past experiences.

Currently reinforcement learning is getting more acceptances for implementing learning in robots from the system-environment interactions.

This learning can be implemented using the concept of both single-agent and multiagent.

This paper describes such a multiagent approach for implementing a type of reinforcement learning using a priority based behaviour-based architecture.

This proposed methodology has been successfully tested in both indoor and outdoor environments.

Today I read a paper titled “Attraction-Based Receding Horizon Path Planning with Temporal Logic Constraints”

The abstract is:

Our goal in this paper is to plan the motion of a robot in a partitioned environment with dynamically changing, locally sensed rewards.

We assume that arbitrary assumptions on the reward dynamics can be given.

The robot aims to accomplish a high-level temporal logic surveillance mission and to locally optimize the collection of the rewards in the visited regions.

These two objectives often conflict and only a compromise between them can be reached.

We address this issue by taking into consideration a user-defined preference function that captures the trade-off between the importance of collecting high rewards and the importance of making progress towards a surveyed region.

Our solution leverages ideas from the automata-based approach to model checking.

We demonstrate the utilization and benefits of the suggested framework in an illustrative example.