Archives for 2016

Planning Random path distributions for ambush games in unstructured environments

Today I read a published paper “Planning Random path distributions for ambush games in unstructured environments”

The abstract is:

Operating vehicles in adversarial environments require non-conventional planning techniques. A two-player, zero-sum non-cooperative game is introduced, which is solved via a linear program. An extension is proposed to construct networks displaying good representations of the environment characteristics, while offering acceptable results for the technique used. Sensitivity of the solution to the LP solver algorithm is identified. The performances of the planner are finally assessed by comparison with those of conventional planners. Results are used to formulate secondary objectives to the problem.

Forward and Inverse Kinematics Seamless Matching Using Jacobian

Today I read a published paper “Forward and Inverse Kinematics Seamless Matching Using Jacobian”

The abstract is:

In this paper the problem of matching Forward Kinematics (FK) motion of a 3 Dimensional (3D) joint chain to the Inverse Kinematics (IK) movement and vice versa has been addressed. The problem lies at the heart of animating a 3D character having controller and manipulator based rig for animation within any 3D modeling and animation software. The seamless matching has been achieved through the use of pseudo-inverse of Jacobian Matrix. The Jacobian Matrix is used to determine the rotation values of each joint of character body part such as arms, between the inverse kinematics and forward kinematics motion. Then moving the corresponding kinematic joint system to the desired place, automatically eliminating the jumping or popping effect which would reduce the complexity of the system.

Efficient Hand Articulations Tracking using Adaptive Hand Model and Depth map

Today I read a published paper “Efficient Hand Articulations Tracking using Adaptive Hand Model and Depth map”

The abstract is:

Real-time hand articulations tracking is important for many applications such as interacting with virtual / augmented reality devices or tablets. However, most of existing algorithms highly rely on expensive and high power-consuming GPUs to achieve real-time processing. Consequently, these systems are inappropriate for mobile and wearable devices. In this paper, we propose an efficient hand tracking system which does not require high performance GPUs. In our system, we track hand articulations by minimizing discrepancy between depth map from sensor and computer-generated hand model. We also initialize hand pose at each frame using finger detection and classification. Our contributions are: (a) propose adaptive hand model to consider different hand shapes of users without generating personalized hand model; (b) improve the highly efficient frame initialization for robust tracking and automatic initialization; (c) propose hierarchical random sampling of pixels from each depth map to improve tracking accuracy while limiting required computations. To the best of our knowledge, it is the first system that achieves both automatic hand model adjustment and real-time tracking without using GPUs.

Low-cost Augmented Reality prototype for controlling network devices

Today I read a published paper “Low-cost Augmented Reality prototype for controlling network devices”

The abstract is:

With the evolution of mobile devices, and smart-phones in particular, comes the ability to create new experiences that enhance the way we see, interact, and manipulate objects, within the world that surrounds us. It is now possible to blend data from our senses and our devices in numerous ways that simply were not possible before using Augmented Reality technology. In a near future, when all of the office devices as well as your personal electronic gadgets are on a common wireless network, operating them using a universal remote controller would be possible. This paper presents an off-the-shelf, low-cost prototype that leverages the Augmented Reality technology to deliver a novel and interactive way of operating office network devices around using a mobile device. We believe this type of system may provide benefits to controlling multiple integrated devices and visualizing interconnectivity or utilizing visual elements to pass information from one device to another, or may be especially beneficial to control devices when interacting with them physically may be difficult or pose danger or harm.

Free-body Gesture Tracking and Augmented Reality Improvisation for Floor and Aerial Dance

Today I read a published paper “Free-body Gesture Tracking and Augmented Reality Improvisation for Floor and Aerial Dance”

The abstract is:

This paper describes an updated interactive performance system for floor and Aerial Dance that controls visual and sonic aspects of the presentation via a depth sensing camera (MS Kinect). In order to detect, measure and track free movement in space, 3 degree of freedom (3-DOF) tracking in space (on the ground and in the air) is performed using IR markers with a method for multi target tracking capabilities added and described in detail. An improved gesture tracking and recognition system, called Action Graph (AG), is described in the paper. Action Graph uses an efficient incremental construction from a single long sequence of movement features and automatically captures repeated sub-segments in the movement from start to finish with no manual interaction needed with other advanced capabilities discussed as well. By using the new model for the gesture we can unify an entire choreography piece by dynamically tracking and recognizing gestures and sub-portions of the piece. This gives the performer the freedom to improvise based on a set of recorded gestures/portions of the choreography and have the system dynamically respond in relation to the performer within a set of related rehearsed actions, an ability that has not been seen in any other system to date.

Free-body Gesture Tracking and Augmented Reality Improvisation for Floor and Aerial Dance

Today I read a published paper titled “Free-body Gesture Tracking and Augmented Reality Improvisation for Floor and Aerial Dance”

Thought it was a rather novel application for a Microsoft Kinect. Not sure if I will ever apply any of the content to my own work. But still an interesting read.

The abstract is:

This paper describes an updated interactive performance system for floor and Aerial Dance that controls visual and sonic aspects of the presentation via a depth sensing camera (MS Kinect). In order to detect, measure and track free movement in space, 3 degree of freedom (3-DOF) tracking in space (on the ground and in the air) is performed using IR markers with a method for multi target tracking capabilities added and described in detail. An improved gesture tracking and recognition system, called Action Graph (AG), is described in the paper. Action Graph uses an efficient incremental construction from a single long sequence of movement features and automatically captures repeated sub-segments in the movement from start to finish with no manual interaction needed with other advanced capabilities discussed as well. By using the new model for the gesture we can unify an entire choreography piece by dynamically tracking and recognizing gestures and sub-portions of the piece. This gives the performer the freedom to improvise based on a set of recorded gestures/portions of the choreography and have the system dynamically respond in relation to the performer within a set of related rehearsed actions, an ability that has not been seen in any other system to date.

Improving Perceptual Color Difference using Basic Color Terms

Today I read a published paper “Improving Perceptual Color Difference using Basic Color Terms”

The abstract is:

We suggest a new color distance based on two observations. First, perceptual color differences were designed to be used to compare very similar colors. They do not capture human perception for medium and large color differences well. Thresholding was proposed to solve the problem for large color differences, i.e. two totally different colors are always the same distance apart. We show that thresholding alone cannot improve medium color differences. We suggest to alleviate this problem using basic color terms. Second, when a color distance is used for edge detection, many small distances around the just noticeable difference may account for false edges. We suggest to reduce the effect of small distances.