justin

Today I read a paper titled “A Light Transport Model for Mitigating Multipath Interference in TOF Sensors”

The abstract is:
Continuous-wave Time-of-flight (TOF) range imaging has become a commercially viable technology with many applications in computer vision and graphics.

However, the depth images obtained from TOF cameras contain scene dependent errors due to multipath interference (MPI).

Specifically, MPI occurs when multiple optical reflections return to a single spatial location on the imaging sensor.

Many prior approaches to rectifying MPI rely on sparsity in optical reflections, which is an extreme simplification.

In this paper, we correct MPI by combining the standard measurements from a TOF camera with information from direct and global light transport.

We report results on both simulated experiments and physical experiments (using the Kinect sensor).

Our results, evaluated against ground truth, demonstrate a quantitative improvement in depth accuracy.

Today I read a paper titled “High-dimensional Black-box Optimization via Divide and Approximate Conquer”

The abstract is:
Divide and Conquer (DC) is conceptually well suited to high-dimensional optimization by decomposing a problem into multiple small-scale sub-problems.

However, appealing performance can be seldom observed when the sub-problems are interdependent.

This paper suggests that the major difficulty of tackling interdependent sub-problems lies in the precise evaluation of a partial solution (to a sub-problem), which can be overwhelmingly costly and thus makes sub-problems non-trivial to conquer.

Thus, we propose an approximation approach, named Divide and Approximate Conquer (DAC), which reduces the cost of partial solution evaluation from exponential time to polynomial time.

Meanwhile, the convergence to the global optimum (of the original problem) is still guaranteed.

The effectiveness of DAC is demonstrated empirically on two sets of non-separable high-dimensional problems.

Today I read a paper titled “The role of RGB-D benchmark datasets: an overview”

The abstract is:
The advent of the Microsoft Kinect three years ago stimulated not only the computer vision community for new algorithms and setups to tackle well-known problems in the community but also sparked the launch of several new benchmark datasets to which future algorithms can be compared 019 to.

This review of the literature and industry developments concludes that the current RGB-D benchmark datasets can be useful to determine the accuracy of a variety of applications of a single or multiple RGB-D sensors.

Today I read a paper titled “How to avoid ethically relevant Machine Consciousness”

The abstract is:
This paper discusses the root cause of systems perceiving the self experience and how to exploit adaptive and learning features without introducing ethically problematic system properties.

Today I read a paper titled “Is swarm intelligence able to create mazes?”

The abstract is:
In this paper, the idea of applying Computational Intelligence in the process of creation board games, in particular mazes, is presented.

For two different algorithms the proposed idea has been examined.

The results of the experiments are shown and discussed to present advantages and disadvantages.

Today I read a paper titled “Quantum machine learning with glow for episodic tasks and decision games”

The abstract is:
We consider a general class of models, where a reinforcement learning (RL) agent learns from cyclic interactions with an external environment via classical signals.

Perceptual inputs are encoded as quantum states, which are subsequently transformed by a quantum channel representing the agent’s memory, while the outcomes of measurements performed at the channel’s output determine the agent’s actions.

The learning takes place via stepwise modifications of the channel properties.

They are described by an update rule that is inspired by the projective simulation (PS) model and equipped with a glow mechanism that allows for a backpropagation of policy changes, analogous to the eligibility traces in RL and edge glow in PS.

In this way, the model combines features of PS with the ability for generalization, offered by its physical embodiment as a quantum system.

We apply the agent to various setups of an invasion game and a grid world, which serve as elementary model tasks allowing a direct comparison with a basic classical PS agent.

Today I read a paper titled “A Simultaneous-Movement Mobile Multiplayer Game Design based on Adaptive Background Partitioning Technique”

The abstract is:
Implementations of mobile games have become prevalent industrial technology due to the ubiquitous nature of mobile devices.

However, simultaneous-movement multiplayer games, games that a player competes simultaneously with other players, are usually affected by such parameters as latency, type of game architecture and type of communication technology.

This paper makes a review of the above parameters, considering the pros and cons of the various techniques used in addressing each parameter.

It then goes ahead to propose an enhanced mechanism for dealing with packet delays based on partitioning the game background into grids.

The proposed design is implemented and tested using Bluetooth and Wi-Fi communication technologies.

The efficiency and effectiveness of the design are also analyzed.

Today I read a paper titled “Massively Parallel Ray Tracing Algorithm Using GPU”

The abstract is:
Ray tracing is a technique for generating an image by tracing the path of light through pixels in an image plane and simulating the effects of high-quality global illumination at a heavy computational cost.

Because of the high computation complexity, it can’t reach the requirement of real-time rendering.

The emergence of many-core architectures, makes it possible to reduce significantly the running time of ray tracing algorithm by employing the powerful ability of floating point computation.

In this paper, a new GPU implementation and optimization of the ray tracing to accelerate the rendering process is presented.

Today I read a paper titled “Verifiable Source Code Documentation in Controlled Natural Language”

The abstract is:
Writing documentation about software internals is rarely considered a rewarding activity.

It is highly time-consuming and the resulting documentation is fragile when the software is continuously evolving in a multi-developer setting.

Unfortunately, traditional programming environments poorly support the writing and maintenance of documentation.

Consequences are severe as the lack of documentation on software structure negatively impacts the overall quality of the software product.

We show that using a controlled natural language with a reasoner and a query engine is a viable technique for verifying the consistency and accuracy of documentation and source code.

Using ACE, a state-of-the-art controlled natural language, we present positive results on the comprehensibility and the general feasibility of creating and verifying documentation.

As a case study, we used automatic documentation verification to identify and fix severe flaws in the architecture of a non-trivial piece of software.

Moreover, a user experiment shows that our language is faster and easier to learn and understand than other formal languages for software documentation.

Today I read a paper titled “Kinects and Human Kinetics: A New Approach for Studying Crowd Behavior”

The abstract is:
Modeling crowd behavior relies on accurate data of pedestrian movements at a high level of detail.

Imaging sensors such as cameras provide a good basis for capturing such detailed pedestrian motion data.

However, currently available computer vision technologies, when applied to conventional video footage, still cannot automatically unveil accurate motions of groups of people or crowds from the image sequences.

We present a novel data collection approach for studying crowd behavior which uses the increasingly popular low-cost sensor Microsoft Kinect.

The Kinect captures both standard camera data and a three-dimensional depth map.

Our human detection and tracking algorithm is based on agglomerative clustering of depth data captured from an elevated view – in contrast to the lateral view used for gesture recognition in Kinect gaming applications.

Our approach transforms local Kinect 3D data to a common world coordinate system in order to stitch together human trajectories from multiple Kinects, which allows for a scalable and flexible capturing area.

At a testbed with real-world pedestrian traffic we demonstrate that our approach can provide accurate trajectories from three Kinects with a Pedestrian Detection Rate of up to 94% and a Multiple Object Tracking Precision of 4 cm.

Using a comprehensive dataset of 2240 captured human trajectories we calibrate three variations of the Social Force model.

The results of our model validations indicate their particular ability to reproduce the observed crowd behavior in microscopic simulations.

Today I read a paper titled “Neurally-Guided Procedural Models: Learning to Guide Procedural Models with Deep Neural Networks”

The abstract is:
We present a deep learning approach for speeding up constrained procedural modeling.

Probabilistic inference algorithms such as Sequential Monte Carlo (SMC) provide powerful tools for constraining procedural models, but they require many samples to produce desirable results.

In this paper, we show how to create procedural models which learn how to satisfy constraints.

We augment procedural models with neural networks: these networks control how the model makes random choices based on what output it has generated thus far.

We call such a model a neurally-guided procedural model.

As a pre-computation, we train these models on constraint-satisfying example outputs generated via SMC.

They are then used as efficient importance samplers for SMC, generating high-quality results with very few samples.

We evaluate our method on L-system-like models with image-based constraints.

Given a desired quality threshold, neurally-guided models can generate satisfactory results up to 10x faster than unguided models.

Today I read a paper titled “Keypoint Encoding for Improved Feature Extraction from Compressed Video at Low Bitrates”

The abstract is:
In many mobile visual analysis applications, compressed video is transmitted over a communication network and analyzed by a server.

Typical processing steps performed at the server include keypoint detection, descriptor calculation, and feature matching.

Video compression has been shown to have an adverse effect on feature-matching performance.

The negative impact of compression can be reduced by using the keypoints extracted from the uncompressed video to calculate descriptors from the compressed video.

Based on this observation, we propose to provide these keypoints to the server as side information and to extract only the descriptors from the compressed video.

First, we introduce four different frame types for keypoint encoding to address different types of changes in video content.

These frame types represent a new scene, the same scene, a slowly changing scene, or a rapidly moving scene and are determined by comparing features between successive video frames.

Then, we propose Intra, Skip and Inter modes of encoding the keypoints for different frame types.

For example, keypoints for new scenes are encoded using the Intra mode, and keypoints for unchanged scenes are skipped.

As a result, the bitrate of the side information related to keypoint encoding is significantly reduced.

Finally, we present pairwise matching and image retrieval experiments conducted to evaluate the performance of the proposed approach using the Stanford mobile augmented reality dataset and 720p format videos.

The results show that the proposed approach offers significantly improved feature matching and image retrieval performance at a given bitrate.

Today I read a paper titled “Learning from the memory of Atari 2600”

The abstract is:
We train a number of neural networks to play games Bowling, Breakout and Seaquest using information stored in the memory of a video game console Atari 2600.

We consider four models of neural networks which differ in size and architecture: two networks which use only information contained in the RAM and two mixed networks which use both information in the RAM and information from the screen.

As the benchmark we used the convolutional model proposed in NIPS and received comparable results in all considered games.

Quite surprisingly, in the case of Seaquest we were able to train RAM-only agents which behave better than the benchmark screen-only agent.

Mixing screen and RAM did not lead to an improved performance comparing to screen-only and RAM-only agents.

Today I read a paper titled “Predictive No-Reference Assessment of Video Quality”

The abstract is:
Among the various means to evaluate the quality of video streams, No-Reference (NR) methods have low computation and may be executed on thin clients.

Thus, NR algorithms would be perfect candidates in cases of real-time quality assessment, automated quality control and, particularly, in adaptive mobile streaming.

Yet, existing NR approaches are often inaccurate, in comparison to Full-Reference (FR) algorithms, especially under lossy network conditions.

In this work, we present an NR method that combines machine learning with simple NR metrics to achieve a quality index comparably as accurate as the Video Quality Metric (VQM) Full-Reference algorithm.

Our method is tested in an extensive dataset (960 videos), under lossy network conditions and considering nine different machine learning algorithms.

Overall, we achieve an over 97% correlation with VQM, while allowing real-time assessment of video quality of experience in realistic streaming scenarios.

Today I read a paper titled “Synchronization and Collision Avoidance in Non-Linear Flocking Networks of Autonomous Agents”

The abstract is:
We introduce and discuss two novel second-order consensus networks with state-dependent couplings of Cucker-Smale type.

The first scheme models flocking to synchronization over a network of agents where the alignment of the agent’s states occurs over a non-trivial limit orbit that is generated by the internal dynamics of each individual agent.

The second scheme models the speed alignment of a group of agents which avoid approaching each other closer than a prescribed distance.

While seemingly different, both of these systems can be analyzed using the same mathematical methods.

We rigorously analyze both examples and reveal their striking similarities.

We arrive at sufficient conditions that relate the initial configurations and the systems’ parameters that give rise to a collective common behavior.

Simulation examples are presented to support our theoretical conclusions.

Today I read a paper titled “3-D Hand Pose Estimation from Kinect’s Point Cloud Using Appearance Matching”

The abstract is:
We present a novel appearance-based approach for pose estimation of a human hand using the point clouds provided by the low-cost Microsoft Kinect sensor.

Both the free-hand case, in which the hand is isolated from the surrounding environment, and the hand-object case, in which the different types of interactions are classified, have been considered.

The hand-object case is clearly the most challenging task having to deal with multiple tracks.

The approach proposed here belongs to the class of partial pose estimation where the estimated pose in a frame is used for the initialization of the next one.

The pose estimation is obtained by applying a modified version of the Iterative Closest Point (ICP) algorithm to synthetic models to obtain the rigid transformation that aligns each model with respect to the input data.

The proposed framework uses a “pure” point cloud as provided by the Kinect sensor without any other information such as RGB values or normal vector components.

For this reason, the proposed method can also be applied to data obtained from other types of depth sensor, or RGB-D camera.

Today I read a paper titled “Virtual World, Defined from a Technological Perspective, and Applied to Video Games, Mixed Reality and the Metaverse”

The abstract is:
There is no generally accepted definition for a virtual world, with many complimentary terms and acronyms having emerged implying a virtual world.

Advances in systems architecture techniques such as, host migration of instances, mobile ad-hoc networking, and distributed computing, bring in to question whether those architectures can actually support a virtual world.

Without a concrete definition, controversy ensues and it is problematic to design an architecture for a virtual world.

Several researchers provided a definition but aspects of each definition are still problematic and simply can not be applied to contemporary technologies.

The approach of this article is to sample technologies using grounded theory, and obtain a definition for a `virtual world’ that is directly applicable to technology.

The obtained definition is compared with related work and used to classify advanced technologies, such as: a pseudo-persistent video game, a MANet, virtual and mixed reality, and the Metaverse.

The results of this article include: a break down of which properties set apart the various technologies; a definition that is validated by comparing it with other definitions; an ontology showing the relation of the different complimentary terms and acronyms; and, the usage of pseudo-persistence to categories those technologies which only mimic persistence.

Today I read a paper titled “Reconfiguration of 3D Crystalline Robots Using O(log n) Parallel Moves”

The abstract is:
We consider the theoretical model of Crystalline robots, which have been introduced and prototyped by the robotics community.

These robots consist of independently manipulable unit-square atoms that can extend/contract arms on each side and attach/detach from neighbors.

These operations suffice to reconfigure between any two given (connected) shapes.

The worst-case number of sequential moves required to transform one connected configuration to another is known to be Theta(n).

However, in principle, atoms can all move simultaneously.

We develop a parallel algorithm for reconfiguration that runs in only O(log n) parallel steps, although the total number of operations increases slightly to Theta(nlogn).

The result is the first (theoretically) almost-instantaneous universally reconfigurable robot built from simple units.

Today I read a paper titled “Hands-free Evolution of 3D-printable Objects via Eye Tracking”

The abstract is:
Interactive evolution has shown the potential to create amazing and complex forms in both 2-D and 3-D settings.

However, the algorithm is slow and users quickly become fatigued.

We propose that the use of eye tracking for interactive evolution systems will both reduce user fatigue and improve evolutionary success.

We describe a systematic method for testing the hypothesis that eye tracking driven interactive evolution will be a more successful and easier-to-use design method than traditional interactive evolution methods driven by mouse clicks.

We provide preliminary results that support the possibility of this proposal, and lay out future work to investigate these advantages in extensive clinical trials.

Today I read a paper titled “Stitching Stabilizer: Two-frame-stitching Video Stabilization for Embedded Systems”

The abstract is:
In conventional electronic video stabilization, the stabilized frame is obtained by cropping the input frame to cancel camera shake.

While a small cropping size results in strong stabilization, it does not provide us satisfactory results from the viewpoint of image quality, because it narrows the angle of view.

By fusing several frames, we can effectively expand the area of input frames, and achieve strong stabilization even with a large cropping size.

Several methods for doing so have been studied.

However, their computational costs are too high for embedded systems such as smartphones.

We propose a simple, yet surprisingly effective algorithm, called the stitching stabilizer.

It stitches only two frames together with a minimal computational cost.

It can achieve real-time processes in embedded systems, for Full HD and 30 FPS videos.

To clearly show the effect, we apply it to hyperlapse.

Using several clips, we show it produces more strongly stabilized and natural results than the existing solutions from Microsoft and Instagram.

Today I read a paper titled “Text Matching as Image Recognition”

The abstract is:
Matching two texts is a fundamental problem in many natural language processing tasks.

An effective way is to extract meaningful matching patterns from words, phrases, and sentences to produce the matching score.

Inspired by the success of convolutional neural network in image recognition, where neurons can capture many complicated patterns based on the extracted elementary visual patterns such as oriented edges and corners, we propose to model text matching as the problem of image recognition.

Firstly, a matching matrix whose entries represent the similarities between words is constructed and viewed as an image.

Then a convolutional neural network is utilized to capture rich matching patterns in a layer-by-layer way.

We show that by resembling the compositional hierarchies of patterns in image recognition, our model can successfully identify salient signals such as n-gram and n-term matchings.

Experimental results demonstrate its superiority against the baselines.

Today I read a paper titled “Simulation of Color Blindness and a Proposal for Using Google Glass as Color-correcting Tool”

The abstract is:
The human visual color response is driven by specialized cells called cones, which exist in three types, viz.

R, G, and B.

Software is developed to simulate how color images are displayed for different types of color blindness.

Specified the default color deficiency associated with a user, it generates a preview of the rainbow (in the visible range, from red to violet) and shows up, side by side with a colorful image provided as input, the display correspondent colorblind.

The idea is to provide an image processing after image acquisition to enable a better perception ofcolors by the color blind.

Examples of pseudo-correction are shown for the case of Protanopia (red blindness).

The system is adapted into a screen of an i-pad or a cellphone in which the colorblind observe the camera, the image processed with color detail previously imperceptible by his naked eye.

As prospecting, wearable computer glasses could be manufactured to provide a corrected image playback.

The approach can also provide augmented reality for human vision by adding the UV or IR responses as a new feature of Google Glass.

Today I read a paper titled “Neural Aggregation Network for Video Face Recognition”

The abstract is:
In this paper, we present a Neural Aggregation Network (NAN) for video face recognition.

The network takes a face video or face image set of a person with variable number of face frames as its input, and produces a compact and fixed-dimension visual representation of that person.

The whole network is composed of two modules.

The feature embedding module is a CNN which maps each face frame into a feature representation.

The neural aggregation module is composed of two content based attention blocks which is driven by a memory storing all the features extracted from the face video through the feature embedding module.

The output of the first attention block adapts the second, whose output is adopted as the aggregated representation of the video faces.

Due to the attention mechanism, this representation is invariant to the order of the face frames.

The experiments show that the proposed NAN consistently outperforms hand-crafted aggregations such as average pooling, and achieves state-of-the-art accuracy on three video face recognition datasets: the YouTube Face, IJB-A and Celebrity-1000 datasets.