scholarly journals Lossy Geometry Compression for High Resolution Voxel Scenes

2020 ◽  
Vol 3 (1) ◽  
pp. 1-13
Author(s):  
Remi van der Laan ◽  
Leonardo Scandolo ◽  
Elmar Eisemann
Keyword(s):  
High Resolution ◽  
Real Time ◽  
High Performance ◽  
State Of The Art ◽  
Directed Acyclic Graphs ◽  
Memory Consumption ◽  
Geometry Compression ◽  
Acyclic Graphs ◽  
Error Ratio ◽  
Voxel Grid

Sparse Voxel Directed Acyclic Graphs (SVDAGs) losslessly compress highly detailed geometry in a high-resolution binary voxel grid by identifying matching elements. This representation is suitable for high-performance real-time applications, such as free-viewpoint videos and high-resolution precomputed shadows. In this work, we introduce a lossy scheme to further decrease memory consumption by minimally modifying the underlying voxel grid to increase matches. Our method efficiently identifies groups of similar but rare subtrees in an SVDAG structure and replaces them with a single common subtree representative. We test our compression strategy on several standard voxel datasets, where we obtain memory reductions of 10% up to 50% compared to a standard SVDAG, while introducing an error (ratio of modified voxels to voxel count) of only 1% to 5%. Furthermore, we show that our method is complementary to other state of the art SVDAG optimizations, and has a negligible effect on real-time rendering performance.

scholarly journals Multiple objects tracking in the UAV system based on hierarchical deep high-resolution network

2021 ◽  
Author(s):  
Wei Huang ◽  
Xiaoshu Zhou ◽  
Mingchao Dong ◽  
Huaiyu Xu
Keyword(s):  
High Resolution ◽  
Object Tracking ◽  
High Performance ◽  
State Of The Art ◽  
Class Imbalance ◽  
Unified Framework ◽  
Multiple Objects ◽  
Tracking Process ◽  
Objects Tracking ◽  
Different Types

AbstractRobust and high-performance visual multi-object tracking is a big challenge in computer vision, especially in a drone scenario. In this paper, an online Multi-Object Tracking (MOT) approach in the UAV system is proposed to handle small target detections and class imbalance challenges, which integrates the merits of deep high-resolution representation network and data association method in a unified framework. Specifically, while applying tracking-by-detection architecture to our tracking framework, a Hierarchical Deep High-resolution network (HDHNet) is proposed, which encourages the model to handle different types and scales of targets, and extract more effective and comprehensive features during online learning. After that, the extracted features are fed into different prediction networks for interesting targets recognition. Besides, an adjustable fusion loss function is proposed by combining focal loss and GIoU loss to solve the problems of class imbalance and hard samples. During the tracking process, these detection results are applied to an improved DeepSORT MOT algorithm in each frame, which is available to make full use of the target appearance features to match one by one on a practical basis. The experimental results on the VisDrone2019 MOT benchmark show that the proposed UAV MOT system achieves the highest accuracy and the best robustness compared with state-of-the-art methods.

A Tour of Lattice-Based Skyline Algorithms

2018 ◽  
pp. 96-122
Author(s):  
Markus Endres ◽  
Lena Rudenko
Keyword(s):  
Real Time ◽  
Data Streams ◽  
High Performance ◽  
State Of The Art ◽  
Experimental Results ◽  
Lattice Structures ◽  
Skyline Query ◽  
Basic Concepts ◽  
Generic Index

A skyline query retrieves all objects in a dataset that are not dominated by other objects according to some given criteria. There exist many skyline algorithms which can be classified into generic, index-based, and lattice-based algorithms. This chapter takes a tour through lattice-based skyline algorithms. It summarizes the basic concepts and properties, presents high-performance parallel approaches, shows how one overcomes the low-cardinality restriction of lattice structures, and finally presents an application on data streams for real-time skyline computation. Experimental results on synthetic and real datasets show that lattice-based algorithms outperform state-of-the-art skyline techniques, and additionally have a linear runtime complexity.

scholarly journals An Effective Algorithm for Denoising Salt-And-Pepper Noise in Real-Time

2020 ◽  
pp. 14-27
Author(s):  
Obed Appiah ◽  
James Benjamin Hayfron-Acquah ◽  
Michael Asante
Keyword(s):  
Real Time ◽  
High Performance ◽  
State Of The Art ◽  
Median Filter ◽  
Image Data ◽  
Poor Performance ◽  
Relevant Information ◽  
Salt And Pepper Noise ◽  
Running Time ◽  
Salt And Pepper

For computer vision systems to effectively perform diagnoses, identification, tracking, monitoring and surveillance, image data must be devoid of noise. Various types of noises such as Salt-and-pepper or Impulse, Gaussian, Shot, Quantization, Anisotropic, and Periodic noises corrupts images making it difficult to extract relevant information from them. This has led to a lot of proposed algorithms to help fix the problem. Among the proposed algorithms, the median filter has been successful in handling salt-and-pepper noise and preserving edges in images. However, its moderate to high running time and poor performance when images are corrupted with high densities of noise, has led to various proposed modifications of the median filter. The challenge observed with all these modifications is the trade-off between efficient running time and quality of denoised images. This paper proposes an algorithm that delivers quality denoised images in low running time. Two state-of-the-art algorithms are combined into one and a technique called Mid-Value-Decision-Median introduced into the proposed algorithm to deliver high quality denoised images in real-time. The proposed algorithm, High-Performance Modified Decision Based Median Filter (HPMDBMF) runs about 200 times faster than the state-of-the-art Modified Decision Based Median Filter (MDBMF) and still generate equivalent output.

scholarly journals DisCountNet: Discriminating and Counting Network for Real-Time Counting and Localization of Sparse Objects in High-Resolution UAV Imagery

2019 ◽  
Vol 11 (9) ◽  
pp. 1128 ◽  
Author(s):  
Maryam Rahnemoonfar ◽  
Dugan Dobbs ◽  
Masoud Yari ◽  
Michael J. Starek
Keyword(s):  
High Resolution ◽  
Real Time ◽  
State Of The Art ◽  
Sparse Matrix ◽  
Aerial Images ◽  
Heat Map ◽  
High Resolution Image ◽  
Current State ◽  
Density Map ◽  
Distinct Features

Recent deep-learning counting techniques revolve around two distinct features of data—sparse data, which favors detection networks, or dense data where density map networks are used. Both techniques fail to address a third scenario, where dense objects are sparsely located. Raw aerial images represent sparse distributions of data in most situations. To address this issue, we propose a novel and exceedingly portable end-to-end model, DisCountNet, and an example dataset to test it on. DisCountNet is a two-stage network that uses theories from both detection and heat-map networks to provide a simple yet powerful design. The first stage, DiscNet, operates on the theory of coarse detection, but does so by converting a rich and high-resolution image into a sparse representation where only important information is encoded. Following this, CountNet operates on the dense regions of the sparse matrix to generate a density map, which provides fine locations and count predictions on densities of objects. Comparing the proposed network to current state-of-the-art networks, we find that we can maintain competitive performance while using a fraction of the computational complexity, resulting in a real-time solution.

scholarly journals Improving Week-2 Forecasts with Multimodel Reforecast Ensembles

2006 ◽  
Vol 134 (8) ◽  
pp. 2279-2284 ◽  
Author(s):  
Jeffrey S. Whitaker ◽  
Xue Wei ◽  
Frédéric Vitart
Keyword(s):  
High Resolution ◽  
Real Time ◽  
State Of The Art ◽  
Horizontal Resolution ◽  
Model Output ◽  
Single Model ◽  
Global Models ◽  
Operational Forecast ◽  
Model Output Statistics ◽  
Ecmwf Model

Abstract It has recently been demonstrated that model output statistics (MOS) computed from a long retrospective dataset of ensemble “reforecasts” from a single model can significantly improve the skill of probabilistic week-2 forecasts (with the same model). In this study the technique is extended to a multimodel reforecast dataset consisting of forecasts from ECMWF and NCEP global models. Even though the ECMWF model is more advanced than the version of the NCEP model used (it has more than double the horizontal resolution and is about five years newer), the forecasts produced by the multimodel MOS technique are more skillful than those produced by the MOS technique applied to either the NCEP or ECMWF forecasts alone. These results demonstrate that the MOS reforecast approach yields benefits for week-2 forecasts that are just as large for high-resolution state-of-the-art models as they are for relatively low resolution out-of-date models. Furthermore, operational forecast centers can benefit by sharing both retrospective reforecast datasets and real-time forecasts.

Fast Strong Planning for FOND Problems with Multi-Root Directed Acyclic Graphs

2014 ◽  
Vol 23 (06) ◽  
pp. 1460028 ◽  
Author(s):  
Andres Calderon Jaramillo ◽  
Jicheng Fu ◽  
Vincent Ng ◽  
Farokh B. Bastani ◽  
I-Ling Yen
Keyword(s):  
State Of The Art ◽  
Solution Space ◽  
Strong Solutions ◽  
Search Space ◽  
Directed Acyclic Graphs ◽  
The State ◽  
Benchmark Problems ◽  
Acyclic Graphs ◽  
Planning Problems ◽  
Improve Planning

Recently, the state-of-the-art AI planners have significantly improved planning efficiency on Fully Observable Nondeterministic planning (FOND) problems with strong cyclic solutions. These strong cyclic solutions are guaranteed to achieve the goal if they terminate, implying that there is a possibility that they may run into indefinite loops. In contrast, strong solutions are guaranteed to achieve the goal, but few planners can effectively handle FOND problems with strong solutions. In this study, we aim to address this difficult, yet under-investigated class of planning problems: FOND planning problems with strong solutions. We present a planner that employs a new data structure, MRDAG (multi-root directed acyclic graph), to define how the solution space should be expanded. Based on the characteristics of MRDAG, we develop heuristics to ensure planning towards the relevant search direction and design optimizations to prune the search space to further improve planning efficiency. We perform extensive experiments to evaluate MRDAG, the heuristics, and the optimizations for pruning the search space. Experimental results show that our strong algorithm achieves impressive performance on a variety of benchmark problems: on average it runs more than three orders of magnitude faster than the state-of-the-art planners, MBP and Gamer, while demonstrating significantly better scalability.

Design and Implementation of the Motor Position Detection System Based on CAN Bus

2013 ◽  
Vol 415 ◽  
pp. 232-235
Author(s):  
Shao Xiong Fan ◽  
Yan Cai ◽  
Wen Tao Jiang ◽  
Chen Hui Wang ◽  
Yan Bin Wen
Keyword(s):  
High Resolution ◽  
Real Time ◽  
High Performance ◽  
Can Bus ◽  
Position Information ◽  
Essential Information ◽  
Rotor Position ◽  
Position Detection ◽  
Position Signal ◽  
Detecting Method

The rotor position of switched reluctance motor (SRM) is an essential information, and especially in high-performance SRM drive, it is important to achieve high resolution and real-time position detection. However, opto-coupler and linear encoder cant meet the demand of position detection accuracy. Moreover, if the controller detects the position directly, it is difficult to achieve a controller for remote control of the position detecting because of interference. In this paper, a novel position detecting method is proposed. Absolute encoder is used to achieve high resolution rotor position signal. MCU collects position signal from encoder, and The position information is transmitted to the master controller DSP via the Controller Area Network (CAN) bus. The experimental results show that the position detecting method meet the requirement of high-precision and real-time function of high-performance SRM drive.

scholarly journals Real-Time Facial Affective Computing on Mobile Devices

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 870 ◽  
Author(s):  
Yuanyuan Guo ◽  
Yifan Xia ◽  
Jing Wang ◽  
Hui Yu ◽  
Rung-Ching Chen
Keyword(s):  
Computational Complexity ◽  
Real Time ◽  
Mobile Devices ◽  
High Performance ◽  
Affective Computing ◽  
State Of The Art ◽  
Light Weight ◽  
Computational Power ◽  
Art Methods ◽  
And Storage

Convolutional Neural Networks (CNNs) have become one of the state-of-the-art methods for various computer vision and pattern recognition tasks including facial affective computing. Although impressive results have been obtained in facial affective computing using CNNs, the computational complexity of CNNs has also increased significantly. This means high performance hardware is typically indispensable. Most existing CNNs are thus not generalizable enough for mobile devices, where the storage, memory and computational power are limited. In this paper, we focus on the design and implementation of CNNs on mobile devices for real-time facial affective computing tasks. We propose a light-weight CNN architecture which well balances the performance and computational complexity. The experimental results show that the proposed architecture achieves high performance while retaining the low computational complexity compared with state-of-the-art methods. We demonstrate the feasibility of a CNN architecture in terms of speed, memory and storage consumption for mobile devices by implementing a real-time facial affective computing application on an actual mobile device.

scholarly journals An AT89C52 Microcontroller Based High Resolution PWM Controller for 3-Phase Voltage Source Inverters

1970 ◽  
Vol 6 (2) ◽  
Author(s):  
K. M. Rahman and S. J. M. Idrus
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Pulse Width ◽  
High Performance ◽  
Lookup Table ◽  
Voltage Source ◽  
Phase Voltage ◽  
Voltage Source Inverter ◽  
Binary Counter ◽  
Wide Range

A novel technique for generating real time high resolution pulse width modulated patterns for a three phase voltage source inverter is presented in this paper. An AT89C52 microcontroller computes the PWM pulse widths on carrier cycle basis based on the input frequency.  The microcontroller send the pulse width information for the three phases to a memory minimized ROM lookup table, which is scanned by a binary counter to generate the real time PWM patterns. The on board timer of AT89C52 is utilized to generate programmed clock for the scanning binary counter. The overall design is hardware minimized and compact that makes it feasible for low cost high performance ac drive applications.  The proposed PWM controller supports variable frequency variable voltage operation for wide range and hence is ideally suited for industrial drives requiring wide speed variations.  Keywords: Pulse width modulation, Microcontroller application, Voltage source inverter, variable speed ac drives.

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