scholarly journals Fast and Accurate Background Reconstruction Using Background Bootstrapping

2022 ◽  
Vol 8 (1) ◽  
pp. 9
Author(s):  
Bruno Sauvalle ◽  
Arnaud de La Fortelle
Keyword(s):  
Moving Objects ◽  
Graphics Processing Unit ◽  
Stochastic Gradient Descent ◽  
Processing Unit ◽  
Current Estimate ◽  
Background Estimation ◽  
Background Reconstruction ◽  
Image Pixels ◽  
Definition Of ◽  
Graphics Processing

The goal of background reconstruction is to recover the background image of a scene from a sequence of frames showing this scene cluttered by various moving objects. This task is fundamental in image analysis, and is generally the first step before more advanced processing, but difficult because there is no formal definition of what should be considered as background or foreground and the results may be severely impacted by various challenges such as illumination changes, intermittent object motions, highly cluttered scenes, etc. We propose in this paper a new iterative algorithm for background reconstruction, where the current estimate of the background is used to guess which image pixels are background pixels and a new background estimation is performed using those pixels only. We then show that the proposed algorithm, which uses stochastic gradient descent for improved regularization, is more accurate than the state of the art on the challenging SBMnet dataset, especially for short videos with low frame rates, and is also fast, reaching an average of 52 fps on this dataset when parameterized for maximal accuracy using acceleration with a graphics processing unit (GPU) and a Python implementation.

scholarly journals Fast and Accurate Background Reconstruction Using Background Bootstrapping

2021 ◽  
Author(s):  
Bruno Sauvalle ◽  
Arnaud de La Fortelle
Keyword(s):  
Gradient Descent ◽  
Moving Objects ◽  
Stochastic Gradient Descent ◽  
Current Estimate ◽  
Background Estimation ◽  
Illumination Changes ◽  
Background Reconstruction ◽  
Image Pixels ◽  
Definition Of ◽  
Background Image

The goal of background reconstruction is to recover the background image of a scene from a sequence of frames showing this scene cluttered by various moving objects. This task is fundamental in image analysis, and is generally the first step before more advanced processing, but difficult because there is no formal definition of what should be considered as background or foreground and the results may be severely impacted by various challenges such as illumination changes, intermittent object motions, highly cluttered scenes, etc. We propose in this paper a new iterative algorithm for background reconstruction, where the current estimate of the background is used to guess which image pixels are background pixels and a new background estimation is performed using those pixels only. We then show that the proposed algorithm, which uses stochastic gradient descent for improved regularization, is more accurate than the state of the art on the challenging SBMnet dataset, especially for short videos with low frame rates, and is also fast, reaching an average of 52 fps on this dataset when parameterized for maximal accuracy using GPU acceleration and a Python implementation.

scholarly journals Electronic Orientation Aid for Visually Impaired using Graphics Processing Unit (GPU)

2020 ◽  
Vol 32 ◽  
pp. 03054
Author(s):  
Akshata Parab ◽  
Rashmi Nagare ◽  
Omkar Kolambekar ◽  
Parag Patil
Keyword(s):  
Real Time ◽  
Visually Impaired ◽  
Graphics Processing Unit ◽  
Human Perception ◽  
Processing Unit ◽  
Surrounding Environment ◽  
Visually Impaired People ◽  
Impaired People ◽  
Definition Of ◽  
Graphics Processing

Vision is one of the very essential human senses and it plays a major role in human perception about surrounding environment. But for people with visual impairment their definition of vision is different. Visually impaired people are often unaware of dangers in front of them, even in familiar environment. This study proposes a real time guiding system for visually impaired people for solving their navigation problem and to travel without any difficulty. This system will help the visually impaired people by detecting the objects and giving necessary information about that object. This information may include what the object is, its location, its precision, distance from the visually impaired etc. All these information will be conveyed to the person through audio commands so that they can navigate freely anywhere anytime with no or minimal assistance. Object detection is done using You Only Look Once (YOLO) algorithm. As the process of capturing the video/images and sending it to the main module has to be carried at greater speed, Graphics Processing Unit (GPU) is used. This will help in enhancing the overall speed of the system and will help the visually Impaired to get the maximum necessary instructions as quickly as possible. The process starts from capturing the real time video, sending it for analysis and processing and get the calculated results. The results obtained from analysis are conveyed to user by means of hearing aid. As a result by this system the blind or the visually impaired people can visualize the surrounding environment and travel freely from source to destination on their own.

Clearance Measurement of 3D Objects Using Accessibility Cone

2019 ◽  
Author(s):  
Masatomo Inui ◽  
Kouhei Nishimiya ◽  
Nobuyuki Umezu
Keyword(s):  
Graphics Processing Unit ◽  
Three Dimensional ◽  
Computation Time ◽  
Depth Information ◽  
Computation Method ◽  
Processing Unit ◽  
Three Dimensional Objects ◽  
Mechanical Products ◽  
Definition Of ◽  
Graphics Processing

Abstract Clearance is a basic parameter in the design of mechanical products, generally specified as the distance between two shape elements, for example, the width of a slot. This definition is unsuitable for evaluating the clearance during assembly or manufacturing tasks, where the depth information is also critical. In this paper, we propose a novel definition of clearance for the surface of three-dimensional objects. Unlike the typical methods used to define clearance, the proposed method can simultaneously handle the relationship between the width and depth in the clearance, and thus, obtain an intuitive understanding regarding the assembly and manufacturing capability of a product. Our definition is based on the accessibility cone of a point on the object’s surface; further, the peak angle of the accessibility cone corresponds to the clearance at this point. A computation method of the clearance is presented and the results of its application are demonstrated. Our method uses the rendering function of a graphics processing unit to compute the clearance. A large computation time necessary for the analysis is considered as a problem regarding the practical use of this clearance definition.

Fast iterative solvers for large compressed-sparse row linear systems on graphics processing unit

2015 ◽  
Vol 10 (1) ◽  
pp. 3-18 ◽  
Author(s):  
Frédéric Magoulès ◽  
Abal-Kassim Cheik Ahamed ◽  
Roman Putanowicz
Keyword(s):  
Linear Systems ◽  
Graphics Processing Unit ◽  
Iterative Solvers ◽  
Processing Unit ◽  
Compressed Sparse Row ◽  
Graphics Processing

Implementing wide baseline matching algorithms on a graphics processing unit.

2007 ◽  
Author(s):  
Fredrick H. Rothganger ◽  
Kurt W. Larson ◽  
Antonio Ignacio Gonzales ◽  
Daniel S. Myers
Keyword(s):  
Graphics Processing Unit ◽  
Processing Unit ◽  
Wide Baseline Matching ◽  
Graphics Processing

scholarly journals Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback?

2021 ◽  
Vol 22 (10) ◽  
pp. 5212
Author(s):  
Andrzej Bak
Keyword(s):  
Molecular Conformation ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Diverse Range ◽  
Current State ◽  
Gpu Clusters ◽  
Pharmacophore Hypothesis ◽  
Rising Power ◽  
Graphics Processing ◽  
Ligand Conformation

A key question confronting computational chemists concerns the preferable ligand geometry that fits complementarily into the receptor pocket. Typically, the postulated ‘bioactive’ 3D ligand conformation is constructed as a ‘sophisticated guess’ (unnecessarily geometry-optimized) mirroring the pharmacophore hypothesis—sometimes based on an erroneous prerequisite. Hence, 4D-QSAR scheme and its ‘dialects’ have been practically implemented as higher level of model abstraction that allows the examination of the multiple molecular conformation, orientation and protonation representation, respectively. Nearly a quarter of a century has passed since the eminent work of Hopfinger appeared on the stage; therefore the natural question occurs whether 4D-QSAR approach is still appealing to the scientific community? With no intention to be comprehensive, a review of the current state of art in the field of receptor-independent (RI) and receptor-dependent (RD) 4D-QSAR methodology is provided with a brief examination of the ‘mainstream’ algorithms. In fact, a myriad of 4D-QSAR methods have been implemented and applied practically for a diverse range of molecules. It seems that, 4D-QSAR approach has been experiencing a promising renaissance of interests that might be fuelled by the rising power of the graphics processing unit (GPU) clusters applied to full-atom MD-based simulations of the protein-ligand complexes.

Parallelization of Global Sequence Alignment on Graphics Processing Unit

2020 ◽  
Author(s):  
Kailash W. Kalare ◽  
Mohammad S. Obaidat ◽  
Jitendra V. Tembhurne ◽  
Chandrashekhar Meshram ◽  
Kuei-Fang Hsiao
Keyword(s):  
Sequence Alignment ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Graphics Processing

Real-time, High-resolution Depth Upsampling on Embedded Accelerators

2021 ◽  
Vol 20 (3) ◽  
pp. 1-22
Author(s):  
David Langerman ◽  
Alan George
Keyword(s):  
High Resolution ◽  
Low Power ◽  
Real Time ◽  
Mixed Reality ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Reconfigurable Logic ◽  
Depth Sensors ◽  
Time Requirements ◽  
Graphics Processing

High-resolution, low-latency apps in computer vision are ubiquitous in today’s world of mixed-reality devices. These innovations provide a platform that can leverage the improving technology of depth sensors and embedded accelerators to enable higher-resolution, lower-latency processing for 3D scenes using depth-upsampling algorithms. This research demonstrates that filter-based upsampling algorithms are feasible for mixed-reality apps using low-power hardware accelerators. The authors parallelized and evaluated a depth-upsampling algorithm on two different devices: a reconfigurable-logic FPGA embedded within a low-power SoC; and a fixed-logic embedded graphics processing unit. We demonstrate that both accelerators can meet the real-time requirements of 11 ms latency for mixed-reality apps. 1

Sign in / Sign up

Export Citation Format

Share Document