ASP modules: cost-effective building blocks for real-time computer vision

1990 ◽  
Author(s):  
R. M. Lea ◽  
Argy Krikelis
Keyword(s):  
Computer Vision ◽  
Real Time ◽  
Cost Effective ◽  
Building Blocks

scholarly journals Cost-Effective CNNs for Real-Time Micro-Expression Recognition

2020 ◽  
Vol 10 (14) ◽  
pp. 4959
Author(s):  
Reda Belaiche ◽  
Yu Liu ◽  
Cyrille Migniot ◽  
Dominique Ginhac ◽  
Fan Yang
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Real Time ◽  
Video Clip ◽  
Cost Effective ◽  
Compact Representation ◽  
Expression Recognition ◽  
Memory Space ◽  
Computational Resources ◽  
Micro Expression

Micro-Expression (ME) recognition is a hot topic in computer vision as it presents a gateway to capture and understand daily human emotions. It is nonetheless a challenging problem due to ME typically being transient (lasting less than 200 ms) and subtle. Recent advances in machine learning enable new and effective methods to be adopted for solving diverse computer vision tasks. In particular, the use of deep learning techniques on large datasets outperforms classical approaches based on classical machine learning which rely on hand-crafted features. Even though available datasets for spontaneous ME are scarce and much smaller, using off-the-shelf Convolutional Neural Networks (CNNs) still demonstrates satisfactory classification results. However, these networks are intense in terms of memory consumption and computational resources. This poses great challenges when deploying CNN-based solutions in many applications, such as driver monitoring and comprehension recognition in virtual classrooms, which demand fast and accurate recognition. As these networks were initially designed for tasks of different domains, they are over-parameterized and need to be optimized for ME recognition. In this paper, we propose a new network based on the well-known ResNet18 which we optimized for ME classification in two ways. Firstly, we reduced the depth of the network by removing residual layers. Secondly, we introduced a more compact representation of optical flow used as input to the network. We present extensive experiments and demonstrate that the proposed network obtains accuracies comparable to the state-of-the-art methods while significantly reducing the necessary memory space. Our best classification accuracy was 60.17% on the challenging composite dataset containing five objectives classes. Our method takes only 24.6 ms for classifying a ME video clip (less than the occurrence time of the shortest ME which lasts 40 ms). Our CNN design is suitable for real-time embedded applications with limited memory and computing resources.

scholarly journals Real-Time Evaluation of Breast Self-Examination Using Computer Vision

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Eman Mohammadi ◽  
Elmer P. Dadios ◽  
Laurence A. Gan Lim ◽  
Melvin K. Cabatuan ◽  
Raouf N. G. Naguib ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Computer Vision ◽  
Real Time ◽  
Cost Effective ◽  
Breast Self Examination ◽  
Cost Effective Approach ◽  
Confirmatory Tests ◽  
Evaluation Algorithm ◽  
Vision Algorithm ◽  
Self Examination

Breast cancer is the most common cancer among women worldwide and breast self-examination (BSE) is considered as the most cost-effective approach for early breast cancer detection. The general objective of this paper is to design and develop a computer vision algorithm to evaluate the BSE performance in real-time. The first stage of the algorithm presents a method for detecting and tracking the nipples in frames while a woman performs BSE; the second stage presents a method for localizing the breast region and blocks of pixels related to palpation of the breast, and the third stage focuses on detecting the palpated blocks in the breast region. The palpated blocks are highlighted at the time of BSE performance. In a correct BSE performance, all blocks must be palpated, checked, and highlighted, respectively. If any abnormality, such as masses, is detected, then this must be reported to a doctor to confirm the presence of this abnormality and proceed to perform other confirmatory tests. The experimental results have shown that the BSE evaluation algorithm presented in this paper provides robust performance.

scholarly journals Computer Vision Applied to Detect Lethargy through Animal Motion Monitoring: A Trial on African Swine Fever in Wild Boar

Animals ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 2241 ◽  
Author(s):  
Eduardo Fernández-Carrión ◽  
Jose Ángel Barasona ◽  
Ángel Sánchez ◽  
Cristina Jurado ◽  
Estefanía Cadenas-Fernández ◽  
...  
Keyword(s):  
Computer Vision ◽  
Infectious Diseases ◽  
Wild Boar ◽  
Real Time ◽  
Disease Surveillance ◽  
Animal Health ◽  
Clinical Signs ◽  
African Swine Fever ◽  
Cost Effective ◽  
Experimental Trials

Early detection of infectious diseases is the most cost-effective strategy in disease surveillance for reducing the risk of outbreaks. Latest deep learning and computer vision improvements are powerful tools that potentially open up a new field of research in epidemiology and disease control. These techniques were used here to develop an algorithm aimed to track and compute animal motion in real time. This algorithm was used in experimental trials in order to assess African swine fever (ASF) infection course in Eurasian wild boar. Overall, the outcomes showed negative correlation between motion reduction and fever caused by ASF infection. In addition, infected animals computed significant lower movements compared to uninfected animals. The obtained results suggest that a motion monitoring system based on artificial vision may be used in indoors to trigger suspicions of fever. It would help farmers and animal health services to detect early clinical signs compatible with infectious diseases. This technology shows a promising non-intrusive, economic and real time solution in the livestock industry with especial interest in ASF, considering the current concern in the world pig industry.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

scholarly journals 2D-3D integration of hexagonal boron nitride and a high-κ dielectric for ultrafast graphene-based electro-absorption modulators

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hitesh Agarwal ◽  
Bernat Terrés ◽  
Lorenzo Orsini ◽  
Alberto Montanaro ◽  
Vito Sorianello ◽  
...  
Keyword(s):  
Boron Nitride ◽  
Hafnium Oxide ◽  
High Speed ◽  
Hexagonal Boron Nitride ◽  
Temperature Stability ◽  
Cost Effective ◽  
Fold Increase ◽  
Building Blocks ◽  
High Quality ◽  
New Device

AbstractElectro-absorption (EA) waveguide-coupled modulators are essential building blocks for on-chip optical communications. Compared to state-of-the-art silicon (Si) devices, graphene-based EA modulators promise smaller footprints, larger temperature stability, cost-effective integration and high speeds. However, combining high speed and large modulation efficiencies in a single graphene-based device has remained elusive so far. In this work, we overcome this fundamental trade-off by demonstrating the 2D-3D dielectric integration in a high-quality encapsulated graphene device. We integrated hafnium oxide (HfO2) and two-dimensional hexagonal boron nitride (hBN) within the insulating section of a double-layer (DL) graphene EA modulator. This combination of materials allows for a high-quality modulator device with high performances: a ~39 GHz bandwidth (BW) with a three-fold increase in modulation efficiency compared to previously reported high-speed modulators. This 2D-3D dielectric integration paves the way to a plethora of electronic and opto-electronic devices with enhanced performance and stability, while expanding the freedom for new device designs.

scholarly journals Cost-Effective Real-Time Metabolic Profiling of Cancer Cell Lines for Plate-Based Assays

Chemosensors ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 139
Author(s):  
Wiktoria Blaszczak ◽  
Zhengchu Tan ◽  
Pawel Swietach
Keyword(s):  
Real Time ◽  
Cell Lines ◽  
Acid Production ◽  
Metabolic Regulation ◽  
Cost Effective ◽  
Culture Conditions ◽  
Metabolic Inhibitors ◽  
Metabolic Phenotype ◽  
Ductal Adenocarcinoma ◽  
Oxygen Utilization

A fundamental phenotype of cancer cells is their metabolic profile, which is routinely described in terms of glycolytic and respiratory rates. Various devices and protocols have been designed to quantify glycolysis and respiration from the rates of acid production and oxygen utilization, respectively, but many of these approaches have limitations, including concerns about their cost-ineffectiveness, inadequate normalization procedures, or short probing time-frames. As a result, many methods for measuring metabolism are incompatible with cell culture conditions, particularly in the context of high-throughput applications. Here, we present a simple plate-based approach for real-time measurements of acid production and oxygen depletion under typical culture conditions that enable metabolic monitoring for extended periods of time. Using this approach, it is possible to calculate metabolic fluxes and, uniquely, describe the system at steady-state. By controlling the conditions with respect to pH buffering, O2 diffusion, medium volume, and cell numbers, our workflow can accurately describe the metabolic phenotype of cells in terms of molar fluxes. This direct measure of glycolysis and respiration is conducive for between-runs and even between-laboratory comparisons. To illustrate the utility of this approach, we characterize the phenotype of pancreatic ductal adenocarcinoma cell lines and measure their response to a switch of metabolic substrate and the presence of metabolic inhibitors. In summary, the method can deliver a robust appraisal of metabolism in cell lines, with applications in drug screening and in quantitative studies of metabolic regulation.

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