Query: Porosity  computer vision

title: Porosity Measurement of Iron Oxide by Using Computer Vision System
abstract: In this work, a proposed Computer Vision System (CVS) has been applied to measure the porosity of iron oxide (Fe3O4) specimens. Measurement of the porosity using CVS reduces time, effort and cost compared to the conventional methods. Fe3O4 was received from iron rolling process in the form of thin layer, and then converted to powder by grinding process and filtering by 200 mm sieve. Fe3O4 specimens were then manufactured by powder metallurgy (PM) technique using steel die subjected to a compression load (300 KN) to form cylindrical discs with 27 mm diameter and 10 mm height. Optical microscope with digital camera has been used in capturing images of the surfaces of Fe3O4 specimens. The proposed CVS uses k means algorithms, which divide the intended surfaces into distinct regions and by computing the ratio between these regions, the specimen porosity is obtained. Layers of the specimens are removed to measure the average porosity inside the specimens. The average value of porosity of specimens has been obtained for the original specimen and its mean value is ~26.39% In comparison, the porosity computed by CVS was very close to that obtained using conventional method, which confirms the efficiency of this technique.

title: Clay Based Brick Porosity Estimation Using Image Processing Techniques
abstract: This work intends to apprehend and emphasize the contribution of image processing techniques and computer vision in the treatment of clay based material known in Meknes region. One of the various characteristics used to describe clay in a qualitative manner is porosity, as it is considered one of the properties that with "kill or cure" effectiveness. For this purpose, we use scanning electron microscopy images, as they are considered the most powerful tool for characterising the quality of the microscopic pore structure of porous materials. We present various existing methods of segmentation, as we are interested only in pore regions. The results show good matching between physical estimation and Voronoi diagram based porosity estimation.

title: GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing
abstract: We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating) These toolkits provide state of the art pre trained models, training scripts, and training logs, to facilitate rapid prototyping and promote reproducible research. We also provide modular APIs with flexible building blocks to enable efficient customization. Leveraging the MXNet ecosystem, the deep learning models in GluonCV and GluonNLP can be deployed onto a variety of platforms with different programming languages. The Apache 2.0 license has been adopted by GluonCV and GluonNLP to allow for software distribution, modification, and usage.

title: Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
abstract: This work presents Kornia an open source computer vision library which consists of a set of differentiable routines and modules to solve generic computer vision problems. The package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse mode auto differentiation to define and compute the gradient of complex functions. Inspired by OpenCV, Kornia is composed of a set of modules containing operators that can be inserted inside neural networks to train models to perform image transformations, camera calibration, epipolar geometry, and low level image processing techniques, such as filtering and edge detection that operate directly on high dimensional tensor representations. Examples of classical vision problems implemented using our framework are provided including a benchmark comparing to existing vision libraries.

title: Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
abstract: While deep neural networks have become the go to approach in computer vision, the vast majority of these models fail to properly capture the uncertainty inherent in their predictions. Estimating this predictive uncertainty can be crucial, for example in automotive applications. In Bayesian deep learning, predictive uncertainty is commonly decomposed into the distinct types of aleatoric and epistemic uncertainty. The former can be estimated by letting a neural network output the parameters of a certain probability distribution. Epistemic uncertainty estimation is a more challenging problem, and while different scalable methods recently have emerged, no extensive comparison has been performed in a real world setting. We therefore accept this task and propose a comprehensive evaluation framework for scalable epistemic uncertainty estimation methods in deep learning. Our proposed framework is specifically designed to test the robustness required in real world computer vision applications. We also apply this framework to provide the first properly extensive and conclusive comparison of the two current state of the art scalable methods: ensembling and MC dropout. Our comparison demonstrates that ensembling consistently provides more reliable and practically useful uncertainty estimates. Code is available at https:/github.com/fregu856/evaluating_bdl.

title: Computer Vision for COVID 19 Control: A Survey
abstract: The COVID 19 pandemic has triggered an urgent need to contribute to the fight against an immense threat to the human population. Computer Vision, as a subfield of Artificial Intelligence, has enjoyed recent success in solving various complex problems in health care and has the potential to contribute to the fight of controlling COVID 19. In response to this call, computer vision researchers are putting their knowledge base at work to devise effective ways to counter COVID 19 challenge and serve the global community. New contributions are being shared with every passing day. It motivated us to review the recent work, collect information about available research resources and an indication of future research directions. We want to make it available to computer vision researchers to save precious time. This survey paper is intended to provide a preliminary review of the available literature on the computer vision efforts against COVID 19 pandemic.

title: Computer vision and machine learning enabled soybean root phenotyping pipeline
abstract: Root system architecture (RSA) traits are of interest for breeding selection; however, measurement of these traits is difficult, resource intensive, and results in large variability. The advent of computer vision and machine learning (ML) enabled trait extraction and measurement has renewed interest in utilizing RSA traits for genetic enhancement to develop more robust and resilient crop cultivars. We developed a mobile, low cost, and high resolution root phenotyping system composed of an imaging platform with computer vision and ML based segmentation approach to establish a seamless end to end pipeline from obtaining large quantities of root samples through image based trait processing and analysis.  This high throughput phenotyping system, which has the capacity to handle hundreds to thousands of plants, integrates time series image capture coupled with automated image processing that uses optical character recognition (OCR) to identify seedlings via barcode, followed by robust segmentation integrating convolutional auto encoder (CAE) method prior to feature extraction. The pipeline includes an updated and customized version of the Automatic Root Imaging Analysis (ARIA) root phenotyping software. Using this system, we studied diverse soybean accessions from a wide geographical distribution and report genetic variability for RSA traits, including root shape, length, number, mass, and angle.  This system provides a high throughput, cost effective, non destructive methodology that delivers biologically relevant time series data on root growth and development for phenomics, genomics, and plant breeding applications. This phenotyping platform is designed to quantify root traits and rank genotypes in a common environment thereby serving as a selection tool for use in plant breeding. Root phenotyping platforms and image based phenotyping are essential to mirror the current focus on shoot phenotyping in breeding efforts.

title: Applications of Computer Vision in Plant Pathology: A Survey
abstract: The real time decision support system can enhance the crop or plant growth, therefore, increasing their productivity, quality, and economic value. This also helps us in serving the nature by supervising the plant growth in balancing the environment. Computer vision techniques have proven to play an important role in the number of applications like medical, defense, agriculture, remote sensing, business analysis, etc. The use of digital image processing methods for simulating the visual capability of the human being has proven to be a dynamic feature in smart or precision agriculture. This concept has provided with the automatic preventing and monitoring of plants, cultivation, disease management, water management etc. to increase the crop productivity and quality. In this paper, we have surveyed the number of articles that adopt the concept of computer vision and soft computing methods for the identification and classification of diseases from the leaf of the plant. Our aim is to present the state of the art of the concepts, applications, and theories associated with the digital image processing and soft computing methodologies. The various outcomes have been discussed separately.

title: Deep Learning in Computer Vision
abstract: Deep learning algorithms have brought a revolution to the computer vision community by introducing non traditional and efficient solutions to several image related problems that had long remained u

title: Computer vision technology in agricultural automation A review
abstract: Computer vision is a field that involves making a machine "see" This technology uses a camera and computer instead of the human eye to identify, track and measure targets for further image processing. With the development of computer vision, such technology has been widely used in the field of agricultural automation and plays a key role in its development. This review systematically summarizes and analyzes the technologies and challenges over the past three years and explores future opportunities and prospects to form the latest reference for researchers. Through the analyses, it is found that the existing technology can help the development of agricultural automation for small field farming to achieve the advantages of low cost, high efficiency and high precision. However, there are still major challenges. First, the technology will continue to expand into new application areas in the future, and there will be more technological issues that need to be overcome. It is essential to build large scale data sets. Second, with the rapid development of agricultural automation, the demand for professionals will continue to grow. Finally, the robust performance of related technologies in various complex environments will also face challenges. Through analysis and discussion, we believe that in the future, computer vision technology will be combined with intelligent technology such as deep learning technology, be applied to every aspect of agricultural production management based on large scale datasets, be more widely used to solve the current agricultural problems, and better improve the economic, general and robust performance of agricultural automation systems, thus promoting the development of agricultural automation equipment and systems in a more intelligent direction.