Image matting atoc
Download as PPTX, PDF0 likes156 views
Image matting is the process of separating the foreground and background of an image by assigning each pixel an alpha value between 0 and 1 indicating its transparency. Traditionally, matting uses a trimap to classify pixels as foreground, background, or uncertain. Early sampling-based methods calculated alpha values based on feature distances of closest foreground and background pixels. More recent approaches use deep learning, where the first deep learning matting method in 2016 took local and non-local information as input, and the 2017 Deep Image Matting method used an RGB image and trimap as input in a fully deep learning framework.
1 of 12
Download to read offline
![Image Matting is
Image foreground background
Only deciding alpha! [0,1]](https://image.slidesharecdn.com/imagemattingatocr-190314094527/85/Image-matting-atoc-3-320.jpg)
![Image Matting is
Image foreground background
Only deciding alpha! [0,1]](https://image.slidesharecdn.com/imagemattingatocr-190314094527/85/Image-matting-atoc-4-320.jpg)
Recommended
Advanced Image Processing: Project 1
Advanced Image Processing: Project 1JosephChang
油
This document discusses several image processing techniques including HDR radiance mapping, panorama image projection correction using cylindrical projection, a dolly zoom effect simulation, and deblurring techniques. It also covers implementing exposure fusion on Android by porting code from MATLAB to C, Java, and the Android SDK to fuse multiple exposures based on pixel weightings of saturation, contrast, and exposure level. Multi-resolution fusion and fusion with flash images are also discussed.Demystifying laser projection for cinema: 5 frequently asked questions, 125+ ...
Demystifying laser projection for cinema: 5 frequently asked questions, 125+ ...Barco
油
Presentation held by Tom Bert, Senior Product Manager Digital Cinema at Barco, on the occasion of the Big Screen Experience Program during the IBC tradeshow and conference in Amsterdam in September 2016. The program allowed for cinema professionals to explore the latest developments in the industry. From STC (Stereo Camera onboard on Bepi Colombo ESA Mission) to Blender
From STC (Stereo Camera onboard on Bepi Colombo ESA Mission) to BlenderEmanuele Simioni
油
- The document discusses the STC (Stereo Camera) instrument onboard the BepiColombo mission to Mercury.
- STC will provide global stereo mapping and digital terrain model reconstruction of Mercury's surface with a spatial resolution of 58.2 m/pixel at periherm.
- The instrument uses a push-frame design with two optical units to achieve a stereo angle of 賊20属 to provide stereo imagery for photogrammetric analysis.
- Calibration and validation efforts show the instrument is capable of generating DTMs with a vertical accuracy of 73 micrometers from test data, corresponding to 36 meters on Mercury's surface.study Active Refocusing Of Images And Videos
study Active Refocusing Of Images And VideosChiamin Hsu
油
This document summarizes a study on active refocusing of images and videos using a single-image depth estimation technique. It presents an active illumination method using projected dot patterns to estimate depth. Camera images of projected dots focused at different depths are analyzed to compute a dense depth map. Segmentation and dot removal are used to complete the depth map. Realistic refocusing is achieved by applying aperture effects based on the depth map while handling partial occlusions. The method produces high resolution refocused images and videos without hardware modifications. Limitations include issues with translucent objects and strong sunlight. Future work involves incorporating the technique into digital cameras.Hable John Uncharted2 Hdr Lighting
Hable John Uncharted2 Hdr Lightingozlael ozlael
油
This document discusses techniques for lighting and tonemapping in 3D graphics to better simulate the human visual system. It covers gamma correction, which accounts for how monitors display light intensities non-linearly. It also discusses filmic tonemapping, which produces crisp blacks, saturated dark tones, and soft highlights similar to film, by applying a tone curve modeled after photographic film. This provides advantages over other tonemapping operators like Reinhard for reproducing accurate colors across a high dynamic range.Beyond RGB: Raster Analytics with FME
Beyond RGB: Raster Analytics with FMESafe Software
油
This document discusses how FME can be used to analyze and manipulate the multiple bands that are delivered by satellite imagery providers. It provides examples of useful FME transformers for assembling images from bands, including changing radiance to reflectance, sorting and combining bands, pansharpening, and applying multipliers. It also demonstrates how FME can be used to calculate the Normalized Difference Vegetation Index (NDVI) from satellite imagery bands to analyze vegetation health.舒从亳仄 舒从仂于从亳亶 "Baking Normal Map: 亟仂磪舒仄 磻仂" GameDev Conference 2018
舒从亳仄 舒从仂于从亳亶 "Baking Normal Map: 亟仂磪舒仄 磻仂" GameDev Conference 2018Lviv Startup Club
油
This document summarizes the process for baking normal maps, including:
1. Preparing high-poly and low-poly 3D models, addressing common issues like bevels, slopes, and floating geometry.
2. Creating UVs for the low-poly model and configuring texture resolution.
3. Baking normal maps in software like Maya or Substance Painter to transfer detail from high-poly to low-poly, and addressing bugs.Stochastic Screen-Space Reflections
Stochastic Screen-Space ReflectionsElectronic Arts / DICE
油
Talks by Tomasz Stachowiak at Electronic Arts for SIGGRAPH 2015 in the Advances in Real-time Rendering course.
What is Texture.pdf
What is Texture.pdfDong Heon Cho
油
The document discusses texture analysis in computer vision. It begins by asking what texture is and whether objects themselves can be considered textures. It then outlines several statistical and Fourier approaches to texture analysis, citing specific papers on texture energy measures, texton theory, and using textons to model materials. Deep convolutional neural networks are also discussed as being able to recognize and describe texture through learned filter banks. The concept of texels is introduced as low-level features that make up texture at different scales from edges to shapes. The document hypothesizes that CNNs are sensitive to texture because texture repeats across images while object shapes do not, and that CNNs act as texture mappers rather than template matchers. It also questions whether primary visual cortexBADGE
BADGEDong Heon Cho
油
This document discusses active learning techniques called Deep Badge Active Learning. It proposes using gradient embeddings to represent samples and k-means++ initialization for sample selection. Specifically, it uses the gradient embedding for feature representation, then performs k-means++ initialization to select samples by finding those with the maximum 2-norm and those farthest from existing samples, adding them to the set iteratively. This aims to select a diverse set of samples, similar to how binary search works. The technique could improve over entropy-based and core-set selection approaches for active learning with convolutional neural networks.Neural Radiance Field
Neural Radiance FieldDong Heon Cho
油
Neural Radiance Fields (NeRF) represent scenes as neural networks that map 5D input (3D position and 2D viewing direction) to a 4D output (RGB color and opacity). NeRF uses an MLP that is trained to predict volumetric density and color for a scene from many camera views. Key aspects of NeRF include using positional encodings as inputs to help model view-dependent effects, and training to optimize for integrated color and density values along camera rays. NeRF has enabled novel applications beyond novel view synthesis, including pose estimation, dense descriptors, and self-supervised segmentation.2020 > Self supervised learning
2020 > Self supervised learningDong Heon Cho
油
Self Supervised Learning after 2020
SimCLR, MOCO, BYOL and etc
Start from SimCLRAll about that pooling
All about that poolingDong Heon Cho
油
The document discusses various pooling operations used in image processing and convolutional neural networks (CNNs). It provides an overview of common pooling methods like max pooling, average pooling, and spatial pyramid pooling. It also discusses more advanced and trainable pooling techniques like stochastic pooling, mixed/gated pooling, fractional pooling, local importance pooling, and global feature guided local pooling. The document analyzes the tradeoffs of different pooling methods and how they can balance preserving details versus achieving invariance to changes in position or lighting. It references several influential papers that analyzed properties of pooling operations.Background elimination review
Background elimination reviewDong Heon Cho
油
This document discusses background elimination techniques which involve three main steps: object detection to select the target, segmentation to isolate the target from the background, and refinement to improve the quality of the segmented mask. It provides an overview of approaches that have been used for each step, including early methods based on SVM and more recent deep learning-based techniques like Mask R-CNN that integrate detection and segmentation. The document also notes that segmentation is challenging without object detection cues and discusses types of segmentation as well as refinement methods that use transformations, dimension reduction, and graph-based modeling.Transparent Latent GAN
Transparent Latent GANDong Heon Cho
油
1. TL-GAN matches feature axes in the latent space to generate images without fine-tuning the neural network.
2. It discovers correlations between the latent vector Z and image labels by applying multivariate linear regression and normalizing the coefficients.
3. The vectors are then adjusted to be orthogonal, allowing different properties to be matched while labeling unlabeled data to add descriptions.Multi object Deep reinforcement learning
Multi object Deep reinforcement learningDong Heon Cho
油
This document discusses multi-objective reinforcement learning and introduces Deep OLS Learning, which combines multi-objective learning with deep Q-networks. It presents Deep OLS Learning with Partial Reuse and Full Reuse to handle multi-objective Markov decision processes by finding a convergence set of policies that optimize multiple conflicting objectives, such as maximizing server performance while minimizing power consumption. The approach is evaluated on multi-objective versions of mountain car and deep sea treasure problems.Multi agent reinforcement learning for sequential social dilemmas
Multi agent reinforcement learning for sequential social dilemmasDong Heon Cho
油
This document summarizes research on multi-agent reinforcement learning in sequential social dilemmas. It discusses how sequential social dilemmas extend traditional matrix games by adding temporal aspects like partial observability. Simulation experiments are described where agents learn cooperative or defective policies for tasks like fruit gathering and wolfpack hunting in a partially observable environment. The agents' learned policies are then used to construct an empirical payoff matrix to analyze whether cooperation or defection is rewarded more, relating the multi-agent reinforcement learning results back to classic social dilemmas.Multi agent System
Multi agent SystemDong Heon Cho
油
This document discusses multi-agent systems and their applications. It provides examples of multi-agent systems for spacecraft control, manufacturing scheduling, and more. Key points:
- Multi-agent systems consist of interacting intelligent agents that can cooperate, coordinate, and negotiate to achieve goals. They offer benefits like robustness, scalability, and reusability.
- Challenges include defining global goals from local actions and incentivizing cooperation. Games like the prisoner's dilemma model social dilemmas around cooperation versus defection.
- The document outlines architectures like the blackboard model and BDI (belief-desire-intention) model. It also provides a manufacturing example using the JADE platform.Hybrid reward architecture
Hybrid reward architectureDong Heon Cho
油
The document discusses Hybrid Reward Architecture (HRA), a reinforcement learning method that decomposes the reward function of an environment into multiple sub-reward functions. In HRA, each sub-reward function is learned by a separate agent using DQN. This allows HRA to learn complex reward functions more quickly and stably compared to using a single reward signal. An experiment is described where HRA learns to eat 5 randomly placed fruits in an environment over 300 steps more effectively than a standard DQN agent.Use Jupyter notebook guide in 5 minutes
Use Jupyter notebook guide in 5 minutesDong Heon Cho
油
Tutorial of Jupyter notebook in a fast way.
Introduce what is notebook and simple shortcuts
- command mode, edit modeAlexNet and so on...
AlexNet and so on...Dong Heon Cho
油
AlexNet 2012 Contributions and Structure with Simple keras Code.
Multi GPU, Pooling, Data Augmentation...Deep Learning AtoC with Image Perspective
Deep Learning AtoC with Image PerspectiveDong Heon Cho
油
Deep learning models like CNNs, RNNs, and GANs are widely used for image classification and computer vision tasks. CNNs are commonly used for tasks like classification, detection, segmentation through learning hierarchical image features. Fully convolutional networks with encoder-decoder architectures like SegNet and Mask R-CNN can perform pixel-level semantic segmentation and instance segmentation by combining classification and bounding box detection. Deep learning has achieved state-of-the-art performance on many image applications due to its ability to learn powerful visual representations from large datasets.LOL win prediction
LOL win predictionDong Heon Cho
油
League of legend win prediction on master 1 tier.
A pipeline of data analytics and how to improve results.How can we train with few data
How can we train with few dataDong Heon Cho
油
The document discusses approaches for using deep learning with small datasets, including transfer learning techniques like fine-tuning pre-trained models, multi-task learning, and metric learning approaches for few-shot and zero-shot learning problems. It also covers domain adaptation techniques when labels are not available, as well as anomaly detection for skewed label distributions. Traditional models like SVM are suggested as initial approaches, with deep learning techniques applied if those are not satisfactory.Domain adaptation gan
Domain adaptation ganDong Heon Cho
油
The document discusses domain adaptation and transfer learning techniques in deep learning such as feature extraction, fine tuning, and parameter sharing. It specifically describes domain-adversarial neural networks which aim to make the source and target feature distributions indistinguishable and domain separation networks which extract domain-invariant and private features to model each domain separately.Dense sparse-dense training for dnn and Other Models
Dense sparse-dense training for dnn and Other ModelsDong Heon Cho
油
Deep Learning Training methods & Squeezing methodsSqueeeze models
Squeeeze modelsDong Heon Cho
油
This document discusses various techniques for compressing and speeding up deep neural networks, including singular value decomposition, pruning, and SqueezeNet. Singular value decomposition can be used to compress fully connected layers by minimizing the difference between the original weight matrix and its low-rank approximation. Pruning techniques remove unimportant weights below a threshold. SqueezeNet is highlighted as designing a small CNN architecture from the start that achieves AlexNet-level accuracy with 50x fewer parameters and less than 0.5MB in size.How to Make an RFID Door Lock System using Arduino
How to Make an RFID Door Lock System using ArduinoCircuitDigest
油
Learn how to build an RFID-based door lock system using Arduino to enhance security with contactless access control.Taykon-Kalite belgeleri
Taykon-Kalite belgeleriTAYKON
油
Kalite Politikam脹z
Taykon elik i巽in kalite, hayallerinizi bizlerle paylat脹脹n脹z an balar. Proje 巽iziminden detaylar脹n 巽旦z端m端ne, detaylar脹n 巽旦z端m端nden 端retime, 端retimden montaja, montajdan teslime hayallerinizin ger巽ekletiini g旦rd端端n端z ana kadar ge巽en t端m aamalar脹, 巽al脹anlar脹, t端m teknik donan脹m ve 巽evreyi i巽ine al脹r KAL聴TE.More Related Content
More from Dong Heon Cho (20)
What is Texture.pdf
What is Texture.pdfDong Heon Cho
油
The document discusses texture analysis in computer vision. It begins by asking what texture is and whether objects themselves can be considered textures. It then outlines several statistical and Fourier approaches to texture analysis, citing specific papers on texture energy measures, texton theory, and using textons to model materials. Deep convolutional neural networks are also discussed as being able to recognize and describe texture through learned filter banks. The concept of texels is introduced as low-level features that make up texture at different scales from edges to shapes. The document hypothesizes that CNNs are sensitive to texture because texture repeats across images while object shapes do not, and that CNNs act as texture mappers rather than template matchers. It also questions whether primary visual cortexBADGE
BADGEDong Heon Cho
油
This document discusses active learning techniques called Deep Badge Active Learning. It proposes using gradient embeddings to represent samples and k-means++ initialization for sample selection. Specifically, it uses the gradient embedding for feature representation, then performs k-means++ initialization to select samples by finding those with the maximum 2-norm and those farthest from existing samples, adding them to the set iteratively. This aims to select a diverse set of samples, similar to how binary search works. The technique could improve over entropy-based and core-set selection approaches for active learning with convolutional neural networks.Neural Radiance Field
Neural Radiance FieldDong Heon Cho
油
Neural Radiance Fields (NeRF) represent scenes as neural networks that map 5D input (3D position and 2D viewing direction) to a 4D output (RGB color and opacity). NeRF uses an MLP that is trained to predict volumetric density and color for a scene from many camera views. Key aspects of NeRF include using positional encodings as inputs to help model view-dependent effects, and training to optimize for integrated color and density values along camera rays. NeRF has enabled novel applications beyond novel view synthesis, including pose estimation, dense descriptors, and self-supervised segmentation.2020 > Self supervised learning
2020 > Self supervised learningDong Heon Cho
油
Self Supervised Learning after 2020
SimCLR, MOCO, BYOL and etc
Start from SimCLRAll about that pooling
All about that poolingDong Heon Cho
油
The document discusses various pooling operations used in image processing and convolutional neural networks (CNNs). It provides an overview of common pooling methods like max pooling, average pooling, and spatial pyramid pooling. It also discusses more advanced and trainable pooling techniques like stochastic pooling, mixed/gated pooling, fractional pooling, local importance pooling, and global feature guided local pooling. The document analyzes the tradeoffs of different pooling methods and how they can balance preserving details versus achieving invariance to changes in position or lighting. It references several influential papers that analyzed properties of pooling operations.Background elimination review
Background elimination reviewDong Heon Cho
油
This document discusses background elimination techniques which involve three main steps: object detection to select the target, segmentation to isolate the target from the background, and refinement to improve the quality of the segmented mask. It provides an overview of approaches that have been used for each step, including early methods based on SVM and more recent deep learning-based techniques like Mask R-CNN that integrate detection and segmentation. The document also notes that segmentation is challenging without object detection cues and discusses types of segmentation as well as refinement methods that use transformations, dimension reduction, and graph-based modeling.Transparent Latent GAN
Transparent Latent GANDong Heon Cho
油
1. TL-GAN matches feature axes in the latent space to generate images without fine-tuning the neural network.
2. It discovers correlations between the latent vector Z and image labels by applying multivariate linear regression and normalizing the coefficients.
3. The vectors are then adjusted to be orthogonal, allowing different properties to be matched while labeling unlabeled data to add descriptions.Multi object Deep reinforcement learning
Multi object Deep reinforcement learningDong Heon Cho
油
This document discusses multi-objective reinforcement learning and introduces Deep OLS Learning, which combines multi-objective learning with deep Q-networks. It presents Deep OLS Learning with Partial Reuse and Full Reuse to handle multi-objective Markov decision processes by finding a convergence set of policies that optimize multiple conflicting objectives, such as maximizing server performance while minimizing power consumption. The approach is evaluated on multi-objective versions of mountain car and deep sea treasure problems.Multi agent reinforcement learning for sequential social dilemmas
Multi agent reinforcement learning for sequential social dilemmasDong Heon Cho
油
This document summarizes research on multi-agent reinforcement learning in sequential social dilemmas. It discusses how sequential social dilemmas extend traditional matrix games by adding temporal aspects like partial observability. Simulation experiments are described where agents learn cooperative or defective policies for tasks like fruit gathering and wolfpack hunting in a partially observable environment. The agents' learned policies are then used to construct an empirical payoff matrix to analyze whether cooperation or defection is rewarded more, relating the multi-agent reinforcement learning results back to classic social dilemmas.Multi agent System
Multi agent SystemDong Heon Cho
油
This document discusses multi-agent systems and their applications. It provides examples of multi-agent systems for spacecraft control, manufacturing scheduling, and more. Key points:
- Multi-agent systems consist of interacting intelligent agents that can cooperate, coordinate, and negotiate to achieve goals. They offer benefits like robustness, scalability, and reusability.
- Challenges include defining global goals from local actions and incentivizing cooperation. Games like the prisoner's dilemma model social dilemmas around cooperation versus defection.
- The document outlines architectures like the blackboard model and BDI (belief-desire-intention) model. It also provides a manufacturing example using the JADE platform.Hybrid reward architecture
Hybrid reward architectureDong Heon Cho
油
The document discusses Hybrid Reward Architecture (HRA), a reinforcement learning method that decomposes the reward function of an environment into multiple sub-reward functions. In HRA, each sub-reward function is learned by a separate agent using DQN. This allows HRA to learn complex reward functions more quickly and stably compared to using a single reward signal. An experiment is described where HRA learns to eat 5 randomly placed fruits in an environment over 300 steps more effectively than a standard DQN agent.Use Jupyter notebook guide in 5 minutes
Use Jupyter notebook guide in 5 minutesDong Heon Cho
油
Tutorial of Jupyter notebook in a fast way.
Introduce what is notebook and simple shortcuts
- command mode, edit modeAlexNet and so on...
AlexNet and so on...Dong Heon Cho
油
AlexNet 2012 Contributions and Structure with Simple keras Code.
Multi GPU, Pooling, Data Augmentation...Deep Learning AtoC with Image Perspective
Deep Learning AtoC with Image PerspectiveDong Heon Cho
油
Deep learning models like CNNs, RNNs, and GANs are widely used for image classification and computer vision tasks. CNNs are commonly used for tasks like classification, detection, segmentation through learning hierarchical image features. Fully convolutional networks with encoder-decoder architectures like SegNet and Mask R-CNN can perform pixel-level semantic segmentation and instance segmentation by combining classification and bounding box detection. Deep learning has achieved state-of-the-art performance on many image applications due to its ability to learn powerful visual representations from large datasets.LOL win prediction
LOL win predictionDong Heon Cho
油
League of legend win prediction on master 1 tier.
A pipeline of data analytics and how to improve results.How can we train with few data
How can we train with few dataDong Heon Cho
油
The document discusses approaches for using deep learning with small datasets, including transfer learning techniques like fine-tuning pre-trained models, multi-task learning, and metric learning approaches for few-shot and zero-shot learning problems. It also covers domain adaptation techniques when labels are not available, as well as anomaly detection for skewed label distributions. Traditional models like SVM are suggested as initial approaches, with deep learning techniques applied if those are not satisfactory.Domain adaptation gan
Domain adaptation ganDong Heon Cho
油
The document discusses domain adaptation and transfer learning techniques in deep learning such as feature extraction, fine tuning, and parameter sharing. It specifically describes domain-adversarial neural networks which aim to make the source and target feature distributions indistinguishable and domain separation networks which extract domain-invariant and private features to model each domain separately.Dense sparse-dense training for dnn and Other Models
Dense sparse-dense training for dnn and Other ModelsDong Heon Cho
油
Deep Learning Training methods & Squeezing methodsSqueeeze models
Squeeeze modelsDong Heon Cho
油
This document discusses various techniques for compressing and speeding up deep neural networks, including singular value decomposition, pruning, and SqueezeNet. Singular value decomposition can be used to compress fully connected layers by minimizing the difference between the original weight matrix and its low-rank approximation. Pruning techniques remove unimportant weights below a threshold. SqueezeNet is highlighted as designing a small CNN architecture from the start that achieves AlexNet-level accuracy with 50x fewer parameters and less than 0.5MB in size.Recently uploaded (20)
How to Make an RFID Door Lock System using Arduino
How to Make an RFID Door Lock System using ArduinoCircuitDigest
油
Learn how to build an RFID-based door lock system using Arduino to enhance security with contactless access control.Taykon-Kalite belgeleri
Taykon-Kalite belgeleriTAYKON
油
Kalite Politikam脹z
Taykon elik i巽in kalite, hayallerinizi bizlerle paylat脹脹n脹z an balar. Proje 巽iziminden detaylar脹n 巽旦z端m端ne, detaylar脹n 巽旦z端m端nden 端retime, 端retimden montaja, montajdan teslime hayallerinizin ger巽ekletiini g旦rd端端n端z ana kadar ge巽en t端m aamalar脹, 巽al脹anlar脹, t端m teknik donan脹m ve 巽evreyi i巽ine al脹r KAL聴TE.
Introduction to Safety, Health & Environment
Introduction to Safety, Health & Environmentssuserc606c7
油
Introduction to Safety, Health &EnvironmentBest KNow Hydrogen Fuel Production in the World The cost in USD kwh for H2
Best KNow Hydrogen Fuel Production in the World The cost in USD kwh for H2Daniel Donatelli
油
The cost in USD/kwh for H2
Daniel Donatelli
Secure Supplies Group
Index
Introduction - Page 3
The Need for Hydrogen Fueling - Page 5
Pure H2 Fueling Technology - Page 7
Blend Gas Fueling: A Transition Strategy - Page 10
Performance Metrics: H2 vs. Fossil Fuels - Page 12
Cost Analysis and Economic Viability - Page 15
Innovations Driving Leadership - Page 18
Laminar Flame Speed Adjustment
Heat Management Systems
The Donatelli Cycle
Non-Carnot Cycle Applications
Case Studies and Real-World Applications - Page 22
Conclusion: Secure Supplies Leadership in Hydrogen Fueling - Page 27
Soil Properties and Methods of Determination
Soil Properties and Methods of DeterminationRajani Vyawahare
油
This PPT covers the index and engineering properties of soil. It includes details on index properties, along with their methods of determination. Various important terms related to soil behavior are explained in detail. The presentation also outlines the experimental procedures for determining soil properties such as water content, specific gravity, plastic limit, and liquid limit, along with the necessary calculations and graph plotting. Additionally, it provides insights to understand the importance of these properties in geotechnical engineering applications.Air pollution is contamination of the indoor or outdoor environment by any ch...
Air pollution is contamination of the indoor or outdoor environment by any ch...dhanashree78
油
Air pollution is contamination of the indoor or outdoor environment by any chemical, physical or biological agent that modifies the natural characteristics of the atmosphere.
Household combustion devices, motor vehicles, industrial facilities and forest fires are common sources of air pollution. Pollutants of major public health concern include particulate matter, carbon monoxide, ozone, nitrogen dioxide and sulfur dioxide. Outdoor and indoor air pollution cause respiratory and other diseases and are important sources of morbidity and mortality.
WHO data show that almost all of the global population (99%) breathe air that exceeds WHO guideline limits and contains high levels of pollutants, with low- and middle-income countries suffering from the highest exposures.
Air quality is closely linked to the earths climate and ecosystems globally. Many of the drivers of air pollution (i.e. combustion of fossil fuels) are also sources of greenhouse gas emissions. Policies to reduce air pollution, therefore, offer a win-win strategy for both climate and health, lowering the burden of disease attributable to air pollution, as well as contributing to the near- and long-term mitigation of climate change.
Engineering at Lovely Professional University (LPU).pdf
Engineering at Lovely Professional University (LPU).pdfSona
油
LPUs engineering programs provide students with the skills and knowledge to excel in the rapidly evolving tech industry, ensuring a bright and successful future. With world-class infrastructure, top-tier placements, and global exposure, LPU stands as a premier destination for aspiring engineers.Structural QA/QC Inspection in KRP 401600 | Copper Processing Plant-3 (MOF-3)...
Structural QA/QC Inspection in KRP 401600 | Copper Processing Plant-3 (MOF-3)...slayshadow705
油
This presentation provides an in-depth analysis of structural quality control in the KRP 401600 section of the Copper Processing Plant-3 (MOF-3) in Uzbekistan. As a Structural QA/QC Inspector, I have identified critical welding defects, alignment issues, bolting problems, and joint fit-up concerns.
Key topics covered:
Common Structural Defects Welding porosity, misalignment, bolting errors, and more.
Root Cause Analysis Understanding why these defects occur.
Corrective & Preventive Actions Effective solutions to improve quality.
Team Responsibilities Roles of supervisors, welders, fitters, and QC inspectors.
Inspection & Quality Control Enhancements Advanced techniques for defect detection.
Applicable Standards: GOST, KMK, SNK Ensuring compliance with international quality benchmarks.
This presentation is a must-watch for:
QA/QC Inspectors, Structural Engineers, Welding Inspectors, and Project Managers in the construction & oil & gas industries.
Professionals looking to improve quality control processes in large-scale industrial projects.
Download & share your thoughts! Let's discuss best practices for enhancing structural integrity in industrial projects.
Categories:
Engineering
Construction
Quality Control
Welding Inspection
Project Management
Tags:
#QAQC #StructuralInspection #WeldingDefects #BoltingIssues #ConstructionQuality #Engineering #GOSTStandards #WeldingInspection #QualityControl #ProjectManagement #MOF3 #CopperProcessing #StructuralEngineering #NDT #OilAndGas
Optimization of Cumulative Energy, Exergy Consumption and Environmental Life ...
Optimization of Cumulative Energy, Exergy Consumption and Environmental Life ...J. Agricultural Machinery
油
Optimal use of resources, including energy, is one of the most important principles in modern and sustainable agricultural systems. Exergy analysis and life cycle assessment were used to study the efficient use of inputs, energy consumption reduction, and various environmental effects in the corn production system in Lorestan province, Iran. The required data were collected from farmers in Lorestan province using random sampling. The Cobb-Douglas equation and data envelopment analysis were utilized for modeling and optimizing cumulative energy and exergy consumption (CEnC and CExC) and devising strategies to mitigate the environmental impacts of corn production. The Cobb-Douglas equation results revealed that electricity, diesel fuel, and N-fertilizer were the major contributors to CExC in the corn production system. According to the Data Envelopment Analysis (DEA) results, the average efficiency of all farms in terms of CExC was 94.7% in the CCR model and 97.8% in the BCC model. Furthermore, the results indicated that there was excessive consumption of inputs, particularly potassium and phosphate fertilizers. By adopting more suitable methods based on DEA of efficient farmers, it was possible to save 6.47, 10.42, 7.40, 13.32, 31.29, 3.25, and 6.78% in the exergy consumption of diesel fuel, electricity, machinery, chemical fertilizers, biocides, seeds, and irrigation, respectively. Multi objective genetic approach with Ranking
Multi objective genetic approach with Rankingnamisha18
油
Multi objective genetic approach with Ranking Lecture -3 Cold water supply system.pptx
Lecture -3 Cold water supply system.pptxrabiaatif2
油
The presentation on Cold Water Supply explored the fundamental principles of water distribution in buildings. It covered sources of cold water, including municipal supply, wells, and rainwater harvesting. Key components such as storage tanks, pipes, valves, and pumps were discussed for efficient water delivery. Various distribution systems, including direct and indirect supply methods, were analyzed for residential and commercial applications. The presentation emphasized water quality, pressure regulation, and contamination prevention. Common issues like pipe corrosion, leaks, and pressure drops were addressed along with maintenance strategies. Diagrams and case studies illustrated system layouts and best practices for optimal performance.
IPC-9716_2024 Requirements for Automated Optical Inspection (AOI) Process Con...
IPC-9716_2024 Requirements for Automated Optical Inspection (AOI) Process Con...ssuserd9338b
油
IPC-9716_2024 Requirements for Automated Optical Inspection (AOI) Process Control for Printed Board Assemblies.pdfMathematics behind machine learning INT255 INT255__Unit 3__PPT-1.pptx
Mathematics behind machine learning INT255 INT255__Unit 3__PPT-1.pptxppkmurthy2006
油
Mathematics behind machine learning INT255 
Optimization of Cumulative Energy, Exergy Consumption and Environmental Life ...
Optimization of Cumulative Energy, Exergy Consumption and Environmental Life ...J. Agricultural Machinery
油
Image matting atoc
- 1. NotKind Introduction for Image Matting Segmentation 手規
- 3. Image Matting is Image foreground background Only deciding alpha! [0,1]
- 4. Image Matting is Image foreground background Only deciding alpha! [0,1]
- 5. Matting is commonly with Trimap! For decide Foreground/Uncertain/Background
- 6. History of Matting A long time ago,
- 7. Approaches Sampling Based > Mishima Method 1993 patent Calculating closest F/B Distance for decide alpha : globally * Feature - First method of matting - Look feature globally - Developed for Blue screen matting
- 8. Approaches Sampling Based > Knockout software - not sure 1998? Calculating closest F/B Distance for decide alpha : locally sampled * Feature - First method of matting with Trimap
- 9. Jumping matting history Closed Form Matting 2006 - Locally Matting KNN Matting 2012 - nonLocal Matting
- 10. Approaches Deep Learning Age > DCNN Matting - 2016 Mix Local-Nonlocal information * Feature - First method of matting with Deep Learning Input is RGB + KNN Matting + Closed Form Matting
- 11. Approaches Deep Learning Age > Deep Image Matting - 2017 * Feature Fully Deep Learning Refinement Stage is for alternative Image Guide Filter Input is RGB + Trimap
- 12. Want more? http://people.inf.ethz.ch/aksoyy/ Meet Ya脹z Aksoy who starts from 2012 or https://www.ecse.rpi.edu/~rjradke/cvfxcourse. html kind lecture for matting