This document discusses texturing in XNA game development. It explains that textures are images applied to surfaces to make objects look more realistic. The document covers UV coordinates, vertex types for textures, loading and applying textures, texture tiling, transparency, and billboarding. It provides code examples for creating and drawing a textured quad and handling transparency using alpha blending.
Volume rendering 3D volume data (medical CT scans) in Unity3D.
Covering the following topics:
- Raymarching
- Maximum Intensity Projection
- Direct Volume Rendering with compositing
- Isosurface rendering
- Transfer functions
- 2D Transfer Functions
- Slice rendering
Source code here: https://github.com/mlavik1/UnityVolumeRendering
Object Shape Representation by Kernel Density Feature Points Estimator cscpconf
This paper introduces an object shape representation using Kernel Density Feature Points
Estimator (KDFPE). In this method we obtain the density of feature points within defined rings
around the centroid of the image. The Kernel Density Feature Points Estimator is then applied to
the vector of the image. KDFPE is invariant to translation, scale and rotation. This method of
image representation shows improved retrieval rate when compared to Density Histogram
Feature Points (DHFP) method. Analytic analysis is done to justify our method and we compared our results with object shape representation by the Density Histogram of Feature Points (DHFP) to prove its robustness.
Faire de la reconnaissance d'images avec le Deep Learning - Cristina & Pierre...Jedha Bootcamp
Reconnaissance de visages sur vos photos Facebook, détection de maladies via imagerie médicale, les applications de la reconnaissance d'images grâce à l'intelligence artificielle offrent de vastes possibilités. Lors de cet événement, Cristina & Pierre - Machine Learning Engineers chez Photobox - vous feront une démonstration des outils de reconnaissance d'images via ces algorithmes de Deep Learning.
In this paper a PDE based hybrid method for image denoising is introduced. The method is a bi-stage filter with anisotropic diffusion followed by wavelet based bayesian shrinkage. Here efficient denoising is achieved by reducing the convergence time of anisotropic diffusion.
Volume rendering 3D volume data (medical CT scans) in Unity3D.
Covering the following topics:
- Raymarching
- Maximum Intensity Projection
- Direct Volume Rendering with compositing
- Isosurface rendering
- Transfer functions
- 2D Transfer Functions
- Slice rendering
Source code here: https://github.com/mlavik1/UnityVolumeRendering
Object Shape Representation by Kernel Density Feature Points Estimator cscpconf
This paper introduces an object shape representation using Kernel Density Feature Points
Estimator (KDFPE). In this method we obtain the density of feature points within defined rings
around the centroid of the image. The Kernel Density Feature Points Estimator is then applied to
the vector of the image. KDFPE is invariant to translation, scale and rotation. This method of
image representation shows improved retrieval rate when compared to Density Histogram
Feature Points (DHFP) method. Analytic analysis is done to justify our method and we compared our results with object shape representation by the Density Histogram of Feature Points (DHFP) to prove its robustness.
Faire de la reconnaissance d'images avec le Deep Learning - Cristina & Pierre...Jedha Bootcamp
Reconnaissance de visages sur vos photos Facebook, détection de maladies via imagerie médicale, les applications de la reconnaissance d'images grâce à l'intelligence artificielle offrent de vastes possibilités. Lors de cet événement, Cristina & Pierre - Machine Learning Engineers chez Photobox - vous feront une démonstration des outils de reconnaissance d'images via ces algorithmes de Deep Learning.
In this paper a PDE based hybrid method for image denoising is introduced. The method is a bi-stage filter with anisotropic diffusion followed by wavelet based bayesian shrinkage. Here efficient denoising is achieved by reducing the convergence time of anisotropic diffusion.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Adaptive Median Filters
Elements of visual perception
Representing Digital Images
Spatial and Intensity Resolution
cones and rods
Brightness Adaptation
Spatial and Intensity Resolution
Image Acquisition and Representation
A Simple Image Formation Model
Image Sampling and Quantization
Image Interpolation
Image quantization
Nearest Neighbor Interpolation
This presentation contains concepts of different image restoration and reconstruction techniques used nowadays in the field of digital image processing. Slides are prepared from Gonzalez book and Pratt book.
Labview with dwt for denoising the blurred biometric imagesijcsa
In this paper, biometric blurred image (fingerprint) denoising are presented and investigated by using
LabVIEW applications , It is blurred and corrupted with Gaussian noise. This work is proposed
algorithm that has used a discrete wavelet transform (DWT) to divide the image into two parts, this will
be increasing the manipulation speed of biometric images that are of the big sizes. This work has included
two tasks ; the first designs the LabVIEW system to calculate and present the approximation coefficients,
by which the image's blur factor reduced to minimum value according to the proposed algorithm. The
second task removes the image's noise by calculated the regression coefficients according to Bayesian-
Shrinkage estimation method.
Tensorflow London 13: Zbigniew Wojna 'Deep Learning for Big Scale 2D Imagery'Seldon
Speaker: Zbigniew Wojna, Deep Learning Researcher and founder of TensorFlight.Inc
Title: Architectures for big scale 2D imagery
Abstract: Zbigniew will present research he conducted during his Ph.D. at University College London and in collaboration with Google. His primary interest lays in the development of neural architectures for 2D imagery problems in big scale. He will present the recently published analysis of different upsampling methods in the decoder part of visual architectures, together with last week ongoing extension for GANs. Will discuss attention mechanism for text recognition and review for what kind of application it can be useful (automatically updating Google Maps based on Google Street View imagery). He will explain the idea behind inception and change in Inception-v3 to have it the best single model on ImageNet 2015 and how does it compare to Resnet architecture which was published 2 weeks after. Together with inception, will present his winning submission to MS COCO 2016 detection challenge and the extensive analysis of different models and backbone architectures inside. At the end will shortly review UCL effort working with 4096x4096 images at The Digital Mammography DREAM Challenge for breast cancer recognition, where they achieved 9th among 1375 teams worldwide and 2nd place in the community phase.
Bio: Zbigniew Wojna is deep learning researcher and founder of TensorFlight Inc. company providing instant remote commercial property inspection (for risk factors for reinsurance enterprises) based on satellite and street view type imagery. Zbigniew is currently in the final stage of his Ph.D. (already with more than 1000 citations) at the University College London under the supervision of Professor Iasonas Kokkinos and professor John Shawe-Taylor. His primary interest lies in finding and solving research problems around 2D machine vision applications usually in big scale. Zbigniew in his Ph.D. career spent most of the time working across different groups in DeepMind, Google Research, and Facebook Research. It includes DeepMind Health Team, Deep Learning Team for Google Maps in collaboration with Google Brain, Machine Perception with Kevin Murphy, Weak Localization Team with Vittorio Ferrari and Facebook AI Research Lab in Paris. His company TensorFlight Inc. was featured as top 2 AI startups among few hundreds by InnovatorsRace50 and closed seed funding last year.
Thanks to all TensorFlow London meetup organisers and supporters:
Seldon.io
Altoros
Rewired
Google Developers
Rise London
[PDF] Automatic Image Co-segmentation Using Geometric Mean Saliency (Top 10% ...Koteswar Rao Jerripothula
Most existing high-performance co-segmentation algorithms are usually complicated due to the way of co-labelling a set of images and the requirement to handle quite a few parameters for effective co-segmentation. In this paper, instead of relying on the complex process of co-labelling multiple images, we perform segmentation on individual images but based on a combined saliency map that is obtained by fusing single-image saliency maps of a group of similar images. Particularly, a new multiple image based saliency map extraction, namely geometric mean saliency (GMS) method, is proposed to obtain the global saliency maps. In GMS, we transmit the saliency information among the images using the warping technique. Experiments show that our method is able to outperform state-of-the-art methods on three benchmark co-segmentation datasets.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Adaptive Median Filters
Elements of visual perception
Representing Digital Images
Spatial and Intensity Resolution
cones and rods
Brightness Adaptation
Spatial and Intensity Resolution
Image Acquisition and Representation
A Simple Image Formation Model
Image Sampling and Quantization
Image Interpolation
Image quantization
Nearest Neighbor Interpolation
This presentation contains concepts of different image restoration and reconstruction techniques used nowadays in the field of digital image processing. Slides are prepared from Gonzalez book and Pratt book.
Labview with dwt for denoising the blurred biometric imagesijcsa
In this paper, biometric blurred image (fingerprint) denoising are presented and investigated by using
LabVIEW applications , It is blurred and corrupted with Gaussian noise. This work is proposed
algorithm that has used a discrete wavelet transform (DWT) to divide the image into two parts, this will
be increasing the manipulation speed of biometric images that are of the big sizes. This work has included
two tasks ; the first designs the LabVIEW system to calculate and present the approximation coefficients,
by which the image's blur factor reduced to minimum value according to the proposed algorithm. The
second task removes the image's noise by calculated the regression coefficients according to Bayesian-
Shrinkage estimation method.
Tensorflow London 13: Zbigniew Wojna 'Deep Learning for Big Scale 2D Imagery'Seldon
Speaker: Zbigniew Wojna, Deep Learning Researcher and founder of TensorFlight.Inc
Title: Architectures for big scale 2D imagery
Abstract: Zbigniew will present research he conducted during his Ph.D. at University College London and in collaboration with Google. His primary interest lays in the development of neural architectures for 2D imagery problems in big scale. He will present the recently published analysis of different upsampling methods in the decoder part of visual architectures, together with last week ongoing extension for GANs. Will discuss attention mechanism for text recognition and review for what kind of application it can be useful (automatically updating Google Maps based on Google Street View imagery). He will explain the idea behind inception and change in Inception-v3 to have it the best single model on ImageNet 2015 and how does it compare to Resnet architecture which was published 2 weeks after. Together with inception, will present his winning submission to MS COCO 2016 detection challenge and the extensive analysis of different models and backbone architectures inside. At the end will shortly review UCL effort working with 4096x4096 images at The Digital Mammography DREAM Challenge for breast cancer recognition, where they achieved 9th among 1375 teams worldwide and 2nd place in the community phase.
Bio: Zbigniew Wojna is deep learning researcher and founder of TensorFlight Inc. company providing instant remote commercial property inspection (for risk factors for reinsurance enterprises) based on satellite and street view type imagery. Zbigniew is currently in the final stage of his Ph.D. (already with more than 1000 citations) at the University College London under the supervision of Professor Iasonas Kokkinos and professor John Shawe-Taylor. His primary interest lies in finding and solving research problems around 2D machine vision applications usually in big scale. Zbigniew in his Ph.D. career spent most of the time working across different groups in DeepMind, Google Research, and Facebook Research. It includes DeepMind Health Team, Deep Learning Team for Google Maps in collaboration with Google Brain, Machine Perception with Kevin Murphy, Weak Localization Team with Vittorio Ferrari and Facebook AI Research Lab in Paris. His company TensorFlight Inc. was featured as top 2 AI startups among few hundreds by InnovatorsRace50 and closed seed funding last year.
Thanks to all TensorFlow London meetup organisers and supporters:
Seldon.io
Altoros
Rewired
Google Developers
Rise London
[PDF] Automatic Image Co-segmentation Using Geometric Mean Saliency (Top 10% ...Koteswar Rao Jerripothula
Most existing high-performance co-segmentation algorithms are usually complicated due to the way of co-labelling a set of images and the requirement to handle quite a few parameters for effective co-segmentation. In this paper, instead of relying on the complex process of co-labelling multiple images, we perform segmentation on individual images but based on a combined saliency map that is obtained by fusing single-image saliency maps of a group of similar images. Particularly, a new multiple image based saliency map extraction, namely geometric mean saliency (GMS) method, is proposed to obtain the global saliency maps. In GMS, we transmit the saliency information among the images using the warping technique. Experiments show that our method is able to outperform state-of-the-art methods on three benchmark co-segmentation datasets.
This is the version of my 3D math talk that I used at CocoaConf Atlanta. This version includes the graphic representations of the different steps in implementing the shader.
Realtime Per Face Texture Mapping (PTEX)basisspace
This presentation shows the original method for implementing Per-Face Texture Mapping (PTEX) in real-time on commodity hardware. PTEX is used throughout the film industry to handle texture seams robustly while simultaneously easing artist workflow.
Benoit fouletier guillaume martin unity day- modern 2 d techniques-gce2014Mary Chan
Using lessons learned from working on AAA 2D games, a 4-strong indie team set out to create a complete pipeline for creating modern 2D games with an organic feel and a high level of polish... on indie-scale resources.
The tools and techniques developed to reach that ambitious goal will be presented, from the innovative animation system, the terrain, vegetation and level art system, to the effective but powerful rendering model, and more.
Intended audience & prerequisites: Anyone working on a 2D game: programmers, animators, level designers, level artists.
The talk will be of particular interest to teams using Unity, but rather than being purely technical, the talk will outline principles that can be applied in any engine.
Session takeaway: I believe 2D has a great future ahead of her, and that we can do much more with it. I intend to demonstrate how to improve the production pipeline, and invest in tools to become more technical the way 3D does, while retaining the unique advantages of 2D.
Game Credits:
Rayman Origins (Ubisoft Montpellier)
Rayman Legends (Ubisoft Montpellier)
Tetrobot and Co. (Swing Swing Submarine)
Seasons After Fall [working title] (Swing Swing Submarine)
The presentation covers three engine systems created for new Shadow Warrior: skinned decals, vegetation, and seawater rendering.
Subsequent presentation here:
https://www.slideshare.net/jaros_p/shadow-warrior-2-and-the-evolution-of-the-roadhog-engine
An image texture is a set of metrics calculated in image processing designed to quantify the perceived texture of an image. Image Texture gives us information about the spatial arrangement of color or intensities in an image or selected region of an image. This presentation consists of its types, uses, methods and approaches.
Ultra Fast, Cross Genre, Procedural Content Generation in Games [Master Thesis]Mohammad Shaker
In my MSc. thesis, I have re-tackled the problem of procedurally generating content for physics-based games I have previously investigated in my BSc. graduation thesis. This time around I propose two novel methods: the first is projection based for faster generation of physics-based games content. The other, The Progressive Generation, is a generic, wide-range, across genre, customisable with playability check method all bundled in a fast progressive approach. This new method is applied on two completely different games: NEXT And Cut the Rope.
Short, Matters, Love - Passioneers Event 2015Mohammad Shaker
Short, Matters, Love is a presentation I prepared for freshmen students at the Faculty of Information Technology in Damascus, Syria organised by Passioneers - 2015
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
2. that appears in a video game needs to be textured; this includes everything from plants
to people. If things aren’t textured well, your game just won’t look right.
4. Texturing
• What’s it?!
Textures are images applied to surfaces that are created using primitive objects
XNA is Perfect at Texturing
textures can be colored, filtered, blended,
and transformed at run time!
5. Texturing
• What’s it?!
Textures are images applied to surfaces that are created using primitive objects
textures can be colored, filtered, blended,
and transformed at run time!
XNA is Perfect at Texturing
XNA supports:
.bmp, .dds, .dib, .hdr, .jpg, .pfm, .png, .ppm, and .tga image formats for textures
6. Texturing
• UV Coordinates
– 2D World
• a texture is a two-dimensional object that is mapped onto a 2D polygon
– 3D World
• a texture is a two-dimensional object that is mapped onto a 3D polygon
10. Texturing
• VertexPositionColorTexture
– This format allows you to apply image textures to your primitive shapes, and you can even
shade your images with color.
– For example, with this vertex type you could draw a rectangle with an image texture and then
you could show it again with a different shade of color!
VertexPositionColorTexture vertex = new
VertexPositionColorTexture(Vector3 position, Color color, Vector2 uv);
11. Texturing
• VertexPositionNormalTexture
– This format allows you to add textures to your primitive objects. The normal data enables
lighting for this textured format.
VertexPositionNormalTexture vertex = new
VertexPositionNormalTexture(Vector3 position, Vector3 normal, Vector2 uv);
12. Texturing
• VertexPositionTexture
– This format only permits storage of position and texture data.
– It may be useful if you don’t need lighting and were concerned about saving space or
improving performance for large amounts of vertices.
VertexPositionTexture vertex = new
VertexPositionTexture(Vector3 position, Vector2 uv);
17. Texturing
• TRANSPARENT TEXTURES
An alpha channel can be used to “mask” all pixels of a specific color in an image. Alpha
data is stored in the last color byte of a pixel after the red, green, and blue bytes.
When alpha blending is enabled in your XNA code and the alpha channel is active,
transparency is achieved for the pixels where the alpha setting is set to 0.
New “Alpha” Channel!
18. Texturing
• TRANSPARENT TEXTURES
An alpha channel can be used to “mask” all pixels of a specific color in an image. Alpha
data is stored in the last color byte of a pixel after the red, green, and blue bytes.
When alpha blending is enabled in your XNA code and the alpha channel is active,
transparency is achieved for the pixels where the alpha setting is set to 0.
New “Alpha” Channel!
52. Texturing
• TEXTURE TILING
Using a small image to cover a large surface makes tiling a useful way
to increase the performance of your textures and decrease the size of
your image files.
53. Texture Tiling
• In Load Content
// Right Top
verts[0] = new VertexPositionTexture(
new Vector3(-1, 1, 0), new Vector2(10, 0));
// Left Top
verts[1] = new VertexPositionTexture(
new Vector3(1, 1, 0), new Vector2(1, 0));
// Right Bottom
verts[2] = new VertexPositionTexture(
new Vector3(-1, -1, 0), new Vector2(10, 10));
// Left Bottom
verts[3] = new VertexPositionTexture(
new Vector3(1, -1, 0), new Vector2(1, 10));
61. Billboarding
float GetViewerAngle()
{
// use camera look direction to get
// rotation angle about Y
float x = cam.view.X - cam.position.X;
float z = cam.view.Z - cam.position.Z;
return (float)Math.Atan2(x, z) + MathHelper.Pi;
}
62. Billboarding
float GetViewerAngle()
{
// use camera look direction to get
// rotation angle about Y
float x = cam.view.X - cam.position.X;
float z = cam.view.Z - cam.position.Z;
return (float)Math.Atan2(x, z) + MathHelper.Pi;
}
63. Billboarding
float GetViewerAngle()
{
// use camera look direction to get
// rotation angle about Y
float x = cam.view.X - cam.position.X;
float z = cam.view.Z - cam.position.Z;
return (float)Math.Atan2(x, z) + MathHelper.Pi;
}
64. Billboarding
float GetViewerAngle()
{
// use camera look direction to get
// rotation angle about Y
float x = cam.view.X - cam.position.X;
float z = cam.view.Z - cam.position.Z;
return (float)Math.Atan2(x, z) + MathHelper.Pi;
}
65. Billboarding
float GetViewerAngle()
{
// use camera look direction to get
// rotation angle about Y
float x = cam.view.X - cam.position.X;
float z = cam.view.Z - cam.position.Z;
return (float)Math.Atan2(x, z) + MathHelper.Pi;
}
69. Billboarding
float GetViewerAngle()
{
// use camera look direction to get
// rotation angle about Y
float x = cam.view.X - cam.position.X;
float z = cam.view.Z - cam.position.Z;
return (float)Math.Atan2(x, z) + MathHelper.Pi;
}
rotationY = Matrix.CreateRotationY(GetViewerAngle());
70. Billboarding
float GetViewerAngle()
{
// use camera look direction to get
// rotation angle about Y
float x = cam.view.X - cam.position.X;
float z = cam.view.Z - cam.position.Z;
return (float)Math.Atan2(x, z) + MathHelper.Pi;
}
rotationY = Matrix.CreateRotationY(GetViewerAngle());