WEBINAR ON FUNDAMENTALS OF DIGITAL IMAGE PROCESSING DURING COVID LOCK DOWN by by K.Vijay Anand , Associate Professor, Department of Electronics and Instrumentation Engineering , R.M.K Engineering College, Tamil Nadu , India
WEBINAR ON FUNDAMENTALS OF DIGITAL IMAGE PROCESSING DURING COVID LOCK DOWN by by K.Vijay Anand , Associate Professor, Department of Electronics and Instrumentation Engineering , R.M.K Engineering College, Tamil Nadu , India
Introduction, graphics primitives :Pixel, resolution, aspect ratio, a frame buffer. Display devices, and applications of computer graphics.
Scan conversion - Line drawing algorithms: Digital Differential Analyzer (DDA), Bresenham’s Circle drawing algorithms: DDA, Bresenham’s, and Midpoint.
Hardware realization of Stereo camera and associated embedded systemIJERA Editor
Stereo camera has two lenses about the same distance apart as human eyes with a separate image sensor for each
lenses. This allows the camera to simulate human binocular vision, and therefore gives it ability to capture three
dimensional images. It detects depth information of the subject which allows user to capture image that are
instantly rendered in 3D. Stereo cameras are also required in stereo vision, a ranging method which finds its
application in almost every field. Still stereo 3D hasn’t yet become a standard because of technical problems,
including agronomy issues, cost, and lack of hardware and software standards. Due to above reasons, it is
important to achieve the low cost and standard hardware for 3D vision for which a novel architecture of a stereo
camera is required. This paper proposes to provide low cost solution to stereo cameras as cameras can be
designed as per requirement and mainly focuses on the processing of sensor raw image data.
Blending of Images Using Discrete Wavelet Transformrahulmonikasharma
The project presents multi focus image fusion using discrete wavelet transform with local directional pattern and spatial frequency analysis. Multi focus image fusion in wireless visual sensor networks is a process of blending two or more images to get a new one which has a more accurate description of the scene than the individual source images. In this project, the proposed model utilizes the multi scale decomposition done by discrete wavelet transform for fusing the images in its frequency domain. It decomposes an image into two different components like structural and textural information. It doesn’t down sample the image while transforming into frequency domain. So it preserves the edge texture details while reconstructing image from its frequency domain. It is used to reduce the problems like blocking, ringing artifacts occurs because of DCT and DWT. The low frequency sub-band coefficients are fused by selecting coefficient having maximum spatial frequency. It indicates the overall active level of an image. The high frequency sub-band coefficients are fused by selecting coefficients having maximum LDP code value LDP computes the edge response values in all eight directions at each pixel position and generates a code from the relative strength magnitude. Finally, fused two different frequency sub-bands are inverse transformed to reconstruct fused image. The system performance will be evaluated by using the parameters such as Peak signal to noise ratio, correlation and entropy
User Interactive Color Transformation between ImagesIJMER
Abstract: In this paper we present a process called color
transfer which can borrow one image’s color
characteristics from another. Most current colorization
algorithms either require a significant user effort or have
large computational time. Here focus on orthogonal color
space i.e. lαβ color space without correlation between the
axes is given. Here we have implemented two global color
transfer algorithms in lαβ color space using simple color
statistical information such as mean, standard deviation
and covariance between the pixels of image. Our approach
is the extension of Reinhard's. Our local color transfer
algorithm uses simple color statistical analysis to recolor
the target image according to selected color range in
source image. Target image’s color influence mask is
prepared. It is a mask that specifies what parts of target
image will be affected according to selected color range.
After that target image is recolored in lαβ color space
according to prepared color influence map. In the lαβ
color space luminance and chrominance information is
separate so it allows making image recoloring optional.
The basic color transformation uses stored color statistics
of source and target image. All the algorithms are
implemented in JAVA object oriented language. The main
advantage of proposed method over the existing one is it
allows the user to recolor a part of the image in a simple &
intuitive way, preserving other color intact & achieving
natural look.
Index Terms: color transfer, local color statistics, color
characteristics, orthogonal color space, color influence
map.
The cause of ww1 happened 1914 this was after the the asssasination of Arc...CharlesMatu2
while Africa and Asian countries had a good raw mterials that would be a supply link which saw the european allies improve there defense millitary power in this rivarly of nations towards ww1
Introduction, graphics primitives :Pixel, resolution, aspect ratio, a frame buffer. Display devices, and applications of computer graphics.
Scan conversion - Line drawing algorithms: Digital Differential Analyzer (DDA), Bresenham’s Circle drawing algorithms: DDA, Bresenham’s, and Midpoint.
Hardware realization of Stereo camera and associated embedded systemIJERA Editor
Stereo camera has two lenses about the same distance apart as human eyes with a separate image sensor for each
lenses. This allows the camera to simulate human binocular vision, and therefore gives it ability to capture three
dimensional images. It detects depth information of the subject which allows user to capture image that are
instantly rendered in 3D. Stereo cameras are also required in stereo vision, a ranging method which finds its
application in almost every field. Still stereo 3D hasn’t yet become a standard because of technical problems,
including agronomy issues, cost, and lack of hardware and software standards. Due to above reasons, it is
important to achieve the low cost and standard hardware for 3D vision for which a novel architecture of a stereo
camera is required. This paper proposes to provide low cost solution to stereo cameras as cameras can be
designed as per requirement and mainly focuses on the processing of sensor raw image data.
Blending of Images Using Discrete Wavelet Transformrahulmonikasharma
The project presents multi focus image fusion using discrete wavelet transform with local directional pattern and spatial frequency analysis. Multi focus image fusion in wireless visual sensor networks is a process of blending two or more images to get a new one which has a more accurate description of the scene than the individual source images. In this project, the proposed model utilizes the multi scale decomposition done by discrete wavelet transform for fusing the images in its frequency domain. It decomposes an image into two different components like structural and textural information. It doesn’t down sample the image while transforming into frequency domain. So it preserves the edge texture details while reconstructing image from its frequency domain. It is used to reduce the problems like blocking, ringing artifacts occurs because of DCT and DWT. The low frequency sub-band coefficients are fused by selecting coefficient having maximum spatial frequency. It indicates the overall active level of an image. The high frequency sub-band coefficients are fused by selecting coefficients having maximum LDP code value LDP computes the edge response values in all eight directions at each pixel position and generates a code from the relative strength magnitude. Finally, fused two different frequency sub-bands are inverse transformed to reconstruct fused image. The system performance will be evaluated by using the parameters such as Peak signal to noise ratio, correlation and entropy
User Interactive Color Transformation between ImagesIJMER
Abstract: In this paper we present a process called color
transfer which can borrow one image’s color
characteristics from another. Most current colorization
algorithms either require a significant user effort or have
large computational time. Here focus on orthogonal color
space i.e. lαβ color space without correlation between the
axes is given. Here we have implemented two global color
transfer algorithms in lαβ color space using simple color
statistical information such as mean, standard deviation
and covariance between the pixels of image. Our approach
is the extension of Reinhard's. Our local color transfer
algorithm uses simple color statistical analysis to recolor
the target image according to selected color range in
source image. Target image’s color influence mask is
prepared. It is a mask that specifies what parts of target
image will be affected according to selected color range.
After that target image is recolored in lαβ color space
according to prepared color influence map. In the lαβ
color space luminance and chrominance information is
separate so it allows making image recoloring optional.
The basic color transformation uses stored color statistics
of source and target image. All the algorithms are
implemented in JAVA object oriented language. The main
advantage of proposed method over the existing one is it
allows the user to recolor a part of the image in a simple &
intuitive way, preserving other color intact & achieving
natural look.
Index Terms: color transfer, local color statistics, color
characteristics, orthogonal color space, color influence
map.
The cause of ww1 happened 1914 this was after the the asssasination of Arc...CharlesMatu2
while Africa and Asian countries had a good raw mterials that would be a supply link which saw the european allies improve there defense millitary power in this rivarly of nations towards ww1
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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/
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
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
When stars align: studies in data quality, knowledge graphs, and machine lear...
Computer graphics.docx
1. a)
i. Computer graphics:
Computer graphics refers to the creation, manipulation, and display of visual content using
computers. It involves the use of software tools and hardware devices to create and render
images, animations, and videos.
ii. Scalar:
Scalar is a mathematical term that refers to a quantity that has only magnitude and no
direction. Scalars can be represented by a single numerical value, such as temperature,
mass, or speed.
iii. Point:
In computer graphics, a point refers to a position in space that is defined by a set of
coordinates. It is the smallest element of a digital image and is typically represented by a
single pixel.
iv. Line:
In computer graphics, a line is a geometric figure that is defined by two points. It is a series
of connected points that extend in a straight or curved direction. Lines can be used to create
shapes, such as triangles, circles, and polygons, and can be used to represent paths, edges,
and boundaries in a digital image.
b)
1. Keyboard
2. Mouse
3. Microphone
4. Joystick or gamepad
c)
In computer graphics, scan conversion is the process of transforming a geometric shape or image into a
digital representation that can be displayed on a computer screen. This process involves breaking down
the shape or image into discrete pixels and determining which pixels to activate to create the desired
image. The process is also known as rasterization or image sampling. The quality of the resulting image is
affected by factors such as the resolution of the display, the size of the pixels, and the complexity of the
shape or image being converted.
d)
a quadric surface is a three-dimensional geometric shape that is defined by a mathematical equation of
second degree, such as a sphere, cylinder, cone, or ellipsoid. Quadric surfaces are used to model 3D
objects in computer graphics and can be manipulated and rendered using specialized software tools.
2. 2
a) The CMY and HSV color models are two popular color models used in computer graphics and image
processing. The main differences between the two models are:
1. Color Representation: In the CMY color model, colors are represented by three primary colors -
cyan, magenta, and yellow. The color values range from 0 to 100%, with 0% representing no
color and 100% representing the full color intensity. In contrast, the HSV color model represents
colors using three parameters - hue, saturation, and value. Hue defines the color, saturation
determines the intensity of the color, and value represents the brightness of the color.
2. Color Space: The CMY color model operates in a subtractive color space, which means that the
colors are created by subtracting light from white. When all three primary colors are combined,
they create black. On the other hand, the HSV color model operates in an additive color space,
which means that the colors are created by adding light together. When all three parameters are
at maximum, they create white.
3. Color Mixing: In the CMY color model, colors are mixed by subtracting the complementary color.
For example, when cyan and magenta are combined, they create blue. When cyan, magenta, and
yellow are combined, they create black. In contrast, in the HSV color model, colors are mixed by
adjusting the hue, saturation, and value parameters to create a new color.
4. Applications: The CMY color model is commonly used in printing and color reproduction, while
the HSV color model is commonly used in computer graphics and image processing, particularly
for color selection and manipulation.
b) Electrostatic plotters are a type of printer used in computer graphics that use electrostatic charges to
transfer toner onto paper or other surfaces. The advantages of electrostatic plotters in computer
graphics include:
1. High Resolution: Electrostatic plotters can produce high-quality, high-resolution prints with
sharp, precise lines and curves. This makes them ideal for printing technical drawings,
schematics, and other detailed graphics.
2. Speed: Electrostatic plotters are faster than other types of plotters and can produce prints
quickly, which is important for printing large or complex images.
3. Versatility: Electrostatic plotters can print on a variety of media, including paper, transparency
film, and other types of materials, which makes them ideal for a range of applications.
4. Low Maintenance: Electrostatic plotters are relatively easy to maintain and require less
maintenance than other types of plotters. They also have fewer moving parts, which reduces the
risk of mechanical failure.
3. 5. Cost-Effective: Electrostatic plotters are more cost-effective than other types of plotters, such as
inkjet or laser plotters, especially when printing large or complex images. This makes them a
popular choice for businesses and organizations that need to produce technical drawings or
other graphics on a regular basis.
c) Random scan displays and raster scan displays are two types of computers displays used to create and
display images. The main differences between the two are:
1. Scanning Method: Raster scan displays use a scanning pattern to draw an image one line at a
time from the top to the bottom of the screen, while random scan displays use a point-to-point
drawing method to draw an image.
2. Refresh Rate: Raster scan displays have a fixed refresh rate, which means that the entire screen
is redrawn at a constant rate, typically 60 times per second. In contrast, random scan displays
have a variable refresh rate, which means that the rate at which the image is drawn depends on
the complexity of the image.
3. Resolution: Raster scan displays have a fixed resolution that is determined by the number of
pixels on the screen. In contrast, random scan displays can have varying resolutions, depending
on the capability of the display hardware.
4. Complexity: Raster scan displays are typically simpler than random scan displays and are often
used for displaying text and simple graphics. Random scan displays are more complex and can be
used for displaying more complex images and animations.
5. Cost: Raster scan displays are generally less expensive than random scan displays because they
are simpler and require less advanced hardware.
d) Bi-directional Reflection Distribution Function (BRDF) is a function that describes the way in which
light is reflected by a surface in different directions. The BRDF is defined as the ratio of the amount of
light reflected in a particular direction to the amount of light that falls on the surface from that direction.
The classes and properties of BRDF can be discussed as follows:
1. Symmetry: The BRDF must be symmetric, i.e., it should remain the same when the directions of
the incident and reflected light are swapped. This property is also known as reciprocity. It
ensures that the amount of light that is reflected in a particular direction is the same as the
amount of light that would be received from that direction.
2. Energy Conservation: The BRDF must satisfy the energy conservation law, which states that the
total amount of light that is reflected by a surface cannot be greater than the total amount of
light that falls on it. This property ensures that the total amount of energy in the system is
conserved.
4. 3. Positivity: The BRDF must be positive, i.e., it cannot have negative values. This property ensures
that the amount of light reflected by a surface is always greater than or equal to zero.
4. Isotropy/Anisotropy: The BRDF can be either isotropic or anisotropic. An isotropic BRDF has the
same reflective properties in all directions, while an anisotropic BRDF has different reflective
properties in different directions.
5. Microfacet Models: BRDFs can be classified based on the underlying microfacet models used to
describe the surface. These models include the Lambertian model, which assumes that the
surface reflects light equally in all directions, and the Cook-Torrance model, which models the
surface as a collection of microfacets with varying orientations and roughness.
e)
A frame buffer is a region of memory in a computer that stores the image data for display on a computer
monitor or other output device. It is a data structure that holds the color and intensity values of
individual pixels, representing the image that is to be displayed on the screen.
Factors to consider when choosing a frame buffer include:
1. Resolution: The resolution of the frame buffer determines the number of pixels that can be
displayed on the screen. A higher resolution frame buffer can display more pixels, resulting in a
higher quality image.
2. Color Depth: The color depth of the frame buffer determines the number of colors that can be
displayed on the screen. A higher color depth frame buffer can display more colors, resulting in a
more vibrant and accurate image.
3. Refresh Rate: The refresh rate of the frame buffer determines how often the image is updated
on the screen. A higher refresh rate frame buffer can display smoother motion and reduce the
perception of flicker.
4. Memory Bandwidth: The memory bandwidth of the frame buffer determines how quickly data
can be read from or written to the frame buffer. A higher memory bandwidth frame buffer can
handle more complex graphics and reduce latency.
5. Compatibility: The frame buffer must be compatible with the display hardware and software
being used. Compatibility issues can lead to display problems and reduced performance.
5. f)
1. JPEG (Joint Photographic Experts Group): JPEG is a lossy compression algorithm that is widely
used for compressing photographic images. It works by analyzing the image data and removing
redundant information, such as high-frequency spatial data and color information that is
imperceptible to the human eye. The degree of compression can be adjusted to balance image
quality and file size.
2. PNG (Portable Network Graphics): PNG is a lossless compression algorithm that is widely used
for compressing images with transparent backgrounds, such as logos and graphics. It works by
encoding the image data using a predictive algorithm that takes advantage of the similarities
between adjacent pixels. Unlike JPEG, PNG does not remove any image data, so the compressed
image is an exact replica of the original. However, this results in larger file sizes than JPEG.