At the end of this lesson, you should be able to;
Describe image.
Describe digital image processing and computer vision.
Compare and Contrast image processing and computer vision.
Describe image sources.
Identify the array of imaging application under the EM Image source.
Describe the components of Image processing system and computer vision system.
Mika Kaukoranta presents what computer vision is and how it can be utilized in software testing by gaining high-level understanding from digital images or videos.
In this presentation we described important things about Image processing and computer vision. If you have any query about this presentation then feels free to visit us at:
http://www.siliconmentor.com/
We create a group presentation for Simulation & Modeling. This presentation has so many related fields as like artificial intelligence ,Information engineering,Neurology, Signal processing etc.
Mika Kaukoranta presents what computer vision is and how it can be utilized in software testing by gaining high-level understanding from digital images or videos.
In this presentation we described important things about Image processing and computer vision. If you have any query about this presentation then feels free to visit us at:
http://www.siliconmentor.com/
We create a group presentation for Simulation & Modeling. This presentation has so many related fields as like artificial intelligence ,Information engineering,Neurology, Signal processing etc.
3D Graphics & Rendering in Computer GraphicsFaraz Akhtar
Computer graphics, 3d rendering,3d graphics,Components of a 3D Graphic System,3D Modeling,3D Rendering,Illumination for scan-line renderers, 3D Graphics and Physics
3D Graphics & Rendering in Computer GraphicsFaraz Akhtar
Computer graphics, 3d rendering,3d graphics,Components of a 3D Graphic System,3D Modeling,3D Rendering,Illumination for scan-line renderers, 3D Graphics and Physics
Computer vision has started to achieve some very impressive results over the last 5-10 years. It is now possible to quickly and reliably detect faces, recognize and localize target images, and even classify pictures of objects into generic categories. Unfortunately, knowledge of these techniques remains largely confined to academia. In this session we’ll go over some of the tools available, placing an emphasis on exploring the ideas and algorithms behind their design.
To show how these components can be put together, a sample system will be developed over the course of the presentation. Starting with standard image descriptors, we’ll first see how to do direct image recognition. We’ll then extend that into a simple object classifier, which will be able to distinguish (for example) between images which contain a bicycle and those that don’t.
Mobile Computer Vision requires deep SoC-based optimization and extensive amount of development resources. This presentation reviews the challenges of mobile computer vision optimization, the vision for a cross-platform API and the current solution of using FastCV
This is the slide that Terry. T. Um gave a presentation at Kookmin University in 22 June, 2014. Feel free to share it and please let me know if there is some misconception or something.
(http://t-robotics.blogspot.com)
(http://terryum.io)
Data Science - Part XVII - Deep Learning & Image ProcessingDerek Kane
This lecture provides an overview of Image Processing and Deep Learning for the applications of data science and machine learning. We will go through examples of image processing techniques using a couple of different R packages. Afterwards, we will shift our focus and dive into the topics of Deep Neural Networks and Deep Learning. We will discuss topics including Deep Boltzmann Machines, Deep Belief Networks, & Convolutional Neural Networks and finish the presentation with a practical exercise in hand writing recognition technique.
Frequently asked questions about using image search engine Immenselab that enables users to search for identical or similar images using a pattern. A test index database of 10 million images is used by www.immenselab.com to show how the engine works. It allows to use different search methods and to control search results on the fly.
At the end of this lesson, you should be able to;
describe the energy and the EM spectrum.
describe image acquisition methods.
discuss image formation model.
express sampling and quantization.
define dynamic range and image representation.
At the end of this lesson, you should be able to;
define segmentation.
Describe edge based in segmentation.
describe thresholding and its properties.
apply edge detection and thresholding as segmentation techniques.
COM2304: Intensity Transformation and Spatial Filtering – II Spatial Filterin...Hemantha Kulathilake
At the end of this lecture, you should be able to;
describe the fundamentals of spatial filtering.
generating spatial filter masks.
identify smoothing via linear filters and non linear filters.
apply smoothing techniques for problem solving.
Here in E2MATRIX , We provide the best coaching & training and IEEE projects. We provide professional courses like matlab, image processing, cloud computing,Android, electrical domain .NET, JAVA, WEKA, NS-2, MATLAB SIMULINK, and our main emphasis is thesis for MTECH , research projects, IEEE projects. Provide Research Help to all Engineering classes in all the fields of electrical , electronics, IT and Computers.
Contact us at:
E2MATRIX
Opp. Bus Stand, Parmar Complex,
Backside Axis Bank, Phagwara - Punjab (INDIA).
Contact: +91 9041262727, 9779363902,
Web: www.e2matrix.com
At the end of this lecture students should be able to;
Define the C standard functions for managing file input output.
Apply taught concepts for writing programs.
At the end of this lecture students should be able to;
Define the declaration C strings.
Compare fixed length and variable length string.
Apply strings for functions.
Define string handling functions.
Apply taught concepts for writing programs.
At the end of this lecture students should be able to;
Define, initialize and access to the C stuctuers.
Develop programs using structures in arrays and functions.
Use structures within structures and structures as pointers.
Define, initialize and access to the C unions.
Compare and contrast C structures and unions.
Define memory allocation and de-allocation methods in C.
Develop programs using memory allocation functions.
Apply taught concepts for writing programs.
At the end of this lecture students should be able to;
Define the C standard functions for managing input output.
Apply taught concepts for writing programs.
At the end of this lecture students should be able to;
Define the C pointers and its usage in computer programming.
Describe pointer declaration and initialization.
Apply C pointers for expressions.
Experiment on pointer operations.
Identify NULL pointer concept.
Experiment on pointer to pointer, pointer arrays, arrays with pointers and functions with pointers.
Apply taught concepts for writing programs.
At the end of this lecture students should be able to;
Describe the C arrays.
Practice the declaration, initialization and access linear arrays.
Practice the declaration, initialization and access two dimensional arrays.
Apply taught concepts for writing programs.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
COM2304: Introduction to Computer Vision & Image Processing
1. COMPUTER GRAPHICS & IMAGE PROCESSING
COM 2304
Introduction to Computer Vision & Image Processing
K.A.S.H.Kulathilake
B.Sc. (Hons) IT (SLIIT), MCS (UCSC), M.Phil(UOM), SEDA(UK)
Rajarata University of Sri Lanka
Faculty of Applied Sciences
Department of Physical Sciences
2. Learning Outcomes
COM 2304 - Computer Graphics & Image
Processing
• At the end of this lecture, you should be able
to;
– Describe image.
– Describe digital image processing and computer
vision.
– Compare and Contrast image processing and
computer vision.
– Describe image sources.
– Identify the array of imaging application under the
EM Image source.
– Describe the components of Image processing
system and computer vision system.
3. What is an Image?
• A rectangular grid of
pixels.
• It has definite width and
height counted in pixels
• Each pixel is a square
and has a fixed size in
given display.
• Each pixel has a color.
COM 2304 - Computer Graphics & Image
Processing
4. What is an Image? (Cont…)
• Image can be
represented using a two
dimensional
function f(x,y) where x
and y are the spatial
coordinates of each
point in the image and
the amplitude of f at
any coordinate is known
as the intensity or gray
value of that point.
COM 2304 - Computer Graphics & Image
Processing
5. What is an Image? (Cont…)
COM 2304 - Computer Graphics & Image
Processing
6. What is Digital Image Processing?
• If the image has discrete and finite quantities of
coordinates and intensity, we call it as a Digital Image.
• Each image contains a finite number of elements each
of which has a location and value. These elements are
referred to as Pixels or Picture elements.
• What is digital image processing?
– Processing a digital image by means of computer is
known as digital image processing.
– Basically we can identify two stems of image processing,
• Processing pictorial information for the human interpretation and
• Processing image data to store transmit and represent for
autonomous machine perception.
COM 2304 - Computer Graphics & Image
Processing
7. What is Computer Vision?
• Computer vision is the transformation of data
from a still or video camera into either a
decision or a new representation.
COM 2304 - Computer Graphics & Image
Processing
8. What is Computer Vision? (cont…)
• Visual scene extract a task relevent
information
• Examples
– Optical character recognition
– Analysis of medical, satellite and microscopic
images
– Surveillance
– Identity verification
– Quality control in manufacturing
COM 2304 - Computer Graphics & Image
Processing
9. What is Computer Vision? (cont…)
COM 2304 - Computer Graphics & Image
Processing
10. Image Sources
• There are various image sources available
which can produce images.
– Electro Magnetic (EM) energy spectrum.
– Acoustic / Ultra-sonic
– Electronics
COM 2304 - Computer Graphics & Image
Processing
11. Imaging in Electro Magnetic (EM)
Energy Spectrum
• Electromagnetic waves are conceptually
sinusoidal and formation of different wave
lengths.
• It can be thought of as a stream of massless
particles, each travelling in a wave like pattern
and moving at the speed of light.
• Each massless particle contains a certain
amount of energy and a bundle of energy is
known as photon.
COM 2304 - Computer Graphics & Image
Processing
12. Imaging in Electro Magnetic (EM)
Energy Spectrum (Cont…)
COM 2304 - Computer Graphics & Image
Processing
13. Imaging in Electro Magnetic (EM)
Energy Spectrum (Cont…)
• Gamma Ray Band
– Gamma rays are
used in nuclear
medicine and
astronomical
observations.
• X-ray Tomography
• Positron Emmition
Tomography (PET)
COM 2304 - Computer Graphics & Image
Processing
14. Imaging in Electro Magnetic (EM)
Energy Spectrum (Cont…)
• X-ray
– X-ray is used in medical diagnostics, industry and
astronomy.
– Bone structure visualization, angiogram and
Computed Axial Tomography (CAT) are common
examples of the X-rays in medical domain.
COM 2304 - Computer Graphics & Image
Processing
15. Imaging in Electro Magnetic (EM)
Energy Spectrum (Cont…)
• Ultra Violet Band
– Imaging in Ultra Violet band includes lithography,
industrial inspections, microscopy, lacers,
biological imaging and astronomical observations.
– One good example of Ultra Violet imaging is
Fluorescence Microscopy.
COM 2304 - Computer Graphics & Image
Processing
16. Imaging in Electro Magnetic (EM)
Energy Spectrum (Cont…)
• Visible and Infrared Bands
– Infrared band is often use in conjunction with visible
band.
– Basically it is used in numerous applications like law
enforcement (figner print reading, reading the
number plate of the vehicles), industrial verifications,
remote sensing, astronomy, light microscopy and
many more.
– Remote sensing, weather observation and predication
are utilizing some energy bands in both visible and
infrared regions.
COM 2304 - Computer Graphics & Image
Processing
17. Imaging in Electro Magnetic (EM)
Energy Spectrum (Cont…)
COM 2304 - Computer Graphics & Image
Processing
18. Imaging in Electro Magnetic (EM)
Energy Spectrum (Cont…)
• Microwave Band
– The common utilization of Microwave is radar.
– Radar can form images virtually any region at any
time.
– It does not care about the lighting conditions and
the weather conditions.
COM 2304 - Computer Graphics & Image
Processing
19. Imaging in Electro Magnetic (EM)
Energy Spectrum (Cont…)
• Radio Band
– Radio signals are used in
medicine for diagnosis
and astronomy.
– Magnetic Resonance
Imaging (MRI)
COM 2304 - Computer Graphics & Image
Processing
20. Components of an Image Processing
System
• With reference to sensing, two devices are required to
acquire digital images.
• The first is a physical device that is sensitive to the
energy radiated by the object we wish to image.
• The second, called the digitizer, is the device for
converting the output of the physical sensing device
into digital form.
– For instance, in a digital video camera the sensors produce
electrical output proportional to the light intensity and the
digitizer converts these outputs to digital data.
COM 2304 - Computer Graphics & Image
Processing
21. Components of an Image Processing
System (Cont…)
• Digitizer and Arithmetic and Logical Unit (ALU)
can be considered as specialized image
processing hardware.
• In the image processing applications, the
computers can be scaling from general
purpose computer to super computer
depending on the nature of the application.
COM 2304 - Computer Graphics & Image
Processing
22. Components of an Image Processing
System (Cont…)
• Software for image processing applications
indicates the specific software modules that help
to the programmer to write efficient programs
easily.
• Storage is required to store the image
temporarily, online storage for fast recall and
archival storage for rarely access.
• Displays are the units which visualize the output
of the image processing application.
• It may be color monitor or graphic card.
COM 2304 - Computer Graphics & Image
Processing
23. Components of an Image Processing
System (Cont…)
• Hardcopy devices are important to recording
images includes laser printers, film cameras,
heat sensitive devices, inkjet units or digital
units such as optical disk or CD-ROM.
• Network provides the media to interchange
the image processing outputs.
COM 2304 - Computer Graphics & Image
Processing
24. Components of an Image Processing
System (Cont…)
COM 2304 - Computer Graphics & Image
Processing
26. Your Reflection
• Your reflection on “Imaging with different
Image sources”?
COM 2304 - Computer Graphics & Image
Processing
27. Reference
• Chapter 01 and 02 of Gonzalez, R.C., Woods,
R.E., Digital Image Processing, 3rd ed.
Addison-Wesley Pub.
COM 2304 - Computer Graphics & Image
Processing
28. Learning Outcomes Revisit
• Now, you should be able to;
– Describe image.
– Describe digital image processing and computer
vision.
– Compare and Contrast image processing and
computer vision.
– Describe image sources.
– Identify the array of imaging application under the
EM Image source.
– Describe the components of Image processing
system and computer vision system.
COM 2304 - Computer Graphics & Image
Processing
29. QUESTIONS ?
Next Lecture – Digital Image Fundamentals
COM 2304 - Computer Graphics & Image
Processing