Monday, 31 October 2016
Digital Image Processing - Color Image processing
13:43
Color Image processing
The use of color in image processing is motivated by two
principal factors. First, color is a powerful descriptor that often simplifies
object identification and extraction from a scene. Second, human can discern
thousands of color shades and intensities, compared to about only two dozen
shades of grat. This factor is particularly important in manual image analysis.
In the first category, the image in question typically are
acquired with full color sensor, such as color TV camera or color scanner. In
the second category, the problem is one of assigning a color to particular
monochrome intensity or range of intensities.
Full color image processing techniques are now used in a
broad range of applications, including publishing, visualization, and the
internet.
Color images processing is divided into two major areas: -
full color image processing and pseudocolor image processing.
Three basic quantities are used to describe the quality of
a chromatic light source is:
1. Radiance
2. Luminance
3. Brightness
Radiance is
the total amount of energy that flows from the light source, and it is usually
measured in watt (W).
Luminance is measured in terms of lumens (lm), gives the measure of
the amount of energy an observer perceives from a light source.
Brightness
is
a subjective descriptor that is practically impossible to measure.
Primary
colors: - Red (R), Green (G) and Blue (B).
Secondary
colors: - Magenta (red + blue), Cyan (green + blue), Yellow (red +
green).
The characteristics generally used to distinguish one color
from another are brightness, hue and saturation. Hue and saturation taken
together are called chromaticity.
And therefore a color may be characterized by its brightness and chromaticity.
Color
Models: - Basically, the colors that humans and some other animals perceive
in an object are determined by the nature of the light reflected from the
object. Characterization of light is central to the science of color. If the
light is achromatic (void of color), its only attribute is its intensity, or
amount. Achromatic light is what viewers see on a black and white television
set.
·
RGB (red, green, blue) Model
·
CMY (cyan, magenta, yellow) Model
·
CMYK (cyan, magenta, yellow, black) Model
·
HSI (hue, saturation, intensity) Model
RGB
Model: - In RGB
model, each color appears in its primary spectral components of red, green and
blue. This model is based on Cartesian coordinate system. Image represented in
RGB color model consist of three components images.
The number of bits used to represent each pixel in RGB
space is called the pixel depth. The
total number of colors in 24-bit RGB image is 2^24 = 14,777,216.
The term full color image
is used often to denote 24-bit RGB color image.
The
CMY Color Model: - Cyan, magenta and yellow are the secondary
color of light or alternatively primary colors of pigments. For example, when a
surface is coated with cyan pigment is illuminated with white light, no red
light is reflected from surface. This is cyan reflects red light from reflected
white light. Which itself is composed of equal amount of red, green and blue
light.
The HIS Model: - Createing colos in RGB and CMY
models and changing from one model to another is strainghtforward process.
These color systems are ideally suited for hardware systems. When human views a
object, we describe it by its hue and saturations.
(Figure: Conceptual relationship between RGB and HSI models)
The key point
to keep in mind regarding the cube arrangment and its corresponding HIS color
space is that HSI space is represented by a vertical intensity axis and locus
of color point that lies on plan perpendicular to this axis.
Database Models
10:22
Emerging Database Models
·
Temporal
Data: - Most database system model the current state of the
world, for instance, current customer, current students and courses currently
being offered. In many applications, it is very important to store and retrieve
information about past states.
·
Spatial
Database: - It includes geographical data,
such as maps and associated information, and computer-aided-design data, such
as integratedcircuit designs or building designs. Application of spatial data
initially stores data as files in a file system. Spatial data supports in
database is important for efficiently storing, indexing and querying of data on
the basis of spatial location. Two types of spatial data: -
· Computer-aided-design(CAD) data: - It includes
spatial information about how objects – such as buildings, car or aircrafts are
constructed. Other important example of CAD databases are integrated circuits
and electronic device layouts.
· Geographical data: - such
as road maps, topographic elevation maps, political maps showing boundaries, land
ownership maps etc.
Application
of Spatial Data: - The ability to store and query large amounts of
data efficiently, concurrency control and durability.
·
Multimedia Database: - A
multimedia database can store multimedia data such as text, images, video,
audio etc. Video and audio data is called continuous-media
data because the display of data requires retrieval at a steady,
predetermined rate.
Multimedia
data formats: - Because of the large number of bytes required
to represent multimedia data, it is essential that multimedia data be stored
and transmitted in compressed form. For image data the most widely used format
is JPEG, named after the standards body that created it, the Joint Picture Experts Group. Video data
can be store by encoding each frame of video in JPEG format.
Continuous
media data: - The most important type of continuous media
data is video data and audio data (for example database of movies). These systems
are characterized by their real time information delivery requirements.
· Data
must be delivered fast without any gap in audio or video.
· Data
must be delivered at a rate that does not cause of overflow of system buffers.
· Synchronization
among different data streams must be maintained.
Saturday, 29 October 2016
FSM—Finite State Machine
11:39
FSM: - FSM Stands for Finite State Machine. A finite state machine
with a sequential circuit with “random” next state logic. An FSM consists of two blocks of combinational
logic — Next state logic and output
logic.
A
FSM is specified by five entities: symbolic state, input signals, output
signals, next state function and output function. The FSM transits from one
state to another. The new state is determined by next state function, which is
a function of next state and input signals.
The block diagram of FSM is similar to
regular sequential circuit. The state register is memory element that store the
state of FSM. It is synchronized by a global clock. The next state logic implements
the next state function. The output logic implements the output function. The
diagram includes both moore output logic. Whose input is the next state.
FSM
can be used to detecting the unique pattern from an input data stream or
generating a specific sequence of output values.
• Finite String Pattern Recognizer
•
Traffic Light Controller
There are two general classes of finite
state machine, characterized by their functional specifications.
1.
In
Moore machine, the outputs depend
only on the current state of the machine.
2.
In
Mealy machine, the output depends on
both the current state and the current inputs.
Finite state machines provide a
systematic way to design synchronous sequential circuits given a functional
specification.
(Figure: Moore machine and Mealy machine)
FSM
Advantages: -
·
Simple
·
Predictable ( deterministic FSM ) - given a set
of inputs and a known current state, the state transition can be predicted,
allowing for easy testing.
·
Due to their simplicity, FSMs are quick to
design, quick to implement and quick in execution.
·
Easy to transfer from a meaningful abstract
representation to a coded implementation.
·
FSM
is an old knowledge representation and system modeling technique.
FSM
Disadvantages: -
·
The conditions for state transitions are
ridged, meaning they are fixed.
·
The
predictable nature of deterministic FSMs can be unwanted in some domains such
as computer games.
·
Not
suited to all problem domains.
05:00
(HTML+CSS) Coding behind Fantasy Diwali with Everyone
Hello, I created a video named as “Fantasy Diwali with
Everyone”. I used HTML and CSS coding to create content of this video. I linked
CSS sheet to make effective layout.
HTML
TAGS USED:
1)
Html tag
2)
Head tag
3)
Title tag
4)
Img tag
5)
Body tag
6)
Div tag
7)
Center tag
8)
Br tag
9)
Style tag
10)
Link tag
CSS SELECTOR USED:
1)
Id
2)
Class
CSS PROPERTIES USED:
3)
Broder-radius
4)
Margin-left
5)
Margin-top
6)
Position
7)
Color
You can see like video on this mini project just click on the link given below.
If you like this mini-project and want to create something like this than you can watch videos i uploaded on you tube. In whole video series i will show you how i created this mini project step by step.
This is introduction part of fantasy project series. just click on the given link below: -
In this video i created first two slides of my mini-project: -
Soon, i will upload other videos. stay connected with my channel if you want want to watch whole video series. :)
Wednesday, 26 October 2016
The Query Compiler
11:56
The
Query Compiler
Compilation
of Queries: - Compilation means turning a query into a
physical query plan, which can be implemented by query engine.
The query- compiler package is
a set of tools for the inspection of the process of query compilation. It shows
how a SQL query is parsed, desugared, translated in relational algebra and
optimized. The user interface is web-based, implemented using strategy-net and xml-tools. Of course the components of the query-compiler are also
available as command-line utile.
The query-compiler is based
on the sql-front and relational-algebra packages. sql-front is used to
parse a SQL query to an abstract syntax for SQL. The relational-algebra
packages implement the optimization of relational algebra and the rendering of
relational algebra expressions in MathML?.
Steps of query compilation:
— Parsing
— Semantic
checking
— Selection
of the preferred logical query plan
— Generating
the best physical plan
The
Parser:
— It
is the first step in query processing.
— Parsing
turns query into parse tree.
— It generates a parse tree.
— It generates a parse tree.
ü
Subject
of Compilation
- Nodes in the parse
tree corresponds to the SQL constructs.
- It is similar to the compiler of a programming language.
Preprocessor:
•
Responsible for semantic checking
–
Check relation uses
–
Check and resolve attribute uses
–
Check types
•
If the parse tree satisfies all the above
tests, it is valid
•
Otherwise, processing stops.
View
Expansion:
- A very critical part of query compilation.
- A very critical part of query compilation.
- Expands the view references in the query tree to the actual view.
- Provides opportunities for the query optimization.
Semantic
Checking:
- Checks
the semantics of a SQL query.
— Examines
a parse tree
Checks:
Checks:
ü
Attributes
ü
Relation
names
ü
Types
- · Resolves attribute references.
Conversion
to a logical query plan:
- Converts
a semantically parsed tree to a algebraic expression.
- Conversion
is straightforward but subqueries need to be optimized.
—Two argument selection approach can be used.
—Two argument selection approach can be used.
Cost
based optimizing:
- Best physical query plan represents the least costly plan.
- Factors that decide the cost of a query plan:
- Best physical query plan represents the least costly plan.
- Factors that decide the cost of a query plan:
ü
Order
and grouping operations like joins, unions and intersections.
ü
Nested
loop and the hash loop joins used.
ü
Scanning
and sorting operations.
ü
Storing
intermediate results.
ü
The
query cost is defined by the time to answer a query.
ü
Different
factors are contributed to query cost like disk access time, CPU time or
network communication time.
Logical and physical query plans: -
· Both are trees representing query evaluation
· Leaves represent data
· Internal nodes are operators over the data
· Logical plan is higher-level and
algebraic
· Physical plan is lower-level and operational
· Logical plan operators correspond to query language
constructs
- · Physical plan operators correspond to implemented access methods
Logical
plan operators: -
- · Extended relational algebra
- · Leaves of logical plans are table names
- · Basic operators: Select, Project, Cross-Product, Union, Difference
- · Abbreviations: Natural-Join, Theta-Join, Intersect
LED BLUB (B22 BASE) SSK-PAG-15W
02:18
LED BLUB (B22 BASE) SSK-PAG-15W
SYSKA LED [LIGHT YEARS AHEAD]
Specifications:
|
|
Rated Wattage
|
15W
|
Input Voltage
|
AC90~300V*,
50Hz
|
Rated Current
|
0.062A
|
Lumens
|
1500**
|
Operating Temp.
|
240°
|
Color Temp.
|
6500K
|
Applications:
|
Home, office, restaurant, mart, gallery
|
LED lamp
substitution for incandescent
|
Features:
|
Upto 90% energy saving
|
No mercury
|
Lasts long (>5000 days*)
|
Wide operating voltage
|
Very low heating
|
Eco-friendly
|
Caution:
|
Do not disassemble the product.
|
Do not use the lamp in place of high temperature,
humidity or moisture and dusty surrounding.
|
Do not touch the product while using it and
immediately after it is turned off.
|
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