Colour model

COLOR MODEL

Nor Hajar Atieqah ZawawiNur Liyana Roslan

Data Modeling

• A data model is a formalism that helps specify the aspects of the data relevant for their organization.

• Data modeling is the act of exploring data-oriented structures. Like other modeling artifacts data models can be used for a variety of purposes, from high-level conceptual models to physical data models.

• Data modeling is conceptually similar to class modeling. With data modeling you identify entity types whereas with class modeling you identify classes. Data attributes are assigned to entity types just as you would assign attributes and operations to classes.

Example of Data Modeling

Traditional Data Modeling

• Traditional data modeling is different from class modeling because it focuses solely on data.

• Class models allow you to explore both the behavior and data aspects of your domain, with a data model you can only explore data issues.

• Data modelers have a tendency to be much better at getting the data “right" than object modelers.

Example of Traditional Data Modeling

Key Problem of Color Model

A color model is a quantitative representation of the colors that are relevant in an application domain. For the applications that involve human vision, the color model needs to represent the colors that the human eye can perceive. Perceiving colors is important for effective visual communication.

Bayesian Color Estimation for Adaptive Vision-based Robot Localization

• The RoboCup domain poses some challenging problems for state estimation, including moving objects, limited computational power, and low resolution camera information.

• To make use of colors, a robot has to map the raw color values observed with its camera to the colors in the map.

• The key problem in this context is that the appearance of these colors can change drastically under different lighting conditions.

Key Color Priority Based Image Recoloring for Dichromate

• People with color vision deficiency (CVD) have difficulty to discriminate certain categories of color combinations.

• To deliver rich and distinguishable visual information for the color blind people, a natural way is to re-color the images.

• The key problem in this context is how to efficiently re-organize the color information thus becomes a critical issue.

Color Base Retrieval and Recognition

• As the world enters the digital age, visual media is becoming prevalent and easily accessible. Factors such as the explosive growth of the World Wide Web, terabyte disk servers, and the digital versatile disk, reveal the growing amount of visual media which is available to society.

• Two key problems in color indexing are determination of color space and finding the best distance measure.

• Most of the attention has been focused on the color model with little or no consideration of the noise models.

A Skin Color Model Based on Modified GLHS Space for Face Detection

• Face detection is a very important key step in fully automated face recognition systems. The purpose of face detection is to determine the locations and sizes of human faces in images.

• Face detection has been successfully used in biometrics, video surveillance, human-computer interface and image database management.

• However, since the color of facial pixels is sensitive to different illumination conditions, it is hard to achieve stable detection performance. Therefore, skin color based detection methods have limitations in practice.

Shortcomings of Traditional Data Modeling Data Storage

• Data Security The data stored in the flat file(s) can be easily accessible and hence it is not secure.• Data Redundancy In this storage model, the same information may get duplicated in two or more files. This may lead to higher storage and access cost. it also may lead to data inconsistency.• Data Isolation Data Isolation means that all the related data is not available in one file. Usually the data is scattered in various files having different formats. Hence writing new application programs to retrieve the appropriate data is difficult.


• Program/Data Dependence

In traditional file approach, application programs are closely dependent on the files in which data is stored. If we make any changes in the physical format of the file(s), like addition of a data field, all application programs needs to be changed accordingly. Consequently, for each of the application programs that a programmer writes or maintains, the programmer must be concerned with data management. There is no centralized execution of the data management functions. Data management is scattered among all the application programs.


• Lack of Flexibility

The traditional systems are able to retrieve information for predetermined requests for data. If we need unanticipated data, huge programming effort is needed to make the information available, provided the information is there in the files. By the time the information is made available, it may no longer be required or useful.


• Concurrent Access Anomalies

Many traditional systems allow multiple users to access and update the same piece of data simultaneously. However this concurrent updates may result in inconsistent data. To guard against this possibility, the system must maintain some form of supervision. But supervision is difficult because data may be accessed by many different application programs and these application programs may not have been coordinated previously.

Shortcomings Of Modeling Media Data With Traditional Data Modeling

Relational Model

• The constraints underlying the database in terms of a set of first-order predicates, defined over a finite set of predicate variables.

• Entity relationship model are limited in what they can express. It helps to be aware of the limitations up front, since it can affect on represent the enterprise of the modeling that chosen.

A simple relational database with two relations


• Performance: A major constraint and therefore disadvantage in the use of relational database system is machine performance. If the number of tables between which relationships to be established are large and the tables themselves effect the performance in responding to the sql queries.

• Slow extraction of meaning from data: if the data is naturally organized in a hierarchical manner and stored as such, the hierarchical approach may give quick meaning for that data.


• Physical Storage Consumption: With an interactive system, for example an operation like join would depend upon the physical storage also. It is, therefore common in relational databases to tune the databases and in such a case the physical data layout would be chosen so as to give good performance in the most frequently run operations. It therefore would naturally result in the fact that the lays frequently run operations would tend to become even more shared.


Object Oriented

• The disadvantages of object oriented model or programming is that is larger than other types of programming. It also requires a lot of work even before the first piece of code is written, so labor is costly. They require an abundance of system resources to run the program and due to their size they are slower than other programs.

• Larger program size: Object-oriented programs typically involve more lines of code than procedural programs.


• Steep learning curve: The thought process involved in object-oriented programming may not be natural for some people, and it can take time to get used to it. It is complex to create programs based on interaction of objects. Some of the key programming techniques, such as inheritance and polymorphism, can be challenging to comprehend initially.

• Slower programs: Object-oriented are typically slower than procedure-based programs, as they typically require more instructions to be executed.


• Not suitable for all types of problems: There are problems that lend themselves well to functional-programming style, logic-programming style, or procedure-based programming style, and applying object-oriented programming in those situations will not result in efficient programs.


Object-Relational Models

• This approach has the obvious disadvantages of complexity and associated increased costs. Further, there are the proponents of the relational approach that believe the essential simplicity and purity of the relational model are lost with these types of extension.


• Object-Relational models vendors are attempting to portray object models as extensions to the relational model with some additional complexities. This potentially misses the point of object orientation, highlighting the large semantic gap between these two technologies. Object applications are simply not as data-centric as relational-based ones.

Models of media

features

The low-level features of the

media are those that can be

extracted from the media object

itself without external domain

knowledge

colour

texture

audio

spatial

Set-based

shape

temporal

All needs a model to be presented,

interpreted and described

Let us look into details of the colour model

Colour model?It is a qualitative

representation of the colours that are relevant in an

application domain. Must represent the

colours that the human eyes can

perceive

The retina of human eyes relies on the

rods and cones to perceive right

signals

Rods are for when the light intensity is low. Example, for night vision.Cones are for when thelight intensity is high.Example, for day vision

3 types of cones : R cones perceive RedG cones perceive GreenB cones perceive Blue

The intensities of the red, green and blue colour are combined to achieve colour perception

Most recording system

(ex:camera)and display system

(ex:monitor) usesimilar additive mechanism to

represent colour information

Let’s take a look at The first model :The RGB model

The origin of the RGB model corresponds to the lack of colour signal; ex:black while the diagonal

of the origin corresponds to maximum signal levels This model is commonly implemented using data

structures that allocatesame number of bits to each colour channel

For example, a 3-byte representation of colour can represent 24 bit different colour

instances,

would allocate 1 byte each to each colour channel thus distinguish 256 intensities of

pure red, green and blue called pallete

An image is represented as 2-D matrix, each cell contains 24-bit

colour instance. The cell is called as pixelEx:

representation of an image of 1000x1000

requires 24x1000x1000

bytes. When the space required is

not available, number of bits

allocated for each pixel must be

brought down!

But…how?

First method is by reducing the precision of the colour channel by allocating 4 bits per

colour channel instead of 8 bits

Hence there will be 4096 different colour instances instead of 16,777,216 colour

instances

Second method is by using the colour table. Colour table is a lookup table that

maps from a less precise colour index to a more precise colour instanceAssume that we

can process all the pixels in an image to identify the best 4096 distinct 24-bit

colours.These colours are put into the lookup

table. For each pixel of the image,

the index corresponds to the colour instance is

recorded

Hence, when we want to display a

photo, the software use the lookup

table and convert the colour indexes

to actual 24-bit RGB

Example of lookup table

Third method is by using the YRB, YUV and YIQ model. Human eyes are more sensitive to contrast than to colours. Hence, colour

model that represents grayscale as explicit component is more effective than

combination of RGB in creating reduced representationsYRB

Luminance = amount of light (y)

in RGB-based colour instance is :

y=0.299R+0.587G+0.114B

Shows that the blue colour is the least contribute to the

perception of light.

Given the luminance(y) and the existing RGB channels; R and B, a

new colour space YRB can be created and represent the same

colour as RGB

YUVSubtract the luminance component from

the colour components:U = 0.492(B-Y)V = 0.877(R-Y)

These equations ensure black and white pictures have no R and B components. U

and V components reflect the chrominance of the corresponding colour instance

YIQFor further reduction of bit representation,

rotate U and V components by 33°I = -0.492(B-Y)sin 33°+0.877(R-Y)cos 33°Q = 0.492(B-Y)cos 33°+0.877(R-Y)sin 33°

Education

Colour model