If you are a Computer Science student, an Electrical Engineering student, or just someone who works in Image Processing, you must have seen and worked on this picture:

Her name is Lena. But almost everyone who had seen this image knows almost nothing about her other than the fact that the image is a great test image because it contains a nice mixture of detail, flat regions, shading, and texture that do a good job of testing various image processing algorithms, and that she makes gentlemen drool- let’s face it, geeks do not get to see that kind of attractive ladies often- and ladies jealous! But really, who is Lena? And how did she become a standard in the boring field of Image Processing?!!

Her name is Lena Söderberg, a Swedish model born in March 31^st, 1951. Lena posed for the November 1972 issue of the Playboy Magazine. The image used nowadays in Image Processing is actually the same image posted in the magazine- cropped, of course.

It was in June or July of 1973 when Alexander Sawchuk, who was then an assistant professor of electrical engineering at the University of Southern California Signal and Image Processing Institute (SIPI), a graduate student, and the SIPI lab manager were searching the lab for a good image to scan for a colleague’s conference paper after they got tired of the usual test images. They wanted some image of a human face that would produce an output with a good dynamic range. It was only then when someone walked in the lab with a recent issue of Playboy. In Lena’s picture, they found exactly what they needed.

The engineers tore away the top third of the centrefold so they could wrap it around the drum of their Muirhead wirephoto scanner, which they had outfitted with three analog-to-digital converters -one for each colour channel- and a Hewlett Packard 2100 minicomputer. The Muirhead had a fixed resolution of 100 lines per inch and the engineers wanted a 512 × 512 image, so they limited the scan to the top 5.12 inches of the picture, effectively cropping it at the Lena’s shoulders. This scan became one of the most used images in computer history, so much that Lena was called the First Lady of the Internet, and was a guest at the 50^th annual Conference of the Society for Imaging Science and Technology in 1997. Someone even wrote her a poem:

Sonnet for Lena

O dear Lena, your beauty is so vast
It is hard sometimes to describe it fast.
I thought the entire world I would impress
If only your portrait I could compress.
Alas! First when I tried to use VQ
I found that your cheeks belong to only you.
Your silky hair contains a thousand lines
Hard to match with sums of discrete cosines.
And for your lips, sensual and tactual
Thirteen Crays found not the proper fractal.
And while these setbacks are all quite severe
I might have fixed them with hacks here or there
But when filters took sparkle from your eyes
I said, “Heck with it. I’ll just digitize!”

Now that the truth is out there, I can almost hear the voices of the radical, extremist Muslims calling for the ban of the use this image and probably killing, then dismembering and burning the corpse of our dean for using it in our Computer Vision curriculum! At some point in the near past, there were also voices calling for retiring the Lena image. That’s because publications of the kind of Playboy are degrading to women. Give me a break! This is just pathetic! We are not using the FULL image of Lena- forgive me for not posting it; extreme content- we are only using a cropped image of a nice looking girl that only shows a face and a shoulder. What is female-degrading or anti-Islamic about that? The source of the image? Newsflash, Mr. Extremist; not we, IEEE, nor any other organization that uses the image for any purpose pays a fee for the Playboy organization in exchange for the use of it, Playboy even waved away its copyrights to this specific image. In other words, it belongs to nobody, so we are not sponsoring the work of Satan!

I know that I will be personally criticized and attacked for my opinion, and I do not really care. Bottom line is, Lena’s image is perfect for testing Image Processing techniques, and until the radicals camp blesses us with another image to use that will not send us burning in hell for eternity, I will continue to use it. Even though I am sure that the proposed new image will be something like this! Not much dynamic range there, no?

21. Define subjective brightness and brightness adaptation?
Subjective brightness means intensity as preserved by the human visual system.
Brightness adaptation means the human visual system can operate only from
scotopic to glare limit. It cannot operate over the range simultaneously. It accomplishes
this large variation by changes in its overall intensity.

22. Define weber ratio
The ratio of increment of illumination to background of illumination is called as
weber ratio.(ie) ∆i/i
If the ratio (∆i/i) is small, then small percentage of change in intensity is needed
(ie) good brightness adaptation.
If the ratio (∆i/i) is large , then large percentage of change in intensity is needed
(ie) poor brightness adaptation.

23. What is meant by machband effect?
Machband effect means the intensity of the stripes is constant. Therefore it
preserves the brightness pattern near the boundaries, these bands are called as machband
effect.

24. What is simultaneous contrast?
The region reserved brightness not depend on its intensity but also on its
background. All centre square have same intensity. However they appear to the eye to
become darker as the background becomes lighter.

25. What is meant by illumination and reflectance?
Illumination is the amount of source light incident on the scene. It is represented
as i(x, y).   Reflectance is the amount of light reflected by the object in the scene. It is
represented by r(x, y).

26. Define sampling and quantization
Sampling means digitizing the co-ordinate value (x, y).
Quantization means digitizing the amplitude value.

27. Find the number of bits required to store a 256 X 256 image with 32 gray levels?
32 gray levels = 25

= 5 bits
256 * 256 * 5 = 327680 bits.

28. Write the expression to find the number of bits to store a digital image?
The number of bits required to store a digital image is
b=M X N X k
When M=N, this equation becomes
b=N^2k
30. What do you meant by Zooming of digital images?
Zooming may be viewed as over sampling. It involves the creation of new pixel
locations and the assignment of gray levels to those new locations.

31. What do you meant by shrinking of digital images?
Shrinking may be viewed as under sampling. To shrink an image by one half, we
delete every row and column. To reduce possible aliasing effect, it is a good idea to blue
an image slightly before shrinking it.
32. Write short notes on neighbors of a pixel.
The pixel p at co-ordinates (x, y) has 4 neighbors (ie) 2 horizontal and 2 vertical
neighbors whose co-ordinates is given by (x+1, y), (x-1,y), (x,y-1), (x, y+1). This is
called as direct neighbors. It is denoted by N4(P)
Four diagonal neighbors of p have co-ordinates  (x+1, y+1), (x+1,y-1), (x-1, y-1),
(x-1, y+1). It is denoted by ND(4).
Eight neighbors of p denoted by N8(P) is a combination of 4 direct neighbors and
4 diagonal neighbors.

33. Explain the types of connectivity.
1. 4 connectivity
2. 8 connectivity
3. M connectivity (mixed connectivity)

34. What is meant by path?
Path from pixel p with co-ordinates (x, y) to pixel q with co-ordinates (s,t) is a
sequence of distinct pixels with co-ordinates.
35. Give the formula for calculating D4 and D8 distance.
D4 distance ( city block distance) is defined by
D4(p, q) = |x-s| + |y-t|
D8 distance(chess board distance) is defined by
D8(p, q) = max(|x-s|, |y-t|).

36. What is geometric transformation?
Transformation is used to alter the co-ordinate description of image.
The basic geometric transformations are
1.  Image translation
2.  Scaling
3.  Image rotation

37. What is image translation and scaling?
Image translation means reposition the image from one co-ordinate location to
another along straight line path.
Scaling is used to alter the size of the object or image (ie) a co-ordinate system is
scaled by a factor.

38. What is the need for transform?
The need for transform is most of the signals or images are time domain signal
(ie) signals can be measured with a function of time. This representation is not always
best. For most image processing applications anyone of the mathematical transformation
are applied to the signal or images to obtain further information from that signal.

39. Define the term Luminance?
Luminance measured in lumens (lm), gives a measure of the amount of energy an
observer perceiver from a light source.
40. What is Image Transform?
An  image can be expanded  in  terms of a discrete set of basis arrays called basis
images. These basis  images can be generated by unitary matrices. Alternatively, a given
NxN  image can be viewed as an N^2×1 vectors. An  image  transform provides a  set of
coordinates or basis vectors for vector space.

Define Image?
An image may be defined as two dimensional light intensity function f(x, y)
where x and y denote spatial co-ordinate and the amplitude or value of f at any point
(x, y) is called intensity or grayscale or brightness of the image at that point.

2. What is Dynamic Range?
The range of values spanned by the gray scale is called dynamic range of an
image. Image will have high contrast, if the dynamic range is high and image will have
dull washed out gray look if the dynamic range is low.
3. Define Brightness?
Brightness of an object is the perceived luminance of the surround. Two objects
with different surroundings would have identical luminance but different brightness.
4. Define Tapered Quantization?
If gray levels in a certain range occur frequently while others occurs rarely, the
quantization levels are finely spaced in this range and coarsely spaced outside of it. This
method is sometimes called Tapered Quantization.
5. What do you meant by Gray level?
Gray level refers to a scalar measure of intensity that ranges from black to grays
and finally to white.
6. What do you meant by Color model?
A Color model is a specification of 3D-coordinates system and a subspace within
that system where each color is represented by a single point.
7. List the hardware oriented color models?
1. RGB model
2. CMY model
3. YIQ model
4. HSI model

8. What is Hue of saturation?
Hue is a color attribute that describes a pure color where saturation gives a
measure of the degree to which a pure color is diluted by white light.
9. List the applications of color models?
1. RGB model— used for color monitor & color video camera
2. CMY model—used for color printing
3. HIS model—-used for color image processing
4. YIQ model—used for color picture transmission

10. What is Chromatic Adoption?
`  The hue of a perceived color depends on the adoption of the viewer. For example,
the American Flag will not immediately appear red, white, and blue of the viewer has
been subjected to high intensity red light before viewing the flag. The color of the flag
will appear to shift in hue toward the red component cyan.
11. Define Resolutions?
Resolution is defined as the smallest number of discernible detail in an image.
Spatial resolution is the smallest discernible detail in an image and gray level resolution
refers to the smallest discernible change is gray level.
12. What is meant by pixel?
A digital image is composed of a finite number of elements each of which has a
particular location or value. These elements are referred to as pixels or image elements or
picture elements or pels elements.

13. Define Digital image?
When x, y and the amplitude values of f all are finite discrete quantities , we call
the image digital image.

14. What are the steps involved in DIP?
1. Image Acquisition
2. Preprocessing
3. Segmentation
4. Representation and Description
5. Recognition and Interpretation

15. What is recognition and Interpretation?
Recognition means is a process that assigns a label to an object based on the
information provided by its descriptors.
Interpretation means assigning meaning to a recognized object.

16. Specify the elements of DIP system?
1. Image Acquisition
2. Storage
3. Processing
4. Display
17. Explain the categories of digital storage?
1. Short term storage for use during processing.
2. Online storage for relatively fast recall.
3. Archical  storage for infrequent access. 18. What are the types of light receptors?
The two types of light receptors are
1.  Cones and
2.  Rods
19. Differentiate photopic and scotopic vision?

Photopic vision  Scotopic vision
1. The human being can resolve
the fine details with these cones
because each one is connected to
its own nerve end.
2. This is also known as bright
light vision.
Several rods are connected to
one nerve end. So it gives the
overall picture of the image.

This is also known as thin light
vision.

20. How cones and rods are distributed in retina?
In each eye, cones are in the range 6-7 million and rods are in the range 75-150
million.

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SUMMARY

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IMAGE   PROCESSING

In this article, the basics of capturing an image, image processing to modify and enhance the image are discussed. There are many applications for Image Processing like surveillance, navigation, and robotics. Robotics is a very interesting field and promises future development so it is chosen as an example to explain the various aspects involved in Image Processing .

The various techniques of Image Processing are explained briefly and the advantages and disadvantages are listed. There are countless different routines that can be used for variety of purposes. Most of these routines are created for specific operations and applications. However, certain fundamental techniques such as convolution masks can be applied to many classes of routines. We have concentrated on these techniques, which enable us to adapt, develop, and use other routines and techniques for other applications. The advances in technology have created tremendous opportunities for visual system and image processing. There is no doubt that the trend will continue into the future.

INTRODUCTION

Image Processing :

Image processing pertains to the alteration and analysis of pictorial information. Common case of image processing is the adjustment of brightness and contrast controls on a television set by doing this we enhance the image until its subjective appearing to us is most appealing. The biological system (eye, brain) receives, enhances, and dissects analyzes and stores mages at enormous rates of speed.

Basically there are two-methods for processing pictorial information. They are:

1. Optical processing
2. Electronic processing.

Optical processing uses an arrangement of optics or lenses to carry out the process. An important form of optical image processing is found in the photographic dark room.

Electronic image processing is further classified as:

1. Analog processing
2. Digital processing.

Analog processing:

These ple of this kind is the control of brightness and contrast of television image. The television signal is a voltage level that varies In amplitude to represent brightness through out the image by electrically altering these signals , we correspondingly alter the final displayed image  appearance.

Digital image processing:

Processing of digital images by means of digital computer refers to digital image processing. Digital images are composed of finite number of element of which has a particular location value. Picture elements, image elements, and pixels are used as elements used for digital image processing.

Digital Image Processing is concerned with processing of an image. In simple words an image is a representation of a real scene, either in black and white or in color, and either in print form or in a digital form i.e., technically a image is a two-dimensional light intensity function.  In other words it is a data intensity values arranged in a two dimensional form, the required property of an image can be extracted from processing an image.  Image is typically by stochastic models. It is represented by AR model. Degradation is represented by MA model.

Other form is orthogonal series expansion. Image processing system is typically non-casual system. Image processing is two dimensional signal processing. Due to linearity Property, we can operate on rows and columns separately. Image processing is vastly being implemented by “Vision Systems” in robotics. Robots are designed, and meant, to be controlled by a computer or similar devices. While “Vision Systems” are most sophisticated sensors used in Robotics. They relate the function of a robot to its environment as all other sensors do.

“Vision Systems” may be used for a variety of applications, including manufacturing, navigation and surveillance.

Some of the applications of Image Processing are:

1.Robotics.                                  3.Graphics and Animations.

2.Medical Field.                          4.Satellite Imaging.

INDEX TERMS

• Image Processing?

Image processing is a subclass of signal processing concerned specifically   with Pictures.Improve image quality for human perception and/or computer interpretation. Image Enhancement

To bring out detail is obscured, or simply to highlight certain features of interest in an image.

Example:

1. Image Restoration

Improving the appearance of an image tend to be based on   mathematical or probabilistic models of image degradation.

Example:

DISTORTED IMAGE                                                            RESTORTED IMAGE

1. Color Image Processing

Gaining in importance because of the significant increase in the use of digital images over the Internet.

1. Wavelets

Foundation for representing images in various degrees of resolution.  Used in image data compression and pyramidal representation  (images are subdivided successively into smaller regions)

1. Compression

Reducing the storage required to save an image or the bandwidth   required to transmit it. Ex. JPEG (Joint Photographic Experts Group) image compression standard.

1. Morphological processing

Tools for extracting image components that are useful in the                          representation and description of shape.

1. Image Segmentation

Computer tries to separate objects separate objects from the image              background from the image background. It is one of the most   difficult tasks in DIP. A rugged segmentation procedure brings the process a long way toward successful solution of an image problem. Output of the segmentation stage is raw pixel data, constituting either the boundary of a region or all the points in the region itself.

ANALYSIS

The following is the overall view and analysis of Image Processing.

IMAGE PROCESSING TECHNIQUES:

Image Processing techniques are used to enhance, improve, or otherwise alter an image and to prepare it for image analysis. Usually, during image processing information is not extracted from the image. The intention is to remove faults, trivial information, or information that may be important, but not useful, and to improve the image.

Image processing is divided into many sub processes, including Histogram Analysis, Thresholding, Masking, Edge Detection, Segmentation, and others.

STAGES IN IMAGE PROCESSING:

_

1.IMAGE ACQUISITION:

An image is captured by a sensor (such as a monochrome or color TV camera) and digitized. If the output of the camera or sensor is not already in digital form, an analog-to digital converter digitizes it.

2.RECOGNITION AND INTERPRETATION:

Recognition is the process that assigns a label to an object based on the information provided by its descriptors. Interpretation is assigning meaning to an ensemble of recognized objects.

3.SEGMENTATION:

Segmentation is the generic name for a number of different techniques that

divide the image into segments of its constituents. The purpose of segmentation is to

separate the information contained in the image into smaller entities that can be used for other purposes.

4.REPRESENTATION AND DESCRIPTION:

Representation and Description transforms raw data into a form suitable for

the Recognition processing.

5. KNOWLEDGE BASE:

A problem domain detailing the regions of an image where the information of

interest is known to be located is known as knowledge base. It helps to limit the search.

THRESHOLDING:

Thresholding is the process of dividing an image into different portions by picking a certain grayness level as a threshold, comparing each pixel value with the threshold, and then assigning the pixel to the different portions, depending on whether the pixel’s grayness level is below the threshold or above the threshold value. Thresholding can be performed either at a single level or at multiple levels, in which the image is processed by dividing it into ” layers”, each with a selected threshold.

Various techniques are available to choose an appropriate threshold ranging from simple routines for binary images to sophisticated techniques for complicated images.

CONNECTIVITY:

Sometimes we need to decide whether neighboring pixels are somehow “connected” or related to each other. Connectivity establishes whether they have the same property, such as being of the same region, coming from the same object, having a similar texture, etc. To establish the connectivity of neighboring pixels, we first have to decide upon a connectivity path.

NOISE REDUCTION:

Like other signal processing mediums, Vision Systems contains noises. Some noises are systematic and come from dirty lenses, faulty electronic components, bad memory chips and low resolution. Others are random and are caused by environmental effects or bad lighting. The net effect is a corrupted image that needs to be preprocessed to reduce or eliminate the noise. In addition, sometimes images are not of good quality, due to both hardware and software inadequacies; thus, they have to be enhanced and improved before other analysis can be performed on them.

CONVOLUTION MASKS:

A mask may be used for many different purposes, including filtering operations and noise reduction. Noise and Edges produces higher frequencies in the spectrum of a signal. It is possible to create masks that behave like a low pass filter, such that higher frequencies of an image are attenuated while the lower frequencies are not changed very much. There by the noise is reduced.

EDGE DETECTION:

Edge Detection is a general name for a class of routines and techniques that operate on an image and results in a line drawing of the image. The lines represented changes in values such as cross sections of planes, intersections of planes, textures, lines, and colors, as well as differences in shading and textures. Some techniques are mathematically oriented, some are heuristic, and some are descriptive. All generally operate on the differences between the gray levels of pixels or groups of pixels through masks or thresholds. The final result is a line drawing or similar representation that requires much less memory to be stored, is much simpler to be processed, and saves in computation and storage costs. Edge detection is also necessary in subsequent process, such as segmentation and object recognition. Without edge detection, it may be impossible to find overlapping parts, to calculate features such as a diameter and an area or to determine parts by region growing.

IMAGE DATA COMPRESSION:

Electronic images contain large amounts of information and thus require data transmission lines with large bandwidth capacity. The requirements for the temporal and spatial resolution of an image, the number of images per second, and the number of gray levels are determined by the required quality of the images. Recent data transmission and storage techniques have significantly improved image transmission capabilities, including transmission over the Internet.

REAL-TIME IMAGE PROCESSING:

In many of the techniques considered so far, the image is digitized and stored before processing. In other situations, although the image is not stored, the processing routines require long computational times before they are finished. This means that, in general, there is a long lapse between the time and image is taken and the time a result obtained. This may be acceptable in situations in which the decisions do not affect the process. However, in other situations, there is a need for real-time processing such that the results are available in real time or in a short enough time to be considered real time. Two different approaches are considered for real time processing. One is to design dedicated hardware such that the processing is fast enough to occur in real time. The other is to try to increase the efficiency of both the software and the hardware and thereby reduce processing and computational requirements.

APPLICATION :

Image Processing is vastly being implemented in Vision Systems in Robotics. Robots capture the real time images using cameras and process them to fulfill the desired action.

A simple application in robotics using Vision Systems is a robot hand-eye coordination system. Consider that the robot’s task is to move an         object from one point to another point. Here the robots are fixed with cameras to view the object which is to be moved. The hand of the robot and the object that is to be captured are observed by the cameras, which are fixed to the robot in position, this real time image is processed by the image processing techniques to get the actual distance between the hand and the object. Here the base wheel of the robot’s hand is rotated through an angle which is proportional to the actual distance between hand and the object. Here a point in the target is obtained by using the Edge Detection Technique. The operation to be performed is controlled by the micro-controller, which is connected to the ports of the fingers of the robot’s hand. Using the software programs the operations to be performed are assigned keys from the keyboard. By pressing the relative key on the keyboard the hand moves appropriately.

Here the usage of sensors/cameras and Edge Detection technique are related to Image Processing and Vision Systems. By this technique the complexity of using manual sensors is minimized to a great extent and thereby sophistication is increased. Hence image processing is used here in the study of robotics.

APPLICATION 2:

In the field of Medicine this is highly applicable in areas like Medical imaging, Scanning, Ultrasound and X-rays etc.

Image Processing is rapidly used for MRI SCAN (Magnetic Resonance Imaging) and CT SCAN (Computer Tomography). Tomography is an imaging technique that generates an image of a thin cross sectional slice of a test pieces

ADVANTAGES

• In medicine by using the Image Processing techniques the sophistication has increased. This lead to technological advancement.
• Vision Systems are flexible, inexpensive, powerful tools that can be used with ease.
• In Space Exploration the robots play vital role which in turn use the image processing techniques.
• Image Processing is used for Astronomical Observations.
• Also used in Remote Sensing, Geological Surveys for detecting mineral resources etc.
• Also used for character recognizing techniques, inspection for abnormalities in industries.

DISADVANTAGES

• A Person needs knowledge in many fields to develop an application / or part of an application using image processing.
• Calculations and computations are difficult and complicated so needs an expert in the field related. Hence it’s unsuitable and unbeneficial to ordinary programmers with mediocre knowledge.

CONCLUSION

It’s a critical study, which plays a vital role in modern world as it is involved with advanced use of science and technology. The advances in technology have created tremendous opportunities for Vision System and Image Processing. There is no doubt that the trend will continue into the future. from the above discussion we can conclude that this field has relatively more advantages than disadvantages and hence is very useful in varied branches.

REFERENCES

• INTRODUCTION TO ROBOTICS, ANALYSIS, SYSTEMS, APPLICATIONS  – SAEED B. NIKU

• INTRODUCTION TO DIGITAL IMAGE PROCESSING – ANIL K.JAIN

• Digital mage Processing – Rafael C. Gonzalez and Richard E. Woods, Addison Wesley 1993.

• Image Processing Analysis, and Machine Vision 2nd edition PWS Publishing, 1998 – Milan Sonka, Vaclav Hlavac and Roger Boyle.

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IrfanView

IrfanView is a free, small, and fast image viewer that can handle virtually any image file format (some require additional plugins). It offers an arsenal of image manipulation features (crop, resize, color adjustment, etc.), as well as a multitude of special features usually found only in expensive packages. There are too many to list here, but some of the best features include complete effects (sharpen, blur, etc.), paint capabilities (draw on images), desktop/window capturing, thumbnail viewer/creator, multimedia player, batch conversion (optional image processing), support for Adobe Photoshop filters, and many plugins available to add even more features.

• Website: Visit Official Site
• Price: Free
• Developer: Irfan Skiljan

http://www.roborealm.com/links/vision_software.php

Reviews of Vision Software
k2bytes’ review of Machine Vision Libraries

http://www.roborealm.com/index.php

RoboRealm® is an application for use in computer vision, image analysis, and robotic vision systems. Using an easy point and click interface RoboRealm simplifies vision programming! Using an inexpensive USB webcam and the PC you already have you can now add machine vision to your robotic projects!

This only costs $89 so it could be used for educational purposes.

Lego NXT Ball Picker
http://www.roborealm.com/tutorial/ball_picker/slide010.php

Author : GUNAWAN, DAVID

Mesin bor otomatis dibuat dengan tujuan agar dapat dilakukan pengeboran PCB secara otomatis. Pengeboran PCB otomatis ini menggunakan kamera sebagai sensor pendeteksi koordinat pad atau via. Kamera berfungsi menangkap gambar print out sebuah PCB kemudian dengan metode image processing dapat diketahui dan diambil koordinat pengeborannya. Tugas akhir ini dibuat untuk mengetahui bagaimana hasil pengeboran apabila mesin bor otomatis tersebut dikontrol sepenuhnya menggunakan PLC (Programmable Logic Controller). Proses pengambilan koordinat akan dilakukan pada PC (Personal Computer) dengan menggunakan tiga macam metode yaitu metode image processing, metode file text, dan metode manual. Hasil dari proses pengambilan koordinat ini akan dikirimkan oleh PC ke PLC menggunakan prosedur Host Link. Pengujian telah dilakukan pada 3 buah PCB dengan jumlah dan posisi hole yang berbeda-beda. Dari hasil pengujian didapat bahwa waktu pengeboran yang dibutuhkan pada sistem ini cukup lama yaitu kurang lebih 28 menit untuk PCB pengujian 16 hole, 19 menit untuk PCB pengujian 21 hole, dan 29 menit untuk PCB pengujian 34 hole. Ini disebabkan adanya keterbatasan pada PLC dan motor stepper yang dipakai. Sedangkan tingkat ketelitian dari sistem ini cukup baik, hal ini diketahui dari nilai rata-rata error yang didapat selalu kurang dari 1 milimeter. Jadi dapat disimpulkan bahwa PLC dapat dipakai untuk mengontrol mesin bor otomatis ini.

Keyword : image processing, automatic drilling machine, pcb, plc, pc

Sumber : http://repository.petra.ac.id/3483/

Tujuan :
Menggunakan histogram yang menampilkan warna suatu gambar sebagai alat untuk mengukur jarak terdekat gambar dan membandingkan gambar dengan gambar yang ada dalam database sistem.

Dasar Teori :
Histogram suatu gambar dalam pemrosesan gambar digitall dapat menampilkan ciri dari gambar tersebut. Ciri yang ditampilkan adalah berupa ciri warna dimana warna suatu gambar pertama dengan gambar yang lainnya pasti mempunyai nilai intensitas yang berbeda.
HISTOGRAM CIRI WARNA

This is our 1st URL image displayed in a windows application (YAY)

We MUST celebrate gurlzz!! it finally worked el7amdolla

Correlation and Convolution are two basic operations used in low level vision operations ,and are used in a large number of image processing operations. Although there are functions available for convolution and 2-d convolution can also be done using filtering library of Video and Image Processing blockset in MATLAB ,I wrote a single file which can correlate or convolve two provided 2-d matrices by you during runtime by asking your choice.

Operation : Usually a lot of time we need to convolve or correlate a window function ( take a window matrix ) with a 2-d matrix during various image processing operations. This window is many times a square matrix. So lets check out the correlation and convolution operation.

1-d Operations :

Take a window matrix (here a simple vector) –> w = [ 1 2 3 ]

Take any other vector to be correlated or convolved with : V = [ 2 3 4 1 2 3 4]

Correlation :

1) Identify the number of columns of window  –> n = size(w) –> 3

2)Pad V with size (n-1) of columns before and after –> V =[ 0 0 2 3 4 1 2 3 4 0 0]

3)Start element wise multiplying the w vector right from first column of V  and sum the values –> for.ex. 1*0 +2*0 +3*2 =6

4)Place this value as an element of a new vector , the correlated vector (say C).

5)Repeat the operation till the last element of w gets multiplied by last element of V.

6)Clamp out n-1 number of columns from beginning and end of C.

7)You have the correlated vector.

Convolution:

For convolution simply rotate the vector w by 18o degrees  i.e. w becomes [3 2 1]

Repeat step 1 to 7.

2-d Operations :

Correlation :

1) 2-d matrix is collection of rows or 1-d matrix.Just extend the above concept  keeping in mind you have large number of vectors. i.e pad both rows and columns up and down , right and left.

2) Do element wise matrix multiplication right from the top left corner to bottom right corner, add the results each time and place them in different matrix.

3) Do clamping of padded rows and columns.

Convolution :

1) Just flip the matrix horizontally to the left by 180 degrees.

2) Then flip the obtained matrix vertically upwards by 180 degrees.

For ex : V =   1   2   3

4   5    6

7   8    9

Flip it horizontally  –>

V =   3  2   1

6  5   4

9  8   7

Flip it vertically      –>

V =   9   8   7

6   5   4

3   2   1

And do the same operation as in correlation. You have your convolution results !!

One file implementation in MATLAB 7.6.0 : This code asks for the number of rows and columns for the matrix first , asks it values, then asks to choose between convolution and correlation , and then takes the values of window.

In output it shows the original matrix , the window which is finally used in operation ( i.e. rotated window in convolution) and the correlated or convoluted output.

The file can be obtained here(Keep the window size greater than 2 and odd).

Save the file as vco.m (matlab format) in your Documents>Matlab ; work folder of Matlab or specify the path if saved somewhere else.

Type vco in the command window and enjoy your correlation/convolution!!

I would imagine many of you have tried the mobile application from Adobe for Android. Its okay I guess but I want to go in a different direction. Some research groups have created new languages that cover the image processing area. One of those groups has started an Andorid branch, namely processing.org, see the android source branch/trunk.

A less complex approach seems to be JJIL which just focuses on image processing itself. The created image processing pipe-lines than can be used both in processing still images and video. The actual math-programming  functions used seems to be based on OpenCV.

What I am attempting to state is there is big difference between put something up and engaging the mobile Android user. Being able to crop and color correct an image is not exactly engaging that mobile Android user. Thus the general idea is to start with an Android Image Gallery application and use JJIL to do some image processing via image processing pipelines and combine that with some new UI ideas I have to see if I can raise the engage the mobile Android user bar higher than Adobe did.

The previous article was about producing an image free of noise, now we can apply a tone mapping [1]. I’m going to use Enfuse to generate a natural HDR/LDR image.

Due to the nature of the image produced by the Noise Suppression technique, we can generate over-exposed images (1EV 2EV 3EV 4EV) without any quality issues. Then we are going to feed the resultant images (with a gamma 2.2 applied) to Enfuse.

convert ${min_ev_name}_fon.tiff -alpha off -gamma 2.2 ${min_ev_name}_tone_a.tiff
letters=( a b c d e f g h i j k l m n o p q r s t u v w x y z )
counter=1
while true; do
  exp=`echo "2^${counter}" | bc`
  if [ "${exp}" -gt "${max_exp}" ]; then
     break
  fi
  convert ${min_ev_name}_fon.tiff -alpha off -evaluate multiply ${exp} -gamma 2.2 ${min_ev_name}_tone_${letters[${counter}]}.tiff
  let counter=${counter}+1
done
convert ${min_ev_name}_fon.tiff -alpha off -evaluate multiply ${max_exp} -gamma 2.2 ${min_ev_name}_tone_${letters[${counter}]}.tiff
tone_files=`ls ${min_ev_name}_tone_*.tiff`
enfuse -v -o ${min_ev_name}_tm.tiff ${tone_files}

Enfuse generates internally the following merging masks.

Finally Enfuse produces a natural looking image.

Additionally we can process the image a little bit further by applying contrast and saturation to the HDR/LDR image. I’m using a sigmoidal contrast curve of 2×50% and a 30% increase of saturation.

convert ${min_ev_name}_tm.tiff -sigmoidal-contrast 2x50% -modulate 100,130,100 ${min_ev_name}_final.tiff

—
[1] Guillermo Lujik – HDR TONE MAPPING

max_ev=`ls -1 *.cr2 | tail -n 1`
max_ev_name=`echo ${max_ev} | cut -d '.' -f1`
dcraw -v a -W -o 1 -q 3 -4 -T -c ${max_ev} > ${max_ev_name}.tiff 2> dcraw.log
cat dcraw.log
rgbg=`grep 'multipliers' dcraw.log | cut -d ' ' -f2,3,4,5`
rm dcraw.log
for other in $( ls -r *.cr2 ); do
  if [ ${other} != ${max_ev} ]; then
    dcraw -v -r $rgbg -W -o $color -q 3 -4 -T ${other}
  fi
done

Second step is to create the merging masks (2 masks for 3 catches). A mask is produced from the negate of over-exposed image which is copied into the alpha channel [2] of the corrected version of the over-exposed image. For example, the corrected version of the +2EV image is 0EV.
To generate the corrected images, I’ve wrote a little program (based on Guillermo’s algorithm) using the ImageMagick C++ API. The program prints the exposure difference between 2 images.

#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <wand/MagickWand.h>

int main(int argc,char **argv) {
  MagickBooleanType status;
  MagickPixelPacket pixel1, pixel2;
  MagickWand *image1, *image2;
  PixelIterator *iterator1, *iterator2;
  PixelWand **pixels1, **pixels2;

  long y;
  register long x;
  unsigned long width;

  double min = 65536 / pow(2, 6);
  double max = 65536 * 0.9;
  double sum1 = 0, sum2 = 0;

  if (argc != 3) {
      fprintf(stdout, "Usage: %s normal-image over-exposed-image\n", argv[0]);
      exit(0);
  }

  MagickWandGenesis();
  image1 = NewMagickWand();
  status = MagickReadImage(image1, argv[1]);
  if (status == MagickFalse) {
    return -1;
  }
  image2 = NewMagickWand();
  status = MagickReadImage(image2, argv[2]);
  if (status == MagickFalse) {
    return -1;
  }

  iterator1 = NewPixelIterator(image1);
  iterator2 = NewPixelIterator(image2);
  if ((iterator1 == (PixelIterator *) NULL) || (iterator2 == (PixelIterator *) NULL)) {
    return -1;
  }
  for (y=0; y < (long) MagickGetImageHeight(image1); y++) {
    pixels1 = PixelGetNextIteratorRow(iterator1, &width);
    pixels2 = PixelGetNextIteratorRow(iterator2, &width);
    if ((pixels1 == (PixelWand **) NULL) || (pixels2 == (PixelWand **) NULL)) {
      break;
    }
    for (x=0; x < (long) width; x++) {
      PixelGetMagickColor(pixels1[x], &pixel1);
      PixelGetMagickColor(pixels2[x], &pixel2);
      if ((pixel1.red >= min) && (pixel1.red <= max) && (pixel2.red >= min) && (pixel2.red <= max)) {
         sum1 += pixel1.red;
         sum2 += pixel2.red;
      }
      if ((pixel1.green >= min) && (pixel1.green <= max) && (pixel2.green >= min) && (pixel2.green <= max)) {
         sum1 += pixel1.green;
         sum2 += pixel2.green;
      }
      if ((pixel1.blue >= min) && (pixel1.blue <= max) && (pixel2.blue >= min) && (pixel2.blue <= max)) {
         sum1 += pixel1.blue;
         sum2 += pixel2.blue;
      }
    }
  }
  if (y < (long) MagickGetImageHeight(image1)) {

    return -1;
  }
  iterator1 = DestroyPixelIterator(iterator1);
  image1 = DestroyMagickWand(image1);
  iterator2 = DestroyPixelIterator(iterator2);
  image2 = DestroyMagickWand(image2);
  MagickWandTerminus();

  printf("%.100g\n", sum1/sum2);
  return(0);
}

min_ev=`ls -1r *.tiff | tail -n 1`
for ev in $( ls *.tiff ); do
  if [ ${ev} != ${min_ev} ]; then
    ev_name=`echo ${ev} | cut -d '.' -f1`
    ec=`exposure ${min_ev} ${ev}`
    convert ${ev} -evaluate multiply $ec ${ev_name}_corrected.tiff
    composite -compose CopyOpacity \( ${ev} -negate \) ${ev_name}_corrected.tiff ${ev_name}_mask.tiff
  fi
done

To get the final image, we need to merge the masks together and overlay the mask to the first image.

mask_files=`ls -r *_mask.tiff`
nb_mask=`ls  -r *_mask.tiff | wc -l`
if [[ $( echo "$nb_mask == 1.00" | bc ) == "1" ]]; then
  mv $mask_files final_mask.tiff
else
  composite ${mask_files} final_mask.tiff
fi
min_ev_name=`echo ${min_ev} | cut -d '.' -f1`
composite final_mask.tiff ${min_ev} ${min_ev_name}_fon.tiff

Here is the final bash script which takes in argument a folder with a number of catches. With the -c option, you can compile the latest dcraw and the exposure program (you need the ImageMagick development package). The script requires the following tools: bash, cut, tail, grep, bc, wget, gcc and ImageMagick Q16.

#!/bin/bash
compile=
color=1
wb=-w
ext=dng

while getopts 'w:e:c' OPTION
  do
    case $OPTION in
      w)      wb=-"$OPTARG"
               ;;
      e)      ext="$OPTARG"
               ;;
      c)      compile=1
               ;;
      ?)      printf "Usage: %s: [-w {dcraw white balance: a|w}] [-e {file extension}] [-c compile] {folder}\n" $(basename $0) >&2
               exit 2
               ;;
      esac
      done
shift $(($OPTIND - 1))

if [ -z $1 ]; then
  printf "Usage: %s: [-w {dcraw white balance: a|w}] [-e {file extension}] [-c compile] {folder}\n" $(basename $0) >&2
  exit 2;
fi

if [ "$compile" ]; then
  echo "--- compile dcraw"
  wget http://cybercom.net/~dcoffin/dcraw/dcraw.c
  gcc -o dcraw -O4 dcraw.c -lm -ljpeg -llcms
  rm dcraw.c
  echo "--- compile exposure"
  gcc `Magick-config --cflags --cppflags` -o exposure exposure.c `Magick-config --ldflags --libs`
fi

echo "--- process folder=${1}"
cd ${1}
rm *.tiff

echo "--- convert raw to tiff"
max_ev=`ls -1 *.${ext} | tail -n 1`
max_ev_name=`echo ${max_ev} | cut -d '.' -f1`
../dcraw -v $wb -W -o $color -q 3 -4 -T -c ${max_ev} > ${max_ev_name}.tiff 2> dcraw.log
cat dcraw.log
rgbg=`grep 'multipliers' dcraw.log | cut -d ' ' -f2,3,4,5`
rm dcraw.log
for other in $( ls -r *.${ext} ); do
  if [ ${other} != ${max_ev} ]; then
    ../dcraw -v -r $rgbg -W -o $color -q 3 -4 -T ${other}
  fi
done

min_ev=`ls -1r *.tiff | tail -n 1`
max_exp=0
for ev in $( ls *.tiff ); do
  if [ ${ev} != ${min_ev} ]; then
    ev_name=`echo ${ev} | cut -d '.' -f1`
    echo -n "--- calculate negative exposure between ${min_ev} and ${ev}="
    ec=`../exposure ${min_ev} ${ev}`
    echo $ec
    max_exp=`echo "1/$ec" | bc`
    echo "--- create a mask for ${ev}"
    convert ${ev} -evaluate multiply $ec ${ev_name}_corrected.tiff
    composite -compose CopyOpacity \( ${ev} -negate \) ${ev_name}_corrected.tiff ${ev_name}_mask.tiff
  fi
done

echo "--- merge the masks"
mask_files=`ls -r *_mask.tiff`
nb_mask=`ls  -r *_mask.tiff | wc -l`
if [[ $( echo "$nb_mask == 1.00" | bc ) == "1" ]]; then
  mv $mask_files final_mask.tiff
else
  composite ${mask_files} final_mask.tiff
fi

echo "--- overlay the mask with ${min_ev}"
min_ev_name=`echo ${min_ev} | cut -d '.' -f1`
composite final_mask.tiff ${min_ev} ${min_ev_name}_fon.tiff

rm *_mask.tiff *_corrected.tiff

—
[1] Guillermo Lujik – ZERO NOISE
[2] ImageMagick – Copy Opacity

Bismillah…
This time I will introduce some image processing toolbox in Matlab. How to process digital image in Matlab, how to get the pixel and so on.
First, u have to install Matlab in your computer. Actually beside Matlab, u can use free software to process the digital image like Scilab or Octave. (in this software, I am not really understand, so I just use Matlab^^)
at the second time, u have to get the image processing’s toolbox and install it in your Matlab.
Open your Matlab then

After that, u can type many kind of command related about image processing. For the example, I want to rea the pixel d and convert my favorite wallpaper into gray scale. This is the example:

well, do you want to know what pixel in my favorite’s wallpaper?
All of this is my favorite’s wallpaper’s pixels.

After that if you want to convert the pixel into gray scale, you just type this coomand I1=rgb2gray (I). and the wallpaper becomes like this

Gray scale Image

that is really so sweet, isn’t it? If you use another software you have to type the formula to convert rgb to gray images. Like this

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