Image matching in MATLAB

Image matching- which means comparing two images for identical features. Often its practical usages are biometric applications such as finger print identification, Iris matching etc. Two images matches when their features coincides and these feature could be edges, corners, blobs, color, shape etc. By identifying these specific features in an image and comparing it with the features of other image, we can determine whether the images matches or not. Below shown is a demonstration of the image matching in the application of finger print recognition in MATLAB,

In this program, the number of edges in the image1 is compared with that of the number of edges in the image2. If the image matches, the GUI returns a 'Match' dialogue box.

Algorithm:

1. Load the image into MATLAB
2. Convert to grayscale image
3. Apply the edge detector of your choice after removing noises, if any.
4. Create a copy of this image for comparison (say img1 and img2)
5. Now traverse through the every pixel of both the images using a looping statement,
6. And, check the pixel value at the corresponding pixel
7. If the comparison percentage if greater than certain percentage( say 90, as per your wish), then the finger prints matches, else, the finger prints are not a match



Note: This not the exact way how the conventional biometric scanners work, but, this method can be used for rough matching purpose

H Bridge simulation in NI multisim

H Bridge configuration has many applications in controlling a motor. Generally, a motor can be either switched ON or OFF(uni directional rotation) based on the needs and the direction of rotation depends on the polarity. But, in the field of Robotics and Medical applications, a single motor must have the ability to rotate in two directions(clockwise and anti clockwise). For this purpose, an H bridge configuration is preferred. As the name indicates, the circuit will be in the shape of alphabet “H”. The motor is made to rotate in two directions by changing the polarity of the motor.

 

Look at the following connections, 

Here, the direction of rotation of the motor varies with the change in its polarity. A simulation of the above schematic is shown below,

A combination of these two schematic gives a H Bridge circuit and the switching between polarities is done using the transistors(electronic switches) as shown below,

From the above circuit, it can be seen that when the switches(SW1 and SW3) closed, the motor will be in one polarity and when the switches(SW2 and SW4) are closed while the other switches are open, the motor will be in another polarity. And this is how an H Bridge circuit works and the transistors are used as the switches.  Below shown is the simulation of the H Bridge circuit in NI multisim,

Here, when the gate is ON, the MOSFET is turned ON, hence forms a closed circuit and the motor receives the polarity based on the above explanations. 

Making of my Autonomous Vehicle

I have built an Autonomous Vehicle as a part of my final year project, which was sponsored by “Analog Devices, Inc”.The vehicle have the ability to move from one point to another point based on the GPS coordinates of both points and also plots the path it travels in google maps. In order to avoid obstacles in its path, the vehicle uses a grid of sensors that includes laptop based RADAR, camera, proximity sensors. Following are few snapshots of the prototype that was built (camera is not shown in the picture),

Front view

Side view

Here are some videos of making of my Autonomous Vehicle,

Initial obstacle avoidance algorithm testing 1 - outdoor

Initial obstacle avoidance algorithm testing 2 - outdoor

Optimized algorithm testing 1 - outdoor

Optimized algorithm testing 2 - indoor

Testing after changing vehicle dynamics 1 (vehicle turning radius test)

Testing after changing vehicle dynamics 2 (vehicle turning radius test)

Testing after changing vehicle dynamics 3 + Acquiring GPS data of start and end points

Image processing algorithm testing ( webcam+ MATLAB Simulink)

Plotted GPS data in google earth (while vehicle is moving)

Plotted GPS data in google earth (while vehicle is stationary)

Preliminary model built to test the algorithms 

Heart rate measurement in LabVIEW

This is one of my project in which I have designed a transducer that can measure the variations in oxygenated hemoglobin and deoxygenated hemoglobin ( simply known as an oximeter).The sensor was developed based on the key principle that these two parameters have two different optical spectra in the range of 500nm to 1000nm. Hence, two light sources of two different wavelength(red-660nm and infra red-940nm) are used. A photo diode was used to sense absorption rate and the output of the photo diode was obtained in the LabVIEW using a data aquisition card(NI 6211).  The photo plethysmograph(PPG) waveform was obtained in LabVIEW by driving the leds using PWM signals of 25% duty cycle. Hence, based on the PPG waveform, the heart rate(Beats Per Mintue) was found.

This is the video of the PPG waveform obtained in the LabVIEW. Since ordinary sensors was used, there were lot of noise interference in the reading and the BPM was very fluctuating.

(the PPG waveform in the video is a recorded measurement file of the actual experiment)

This is the PWM pulse which was generated to drive the LEDs from the LabIEW. LEDs are switched alternatively at same duty cycle.

Shown below is the transducing part which I have developed in my college laboratory,

“Simulation” is always a good idea before an actual execution of a project. I have simulated the transducing circuit in NI Multisim and it helped me a lot in actual implementation. Following is the video of the simulation. 

I have used the current source since the output of a photo diode is current and used an I-V converter to convert it into voltage( since input of NI 6211 must be in volts). Here I have also added a sample and hold circuit to hold one led`s value while other is being sampled and vice versa. The manual switch in the simulation was replaced with the control signal from the LabVIEW.

Though the acquired results got some flaws, obtaining a biological parameter was really awesome!

The Free Radicals

Human body consists of n number of feedback loops right from a single cell to the  major parts of the body. Alteration to any of these cells, vessels or parts, leads the regular bio feedback loop to collapse and in some cases, may even results in death. These process often acts like a butterfly effect.

One of the major issue to consider is our immune system and free radicals. Our body`s defense system releases free radicals(a by-product of metabolic process of oxidation)to fight against viruses and bacteria. But the excess free radicals produced due to pollution, smoking, stress, along with the indigenous unterminated  free radical chain tends to steal(in order to get paired) electrons available in the body in every part of the tissue. And this chain goes on until all the free radicals are perfectly bonded.

This is the root cause for majority of heart disease, artery blockage and cancer since these free radicals are more fond of electrons in the region of heart and brain. Unfortunately there are no effective drugs have been developed so far to address this issue. And since free radicals are essential, it cannot be terminated too. But the excess free radicals bonded with the oxygen to form oxygen free radicals can be reversed or neutralized with proper intake of glutathione peroxidase and anti oxidants.

One of the impact of these excess free radicals is that, its tendency to steal electrons from the DNA. If this process is succeeded, the DNA is subjected to mutation and incase of pregnant women, it could even damage the fetus. Thus the effective control of the free radicals must be considered as a preventive step for proper health condition. To do so, keep away from stress and pollution. Also consume anti oxidant rich foods. Vitamin C, E and glutathione peroxidase can reverse the oxygen free radicals into pure oxygen and also prevent excess free radicals and heart blockage. Hope this information helps!!

(I have written this article for my college magazine during my third year of Engineering. These are my perception of free radicals and body chemistry. and I am not an expert in medicine or biochemistry)

Increasing the voltage levels of PWM signals

PWM signals are often used in robotics for the purpose of controlling the DC motor speed and to drive the servo motors. Most of the microcontrollers have the output voltage of 5V. When  the PWM signals are generated from these controllers, the average output voltage for maximum duty cycle will be around 3.3 to 4.2 V(approx). This voltage level cannot drive a 5 or 12V motor efficiently. Hence the voltage level must be boosted with appropriate external circuits.

Following is a simulation that demonstrates how to increase the voltage level of the PWM signals generated from a microcontroller. Here, I have used CCP module of PIC microcontroller to generate the PWM pulses. The DC motor is driven by the MOSFET based on the PWM signals. 

From the video, you can see the voltage level from the microcontroller and the boosted voltage in the oscilloscope.  It can be noted that only the amplitude is increased and the duty cycle remains unchanged. 

Voltage increase can also be seen by connecting a voltmeter across the motor terminals, as shown below,

Image processing based robot using MATLAB and Simulation using Proteus ISIS

Build a robot using MATLAB

For the beginners, who are attempting to make an image processing based  robots, here comes few steps to guide you through the process. Remember that the robots sees what actually  the programmer wanted it to see by using a camera as its sensor along with proper image processing algorithms. Usually it involves high computation and hence a normal microcontroller would not be enough. So let us use MATLAB for processing the images and an ordinary microcontroller to execute the commands from the MATLAB.

As I always follow the KISS concept(Keep It Simple Stupid), I will first explain how to control the motors from MATLAB using serial communication(RS232). In the explanation, I have used the basic 8051 microcontroller to execute the command from the MATLAB(you can use any controller of your choice). To reduce the time in setting up the hardware assembly of motors and microcontroller, I have interfaced proteus ISIS and MATLAB(since our aim is to check whether proper control commands are sent to the controller based on the image processing algorithm developed). The following video shows how the commands from MATLAB is received by the controller via serial com port and which in turn controls the motors actions,

And now, we know how to control a motor from MATLAB. All we have to do now is to develop a suitable image processing algorithm based on our needs and to introduce the motor command function ‘fwrite()’ at appropriate places.

Image Processing:

Let us consider an image is an MxN matrix. So,processing an image is nothing but manipulating the values in the MxN matrix as per their needs. The entire picture that can be seen from a camera is the Field Of View(FOV) of the camera and the desired region in which the processing has to be done(or a feature to be extracted) is known as the Region Of Interest(ROI). There are lot of resources available in internet to learn image processing, but, after gaining some basic knowledge about image processing, Please do think and try to develop your own algorithm or just try to combine several algorithms and check for your output(Am sure it will be more fun than just implementing an already existing algorithm).

In the following video, I have used the lane detection sample video of MATLAB and applied global thresholding on it. I have marked and extracted the ROI from the entire FOV of the video. Let us assume, this ROI is a few meters ahead of the robot in which obstacles has to be detected. The threshold plot from the label matrix gives the obstacles on the road(search google for label matrix and thresholding). The pink line indicates the preset threshold value for obstacles and when the real time threshold exceeds the preset value, it can be taken as an indication of an obstacle and appropriate motors can be activated.( this is where you have to use the motor commands). Here, the white color is considered as an obstacle and black color indicates obstacle free area.

To know whether the direction of the obstacle, multiple ROIs can be used(left and right ROI).  So, based on the direction of the obstacle, activate the respective motors. The following MATLAB commands will capture the images from the selected camera,

vid=videoinput('winvideo',1, 'YUY2_160x120');

preview(vid);

pause(3);

while(1)

img=getsnapshot(vid);

% Do all image processing and analysis here

end

‘img’ is the image matrix and you can apply all your algorithms to it inside the while loop. And, as usual, when you are aiming for higher accuracy, you may focus more on the computation part and a better control loop for the motors(PID controlling is often used most cases).

Snap shots of the videos are given below,