![Study with Dr. Dafda](/img/default-banner.jpg)
- 197
- 376 268
Study with Dr. Dafda
India
Приєднався 3 кві 2021
This channel is an educational channel, designed for students of School and Engineering. I am Dr. Alpesh H. Dafda. I have done my PhD in Electronics and Communication Engineering, and I have a teaching experience of 20 years.
Typical AI Problems/Task domains of Artificial Intelligence: Mundane, Formal & Expert tasks/problems
Video lecture series on Artificial Intelligence and Machine Learning in Hindi Language, Lecture: 7
Typical AI Problems/Task domains of Artificial Intelligence: Mundane, Formal & Expert tasks/problems || In Hindi Language || हिंदी में ||
#artificialintelligenceandmachinelearning
#machinelearning
Link to download ppts/lecture notes and Python codes:
drive.google.com/drive/folders/1AtR1eq6ZvQf-5vjXEMUPXNj2S0d9rAMZ?usp=share_link
Lecture 1 details:
Introduction to Artificial Intelligence and Machine Learning along with Python||#AIML|| Google Colab || Deep Learning || UA-cam Music Songs recommendation system || Tic-Tac-Toe game in Python
What is Artificial Intelligence?
What is Machine Learning?
What is Deep Learning?
What is Python?
आर्टिफीसियल इंटेलिजेंस क्या है?
मशीन लर्निंग क्या है?
पाइथन क्या है?
डीप लर्निंग क्या है?
What is Google Colab?
How to use Google Colab?
Google Colab का इस्तेमाल कैसे करे?
What is Jupyter Notebook?
How to program Tic-Tac-Toe game in Python?
Python code used in the video is present at the end in Description
Artificial Intelligence and Machine Learning (AIML) using/in Python
#aiml
#aimlinhindilanguage
#AIMLwithPython
#ArtificialIntelligenceandMachineLearningUsingPython
#artificialintelligence
#machinelearning
#studywithdrdafda
#artificialintelligenceandmachinelearning
#artificialintelligenceandmachinelearninglecturesin4k
Typical AI Problems/Task domains of Artificial Intelligence: Mundane, Formal & Expert tasks/problems || In Hindi Language || हिंदी में ||
#artificialintelligenceandmachinelearning
#machinelearning
Link to download ppts/lecture notes and Python codes:
drive.google.com/drive/folders/1AtR1eq6ZvQf-5vjXEMUPXNj2S0d9rAMZ?usp=share_link
Lecture 1 details:
Introduction to Artificial Intelligence and Machine Learning along with Python||#AIML|| Google Colab || Deep Learning || UA-cam Music Songs recommendation system || Tic-Tac-Toe game in Python
What is Artificial Intelligence?
What is Machine Learning?
What is Deep Learning?
What is Python?
आर्टिफीसियल इंटेलिजेंस क्या है?
मशीन लर्निंग क्या है?
पाइथन क्या है?
डीप लर्निंग क्या है?
What is Google Colab?
How to use Google Colab?
Google Colab का इस्तेमाल कैसे करे?
What is Jupyter Notebook?
How to program Tic-Tac-Toe game in Python?
Python code used in the video is present at the end in Description
Artificial Intelligence and Machine Learning (AIML) using/in Python
#aiml
#aimlinhindilanguage
#AIMLwithPython
#ArtificialIntelligenceandMachineLearningUsingPython
#artificialintelligence
#machinelearning
#studywithdrdafda
#artificialintelligenceandmachinelearning
#artificialintelligenceandmachinelearninglecturesin4k
Переглядів: 11
Відео
Typical AI Problems/Task domains of Artificial Intelligence: Mundane, Formal & Expert tasks/problems
Переглядів 17День тому
Video lecture series on Artificial Intelligence and Machine Learning, Lecture: 7 Typical AI Problems/Task domains of Artificial Intelligence: Mundane, Formal and Expert tasks/problems ||#artificialintelligence||#machinelearning Link to download ppts/lecture notes and Python codes: drive.google.com/drive/folders/1AtR1eq6ZvQf-5vjXEMUPXNj2S0d9rAMZ?usp=share_link #AIML #AIMLwithPython #ArtificialIn...
Types of AI: Weak AI, Strong AI and Super-Intelligent AI || In Hindi Language||हिंदी में ||
Переглядів 1214 днів тому
Video lecture series on Artificial Intelligence and Machine Learning in Hindi Language, Lecture: 6 Types of AI: Weak AI, Strong AI and Super-Intelligent AI || In Hindi Language||हिंदी में ||#artificialintelligence #artificialintelligenceandmachinelearning #machinelearning Link to download ppts/lecture notes and Python codes: drive.google.com/drive/folders/1AtR1eq6ZvQf-5vjXEMUPXNj2S0d9rAMZ?usp=s...
Types of AI: Weak AI, Strong AI and Super-Intelligent AI ||#artificialintelligence||#machinelearning
Переглядів 1721 день тому
Video lecture series on Artificial Intelligence and Machine Learning, Lecture: 6 Types of AI: Weak AI, Strong AI and Super-Intelligent AI ||#artificialintelligence||#machinelearning Link to download ppts/lecture notes and Python codes: drive.google.com/drive/folders/1AtR1eq6ZvQf-5vjXEMUPXNj2S0d9rAMZ?usp=share_link #AIML #AIMLwithPython #ArtificialIntelligenceandMachineLearningUsingPython #artif...
The Applications of Artificial Intelligence|| In Hindi Language||हिंदी में|| #artificialintelligence
Переглядів 35Місяць тому
Video lecture series on Artificial Intelligence and Machine Learning in Hindi Language, Lecture: 5 The Applications of Artificial Intelligence|| In Hindi Language||हिंदी में|| #artificialintelligence#artificialintelligence #artificialintelligenceandmachinelearning #machinelearning Link to download ppts/lecture notes and Python codes: drive.google.com/drive/folders/1AtR1eq6ZvQf-5vjXEMUPXNj2S0d9r...
The Applications of Artificial Intelligence || #artificialintelligence || #machinelearning
Переглядів 14Місяць тому
Video lecture series on Artificial Intelligence and Machine Learning, Lecture: 5 The Applications of Artificial Intelligence Link to download ppts/lecture notes and Python codes: drive.google.com/drive/folders/1AtR1eq6ZvQf-5vjXEMUPXNj2S0d9rAMZ?usp=share_link #AIML #AIMLwithPython #ArtificialIntelligenceandMachineLearningUsingPython #artificialintelligence #machinelearning #studywithdrdafda #art...
The History of Artificial Intelligence || In Hindi Language || हिंदी में || #artificialintelligence
Переглядів 23Місяць тому
Video lecture series on Artificial Intelligence and Machine Learning in Hindi Language, Lecture: 4 The History of Artificial Intelligence || In Hindi Language || हिंदी में || #artificialintelligence #artificialintelligenceandmachinelearning #machinelearning Link to download ppts/lecture notes and Python codes: drive.google.com/drive/folders/1AtR1eq6ZvQf-5vjXEMUPXNj2S0d9rAMZ?usp=share_link Lectu...
The History of Artificial Intelligence || #artificialintelligence || #machinelearning
Переглядів 25Місяць тому
The History of Artificial Intelligence || #artificialintelligence || #machinelearning
The Foundations of Artificial Intelligence||#artificialintelligence|| In Hindi Language || हिंदी में
Переглядів 19Місяць тому
The Foundations of Artificial Intelligence||#artificialintelligence|| In Hindi Language || हिंदी में
The Foundations of Artificial Intelligence || #artificialintelligence || #machinelearning
Переглядів 1182 місяці тому
The Foundations of Artificial Intelligence || #artificialintelligence || #machinelearning
What is Artificial Intelligence? Approaches to AI: Thinking & Acting Humanly & Rationally| हिंदी में
Переглядів 612 місяці тому
What is Artificial Intelligence? Approaches to AI: Thinking & Acting Humanly & Rationally| हिंदी में
What is Artificial Intelligence? Approaches to AI:Thinking&Acting humanly|Thinking&Acting rationally
Переглядів 1712 місяці тому
What is Artificial Intelligence? Approaches to AI:Thinking&Acting humanly|Thinking&Acting rationally
आर्टिफीसियल इंटेलिजेंस और मशीन लर्निंग क्या होता है?Artificial Intelligence,Machine Learning kya hai
Переглядів 162 місяці тому
आर्टिफीसियल इंटेलिजेंस और मशीन लर्निंग क्या होता है?Artificial Intelligence,Machine Learning kya hai
Introduction to Artificial Intelligence and Machine Learning along with Python||#AIML|| Google Colab
Переглядів 2073 місяці тому
Introduction to Artificial Intelligence and Machine Learning along with Python||#AIML|| Google Colab
Homomorphic Filtering in Digital Image Processing and its Implementation in MATLAB||#DIP|| हिंदी में
Переглядів 4113 місяці тому
Homomorphic Filtering in Digital Image Processing and its Implementation in MATLAB||#DIP|| हिंदी में
Laplacian, Unsharp masking/High Boost filtering in frequency domain filtering & in MATLAB||हिंदी में
Переглядів 1723 місяці тому
Laplacian, Unsharp masking/High Boost filtering in frequency domain filtering & in MATLAB||हिंदी में
Image Sharpening(HPF) in frequency domain filtering and in MATLAB|| IHPF || BHPF || GHPF|| हिंदी में
Переглядів 933 місяці тому
Image Sharpening(HPF) in frequency domain filtering and in MATLAB|| IHPF || BHPF || GHPF|| हिंदी में
Image Smoothing in freq. domain filtering & in MATLAB|| Ideal, Butterworth & Gaussian LPF||हिंदी में
Переглядів 1784 місяці тому
Image Smoothing in freq. domain filtering & in MATLAB|| Ideal, Butterworth & Gaussian LPF||हिंदी में
Intro. to Image Enhancement in the frequency domain & Steps for filtering in freq. domain||हिंदी में
Переглядів 1674 місяці тому
Intro. to Image Enhancement in the frequency domain & Steps for filtering in freq. domain||हिंदी में
Image Sharpening(High Pass)spatial filter(Laplacian)with Example&implementation in MATLAB||हिंदी में
Переглядів 2324 місяці тому
Image Sharpening(High Pass)spatial filter(Laplacian)with Example&implementation in MATLAB||हिंदी में
Order statistics/Non-linear(Median, Minimum & Maximum) spatial filters with Ex.&in MATLAB||हिंदी में
Переглядів 2644 місяці тому
Order statistics/Non-linear(Median, Minimum & Maximum) spatial filters with Ex.&in MATLAB||हिंदी में
Fundamentals of Spatial filtering &Smoothing Spatial filters with example &MATLAB|In Hindi|हिंदी में
Переглядів 4174 місяці тому
Fundamentals of Spatial filtering &Smoothing Spatial filters with example &MATLAB|In Hindi|हिंदी में
Histogram Matching/Specification in DIP with example and perform in MATLAB || In Hindi ||हिंदी में||
Переглядів 1285 місяців тому
Histogram Matching/Specification in DIP with example and perform in MATLAB || In Hindi ||हिंदी में||
Histogram Equalization/Processing/Modelling in Image Processing & in MATLAB|| In Hindi ||हिंदी में||
Переглядів 1665 місяців тому
Histogram Equalization/Processing/Modelling in Image Processing & in MATLAB|| In Hindi ||हिंदी में||
Piecewise linear transformation function : Bit-plane Slicing in DIP & its implementation in MATLAB
Переглядів 1145 місяців тому
Piecewise linear transformation function : Bit-plane Slicing in DIP & its implementation in MATLAB
Steganography in Digital Image Processing & its implementation in MATLAB || LSB based ||Watermarking
Переглядів 1,7 тис.5 місяців тому
Steganography in Digital Image Processing & its implementation in MATLAB || LSB based ||Watermarking
Piecewise linear transformation function:Intensity Level Slicing in DIP&its implementation in MATLAB
Переглядів 1505 місяців тому
Piecewise linear transformation function:Intensity Level Slicing in DIP&its implementation in MATLAB
Piecewise linear transformation function : Contrast stretching in DIP &its implementation in MATLAB
Переглядів 3726 місяців тому
Piecewise linear transformation function : Contrast stretching in DIP &its implementation in MATLAB
Image negatives,Log and Power-Law transformations for DIP &its implementation in MATLAB||हिंदी में||
Переглядів 1736 місяців тому
Image negatives,Log and Power-Law transformations for DIP &its implementation in MATLAB||हिंदी में||
Basics of Intensity transformations & Spatial filtering & its implementation in MATLAB||हिंदी में||
Переглядів 2086 місяців тому
Basics of Intensity transformations & Spatial filtering & its implementation in MATLAB||हिंदी में||
Sir what is the benefit of image negative. Though we can see the same detail in original image..
Image negatives can make certain details more visible, especially in images where the background is dark and the foreground is light. In medical imaging, such as X-rays, image negatives can help highlight specific features that might be less visible in the original image.
@@StudywithDrDafdathank you so much sir..
thank you for your videos, i hope to share and upload more videos about any topic that considerable as important for now days, thank you again for your efforts I appreciated
Thank you.
There is no slide available for frequency domain filtering
In chapter 2 Image enhancements and filtering, both spatial domain and frequency domain filtering ppts are present.
Is N8(P) is same as ND(P)
No. ND(P) are four diagonal neighbours, while N8(P) is all eight neighbours of P. That is N8(P)=N4(P)+ND(P). While considering neighbourhood pixels.
Yes, you can say that, while considering adjacency.
😊 tq
Sir having a doubt as we considering gray scale image with 256 intensity value and we are taking v as a set range from 0 to 10..dat means the set of v is constant for that ranging or it can differs between 0 to 255
The set V is constant for range {0 to 10}, which means V can range only from 0 to 10. And we have to consider only values in between 0 to 10.
Where is the code?
Code is present in the description box.
How do you write the huffmandict(symbols,pro) function?
It is a inbuilt MATLAB function.
why are we putting last char value in the encoded output? like at 7:28, the 7 output value has fda in dictonary, so decoding it will give fda directly, so why 1 in the end
At 7:28, the 7 output value has FD in the dictionary, and not FDA. Check dictionary location number 7, it is FD and therefore always last character is required for correct decoding.
Dont just read PDF bro
Thank you.
at 3.47 why did you take 3 bit for prob 0,25? at it can be written as 10
ua-cam.com/video/uTdBFr8Fn-w/v-deo.html
Thank you
Hello sir, Hope you are doing well. I watched this video of yours and first of all let me thank you for amazing explanation.
You are most welcome
🎉 Nice 👍
❤ Nice Shorts Video
❤ Thank you Sir 🙏
This chapter is the worst from exam point of view. we are expected to remember so many formulae , and it makes NO SENSE to mug up all of it !!!!!
You can try to remember the concepts.
Thanku soo much sir ,and respect
9:31 i have error in line 8 I = imadd(I, 100);
Replace that line with I=I+100; This should solve your error.
@@StudywithDrDafda still error sir imadd requires Image Processing Toolbox. Error in untitled (line 6) I = imadd(I=I+100);
Yes, it will require Image Processing Toolbox for execution. You need to install it .
Great video👍🏻
Thank you sir, Your videos are really helpful. I just have a small suggestion for you, Your DIP playlist starts with last lecture and ends with first lec. Can you rectify this ? 😃
Done sorting. Thanks for the suggestion.
JESUS CHRIST CHRIST I KING JESUS CHRIST
😂
👍
Great Explanation Sir !
well explained. Thanks a lot
thanks very much for the great explanation
Hello sir, Thanks for the beautiful explanation. was wondering if you could drop the codes for BHPF and GHPF
% Matlab program for Butterworth High Pass Filter(BHPF) clc; clear; close all; % Read the image %a = imread('Maulik.png'); %a = rgb2gray(a); a = imread('Cameraman.tif'); a = im2double(a); %convert the range of colors from 0-255 to 0-1 [m,n] =size(a); %Get size of image subplot(2,3,1) imshow(a) title('Input image'); A = fft2(a); %fourier transform of image subplot(2,3,2); imshow(uint8(abs(A))); title('F.T. of i/p without shift'); A_shift = fftshift(A); %shifting origin A_real = abs(A_shift); %Magnitude of A_shift(Frequency domain representation of image) subplot(2,3,3) imshow(uint8(A_shift)); title('Frequency domain image'); D0 = 30; %Cut-Off frequency D = zeros(m,n); order=20;% order of butterworth filter % Butterworth High pass filtering for i=1:m for j=1:n D(i,j) = sqrt(((i-(m/2))^2+(j-(n/2))^2)); H(i,j)=1/((1+(D0/D(i,j))^(2*order))); end end butter_HPF = A_shift.*H; butter_HPF_inverse = ifft2(butter_HPF); butter_HPF_real = abs(butter_HPF_inverse); subplot(2,3,4) imshow(H) title('Butterworth HP Filter'); subplot(2,3,5); mesh(H) title('surface plot BHPF') subplot(2,3,6) imshow((butter_HPF_real)); title('Butterworth HP Filtered image');
% Matlab program for Gaussian High Pass Filter(GHPF) clc; clear; close all; % Read the image a = imread('Maulik.png'); a = rgb2gray(a); %a=imread('Cameraman.tif'); a = im2double(a); %convert the range of colors from 0-255 to 0-1 [m,n] =size(a); %Get size of image subplot(2,3,1) imshow(a) title('Input image'); A = fft2(a); %fourier transform of image subplot(2,3,2); imshow(uint8(abs(A))); title('F.T. of i/p without shift'); A_shift = fftshift(A); %shifting origin A_real = abs(A_shift); %Magnitude of A_shift(Freq. domain repre.) subplot(2,3,3) imshow(uint8(A_real)); title('Frequency domain image'); D0 = 30; %Cut-Off frequency OR Standard deviation sigma for u=1:m for v=1:n D = sqrt((u-m/2).^2+(v-n/2).^2); H(u,v) = 1 - exp(-(D^2)/(2*D0.^2)); end end H_high = H.*A_shift; H_high_real = H.*A_real; H_high_shift = fftshift(H_high); H_high_image = ifft2(H_high_shift); subplot(2,3,4) imshow(H) title('Gaussian High pass Filter'); subplot(2,3,5); mesh(H) title('surface plot GHPF') subplot(2,3,6) imshow(abs(H_high_image)); title('Gaussian High pass Filtered image');
very informative and interesting explanation. Please post the codes for BLPF and GLPF
clc; clear; close all; % Read the image %a = imread('Maulik.png'); %a = rgb2gray(a); a=imread('Cameraman.tif'); a = im2double(a); %convert the range of colors from 0-255 to 0-1 [m,n] =size(a); %Get size of image subplot(2,3,1) imshow(a) title('Input image'); A = fft2(a); %fourier transform of image subplot(2,3,2); imshow(uint8(abs(A))); title('F.T. of i/p without shift'); A_shift = fftshift(A); %shifting origin A_real = abs(A_shift); %Magnitude of A_shift(Frequency domain representation of image) subplot(2,3,3) imshow(uint8(A_real)); title('Frequency domain image'); D0 = 50; %Cut-Off frequency D = zeros(m,n); order=2;% order of butterworth filter % Butterworth Low pass filtering for u=1:m for v=1:n D(u,v) = sqrt(((u-(m/2))^2+(v-(n/2))^2)); H(u,v) = 1/((1+(D(u,v)/D0)^(2*order))); end end subplot(2,3,4) imshow(H) title('Butterworth LP Filter'); subplot(2,3,5); mesh(H) title('surface plot BLPF') butter_LPF = A_shift.*H; butter_LPF_inverse = ifft2(butter_LPF); butter_LPF_real = abs(butter_LPF_inverse); subplot(2,3,6) imshow((butter_LPF_real)); title('Butterworth LP Filtered image');
clc; clear; close all; % Read the image %a = imread('Maulik.png'); %a = rgb2gray(a); a=imread('Cameraman.tif'); a = im2double(a); %convert the range of colors from 0-255 to 0-1 [m,n] =size(a); %Get size of image subplot(2,3,1) imshow(a) title('Input image'); A = fft2(a); %fourier transform of image subplot(2,3,2); imshow(uint8(abs(A))); title('F.T. of i/p without shift'); A_shift = fftshift(A); %shifting origin A_real = abs(A_shift); %Magnitude of A_shift(Freq. domain repre.) subplot(2,3,3) imshow(uint8(A_real)); title('Frequency domain image'); D0 = 50; %Cut-Off frequency OR Standard deviation sigma % Gaussian Low pass filtering for u=1:m for v=1:n d = sqrt((u-m/2).^2+(v-n/2).^2); H(u,v)=exp(-(d^2)/(2*D0.^2)); end end H_low = H.*A_shift; H_low_real = H.*A_real; H_low_shift = fftshift(H_low); H_low_image = ifft2(H_low_shift); subplot(2,3,4) imshow(H) title('Gaussian Low pass Filter'); subplot(2,3,5); mesh(H) title('surface plot GLPF') subplot(2,3,6) imshow(abs(H_low_image)); title('Gaussian Low pass Filtered image');
The forward and inverse formula how to compute. I haven't watched u didn't used them in example
Plz clarify my dout
We are working on term paper ieee lossless compression of jpeg coded photo collection did you have any idea on it
The calculations are done as per the steps explained in the video.
The MATLAB calculations are done as per the steps discussed in the video.
@StudywithDrDafda these formulas i.e forward and inverse are used to find quantization table ?
🎉🎉🎉🎉🎉🎉🎉🎉🎉😊😊😊😊
In this before compression original image size is 800 bits and after compression 220 bits am i right . As u mentioned 8 bit image 10x10 pixel and after applying it 2.2 bits/symbol Length
Yes, correct.
Decoded image means a Compressed Image only na
No. For Huffman coding, decoded image is same as input image. Coded image is compressed image.
@@StudywithDrDafda yes tanq
@StudywithDrDafda sir for jpeg compression Huffman coding and arithmetic encoding or else any existing methods can you say plz
All topics covered in the playlist: Digital Image Processing: ua-cam.com/play/PL0rq1NWOtSWdMxKHShC5iBitElZaaJOpw.html
How to save the image
For saving the image you can use imwrite().
Most in-depth video on this topic I found on UA-cam. Thank you very much sir!
Promo>SM
Thank you.
😂 good 😢
Aslmualikumm srr apny image negative mn 8 bit li haii AGR hmm 7 bit lyy tuu frr kesyy kryy gayy srr kya jesy 2× 2 ka matrix haii hmmny find krna haii image negative tu srr [3 4] [4 5] srr uska image negative nikally kr reply kryy
Thankyou sir <3
Thankyou sir
Hello sir, why the image quality measures like SNR / PSNR are represented in decibels. Or on a logarithmic scale?
Image quality measures are represented in decibels or on a logarithmic scale because human perception of image quality is nonlinear and very wide. Logarithmic scale accounts for varying sensitivity across intensity levels and offers a compact representation for wide dynamic ranges. It simplifies comparison between images and systems, facilitates standardization, and enables mathematical modeling. In addition, it helps in compressing pixel intensity values and provides insight into system behavior.
Is that image is blur or u make it blur
thank you so much sir
thank you so much, sir. I hope to continue sharing and making these useful tutorials.
Hello Sir, Can you please explain video compression methods also - may be an overview.
ua-cam.com/video/Sq-2quHpkE0/v-deo.html
Thanku soo much sir 🙏
thank u so much Dr. Dafda, your video really helped me in the image processing course.
Think you sir🥰🥰🥰
Why Homomorphic filter is always applied on Gray-scale images? What will happen if a color image is input?
Homomorphic filters are often applied to grayscale images in image processing because they are designed to enhance the contrast in an image by separating its illumination and reflectance components. When applied to color images, the filtering process may disrupt the natural color balance, leading to unintended color artifacts or distortions. Additionally, the complexity of handling multiple color channels makes the application of homomorphic filters on color images more challenging and less straightforward.
Thank you 🙏 sir