Lecture 2.pptx
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This document discusses key concepts in digital image processing including sampling, quantization, and resolution. It explains that sampling relates to coordinate values and spatial resolution, while quantization relates to intensity values and grey-level resolution. Code examples are provided to generate figures demonstrating the effects of resizing and quantizing an image at different levels. In the next lecture, topics will include zooming and interpolation.
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import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
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#Loading the CIFAR10 data
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model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
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r, c = 4,4
noise = np.random.normal(0, 1, (r * c,latent_dimensions))
generated_images = generator.predict(noise)
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for i in range(r):
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Can someone please explain what the code below is doing and comment on its network
performance? Please and thank you.
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
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image_shape = (32, 32, 3)
latent_dimensions = 100
#Define a utility function to build the generator.
def build_generator():
model = Sequential()
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#Generating the output image
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model.add(Conv2D(32, kernel_size=3, strides=2,
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model.add(LeakyReLU(alpha=0.2))
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model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
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model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display the generated images.
def display_images():
r, c = 4,4
noise = np.random.normal(0, 1, (r * c,latent_dimensions))
generated_images = generator.predict(noise)
#Scaling the generated images
generated_images = 0.5 * generated_images + 0.5
fig, axs = plt.subplots(r, c)
count = 0
for i in range(r):
for j in range(c):
axs[i,j].imshow(ge.
Image retrieval using vlad with
The objective of this paper is to propose a combinatorial encoding method based on VLAD to
facilitate the promotion of accuracy for large scale image retrieval. Unlike using a single
feature in VLAD, the proposed method applies multiple heterogeneous types of features, such as
SIFT, SURF, DAISY, and HOG, to form an integrated encoding vector for an image
representation. The experimental results show that combining complementary types of features
and increasing codebook size yield high precision for retrieval.
Image Retrieval Using VLAD with Multiple Features
The objective of this paper is to propose a combinatorial encoding method based on VLAD to
facilitate the promotion of accuracy for large scale image retrieval. Unlike using a single
feature in VLAD, the proposed method applies multiple heterogeneous types of features, such as
SIFT, SURF, DAISY, and HOG, to form an integrated encoding vector for an image
representation. The experimental results show that combining complementary types of features
and increasing codebook size yield high precision for retrieval.
Image Processing Using MATLAB
The document discusses a workshop on image processing using MATLAB. It provides an overview of MATLAB and its image processing toolbox. It describes how to read, display, and convert between different image formats in MATLAB. It also demonstrates various image processing operations that can be performed, such as arithmetic operations, conversion between color and grayscale, image rotation, blurring and deblurring, and filling regions of interest. The document aims to introduce the basics of working with images in the MATLAB environment.
Image processing in MATLAB
The document discusses a workshop on image processing using MATLAB. It provides an overview of MATLAB and its image processing toolbox. It describes how to read, display, and convert between different image formats in MATLAB. It also demonstrates various image processing operations like arithmetic operations, conversion between color and grayscale, image rotation, blurring and deblurring, and filling regions of interest. The document aims to introduce the basics of image processing using MATLAB.
Digital Image Processing: Programming Fundamentals
Prerequisites: A brief knowledge of C/C++ language(s).
This text describes the basic computer programming (C/C++) techniques required to begin practical implementations in Digital Image Processing.
Reference Toolkit: ADISL's Image Apprentice, www.adislindia.com/pdts.html
Python program to build deep learning algorithm using a CNNs model to.docx
Python program to build deep learning algorithm using a CNNs model to detect malware attacks.
The code is working, but I faced issues with the last part of the code, and I needed to know precisely where the problem was.
I have the dataset of malware and benign in exe, I convert them to grayscale images, and it goes through the filters, and it shows me the output.
this is the code
import keras from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Conv2D, MaxPooling2D import numpy as np from os import listdir from PIL import Image import os.path import numpy as np path = '/media/malkuwari/New Volume/Ransomware_Detection_using _CNN/mixEXE' h = 256 #height of image w = 256 #width of image # be careful with using this function, it will consume memory, access to disk and time images = [] for f in listdir('/media/malkuwari/New Volume/Ransomware_Detection_using _CNN/mixEXE'): with open(os.path.join('/media/malkuwari/New Volume/Ransomware_Detection_using _CNN/mixEXE', f), 'rb') as img_set: img_arr = img_set.read(h*w) while img_arr: if len(img_arr) == h*w and img_arr not in images: images.append(img_arr) img_arr = img_set.read(h*w) #And you can save them into png files # count = 0 # for img in images: # png = Image.fromarray(np.reshape(list(img), (h,w)).astype('float32'), mode='L') # png.save('image_l%d.png'%count) # count += 1 #reshape images to fit into the CNN model img_list = np.zeros(shape = (len(images), h,w,1), dtype = np.uint8) for j in range(len(images)): img_list[j,:,:,0] = np.reshape(list(images[j]), (h,w)) img_list = img_list.astype('float32') img_list /= 255 # ################################################################ #approca 1 failed go back from scrtuch # implement the architecture of the CNN model = Sequential() #Conv2D Layers model.add(Conv2D(12, (25, 25), padding='same',input_shape=img_list.shape[1:], activation = 'relu')) model.add(Conv2D(12, (25, 25), activation = 'relu')) #Max Pooling Layer model.add(MaxPooling2D(pool_size=(2, 2))) #Conv2D Layer model.add(Conv2D(12, (13, 13), padding='same', activation = 'relu')) model.add(Conv2D(12, (13, 13), activation = 'relu')) #Max Pooling model.add(MaxPooling2D(pool_size=(2, 2))) #Flattening Layer model.add(Flatten()) #Dense Layer model.add(Dense(1024, activation = 'relu')) model.add(Dense(1, activation = 'sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['binary_accuracy']) model.summary()
''' FINALLY If you have a set of executable files from both types (benign and malicious) you can try to fit the model. You should prepare a list of images for malwares and a list for benign and here how you fit the model. ''' #Change the parameters to whatever suits you
I already have to files that contain benign files and malicious images, but it shows me syntax error!
batch_size = 512 epochs = 100 labels = [0 for _ in benign_images ] + [1 for _ in malicious_images ] model.fit( benign_images + maliciou.
Computer graphics
Computer graphics deals with generating, manipulating, and displaying images using computers. It has revolutionized graphic design by moving the industry from physical tools like pasteboards to digital tools using computers and software. Now designers use computers and graphics software to do everything from page layouts to preparing documents for printing. Some key features of computer graphics include vector graphics which use lines and shapes, raster graphics which use pixels, and transformations which allow simulated spatial manipulation of objects.
Design and Implementation of VLSI Architecture for Image Scaling Processor
In many Digital image processing applications, for processing of images or displaying the
images on different size displays, they are use image scaling techniques in order to scale up/scale down
of an image. In this paper, we are implement the 7 stage VLSI architecture at low cost with the edge
oriented area-pixel scaling technique for achieve better image quality compared to other techniques. This method achieves better visual quality compared to previous methods. The seven-stage VLSI architecture of our image scaling processor yields a processing rate of about 200 MHz by using TSMC 0.18- m technology
2.ppt
The document discusses how images are represented digitally in computers. It begins by describing how images are formed using cameras and the electromagnetic spectrum. It then explains that images are converted to digital form through sampling and quantization. Sampling means measuring image intensity at discrete points, while quantization represents these values as integers. The document provides examples of sampling an image at different rates and quantizing to different numbers of gray levels.
COVID-19 detection using Chest X-rays.pdf
The recent COVID-19 pandemic is a major threat to the global population. The healthcare sectors are struggling to cope with rising daily cases due to limited medical supply and lack of facilities. Therefore, there is a need for an alternative efficient diagnosis method for detecting COVID-19. Usually, patients with COVID-19 have a characteristic abnormality in the chest radiography. The use of Chest X-rays not only identifies these abnormalities but also results in a faster diagnosis. In this project, with the aid of transfer learning methods, a convolutional neural network (CNN) architecture-based VGG16 model pre-trained on the ImageNet dataset is used to diagnose patients with COVID-19. The proposed model is trained and tested using a publicly available chest X-ray database. A python-based graphical user interface (GUI) is developed to classify a given chest X-ray either as COVID positive or COVID negative. By proper hyper-parameter tuning, the model is able to provide a training accuracy of 98.72%.
ppt 20BET1024.pptx
This document describes a project to implement real-time facial recognition using OpenCV and Python. The goals are to train an image dataset with face IDs, detect faces in webcam video frames, predict faces based on a confidence level, and improve the model with more training data. The process involves creating training and test datasets, training the data using LBPH to extract features, and predicting faces in new frames in real-time by comparing histograms. The project uses Anaconda, OpenCV libraries, and common functions for detection, drawing, labeling, and training. Further enhancements could include more training data and implementing CNNs with TensorFlow.
Gabor Filter
Gabor filter is a powerful way to enhance biometric images like fingerprint images in order to extract correct features from these images, Gabor filter used in extracting features directly asin iris images, and sometimes Gabor filter has been used for texture analysis. In fingerprint images The even symmetric Gabor filter is contextual filter or multi-resolution filter will be used to enhance fingerprint imageby filling small gaps (low-pass effect) in the direction of the ridge (black regions) and to increase the discrimination between ridge and valley (black and white regions) in the direction, orthogonal to the ridge, the proposed method in applying Gabor filter on fingerprint images depending on translated fingerprint image into binary image after applying some simple enhancing methods to partially overcome time consuming problem of the Gabor filter.
Matlab intro
This document provides an introduction to the basics of MATLAB. It discusses where to find help in MATLAB, how to work with matrices and perform basic operations on them. It also covers logical conditions, different types of loops (for, while, if/else), how to create scripts and functions. Finally, it provides an overview of visualization and graphics in MATLAB as well as an introduction to the image processing toolbox.
Optical Watermarking Literature survey....
1. The document discusses optical watermarking, a technique for protecting copyright of real-world objects like artwork by embedding watermarks in the illumination of the objects. When photos are taken of the illuminated objects, the watermarks are captured in the digital images and can be extracted.
2. Optical watermarking works by transforming binary data into patterns of light projected onto objects. The patterns differ based on 1s and 0s in the data. When photos of the illuminated objects are taken, the patterns can be read from the captured images. Visible light is used with fine or low-contrast patterns to make the watermarks imperceptible.
3. Optical watermarking provides copyright protection for valuable
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Lecture 2.pptx
- 1. Engr. Dr. Ahmed Sohaib Institute of Computer & Software Engineering KFUEIT Digital Image Processing Fall 2022
- 2. key stages in digital image processing Sampling : related to coordinates values (Nyquist frequency) Quantization : related to intensity values
- 3. © 2002 R. C. Gonzalez & R. E. Woods y (intensity values) Generating a digital image. (a) Continuous image. (b) A scaling line from A to B in the continuous image, used to illustrate the concepts of sampling and quantization. (c) sampling and quantization. (d) Digital scan line. a b c d
- 4. © 2002 R. C. Gonzalez & R. E. Woods (a) Continuous image projected onto a sensor array. (b) Result of image sampling and quantization. a b
- 5. 0 0 0 75 75 75 128 128 128 128 0 75 75 75 128 128 128 255 255 255 75 75 75 200 200 200 255 255 255 200 128 128 128 200 200 255 255 200 200 200 128 128 128 255 255 200 200 200 75 75 175 175 175 225 225 225 75 75 75 100 175 175 100 100 100 225 225 75 75 100 75 75 75 35 35 35 0 0 0 35 35 35 35 0 0 0 35 35 35 75 75 75 75 100 100 100 200 200 200 200
- 7. Sampling 1024 512 256 128 64 32
- 8. imread() – reading an image with different postfixes imresize() – resizing an image to any given size figure – opening a new graphical window subplot(#of row, # of col, location) – showing different plots/images in one graphical window imshow() – displaying an image
- 9. generating figures of slide 6 im=imread('obelix.jpg'); im=rgb2gray(imread('obelix.jpg')); im1=imresize(im, [1024 1024]); im2=imresize(im1, [1024 1024]/2); im3=imresize(im1, [1024 1024]/4); im4=imresize(im1, [1024 1024]/8); im5=imresize(im1, [1024 1024]/16); im6=imresize(im1, [1024 1024]/32); figure;imshow(im1) figure;imshow(im2) figure;imshow(im3) figure;imshow(im4) figure;imshow(im5)
- 10. generating figure of slide 7 figure; subplot(2,3,1);imshow(im1);subplot(2,3,2);imshow(im2) subplot(2,3,3);imshow(im3);subplot(2,3,4);imshow(im4) subplot(2,3,5);imshow(im5);subplot(2,3,6);imshow(im6)
- 11. Quantization 8-bit 7-bit 6-bit 5-bit 4-bit 3-bit 2-bit 1-bit
- 12. generating figure of slide 11 im=imread('obelix.jpg'); im=rgb2gray(imread('obelix.jpg')); im1=imresize(im, [1024 1024]); im2= gray2ind(im1,2^7); im3= gray2ind(im1,2^6); im4= gray2ind(im1,2^5); im5= gray2ind(im1,2^4); im6= gray2ind(im1,2^3); im7= gray2ind(im1,2^2); im8= gray2ind(im1,2^1);
- 14. Summary We have looked at: What is sampling? What is spatial resolution? What is quantization? What is grey-level resolution? Next time we will start to see zooming and interpolation
- 15. References • Some content of these slides are taken from Assoc. Prof. Dr. Gholamreza Anbarjafari (Shahab) University of Tartu • Database Images from Sycllarus, National ICT Australia