Images - Science topic

https://join.skype.com/invite/K64uqT3mCBFS my skype id and i love your article i love meeting people and if you need help contact on skype for life call and voice call okay

@Yousef Bahrambeigi

Hi Mr Yousef Bahrambeigi

Thank you for your patience and taking the time to respond

Your answers are thorough and

helpful I had another question,

how can I create a dose vector؟

As you mentioned :

Next, you need to plot the OD values against the known doses for each channel and fit a curve to them. You can use the plot function to plot the data and the polyfit function to fit a polynomial curve. For example, if you have a vector of doses named dose and a vector of OD values for the red channel named OD_red

Python is one of the widely used programming languages for this purpose. Its amazing libraries and tools help in achieving the task of image processing very efficiently. MATLAB is the best language for doing project on image processing. It has so many existing image processing functions. It is user friendly and you can build project easily. C++ is a multi-paradigm language that enables the programmer to set up efficient im- age processing algorithms easily. This language strength comes from many aspects. C++ is high-level, so this enables developing powerful abstractions and mixing different programming styles to ease the development. A convolutional neural network (CNN) is a type of artificial neural network used primarily for image recognition and processing, due to its ability to recognize patterns in images. Machine learning algorithms used for image processing: Artificial neural networks. Convolutional neural networks (CNNs) Scale-invariant feature transform (SIFT) algorithm. Digital image processing deals with manipulation of digital images through a digital computer. The digital image processing deals with developing a digital system that performs operations on a digital image. A digital image is an image composed of picture elements, also known as pixels, each with finite, discrete quantities of numeric representation for its intensity or gray level that is an output from its two-dimensional functions fed as input by its spatial coordinates denoted with x, y on the x-axis and y-axis. There generally three types of processing that are applied to an image. These are: low-level, intermediate-level and high-level processing which are described. Most of the common image processing functions available in image analysis systems can be categorized into the following four categories: Preprocessing. Image Enhancement. Image Transformation. Image Classification and Analysis.

Normalizing an image dataset for CNN means adjusting the pixel values of images so they fall in a similar range, typically between 0 and 1. This helps the CNN learn faster and perform better. Here's how to do it:

1. Load the image dataset.

2. Convert each pixel value from its current range (usually 0-255) to a range between 0 and 1 by dividing by 255.

3. Use the normalized images as input for the CNN.

This process ensures consistent data scaling, making training more stable and efficient.

To enhance the performance of your VGG16 model during training and validation, you can start by applying data augmentation techniques to increase dataset diversity and reduce overfitting. It's crucial to ensure the dataset's cleanliness, correct labeling, and appropriate division into training, validation, and test subsets. Experiment with different learning rates and optimizers, and consider using learning rate schedulers if necessary. Employ regularization methods like dropout and L2 regularization to tackle overfitting issues. Keep a close eye on the training process, implement early stopping, and adjust the batch size as needed. You might also want to explore alternative model architectures or smaller models that could better suit your dataset. Lastly, make sure your hardware resources are utilized effectively, and explore ensemble methods to potentially enhance model performance. These strategies should help you overcome the low accuracy challenge with your VGG16 model.

Dear Researchers and postgraduate students

MESOPOTAMIAN JOURNAL OF BIG DATA (MJBD) issued by Mesopotamian Academic Press, welcomes the original research articles, short papers, long papers, review papers for the publication in the next issue the journal doesn’t requires any publication fee or article processing charge and all papers are published for free

Journal info.

1 -Publication fee: free

2- Frequency: 1 issues per year

3- Subject: computer science, Big data, Parallel Processing, Parallel Computing and any related fields

4- ISSN: 2958-6453

5- Published by: Mesopotamian Academic Press.

6- Contact: email: ahmed.h.ali@mesopotamian.press

Managing Editor: Dr. Ahmed Ali

The journal indexed in

1- Croosref

2- DOAJ

3- Google scholar

4- Research gate

I can't open .txm file with Dragonfile Pro Workstation. I don't know what am i doing wrong. When i go to import image, it doesn't show any .txm file on the folder.

It should be noted that the silence(meaning that you do not provide objection ) is a sign of approval.

It works, you just have to copy the whole line, including the aspx in the end.

you can do it in python easily

by importing object recognition library

To perform quaternion color image processing, you can follow these steps:

1. Convert the RGB color image to quaternion representation: Each pixel in the RGB image is represented by a quaternion, which consists of four components (r, g, b, a). The r, g, and b components represent the red, green, and blue channels respectively, while the a component represents the alpha channel for transparency.

2. Apply quaternion operations: Perform various operations on the quaternion representation of the image. These operations can include addition, subtraction, multiplication, division, and other mathematical transformations specific to quaternions.

3. Color space transformations: Perform color space transformations on the quaternion representation of the image. This can involve converting between different color spaces such as RGB, CMYK, HSV (Hue-Saturation-Value), or any other desired color space.

4. Filtering and enhancement: Apply filtering techniques such as blurring or sharpening to enhance or modify specific features in the image. You can use filters designed specifically for quaternion images or adapt existing filters for use with quaternions.

5. Image compression: Develop compression algorithms specifically designed for quaternion images to reduce storage requirements while preserving important visual information.

6. Visualization: Convert the processed quaternion image back to RGB representation for visualization purposes or further analysis if needed.

It's worth noting that performing quaternion color image processing requires a good understanding of quaternions and their mathematical properties. Additionally, there may be specific libraries or software tools available that provide built-in support for quaternion image processing operations that you can utilize in your implementation.

Digital image processing is essential for a variety of reasons:

There are numerous algorithms used in image processing, catering to various tasks and requirements:

Digital image processing involves several steps in the manipulation of digital images. These steps can vary based on the specific application and goals, but here is a general overview of the typical steps:

Types of digital image processing include:

have u fond the answer or we shall discuss here.Maybe I shall help here !!!

Well, it is very good question. My decision was for demonstration only. If I need to do it again on big set of images I would use algorithm rather than decision. f.e. Huang's approach on bright background giving threshold around 234-237.

Actually, it depends on the software you have used for the Rietveld refinement. As a JANA2006 user, I would try to export a CIF file and open it with any text editor (e.g., Notepad) afterwards. Such information is usually automatically written into the CIF file. If not, you may try to read the log files for the refinement procedure (for instance, in JANA2006, full details on the refinement procedure are given in the respective *.ref file)

Dr Tajinder Kumar Saini thank you for your contribution to the discussion

Dear Refat Eshaq ,

When the level set algorithm is used to segment an image, the level set function must be initialized periodically to ensure that it remains a signed distance function (SDF).

Regards,

Shafagat

The most common compression algorithm for images is the JPEG (Joint Photographic Experts Group) algorithm. JPEG is a widely used lossy compression method that reduces the file size of images while maintaining a reasonable level of visual quality. It achieves compression by analyzing and quantizing the color and spatial information in the image.

As for machine learning algorithms in image processing, there isn't a single "best" algorithm, as the choice depends on the specific task and data at hand. However, certain algorithms are commonly used in various image processing applications:

The "best" algorithm depends on factors like the nature of the image processing task, the amount of available data, computational resources, and the desired level of accuracy. In many modern applications, deep learning approaches like CNNs often outperform traditional machine learning algorithms due to their ability to capture intricate patterns and features in images.

Please check the file

Thank you very much for your precisation. I then also found this paper demonstrating that it is sufficient a very smaller area: Blatchford, M. L., Mannaerts, C. M., & Zeng, Y. (2021). Determining representative sample size for validation of continuous, large continental remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 94, 102235.

Maybe you should study the SENTINEL-1 PRODUCT DATA TYPES.

Candidate Reference:

Regards,

Provided your PCR conditions and Primers are perfect, the only reason I can think of for this kind of result is DNA overdose that might have changed the pH of your mastermix despite buffering. To avoid this, pick a single colony and mix into 50 ul nuclease-free water. Boil the sample at 95 C for 10 min. Vortex thoroughly and pellet down all the debris. Take 2 ul of supernatant as DNA in your PCR. This will reduce initial dna input and will yield a better pcr product.

All the best

Siva

Digital image processing is not machine learning, but it is a field of computer science that uses algorithms to manipulate and enhance digital images. Machine learning is a type of artificial intelligence that allows computers to learn without being explicitly programmed.

There are many different algorithms that can be used for image processing in machine learning. Some of the most common algorithms include:

The best algorithm for a particular image processing task will depend on the specific requirements of the task. For example, if the task is to detect objects in an image, then a CNN would be a good choice. If the task is to segment an image, then a random forest might be a better choice.

Here are some examples of how machine learning is used in image processing:

Machine learning is a powerful tool that can be used to automate many image processing tasks. As machine learning algorithms continue to improve, they will become even more useful for image processing applications.

Convolutional Neural Networks (CNNs) are typically better than Support Vector Machines (SVMs) for image classification because they are able to learn more complex features from images. CNNs are specifically designed to extract features from images, while SVMs are more general-purpose classifiers.

Here are some of the reasons why CNNs are better than SVMs for image classification:

In general, CNNs are better for image classification than SVMs. However, there are some cases where SVMs may be a better choice. For example, if the image dataset is small or if the images are very noisy, then SVMs may be able to achieve better accuracy than CNNs.

As for machine learning vs. deep learning for image classification, deep learning is generally better than machine learning. This is because deep learning models are able to learn more complex features from images than machine learning models. However, deep learning models are also more complex and require more data to train.

In general, if you have a large image dataset and you need to achieve high accuracy, then you should use a deep learning model for image classification. If you have a small image dataset or you don't need to achieve very high accuracy, then you can use a machine learning model. Here are some specific examples of where CNNs have been used for image classification:

These are just a few examples of the many applications of CNNs. CNNs are a powerful tool that can be used to solve a wide variety of problems.

Yes, digital image processing is a part of AI and is used in computer vision.

Digital image processing is the manipulation of digital images using computer algorithms. It is used to improve the quality of images, extract information from images, and create new images. Some common image processing tasks include:

Computer vision is the field of computer science that deals with the extraction of information from digital images and videos. It is used in a variety of applications, such as:

Digital image processing is used in computer vision to perform tasks such as:

So, to answer your question, digital image processing is both a part of AI and is used in computer vision. Here are some specific examples of how digital image processing is used in computer vision:

I would like to use the program adehabitat HS but failed after downloading the program. Is there any example either in video or ppt of the codes?

Thank you for the time and help.

Hello Huda Ghassan Hameed ,

Even though I don't have the slightest idea of what CAM or RAAR means,

Is this of any help?

The paper is here:

Maybe you can also ask for help at the Image analysis forum...

I hope this helps,

Cheers,

J

Unfortunately it is not of my interest

script that demonstrates how to create a polar plot:

```python

import numpy as np

import matplotlib.pyplot as plt

# Sample data for Young's modulus and Poisson's ratio

theta = np.linspace(0, 2*np.pi, 100) # Angular values

young_modulus = np.random.rand(100) # Young's modulus values (replace with your own data)

poissons_ratio = np.random.rand(100) # Poisson's ratio values (replace with your own data)

# Create the polar plot

fig = plt.figure()

ax = fig.add_subplot(111, polar=True)

# Plot Young's modulus

ax.plot(theta, young_modulus, label="Young's Modulus")

ax.fill(theta, young_modulus, alpha=0.25)

# Plot Poisson's ratio

ax.plot(theta, poissons_ratio, label="Poisson's Ratio")

ax.fill(theta, poissons_ratio, alpha=0.25)

# Set the labels and title

ax.set_xticklabels([])

ax.set_yticklabels([])

ax.set_title("Polar Diagram for Young's Modulus and Poisson's Ratio")

# Add a legend

ax.legend()

# Show the plot

plt.show()

```

matplotlib library should been installed (`pip install matplotlib`) before running this script. You can replace the sample data (`young_modulus` and `poissons_ratio`) with your own data accordingly.

This script generates a polar plot with Young's modulus and Poisson's ratio represented as two separate curves. Each data point is plotted at a specific angle (`theta`) around the polar axis. The `fill` function is used to fill the area enclosed by the curves.

Good luck

I am afraid that without knowing either the individual observations or the sum-of-squares (sum of the squared discrepancies around the mean), there is no way of identifying the sample standard deviation. Let me tell you an example. Consider the two datasets (1, 3, 5, 7 and 9) and (3, 4, 5, 6 and 7). Both of them form a sample of n=5 units and possess the same sample mean of 5. Their standard deviation, however, is not the sample. Unfortunately, the sample mean and the sample size are not sufficient to identify the corresponding standard deviation.

You can search on Kaggle for data

Thank you

Best regards

For analyzing the diameter of ocular infected areas in your experimental groups' corneal images, you might consider using image analysis software such as ImageJ or FIJI. These tools are widely used and offer a variety of features for measurements and analysis of images, including area and diameter measurements.

determining causality direction between two events is a challenging task that requires careful consideration, domain knowledge, and appropriate statistical and machine learning techniques. While artificial intelligence and machine learning can assist in causal inference, they are not a substitute for sound experimental design and understanding of the underlying context. A combination of correlation analysis, causal inference techniques, experimental design, and expert validation is often necessary to establish meaningful causality. Additionally, creating a causal map or directed graph can visually represent the causal relationships between the events. However, it is crucial to exercise caution when making causal claims and be aware of the limitations and assumptions of the methods used. Causality is a powerful concept that requires rigorous analysis and continuous refinement.

follower

Relaxivity is a fundamental parameter used in magnetic resonance imaging (MRI) to describe the relationship between the relaxation rate of nuclear spins and the concentration of a contrast agent. It is typically expressed in units of mM^(-1)s^(-1) or similar. To calculate relaxivity from image intensity of an MRI phantom image, you will need to perform a series of steps as follows:

Keep in mind that the calibration process and relaxivity calculation may vary based on the specific MRI scanner, contrast agent, and imaging sequence used. Additionally, the accuracy of the relaxivity calculation depends on the quality of the phantom images, the proper selection of ROIs, and the accuracy of the known concentrations in the phantom compartments.

See Attachment

Signal detection in a moving video stream, particularly for the purpose of automatically recording when a given object appears, involves various researchers, institutions, and industries in the fields of computer vision, machine learning, and artificial intelligence. Over the years, numerous methods and approaches have been developed to achieve object detection and recognition in video streams. Some of the key contributors and techniques include:

The methods employed for automatic recording when a given object appears in a video stream are diverse and continually evolving. Some of the popular techniques include:

a. Single Shot Detectors (SSD): These methods perform object detection with a single forward pass of the neural network, enabling real-time processing in video streams.

b. You Only Look Once (YOLO): YOLO is another real-time object detection algorithm that can detect multiple objects in an image or video frame simultaneously.

c. Region-based Convolutional Neural Networks (R-CNN): R-CNN and its variants use region proposal methods to identify potential object locations before performing object detection.

d. Faster R-CNN: Faster R-CNN introduced the concept of Region Proposal Networks (RPNs) to speed up the object detection process.

e. Mask R-CNN: This extension of Faster R-CNN includes an additional mask prediction branch, allowing for pixel-level segmentation.

f. Feature Pyramid Networks (FPN): FPN enhances the performance of object detection in multi-scale scenarios by using feature pyramids.

g. EfficientDet: This model aims to achieve high accuracy and efficiency by balancing network depth, width, and resolution using compound scaling.

It's important to note that the field of object detection in video streams is continually evolving, and new methods are being researched and developed. Collaboration among academia, industry, and open-source communities plays a crucial role in advancing the capabilities of automatic object detection in video streams.

Lea Lea

This was my question too. But I understood this;

To obtain "LST" from Collection 2 Level 2 Landsat images, the following formula is sufficient: (DN * 0.00341802 + 149) - 273.15.

However, if you want to calculate LST using NDVI, Pv, and emissivity, you will need to download the Level 1 collection of Landsat.

For the specific case of the requested value no one can say. We do not know about the unit of your coordinate system.

Regarding the general question: It depends on your target audience. While Angström is no valid SI unit it is often replaced by/converted to nanometre (nm).

The DIN norm 1301.3 lists the Angström as a no longer valid unit and explicitely notes that the use should be avoided. The same is found for several other national specifications on weighing and measurements.

Nevertheless, the Angström is still common in chemstry, as it is handy on the level of molecular/atomic distances....

I would prefer the nm unit; maybe you can give the converted Angström value in brackets behind the SI representation...

Thank you for your response. No, i do not have that software. i am using only imagej. will that suffice?. one more question should i use whole neuron to count the apical dendrites or only the portion of dendrite would be fine?.

The equations provided are variations of the radiative transfer equation used for estimating Land Surface Temperature (LST) from satellite imagery, such as that from the Landsat series of satellites. The equation is also known as the Radiative Transfer Equation for Temperature (RTE for T), and it's frequently used in remote sensing applications.

The formula includes variables as follows:

LST represents the land surface temperature,

BT is the at-sensor brightness temperature,

w represents the wavelength of emitted radiance,

p is the constant Planck's constant, and

ε is the emissivity of the surface.

Formula 1 is a general form of the radiative transfer equation for temperature, while Formula 2 is a specialized form specifically tailored for Landsat 8 data.

Comparing Formula 1 and Formula 2, you can see that the terms w(BT/p) and 0.00115BT/1.4388 are similar in their purpose. They are both corrections for the wavelength of emitted radiance, but the actual values used (and their unit) will differ because the second formula is specifically calculated for Landsat 8's thermal bands. The 1.4388 value in Formula 2 represents the Wien's displacement constant in micrometers Kelvin units.

The other difference is that Formula 1 uses a natural logarithm of ε (emissivity), while Formula 2 doesn't include this term. This suggests that Formula 2 assumes a constant emissivity (ε) for Landsat 8, which might not necessarily be the case for all land cover types.

So, if you use Formula 1 and Formula 2 separately to calculate the LST of one Landsat 8 image, the results are likely not to be exactly the same due to the differences in the assumptions made in each formula. The difference in results would be based on how much the assumptions made for each formula match the reality of the particular Landsat image being processed.

In conclusion, the choice of formula should be guided by the specific details of your Landsat image, and your knowledge about the land cover types present, their emissivity, and the specific spectral characteristics of the Landsat platform being used.

Jens Kleb thank you for your recommendation . I will check it

Below is a sample questionnaire to assess the self-image variable. The questions are designed to understand how individuals perceive themselves and their self-image. Participants can respond using a Likert scale (e.g., 1 to 5) to indicate the extent to which they agree or disagree with each statement.

Text-to-Image GAN for converting text to a special form:

Here's a basic code structure using Python, TensorFlow, and Keras for creating a Text-to-Image GAN:import numpy as np import tensorflow as tf from tensorflow.keras import layers, models # Define the Generator and Discriminator architecture (you may customize based on your requirements) # Generator network def create_generator(): # Your generator architecture here # Input: Text embeddings # Output: Generated images pass # Discriminator network def create_discriminator(): # Your discriminator architecture here # Input: Real or generated images # Output: Binary classification (real or fake) pass # Create and compile the GAN model def create_gan(generator, discriminator): discriminator.trainable = False gan_model = models.Sequential([generator, discriminator]) # Compile the GAN model with appropriate loss functions and optimizers pass # Data preparation and training (you should customize this based on your dataset) def preprocess_text(text_data): # Your text preprocessing code here # Convert text descriptions to numerical representations (embeddings or one-hot encodings) pass def preprocess_images(image_data): # Your image preprocessing code here # Normalize and resize images as needed pass # Main function def main(): # Load the paired text-image dataset and preprocess it text_data = ... # Your text descriptions image_data = ... # Your corresponding images text_embeddings = preprocess_text(text_data) image_data = preprocess_images(image_data) # Create and compile the generator and discriminator generator = create_generator() discriminator = create_discriminator() gan_model = create_gan(generator, discriminator) # Number of epochs and batch size epochs = 20000 batch_size = 128 # Training loop for epoch in range(epochs): # Sample a batch of text embeddings and corresponding images batch_indices = np.random.randint(0, len(text_embeddings), size=batch_size) batch_text_embeddings = text_embeddings[batch_indices] batch_images = image_data[batch_indices] # Train the discriminator on real images d_loss_real = discriminator.train_on_batch(batch_images, np.ones((batch_size, 1))) # Train the generator-discriminator stack on random text embeddings to generate fake images random_text_embeddings = np.random.randn(batch_size, embedding_dim) # Random text embeddings generated_images = generator.predict(random_text_embeddings) d_loss_fake = discriminator.train_on_batch(generated_images, np.zeros((batch_size, 1))) # Update the generator using the GAN model g_loss = gan_model.train_on_batch(random_text_embeddings, np.ones((batch_size, 1))) # Print the progress if epoch % 100 == 0: print(f"Epoch: {epoch}, D Loss Real: {d_loss_real}, D Loss Fake: {d_loss_fake}, G Loss: {g_loss}") if __name__ == "__main__": main()

Convolutional Neural Networks (CNNs) have been highly successful in various image analysis tasks, including cancer detection. However, traditional CNNs treat all image regions equally when making predictions, which might not be optimal when certain regions contain critical information for cancer detection. To address this, incorporating an attention mechanism into CNNs can significantly improve performance.

Attention mechanisms allow the model to focus on the most informative parts of the image while suppressing less relevant regions. The attention mechanism can be applied to different levels of CNN architectures, such as at the pixel level, spatial level, or channel level. By paying more attention to relevant regions, the CNN with an attention mechanism can enhance the model's ability to detect subtle patterns and features associated with cancerous regions in medical images.

When using CNNs with attention mechanisms for cancer detection, it is crucial to have a sufficiently large dataset with labeled medical images to train the model effectively. Transfer learning with pre-trained models on large-scale image datasets can also be useful to leverage existing knowledge and adapt it to the cancer detection task with a smaller dataset.

Remember that implementing and training deep learning models for cancer detection requires expertise in both deep learning and medical image analysis. Additionally, obtaining annotated medical image datasets and ensuring proper validation and evaluation are essential for developing an accurate and robust cancer detection system. Collaborating with medical professionals and researchers is often necessary to ensure the clinical relevance and accuracy of the developed methods.

To create a neural network with numerical values as input and an image as output, we can use a deep learning library like TensorFlow or PyTorch. In this example, I'll provide you with a simple implementation using TensorFlow and Keras. This implementation will demonstrate how to generate images from random numerical values using a fully connected neural network. Keep in mind that for more complex image generation tasks, you might need a more sophisticated architecture like a Variational Autoencoder (VAE) or a Generative Adversarial Network (GAN).

Before running the code, make sure you have TensorFlow and Keras installed. You can install them using pip:

pip install tensorflow keras

import numpy as np import tensorflow as tf from tensorflow.keras import layers, models # Define the input size for the numerical values input_size = 100 # Define the output image size (e.g., 28x28 grayscale image) output_image_size = (28, 28, 1) # Function to create the generator model def create_generator(): model = models.Sequential() model.add(layers.Dense(256, input_dim=input_size, activation='relu')) model.add(layers.Dense(512, activation='relu')) model.add(layers.Dense(1024, activation='relu')) model.add(layers.Dense(np.prod(output_image_size), activation='sigmoid')) model.add(layers.Reshape(output_image_size)) return model # Function to create random noise as input for the generator def generate_random_noise(batch_size, input_size): return np.random.rand(batch_size, input_size) # Function to create and compile the combined model def create_combined_model(generator, optimizer): generator.trainable = False model = models.Sequential([generator]) model.compile(loss='binary_crossentropy', optimizer=optimizer) return model # Main function def main(): # Generator model generator = create_generator() # Optimizer for the generator optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002, beta_1=0.5) # Combined model combined_model = create_combined_model(generator, optimizer) # Number of epochs and batch size epochs = 20000 batch_size = 128 # Training loop for epoch in range(epochs): # Generate random noise as input for the generator noise = generate_random_noise(batch_size, input_size) # Generate fake images using the generator generated_images = generator.predict(noise) # Your code here: Instead of random noise, you should use your numerical values as input and preprocess them accordingly. # Train the combined model by passing the generated images as inputs and all 1s as the target (since they are fake) d_loss_fake = combined_model.train_on_batch(generated_images, np.ones((batch_size, 1))) # Print the progress if epoch % 100 == 0: print(f"Epoch: {epoch}, Loss: {d_loss_fake}") # Save generated images occasionally if epoch % 1000 == 0: # Your code here: Save the generated images using your preferred method (e.g., matplotlib or OpenCV). pass if __name__ == "__main__": main()

@Syed Hussain Yes, it is made by Chat GPT, I just want to help you

you can follow these steps:

Keep in mind that the specific steps may vary based on the data format and the type of QA layer provided. It's crucial to refer to the data provider's documentation or metadata to understand the information stored in the QA layer and how to interpret and use it effectively to estimate reliable pixels in the EVI image.

Hello Karen,

There are numerous ways folks have attempted to capture perceived body image. Here are a couple of links that you may find helpful:

Good luck with your work.

DSP (Digital Signal Processing) techniques are widely used in both audio and image processing to manipulate, analyze, and enhance signals in the digital domain. Here's a brief overview of how DSP techniques are applied in each domain:

Audio Processing:

Image Processing:

Both audio and image processing rely on a wide range of DSP algorithms and techniques to achieve specific objectives, and advancements in DSP continue to drive innovations in these fields, enabling a variety of applications from entertainment to medical imaging to security.

José Jorge Caracheo González

Hi ,

There are a few reasons why the Radiometric Calibration tool might not be displaying in ENVI. Here are a few things to check:

Here are some additional troubleshooting tips:

I hope this helps !

Hi Joshua Nicholls,

Calibration bar cannot be applied to RGB or Composite image, You need to first reduce the colour depth then apply gradient to the image before inserting the Calibration bar.

Try the following steps:

1. Convert the Image to 32-bit; Image>Type>32-bit

2. Apply LUT; Image>Lookup Tables>Viridis (or any 3-level colouring gradient is suggested although 2-level gradient will also work fine)

3. Insert Calibration bar; Analyze>Tool>Calibration bar.

I hope this will help.

Md. Asif Haider You can either modify your model input layer to accept one channel images. (So e.g., 128x128x1x1, instead of 128x128x3x1)

Or just convert your images to RGB

rgb_image = cv2.cvtColor(gray_image, cv2.COLOR_GRAY2RGB)

Discovering that your research results (an image) have been used without your permission by researchers from your previous university can be a concerning situation. Here are the recommended steps you can take to address the issue:

Remember that each situation may be unique, and the best course of action will depend on the specifics of your case. It's essential to handle the matter professionally and calmly, seeking resolution through appropriate channels.

import numpy as np import matplotlib.pyplot as plt from skimage import io, filters # Load the OCT image image_path = 'path_to_your_image.png' image = io.imread(image_path) # Display the original image plt.figure() plt.imshow(image, cmap='gray') plt.title('Original OCT Image') plt.axis('off') # Preprocessing # Apply a median filter to reduce noise image_filtered = filters.median(image, selem=np.ones((3, 3))) # Image segmentation # Apply a threshold to segment the image threshold = filters.threshold_otsu(image_filtered) image_segmented = image_filtered > threshold # Display the segmented image plt.figure() plt.imshow(image_segmented, cmap='gray') plt.title('Segmented OCT Image') plt.axis('off') # Further analysis and measurements can be performed on the segmented image # Show the plots plt.show()

The Pauli group is a mathematical concept that was first introduced and defined through matrix calculations, rather than through drawings or diagrams. The concept of the Pauli group is based on a set of 2x2 matrices, known as the Pauli matrices, which were first introduced by physicist Wolfgang Pauli in the 1920s to describe the behavior of elementary particles.

The Pauli matrices are defined as follows:

σ_x = [ 0 1 ]

[ 1 0 ]

σ_y = [ 0 -i ]

[ i 0 ]

σ_z = [ 1 0 ]

[ 0 -1 ]

These matrices have several important properties, including that they are Hermitian (equal to their own conjugate transpose) and unitary (their inverse is equal to their conjugate transpose). These properties make them useful for describing quantum states and operations.

The Pauli group is a group of operations that can be performed on a quantum system using the Pauli matrices. It is generated by the tensor product of the Pauli matrices with themselves, and it has several important applications in quantum information processing and quantum computing.

While diagrams and drawings are sometimes used to visualize the operations of the Pauli matrices and the Pauli group, the concept itself was first defined and developed through matrix calculations and algebraic operations.

When using image classification models in real-world applications, several ethical considerations arise. Here are some of the key ethical considerations:

The Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are two commonly used metrics for evaluating the quality of reconstructed or processed images.

In image processing, the sigma value is often associated with the amount of smoothing or blurring applied to an image. Increasing the sigma value leads to a higher degree of smoothing or blurring. The effect of increasing the sigma value on PSNR and SSIM can be explained as follows:

When the sigma value is increased, more smoothing or blurring is applied to the image. This blurring can cause loss of high-frequency details and fine textures in the image. As a result, the mean squared error between the original and processed images increases, leading to a decrease in PSNR. In other words, the blurring introduced by a higher sigma value results in a larger difference between the original and processed images, which lowers the PSNR value.

When the sigma value is increased, the blurring effect can help to reduce noise and enhance the overall structural similarity between the original and processed images. The blurring can smooth out noise and minor variations, making the images more similar in terms of structure. Therefore, increasing the sigma value tends to improve the SSIM value because the structural similarity is enhanced.

Biomedical image processing encompasses a wide range of techniques used to analyze and manipulate images obtained from various medical imaging modalities. Here are some of the different types of biomedical image processing techniques:

Hello,

One thing you can do is to convert the images to tabular data. That way you'll end up with a coherent data. However, doing so will going to probably undermine your overall accuracy.

Another thing, and possibly the most logical solution is to either use multi-modular models or to use three models, one for tabular, one for images and one as the final decision maker. Given you chose to do the second option, you need to construct the final layer of both of the initial models in a way that they'll map, transform and output the results in a format that is coherent enough for the final decision making model to receive.

Sorry outside my field