Image Processing - Science topic

"Advances in artificial intelligence (AI) promise to transform biomedical research, but could pose significant biosafety and biosecurity risks, argue three public health researchers and two policy researchers. They urge governments and AI developers to work with safety and security experts to mitigate harms that could result in the greatest loss of life and disruption to society, such as outbreaks of transmissible pathogens. That means building a scientific consensus through processes that engage diverse, independent experts..."

Dear Doctor

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[Abstract

If you read that quantum machine learning applications solve some traditional machine learning problems at an amazing speed, be sure to check if they return quantum results. Quantum outputs, such as magnitude-encoded vectors, limit the use of applications and require additional specifications on how to extract practical and useful results. In satellite images, degradation of image contrast and color quality is a common problem, which brings great difficulties for information extraction and visibility of image objects. This is due to atmospheric conditions, which can cause a loss of color and contrast in the image. Regardless of the band, image enhancement plays a vital role in presenting details more clearly. The research includes new computational algorithms that accelerate multispectral satellite imagery to improve land cover mapping and study feature extraction. The first method is a quantum Fourier transform algorithm based on quantum computing, and the second method is a parallel modified data algorithm based on parallel computing. These calculation algorithms are applied to multispectral images for improvement. The quantum-processed image is compared with the original image, and better results are obtained in terms of visual interpretation of the extracted information. Quantitatively evaluate the performance of the improved method and evaluate the applicability of the improved technology to different land types.]

Welcome

You could try inserting a sheet of white paper (not too thick) between your wafer and the light source. This should minimize the reflection on your wafer. Otherwise maybe an illumination with a very small incident angle?

Interested, I am interested in collaboration for an editorial book. Kindly contact me at mkhankku@gmail.com for further discussion. Pranshu Saxena

Dr Mudassir Khan

Yes, there are several databases available that provide images and data related to landslides and slope stability, often used for geotechnical analysis and image processing research. Here are some notable options:

1. Landslide Inventory Maps – The US Geological Survey (USGS) and the European Space Agency (ESA) provide comprehensive landslide inventories with georeferenced images, which are valuable for analyzing landslide occurrences and patterns.

2. Global Landslide Catalog (GLC) – Managed by NASA, this catalog contains landslide data with images and geolocations, primarily associated with rainfall-triggered landslides, but it also includes a variety of slope stability scenarios.

3. Japanese Landslide Inventory – Available through the National Research Institute for Earth Science and Disaster Resilience (NIED) in Japan, this database focuses on earthquake-triggered landslides and offers high-resolution images that are highly relevant for seismic studies.

4. SLID: Landslide Image Dataset – This dataset is curated for machine learning purposes and includes labeled images of landslides, making it suitable for image processing and classification tasks.

These databases are widely used in geotechnical research and offer credible, high-quality resources for data-driven analyses of slope stability and landslide risks.

First of all, we have to consider the possibility of averaging a stack of several captures (>10) to eliminate temporal noise. So when I say "image", I mean the average of the stack. In this way, we can minimize the problem of spatial non-uniformity. What we have to do is:

1) to obtain an image in darkness, I'll call it DSNU;

2) to obtain an image in front of a Flat Field at the end of the scale in the dynamic range of the instrument, but making sure that there is no pixel in the image that enters into saturation; I'll call this FF;

3) to obtain PRNU = FF - DSNU;

4) to obtain the average value of PRNU, I'll call it PRNUm;

5) and considering that we already have the reference image, a raw image, which I'll call RAW;

6) finally you can obtain your corrected flat field image (Icorr), assuming a linear behavior throughout the entire dynamic range of the sensor, as follows: Icorr = (RAW-DSNU) x PRNUm / (FF - DSNU).

Regards!

Hi Liew Keng Leong ,

For more details about the conference, please visit the official website of the conference: https://ais.cn/u/IJfQVv

To get your thesis accepted in a reputable ISI journal, ensure that your implementation is:

1. Original: Offers a novel contribution or solution.

2. Well-Researched: Thoroughly reviews and builds on existing literature.

3. Methodologically Sound: Uses clear, reproducible methods.

4. Significant: Demonstrates clear advantages or improvements over existing work.

5. Well-Written: Communicates ideas clearly and is well-organized.

Meeting these criteria increases your chances of acceptance.

Wishing you every success, International Journal of Complexity in Applied Science and Technology

Thanks for responding Fulgence Hatangimana your answer provides a brief overview of first doing the radiometric correction by following the the GitHub repo by Micasense. However, my question was trying to get into the details of radiometric correction that is done by Metashape. For example, if I apply the row gradient correction before processing my images in Metashape, how do I know that Metashape is not trying to repeat the same correction?

Thank you sir@Alexey chernyavskiy.

I think Python is perfect for it.

Thanks for sharing. I wish you every success in your task.

I can suggest the following approach.

1. Cells detection using instance segmentation or just detection. There are a lot of approaches.

2. Track cells. The simplest approach is SORT track algorithm.

3. Analyze a cell (each track)

3.1 Align cell crops using circle detection, build "video" for each cell

3.2 Calculate optical flow

3.3 Convert the optical flow from cartesian to circular coordinates

3.4 The <mean> amplitude of result vector field is rotation speed.

Probably, you will need to crop this field or do some additional transformation in another steps.

Dear Kazi Redwan ,Regular Registration(4 - 6 pages) fee is 485 USD. Online presentation is accepted. All accepted papers will be published in the Conference Proceedings, and submitted to EI Compendex, Scopus for indexing.

For More Details about registration please visit: http://www.iccrsa.org/registration_all

Dear Paulo Bolinhas Your enthusiasm for the topic is contagious, and we couldn't agree more that this special issue is a treasure trove of ideas and discoveries. We hope it will inspire further research and innovation in the field.

Dear Mohamed Shehata, you call yourself successful people, but unable to answer my simple questions regarding your roles as an editor?!

Remember now and forever Mohamed that, real scientists, don't leave or block but certainly stay and answer their critics scientifically and logically, also remember Mohamed that real research is product and/or services oriented but not just some manuscripts that were published with force of money, and change nothing in our real world!!!

Thank you

Please let me know if anyone is interested to o

Useful thing

Dear Sergey Alexandrovich Shoydin ,only English manuscripts are accepted in the conference.

Dear Renjith Vijayakumar Selvarani and Dear Qamar Ul Islam,

Many thanks for your notice.

Can you show some example images?

First up, blockchain. It's like the trusty sidekick ensuring data integrity. By nature, it's transparent and tamper-proof. So, when you have a bunch of IoT devices communicating, blockchain can help keep that data exchange secure and verifiable. It's like having a digital ledger that says, "Yep, this data is legit and hasn't been messed with."

Then, enter machine learning. ML is the brains of the operation, constantly learning and adapting. It can analyze data patterns from IoT devices to spot anything unusual. Think of it as a detective that's always on the lookout for anomalies or suspicious activities.

And finally, there's NLP. It's a bit like the communicator of the group. In this context, NLP can be used to sift through tons of textual data from IoT devices or networks, helping to identify potential security threats or unusual patterns that might not be obvious at first glance.

Put them all together, and you've got a powerful team. Blockchain keeps the data trustworthy, ML hunts down anomalies, and NLP digs deeper into the data narrative. This combo can seriously level up cybersecurity in IoT, making it harder for bad actors to sneak in and cause havoc. Cool, right?

Combining image-trained neural networks, bioinformatics breaches, and quantum-resistant backdoors has major limitations.

Moving from image-trained neural networks to bioinformatics data requires significant domain transfer, which is not straightforward due to the distinct nature of these data types and tasks.

Secure IoT medical devices are designed with robust security features in mind and deployed. Successful attacks requires exploiting a specific vulnerability in the implementation of security measures, rather than the reliance on neural network capabilities.

Deliberately inserting backdoors and to the extent, even quantum-resistant ones, poses ethical and legal questions that would go against norms and standards of cybersecurity practitioners. The actions would violate privacy rights on the federal level, ethical standards and codes of conduct and pose severe legal consequences. Those would be the domestic ones; assuming we're keeping the products in the US.

Quantum computers with sufficient power to break current cryptographic systems are not yet available. Developing quantum-resistant backdoors knowingly anticipates a future scenario to be truth that is still today largely theoretical, without being proven or true.

Great opportunity!

Dear Shazed Muntashir Ovi ,

At its core, AI image processing is the marriage of two cutting-edge fields: artificial intelligence (AI) and computer vision. It's the art and science of bestowing computers with the remarkable ability to understand, interpret, and manipulate visual data—much like the human visual system. Imagine an intricate dance between algorithms and pixels, where machines "see" images and glean insights that elude the human eye.

Regards,

Shafagat

MDPI is a publisher that allows you to publish in a very short time. Specifically, I suggest considering the journals 'Mathematics,' 'Applied Sciences,' and 'Journal of Imaging.' These publications are notable for their quick turnaround times. Personally, I have had the opportunity to publish in all three and have also served as a reviewer for articles in the first two journals, all of which have been positive experiences.

If you are interested in publishing in 'Mathematics' and 'Applied Sciences,' I would recommend exploring special issues related to imaging.

This is a fourier domain filtering technique. Note the area that is zeroed out in the IFFT is the frequency component of that noise frequency. This technique is used in medical imaging for mamo, ct, and mri.

Dear Onkar Kulkarni ,

To optimize the bending curvature by controlling the scribing parameters—the depth, number, and interval of the scribed grooves, finite element analysis was conducted on the bending tests of scribed polyethylene terephthalate films. Moreover, the influences of the parameters on the stress/strain near the grooves were investigated. The maximum stress/strain and curvature generally increased with an increase in depth, whereas these values decreased with an increase in number and intervals.

Regards,

Shafagat

Use the command line:

save filename [ format format-name ] [[ width w ][ height h ] | pixelSize p ] [ supersample N ] [ quality M ] [ transparentBackground  true | false ]

The answer provided by Qamar Ul Islam is obviously AI generated, but the proposed code would somehow work. If you need real help with the software I have experience doing similar things in Matlab, so you may contact me. :)

Murana Awad Dealing with events from an event camera in real-time applications often requires specialized processing due to their asynchronous and continuous nature. Event cameras provide data in the form of pixel-level events, such as changes in brightness (polarity), and timestamps when these changes occur. To utilize this data effectively, you have several options:

The choice of the best approach depends on your specific real-time application and its requirements. Consider factors such as the desired output, computational resources available, and the nature of the tasks you need to perform. It's often beneficial to start with existing libraries and frameworks tailored to event cameras, as they can save you significant development time. Additionally, experimenting with different approaches and assessing their performance is essential for optimizing your real-time event-based system.

Jonas Petersen cool! some answers after years XD

Dear Professor Mahendra Kumar,

Could you please have a look at my following article:

Best regards,

Nidhal

The choice of MRI modality for the detection of brain tumors depends on the specific characteristics of the tumor and the clinical question being addressed. MRI is a versatile imaging modality that offers different sequences, each providing unique information about the brain and its pathologies. In the context of brain tumor detection, the following MRI sequences are commonly used:

In clinical practice, a combination of these MRI sequences is often used to improve the accuracy of brain tumor detection and characterization. The initial evaluation usually includes T1W, T2W, and FLAIR sequences, which provide essential information about the tumor's location and extent. The addition of contrast-enhanced T1-weighted imaging (T1-Gd) can enhance the visibility of tumors and provide additional information about tumor vascularity.

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.

Mohammad Imam , thanks for the input, I'll look into this

Professionally nobody is teaching these tools yet in our industry... but if you are willing to do that, you must learn any programming language like Python, VBA, or MATLA. Then, you can start writing the API code for the specific task you want to do. OAPI changed my life since I implemented that in my research studies. I hope this is useful.

Firstly go deep into the minor details of image processing. and what is the history of it, how it used to work and how it is working right now. based on this, you can dig up for the solutions that you can improve in it. Then with that solution you can opt for writing research paper on it...

I would say with this you can atleast start......

There are two commonly used extraction techniques that can be appropriate for rice plant disease detection: (1) Color-based Extraction and (2) Texture-based Extraction.The most appropriate extraction technique for rice plant disease detection depends on the specific requirements and characteristics of the dataset and the detection algorithm being used.

Sure, I'd be happy to provide you with guidelines for your Matlab project. Please reach out to me via email at erickkirui@kabarak.ac.ke, and I will promptly assist you with the necessary guidance for your project

Dear George Todorov ,

The ability of bacteria to recognize kin provides a means to form social groups. In turn these groups can lead to cooperative behaviors that surpass the ability of the individual. Kin recognition involves specific biochemical interactions between a receptor(s) and an identification molecule(s). To ensure that nonkin are excluded and kin are included, recognition specificity is critical and depends on the number of loci and polymorphisms involved.

Regards,

Shafagat

You bring up a good point. In the case where the tomography dataset is provided with a resolution in unit length, it may not be straightforward to estimate the measurement uncertainty of more complex properties such as permeability or porosity.

In this case, one approach is to use the resolution as a guide and estimate the measurement uncertainty based on the expected level of variation in the property. For example, if the resolution of the tomography dataset is 1 micrometer and the expected level of variation in the permeability or porosity is on the order of 10%, then a reasonable estimate for the measurement uncertainty might be on the order of 0.1 times the average value.

When reporting physical properties derived from tomography datasets, it is important to balance the need for accuracy and precision with the practical limitations of the measurement and the significance of the results. In general, it is recommended to report physical properties with the appropriate number of significant digits to convey the level of uncertainty and enable meaningful comparison with other results, but not to report more digits than necessary.

Ultimately, the appropriate number of significant digits to report will depend on the specific context and level of uncertainty associated with the measurement. If there is uncertainty about the appropriate number of significant digits to use, it may be helpful to consult with a subject matter expert or refer to relevant standards or guidelines in the field.

I hope this helps you.

Thank you

As far as I know, mIoU is just the mean IoU computed over a batch of data.

Please read my paper

An Adaptive Filter to Pick up a Wiener Filter from the Error using MSE with and Without Noise

This is a system that is able to learn.

The paper is a singles and systems.

The topic is AI.

I think the two fields support each other.

Thank you

Ziad

Thank you Cristina, your work is interesting and helpful.

Dear Andra Marginean ,

you may have a look at:

Good luck and

best regards

G.M.

There are several fast publication journals that focus on image processing, including:

IEEE Transactions on Image Processing: This journal is published by the Institute of Electrical and Electronics Engineers (IEEE) and focuses on research related to image processing, including image enhancement, restoration, segmentation, and analysis. It typically takes around 3-6 months to get a decision on a submitted manuscript.

IEEE Signal Processing Letters: Another publication from IEEE, this journal focuses on research in signal processing, including image processing, audio processing, and speech processing. The journal aims to provide a rapid turnaround time for accepted manuscripts, with a typical review time of around 2-3 months.

Journal of Real-Time Image Processing: This Springer journal focuses on research related to real-time image and video processing, including algorithms, architectures, and systems. The journal has a fast publication process, with accepted papers published online within a few weeks of acceptance.

Hi, the answer provided by Prof. Janak Trivedi is pretty comprehensive, agree with that. The ideal approach would be to capture equal number of slices for each stack, but I guess some samples have the signal spread over a greater depth (axially) so you don't want to miss out that signal. Also, you mentioned you make "a maximum intensity profile of Z- stack". So I suggest you average out and also make a montage of your stacks (ImageJ options) and then compare the intensity profiles. Additionally. check out this article:

Hope it helps.

Dear Tanaji Mane ,

Look the link, maybe useful.

Regards,

Shafagat

Hi,

https://www.reddit.com/r/ImageJ/comments/iypg2g/split_roi_into_quadrants/

https://petebankhead.gitbooks.io/imagej-intro/content/chapters/rois/rois.html

Best regards

I suggest you use deep neural network models for disease diagnosis.

This field is very interesting.

Often known datasets like COCO are used for testing because it's well standardized and balanced. I don't know what kind of research you are doing, but you can see popular datasets here: https://imerit.net/blog/22-free-image-datasets-for-computer-vision-all-pbm/

If this is not what you are looking for, then you can search on Roboflow or Kaggle.

It depends on whether the results of image identification are invariant to the software system with respect to rotation, scaling and image transfer

Therefore, you have 20x20 = 400 control points. If you do georeferencing in Qgis, you can use all control points or some of them, like every 5 Km (16-points). During resampling, all pixels have coordinates in the ground system.

If you do not do georeferencing (no resampling), then you could calculate the coordinates of unknown pixels by interpolation. Suppose a pixel size a [m], then in one km, you have p = 1000/a pixels, and therefore known coordinates have the first(x1,y1) and the last(x2,y2) pixel. The slope angle between the first and last pixel is:

s = arc-tan[(x2-x1)/(y2-y1)]. Therefore, a pixel of a distance d from the first pixel has coordinates x = x1 + d.sin(s) and y = y1 +d.cos(s). You can do either row of column interpolation or both and take the average.

Satellite magnetometers some of the time elapse through locales of plasma, like the terrestrial ionosphere, where the ionization is huge enough that a portion of the first surrounding field is prohibited from the plasma. This decrease of field inside the plasma region comes from the 'diamagnetic' impact of the charged particles in their helical trajectory around the magnetic field lines.

CNN is a strong algorithm for image processing. These algorithm are presently the best algorithm we have for the automated processing of pictures. You can used that for your work.

Our Flow visualization Stack exchange is up and running!

Here's the link: https://area51.stackexchange.com/proposals/127312/flow-visualization?referrer=N2ZlMTMyMDhiZGUzZWI5YTBmMGQxNjc5YzI4M2M1NjZiNjRjNDY2NTk3YWE2YTE0ZGU4YTI0MDZjYTc2MmI2ZeegsNYvAbeaIAEB2y4GBguHxhiNLnsacfJIvdPkv8Qb0

We need 5 example questions and 5 users within the first 3 days lest to be taken down. Those interested, please, hurry up.

Note, Stack exchange didn't give me specific instructions how to register. Just gave me the link that I have provided above. Go ahead try it, if you experience any issues with it, please post your experience here.

Danishtah Quamar centers = imfindcircles(A, radius) locates the circles in image A with radii that are about equal to the radius. The result, centers, is a two-column matrix holding the (x,y) coordinates of the image's circular centers.