Questions related to Image Processing
I am trying to make generalizations about which layers to freeze. I know that I must freeze feature extraction layers but some feature extraction layers should not be frozen (for example in transformer architecture encoder part and multi-head attention part of the decoder(which are feature extraction layers) should not be frozen). Which layers I should call “feature extraction layer” in that sense? What kind of “feature extraction” layers should I freeze?
I have performed a Digital Image Correlation test on a rectangular piece of rubber to test the authenticity of my method. However, I faced this chart most of the time. Can anyone show me why this is happening? I am using Ncorr and Post Ncorr for Image processing.
These days machine learning application in cancer detection has been increased by developing a new method of Image processing and deep learning. In this regard, what is your idea about a new image processing method and deep learning for cancer detection?
Thank you in advance for participating in this discussion.
As a student who wants to design a chip for processing CNN algorithms, I ask my question. If we want to design a NN accelerator architecture with RISC V for a custom ASIC or FPGA, what problems or algorithms do we aim to accelerate? It is clear to accelerate the MAC (Multiply - Accumulate) procedures with parallelism and other methods, but aiming for MLPs or CNNs makes a considerable difference in the architecture.
As I read and searched, CNN are mostly for image processing. So anything about an image is usually related to CNN. Is it an acceptable idea if I design architecture to accelerate MLP networks? For MLP acceleration which hw's should I work on additionally? Or is it better to focus on CNN's and understand it and work on it more?
I have a large DICOM dataset, around 200 GB. It is stored in Google Drive. I train the ML model from the lab's GPU server, but it does not have enough storage. I'm not authorized to attach an additional hard drive to the server. Since there is no way to access Google Drive without Colab (if I'm wrong, kindly let me know), where can I store this dataset so that I will be able to access it for training from the remote server?
Could you tell me please what is the effect of electromagnetic waves on a human cell? And how to model the effect of electromagnetic waves on a human cell using image processing methods?
I am currently working on Image Processing of Complex fringes using MATLAB. I have to do the phase wrapping of images using 2D continuous wavelet transform.
I have a salt(5grains) which undergoes hydration and dehydration for 8 cycles. I have pictures of them swelling and shrinking taken every five minutes under microscope. I can see in the video that salt is swelling and shrinking if i compile the images. But I need to quantify how much increase or decrease in size takes place. Can anyone explain about how I can make use of the pictures
I am working on a classification task and I used 2D-DWT as a feature extractor. I want to ask about more details why I can concatenate 2D-DWT coefficients to make image of features. I am thinking to concatenate these coefficients(The horizontal,vertical and diagonal coeeficients) to make an image of features then fed this to CNN but I want to have an convincing and true evidence for this new approach.
I would appreciate it if someone can help me choose a topic in AI Deep Learning or Machine Learning.
I am looking for an Algorithm that can be used in different application and have some issues in terms of accuracy and result, to work on its improvement.
recommend me some papers that help me to find some gaps so I can write my proposal.
I'm looking for the name of an SCI/SCIE journal with a quick review time and a high acceptance rate to publish my paper on image processing (Image Interpolation). Please make a recommendation.
As you can see that the image is taken by changing the camera angle to include the building in the scene. The problem with this is that the measurements are not accurate with the perspective view.
How can I fix this image for the right perspective (centered)?
Suppose I use laplacian pyramid for image denoising application, how would it be better than wavelets? I have read some documents related to laplacian tools in which laplacian pyramids are said to have better selection for signal decomposition than wavelets.
I would like to know about the best method to follow for doing MATLAB based parallel implementation using GPU of my existing MATLAB sequential code. My code involves several custom functions, nested loops.
I tried coverting to cuda-mex function using MATLAB's GPU coder, but I observed that it takes much more time (than CPU) to run the same function.
Proper suggestions will be appreciated.
If you are researcher who is studying or already published on Industry 4.0 or digital transformation topic, what is your hottest issue in this field?
Your answers will guide us in linking the perceptions of experts with bibliometric analysis results.
Thanks in advance for your contribution.
Lane detection is a common use case in Computer Vision. Self-driving cars rely heavily on seamless lane detection. I attempted a road lane detection inspired use case, using computer vision to detect railway track lines. I am encountering a problem here. In the case of road lane detection, the colour difference between road (black) and lane lines (yellow/ white) makes edge detection and thus lane detection fairly easy. Meanwhile, in railway track line detection, no such clear threshold for edge detection exists and the output is as in the second image. Thus making the detection of track lines unclear with noise from the track slab detections etc. This question, therefore, seeks guidance/ advice/ Knowledge exchange to solve this problem. Any feedback on the approach taken to attempt the problem is highly appreciated. Tech: OpenCV
I'm about to start some analyses of vegetation indexes using Sentinel-2 imagery through Google Earth Engine. The analyses are going to comprise a series of images from 2015/2016 until now, and some of the data won't be available in Level-2A of processing (Bottom-of-Atmosphere reflectance).
I know there are some algorithms to estimate BOA reflectance. However, I don't know how good these estimates are, and the products generated by Sen2Cor look more reliable to me. I've already applied Sen2Cor through SNAP, but now I need to do it in a batch of images. Until now, I couldn't find any useful information about how to do it in GEE (I'm using the Python API).
I'm a beginner, so all tips are going to be quite useful. Is it worth applying Sen2Cor or the other algorithms provide good estimates?
Thanks in advance!
I am publishing paper in scopus journal and got one comment as follows:
Whether the mean m_z is the mean within the patches 8x8? If the organs are overlap then how adaptive based method with patches 8x8 is separated? No such image has been taken as a evidence of the argument. Please incorporate the results of such type of images to prove the effectiveness of the proposed method. One result is given which are well separated.
Here I am working on method which takes patches of given image and takes mean of them. This mean is used for normalizing the data.
However, I am unable to understand the meaning of second sentence. As per my knowledge, the MRI image is kind of see through, so how will be any overlap of organs?
During preprocessing medical image data different techniques should be considered such as cropping, filtering, masking, augmentation. My query is, which techniques are frequently applied to medical image datasets during pre-processing?
I'm looking to generate synthetic diffusion images from T1 weighted images of the brain. I read that diffusion images are a sequence of T2 images but with gradients. Maybe could be something related to this. I'm not sure how to generate these gradients too. I'm trying to generate "fake" diffusion images from T1w because of the lack of data from the subjects I'm evaluating.
Can someone please help me?
I have been working on computer vision. I used datasets from Kaggle or other sites for my projects. But now I want to do lane departure warning, and real-time lane detection with real-time conditions(illuminations, road conditions, traffic, etc.). Then the idea to use simulators comes to my mind but there are lots of simulators on online but I'm confused about which one would be suitable for my work!
It would be very supportive if anyone guide me through picking up the best simulator for my works.
Is it because the imaging equation used by the color constancy model is built on RAW images? Or is it because the diagonal model can only be applied to RAW images? When we train a color constancy model using sRGB images, can we still use certain traditional color constancy models such as gamut mapping, correction moments, or CNN?
Greetings for the day,
With great privilege and pleasure, i request anyone belonging to Image Processing domain to review my Ph.D thesis. I hope you will be kind enough to review my research work. Please revert me back on my email id: firstname.lastname@example.org at your leisure.
Thanking you in advance.
Hi. I'm working on 1000 images of 256x256 dimensions. For segmenting I'm using segnet, unet and deeplabv3 layers. when I trained my algorithms it takes nearly 10 hours of training. I'm using 8GB RAM with a 256GB SSD laptop and MATLAB software for coding. Is there any possibility to speed up training without GPU?
I'd like to measure frost thickness on fins of a HEX based on GoPro frames.
I got the ImageJ software. But I don't know if there is a way to select a zone, (a frosted fin) and deduce the average length in one direction.
Currently I do random measurements on the given fin and do the average. However, the random points may not be representative.
I attached two pictures of the fins and frost to illustrate my question.
In advance, thank you very much,
Currently, I'm working on a Deep Learning based project. It's a multiclass classification problem. The dataset can be found here: https://data.mendeley.com/datasets/s8x6jn5cvr/1
I have used Transfer Learning mostly, but couldn't able to get a higher accuracy on the test set. I have used Cross-Entropy and Focal Loss as loss functions. Here, I have 164 samples in the train set, 101 samples in the test set, and 41 samples in the validation set. Yes, about 33% of samples are in the test partition (data partition can't be changed as instructed). I could able to get an accuracy score and f1 score of around 60%. But how can I get higher performance in this dataset with this split ratio? Can anyone suggest me some papers to follow? Or any other suggestion? Suggest me some papers or guidance on my Deep Learning-based multiclass classification problem?
I am working on CTU (Coding Tree Unit) partition using CNN for intra mode HEVC. I need to prepare database for that. I have referred multiple papers for that. In most of papers they are encoding images to get binary labels splitting or non-splitting for all CU (Coding Unit) sizes, resolutions, and QP (Quantization Parameters).
If any one knows how to do it, please give steps or reference material for that.
In my research, I have created a new way of weak edge enhancement. I wanted to try my method on the image dataset to compare it with the active contour philosophy.
So, I was looking for images with masks, as shown in the below paper.
If you can help me to get this data, it would be a great help.
Thanks and Regards,
I'm looking for a PhD position and opportunity in one of the English speaking university in European countries (or Australia).
I majored in artificial intelligence. I am in the field of medical image segmentation and My thesis in master was about retinal blood vessels extraction based on active contour. Skilled in Image processing, machine learning, MATLAB and C++.
So could anybody helps me to find a prof and PhD position related on my skills in one of the English speaking university?
recently i am collecting red blood cells dataset for classifying into 9 categories of Ninad Mehendale research paper. can anyone suggest the dataset for Red Blood Cell Classification Using Image Processing and CNN this papeer?
In the remote sensing application to a volcanic activity wherein, the objective is to determine the temperature, which portion (more specifically the range) of the EM spectrum can detect the electromagnetic emissions of hot volcanic surfaces (which are a function of the temperature and emissivity of the surface and can achieve temperature as high as 1000°C)? Why?
There are shape descriptors: circularity, convexity, compactness, eccentricity, roundness, aspect ratio, solidity, elongation.
1) What are the real formulas for determining these descriptors?
2) circularity = roundness? solidity = ellipticity?
I compared lectures (M.A. Wirth*) with ImageJ (Fiji) user guide and completely confused: descriptors are almost completely different! Which source to trust?
*Wirth, M.A. Shape Analysis and Measurement. / M.A. Wirth // Lecture 10, Image Processing Group, Computing and Information Science, University of Guelph. – Guelph, ON, Canada, 2001 – S. 29
I have grayscale images obtained from SHG microscopy for human cornea collagen bundles, and I have them as tiff stack images and their Czi format. I want to convert those 2D images into a 3D volume but I could not find any method that can be done using MATLAB, Python, or any other program?
Hello dear researchers.
It seems that siam rpn algorithm is one of the very good algorithms for object tracking that its processing speed on gpu is 150 fps.But the problem is that if your chosen object is a white phone, for example, and you are dressed in white and you move the phone towards you, the whole bunding box will be placed on your clothes by mistake. So, low sensitivity to color .How do you think I can optimize the algorithm to solve this problem? Of course, there are algorithms with high accuracy such as siam mask, but it has a very low fps. Thank you for your help.
I'm trying to acquire raw data from Philips MRI.
I followed the save raw data procedures and then I obtained a .idx and a .log file.
I'm not sure if I implemented the procedure correctly.
Are .idx and .log file the file format of Philips MRI raw data?
If so, how to open these files? Is it possible to open these files in matlab?
2 Logistic chaotic sequences generation, we are generating two y sequence(Y1,Y2) to encrypt a data
2D logistic chaotic sequence, we are generating x and y sequence to encrypt a data
whether the above statement is correct, kindly help in this and kindly share the relevant paper if possible
How can I tell the distance and proximity as well as the depth of image processing for object tracking? One idea that came to my mind was to detect whether the object was moving away or approaching based on the size of the image.But I do not know if there is an algorithm that I can implement based on?
In fact, how can I distinguish the x, y, z coordinates from the image taken from the webcam?
Thank you for your help
What are the main image processing journals that publish work on the collection, creation and classification of medical imaging databases such as Medical Image Analysis Journal.
Thank you for your support,
I am using transfer learning using pre-trained models in PyTorch for the Image classification task.
When I modified the output layer of the pre-trained model (e,g, alexnet) as per our dataset and run the code for seeing the modified architecture of alexnet it gives output as "none".
Hi Everyone, I'm currently converting video into images where I noticed 85% of the images doesn't contain the object. Is there any algorithm to check whether an image contains an object or not using the objectness score?
Thanks in advance :)
I'm currently practising an object detection model which should detect a car, person, truck, etc. in both day and night time. Now, I have started gathering data for both day and night time. I'm not sure whether to train a separate model for daylight and another model for the night-light or to combine together and train it?
can anyone suggest to me the data distribution for each class at day and night light? I presume it should be a uniform distribution. Please correct me if I'm wrong.
Eg: for person: 700 images at daylight and another 700 images for nightlight
Any suggestion would be helpful.
Thanks in Advance.
Hi. I'm doing a classification problem using deep learning. so that need to train 512x512 images but when i trained my algorithm shows out of memory error. I want to know how much memory size needed to train 512x512 images in MATLAB.
I want to generate a Lyapunov-exponents-Diagram for my new chaotic map using matlab code. i am unable understand the concept which some of the matlab codes used to get Lyapunov-exponents-Diagram for any chaotic map . kindly help me
Hi! I'm trying to train a convolutional neural network (CNN) using Keras for leaves disease classification from images. There are very few plant disease image datasets, so I need to use one of the available TensorFlow datasets for training my model: specifically, two TensorFlow datasets are suitable for this task: 'plant_village' dataset and 'plant_leaves' dataset.
The problem is I don't know how to explore that datasets for see the classes, features, labels... and I don't know how to split them in training, validation and test datasets. I've tried to use the code that is used in TensorFlow docs to explore and manage 'CIFAR10' or 'mnist' datasets, but it doesn't work with the plants image datasets...
Can someone suggest me how to explore and manage 'plant_village' and/or 'plant_leaves' datasets, please?
I have performed all the attack for my image cryptography algorithm. finally i need to test NIST results for my cryptography algorithm. if any one have the code kindly share the code. please do the needful
I am trying to delineate agricultural fields using Sentinel 2 imagery. I have been implementing different image segmentation algorithms to a time series of this data set. My best output so far has false positive errors of some non-agricultural zones (like forests). Hence, I'm looking for the best way to distinguish forests from ag-fields as a post-processing step.
Paddy rice and millet both plants are transplanted during the monsoon (June - August) and harvested in post-monsoon (October - December). You have a cloud-free timeseries Sentinel 2 A/B images of the study area for every 5 days during the whole crop cycle. Is it possible to develop rice and millet crop maps separately? If not, which satellite/sensor’s images would be need more? Write down the steps need to follow
Dear community, after using the wavelet transform to extract the important features from my EEG signals , i'm wondering about how to calculate the Shanon entropy of each value of my coefficients (cD1,cD2,....cA6), another thing is how to use the Shanon entropy for dimension reduction ?
Thank you .
How can various features (including texture, color and shape) from different components or objects in an image be extracted/selected from images for multi label learning task
- Suppose I have several 1000*1000 grids, and at each grid-point, there is some value (in my case it's the number of copies of a specific gene expressed at that pixel location, note that the locations are the same for every grid). What I want is to quantify the similarity between two 2D spatial point-patterns of this kind (i.e., the spatial expression patterns of two distinct genes), and rank all pairs of genes in a "most similar" to "most dissimilar" manner. Note that it is not the spatial pattern in terms of the absolute value of expression level that I care about, rather, it's the relative pattern that I care about. As a result, I might need to utilize some correlation instead of distance metrics when comparing corresponding pixels.
- The easiest method might be directly viewing all pixels together as a vector and calculate some correlation metric between the two vectors. However, this does not take the spatial information into account. Those genes that I am most interested in have spatial patterns, i.e., clustering and autocorrelation effects their expression pattern (though their "cluster" might take a very thin shape rather than sticking together, e.g., genes specific to the skin cells), which means usually the image would have several peak local regions, while expression levels at other pixels would be extremely low (near 0).
- I am not exactly sure if I should consider applying (1) image similarity comparison algorithms from image processing that take local structure similarity into account (e.g., SURF, SIFT, SSIM, etc.), or (2) spatial similarity comparison algorithms from spatial statistics in GIS (there are some papers about this, but I am not sure if there are any algorithms dealing with simple point data rather than the normal region data with shape (they seem to call it polygon map in GIS)), or (3) statistical methods that deal with discrete 2D distributions, which I think might be a bit crude (seems to disregard the regional clustering/autocorrelation effects, ~ Tobler's First Law of Geography).
- For direction (1), I am thinking about a simple method, that is, first find some "peak" regions in the two images respectively and regard their union as ROIs, and then compare those ROIs in the two images specifically in a simple pixel-by-pixel way (regard them together as a vector), but I am not sure if I can replace the distance metrics with correlation metrics, and am a bit worried that many methods of similarity comparison in image processing might not work well when the two images are dissimilar. For direction (2), I think this direction might be more appropriate because this problem is indeed related to spatial statistics, but I do not yet know where to start in GIS.
A possible caveat of GIS methods: The expression of certain marker genes of a specific cell type might not be clustered in a bulk, but in the shape of a thin layer or irregularly. For example, if the grid is a section of the brain, then the high-expression peak region for cortex layer-specific genes (e.g., Ctip2 for layer V) might form a thin arc curved layer in the 1000*1000 grid.
Original question posted on stackoverflow: https://stackoverflow.com/questions/65912256/how-do-i-quantify-the-similarity-of-spatial-patterns
Any suggestion would be greatly appreciated!
I'm pursuing a Ph.D. in the area of image processing. As per my academic regulations I have to publish two papers in SCI/SCOPUS indexed journals. Already I sort out some good journals but their publication time is so long. I want to complete my course as early as possible. So kindly suggest to me some rapid/fast publications in the area of image processing using deep learning. kindly help me in this regard. thank you for your consideration.
What feature analysis techniques or new approaches can be applied to analyzing cardic ultrasound images for detection of a defect.
I have a pile of powder and I'm trying to calculate its volume with image processing.
I'm not quite sure how to determine its height in each centimeter with image processing.
the picture might be from different angles. can u help me, please?
I am working on dental disease prediction by using Image Processing and Deep Learning. I need a dataset of camera images labeled with the dental disease. Any Kind of reply will be very important to my work and will be very much appreciated. Thanks in advance.
I have a computer vision task in hand. Much as it's quite simple in my opinion, I'm very naive in this area thus looking for the simplest and fastest methods.
There's a laser pointer projected on a screen that keeps bouncing around. I need to capture the location and the velocity of the projected dot with respect to some reference point. I would really appreciate it if someone elaborates on the procedure in simple terms.
I hope you are doing well.
I am using a Vantage Verasonics Research Ultrasound System to do Ultrafast Compound Doppler Imaging. I acquire the beamformed IQData with compounding angles (na = 3) and ensemble size of (ne = 75) which are transmitted at the ultrafast frame rate (PRFmax = 9kHz) and (PRFflow = 3kHz). Can I used the Global SVD clutter filter to process the beamformed IQData instead of conventional high-pass butterworth filter.
Your kind responses will be highly appreciated.
How can Enhance my segmentation results? I calculated the maximum filtering response for my 3D volume and then I performed adaptive thresholding to segment the bundles, but my segmentation results I not that good. I have tried k-mean clustering-based segmentation but it failed. I need to segment the image then extract some features for classification purposes.
I have attached a couple of images as an example.
I want to Identify darkest object in uploaded image. I have tried Imagej. In IMAGEJ, for each image I have to do different threshold and analyzing. Here some are getting excluded for different images with same value of threshold. I want to learn if counting is possible very accurate automatically with some image processing technique. Is it possible to identify with OpenCV?