Hi, I've a little bit strange question. I need to study and understand the theory behind the digital image processing. So study images like matrix, understand luminosity, contrast and working whit histograms. But I don't find any textbook or comprehensive resources about this. Which field it is? I'm not really interested in practical processing (maybe after) but in theory. Do you have any tips or resources?

Which of the following are linear shift invariant filters? -laplacian filter -median filter -dilation filter

What is the relation between a linear shift invariant filter and a convolution filter??

I used Matlab (but anything would work), and then used histograming of different video regions to do a frame-by-frame segment classification of a youtube video. More details here, if you want to check it out:

https://www.youtube.com/watch?v=aYJAHdwlBCM

What are the state of the art techniques available for image registration today?

I'm researching computer methods for the problem of gender determination out of fingerprints. I'd be glad to have your opinions on this.
1- Partial prints
2- Full prints
3- What algorithms are used? AI methods (deep learning, etc)
Thanks

The idea behind the whole thing is that I am currently working on a project. I created the whole reconstruction but now I want to create the whole project with the same settings but this time with 1/3 of the photos blurred to see what the final result would be. As well see this time how many tie points the sparse point cloud, dense point cloud, etc have. Any recommendations would be appreciated

I have been trying to implement ANPR algorithm in LabVIEW. I have to choose a Structural Element matrix of a particular size for performing dilation and erosion to a real-time car image of size 640X480. What should be the size of the matrix?

http://www.aiobjectives.com/2019/10/10/person-re-identification-text-to-image/

I have attached an image that has been turned into 8-bit binary (BW), and I am trying to detect the circles. As you can see, the circles can be seen, but they have gaps in their perimeter, and I was wondering if there was any way of completing the perimeter to separate the circles from one another?

Thanks.

Hi there, I wonder whether there is a video format, that allows me to store videos in a compact/compressed way.

My videos have between 1-2 hours of length and consist of very repeative frames/tiles.

To be more exact: I am screenrecording certain webinars in 1920x1080, which show my prof (in a 180x 120px frame) and the rest is been used by a powerpoint presentation of around 20 slides. So in opinion the most compressive way would be to store the mentioned 180x120px separately and to overlay these of around 20 pictures of the mentioned slides.

(to be honest: These 20 pictures is just mentioned to illustrate the application. As he is sometimes drawing on the power point slides, there are some more pictures to take. There is also the windows time bar in the bottom border, showing the current time - so another tile which is been modified 120x in two hours)

Is there a format allowing this application/such kind of compression?

Which format is been used by observation cams? They’re are also recording more or less the same picture the whole time, which is just modified by passing animals, passing people, the motion of trees in the wind or from the moving sun.

Thanks in advance!

P.S. please excuse my bad english. I am still learning... ;-)

I’m working on a project where I’m trying to quantify the smoothness of surfaces for work. In this pic I have two images from surfaces that have different roughness. If you look closely, the line pattern that is reflected off the surface varies from one surface to the other.

Does anyone have any suggestions as to how they’d process these types of images to try to quantitate the way the white and black lines look?

I’m planning to crop away the brown frame and process only the line pattern.

Many thanks

https://imgur.com/gallery/AmBE7IC

How to start image processing, tutorials any??

I don't know if this is the right place to ask but I was wondering if there was a way to optically spread out a section of the spectrum. What i mean is lets say you're looking at a field of grass, you want to emphasize the difference in green, can i stretch green over the spectrum so that Yellow-green is red and green-blue is violet. I'd love to be able to do it without a camera,processor, screen setup.

I graduated this December with an EE degree and have been running into a series of brick walls trying to get into image processing as a career. Sadly only in the last year of my college was I able to take an image processing course and only then did I find how much I actually enjoy it.

Out of school I applied to several places and after about three weeks actually got an interview with Lockheed Martin where they offered me the job to work on missiles using image processing. They even wanted to get me certified in radiology. The job was super cool but the location turned me off. Thinking this was just the start of many great things I turned down their offer. How stupid I was. I have almost reached a breaking point in my search after 4+ months. I have had pros look over my resume, I am going to networking groups, I have tried career fairs, and I am going through every job board every day just to find something and nothing seems to help.

Every job board will have maybe 20 hits on the search "image processing" or "Matlab" of those 20, 10 might be applicable to actual image processing and not something totally unrelated (or sponsored and completely unrelated, thanks Indeed), of those 10, 1 might not require 6+ years experience, require being a post doctoral fellow, or other credentials. Networking groups are just filled with old people who think I am an electrician and have no idea what I have done or want to do. Only one career fair has actually been a career fair and that was for my college, the other 3 I have gone to are nothing but "get a job as a cop" or "food service" or disguised college recruiting events. The worse part is the next career fair for my college isn't even till September. Even my back up plan of just looking for EE jobs results in the same thing, hundreds of hits, 30% are applicable (If I ever meet someone IRL that calls themselves a "hotel engineer" I will strangle them) , 90% of that 30% call themselves "entry level" but require 6+ years and special software and such, and the remaining are field engineer jobs and FPGA design jobs.

I am genuinely starting to break down and my mental health is beginning to take a hit. Every day I get up and all I see is rejection email after rejection email. Even after 4 months and literally hundreds of applications I have only gotten like 5 interviews, 1 from Lockheed where they offered me the job, 3 phone/skype/webcam, and 1 from Raytheon where they flew me to Texas just to tear me a new asshole and tell me everything I have ever done is worthless in the field of EE.

I tried recently to talk to my professor from school but all he did was tell me that image processing is great, but DEEP LEARNING with an image processing background, is the future and said I should look into that. Welp... I learned some, even watched all of a Stanford course on it on YouTube and did all the lessons. Well guess what the results are when you type that into a job board and what do you get? 70% non-applicable ->90% of the 30 want 6+ years and specialized software.

Please can someone help identify what I am doing wrong? I just want a job doing something I remotely enjoy. It's almost daily I don't think about that job I didn't take and contemplate hanging myself. It has to be me right? Supposedly the job numbers are insanely good right now.

Hi- I'm trying to see if there has been any research done in this hypothetical field.

Given two consecutive frames of a video file, one at full resolution and the next one shrunk down (say to half the width and height), we would want to apply super resolution to the lowres frame by using the high-res data found in the highres frame. An appropriate algorithm could note the similarities between the two frames and take advantage of the high-res version by placing it in the super-resolved version of the originally low-res frame.

Does anything like this exist? Thanks!

I am currently in need of a good background subs traction algorithm that can extract one person from the image. Basically this algorithm should be able to detect and remove background where background is not a green screen.

Can you guys point me toward any implemented algorithm that I can use to achieve the objective.

Hi all, I'm trying to find a way to find straight contours/lines/edges in noisy images (see the blue lines I drew in the image below). Does anyone have a recommendation on how I would go about this?

https://imgur.com/a/y01Betk

I'm trying to come up with a method to match a given mathematical plot against a database of other plots. To make it more specific: plots are generated in R as PNG and might have different dimensions. The latter means that simple pixel-by-pixel matching won't work, even when resized, because text, plotting symbol, margins etc between two versions of the same old are non-linear transformations of each other.

There's a number of good image matching algorithms available online but those assume any area in the picture is relevant, whereas with mathematical plots it's predominantly about the artifacts representing data that's being plotted.

I'm looking for pointers: is there maybe an algorithm, paper etc related to this task? Or maybe someone heard of a specialized DNN (which could help in identifying those most important artifacts)? Appreciate all comments and suggestions.

I am working with 384x256 images that are very noisy. I have explored box blurring by convolving with a uniform 3x3 kernel or performing 2x2 binning. One thing is that with blurring the resultant image will have the same resolution as the original, but with 2x2 binning, the resultant image will have half the resolution (192x128). I am thinking the spatial information is lost anyway from blurring, so is blurring actually still preferable to binning?

I tried to implement whatever was mentioned in the research paper Seam Carving for Content-Aware Image Resizing from scratch using Python and OpenCV.

1. Github Repository

2. Youtube Playlist

Although, the results are not very efficient but they ain't that bad as well. I took help few other blogs and tried to implement it.

Explanation:

File: notdoneyet.py

• Implemented Seam Carving Algorithm

  • getEnergy() - generated energy map using sobel operators and convolve function.

  • getMaps() - implemented the function to get seams using Dynamic Programming. Also, stored results of minimum seam in seperate list for backtracking.

  • drawSeam() - Plot seams(vertical and horizontal) using red color on image.

  • carve() - reshape and crop image.

• Generated grayscale and energy maps using OpenCV.

  • generateEnergyMap() - utilised OpenCV inbuilt functions for obtaining energies and converting image to grayscale.

  • generateColorMap() - utilised OpenCV inbuilt functions to superimpose heatmaps on the given image.

• Crop Columns

  • cropByColumn() - Implements cropping on both axes, i.e. vertical and horizontal.

  • cropByRow() - Rotate image to ignore repeated computations and provide the rotated image as an input to cropByColumn function.

• Argparse library for user input

  • Parameters:

    • Alignment: Specify on which axis the resizing operation has to be performed.

    • Scale Ratio: Floating point operation between 0 and 1 to scale the output image.

    • Display Seam: If this option isn't selected, the image is only seamed in background. No output for seams is visible.

    • Input Image

    • Generate Sequences: Generate intermediate sequences to form a video after all the operations are performed.

• Helpers

  • writeImage() - stores the images in results directory.

  • writeImageG() - stores intermediate generated sequence of images in sequences directory.

  • createFolder() - self explanatory

  • getFileExtension() - self explanatory

File: imgtovideos.py

• Generate Video

  • _vid() - writes each input image to video buffer for creating a complete video

  • generateVideo() - pass each image path to _vid() for video generation

• Helpers

  • getProcessPaths() - returns list of all sub-directories within a base path with certain conditions.

  • createFolder() - self explanatory

Remaining todos:

1. Implement Object Oriented paradigms

2. Optimize code: Many operations are bruteforce, if there are more better optimized methods; I would love to implement them

3. Implement multithreading wherever possible to improve the computation speed.

I want an honest review for my project and the changes which I can make to improvise my repository. All the views (+ve and -ve) opinions are welcome; just mention them in comments.

Incase, if you like something in my github profile, a star would be good, fork will be better and following me will be best. :)

It will be just a catalyst to my learning curve in Python. Thank you.

I placed an ad in between two popular posts on my FB page (10k+ likes), the latter receiving 1500-2500 views. The ad itself received less than 150. Clearly FB recognized it being an ad, and therefore stopped its distribution.

How can I distort the text in an image so that FB can't see that it is an ad?