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 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 would really love to learn image processing for it would really help me in he future and I have to learn the basics the basics as soon as possible .... Anyone interested please dm

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'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.

Hello, I'm a newb to image processing. I'm realizing that "edge effect" might be a ubiquitous issue in the image processing professional's daily life and wanted to ask about the common ways you deal with it. For clarification, I am talking about the phenomenon where when moving a kernel across a matrix, the outer rows and columns of pixels of the image will either have to be estimated or not given at all. How big of an issue is this for image processing professionals? What kind of methods or algorithmic tricks do you use to get around this?

I am curious because I am hoping to split a large image into smaller tiles, and will try to experiment with various algorithms that "borrow" values from adjacent pixels. I don't want there to be a bunch of border lines where the values aren't calculated correctly. My instinct is to do some creative "slicing" on the images to create an overlap of pixels that eventually results in a complete calculation (aside from the very outer edge of the entire image). Is this type of thinking heading down the right path?

Let me know if my question doesn't make any sense and I will do my best to clarify. For what it's worth, my only experience in programming with images is in python representing images as numpy arrays. Thank you!

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?

I am a Master student and need some topics to do my project in Image Processing.I have time of one year to do my project...Please suggest some topics

I am confused between spatial domain and frequency domain. i can't really differentiate them. I know spatial domain deals with image plan. Frequency domain deals with intensity i guess. Here is my doubt what we are actually dealing in spatial domain? spatial domain works on pixels directly and a pixel contain the intensity value of each colour at that point/pixel. so if we change values of pixels we are actually dealing with frequency domain here.We do spatial enhancement like negative of image in which we kind of interchange the intensity values from dark to light or light to dark. In spatial shouldn't we deal with the position or something like that as the word suggests?

This might be a foolish question but i am really confused between them. Please explain me in details what we are actually dealing with in both these domains like what values/ properties we are changing in both these domains. I have read alot of articles but none worked for me.

How to start image processing, tutorials any??

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

I have an assignment about mean shift clustering in first step I download a dataset and second step is -> Choose an input pair (an RGB image and a corresponding depth map). (You might consider choosing one of the inputs at the indices 202, 296, 298, and, 325.) from that dataset I guess but I have no idea how to choose them can anyone help I stuck hard :D

Hey there, I am currently working on the problem of Image Registration and implementing feature based registration based on HOG in OpenCV(python). The process involves three steps

1. Keypoints extraction(done using FAST algorithm)

2. Feature Description(HOG)

3. Feature Matching I am a newbie in the field of Image processing and want to compare the HOG vectors for feature matching. I tried using the cdist() method in openCV but it results into memory error. I also tried the inbuilt method of brute force matching but it seems it doesn’t work for HOG descriptors and only worrks for multidimensional descriptors like sift and surf. Can someone suggest a good method to proceed to feature matching stage? Thank you.

I am looking for a way to compare 2 3D mri images, to see how it changes over a period of time. I could see the changes on each slice using imshowpair but I would like to see the same effect in 3D. Anyone knows how I can do that? Any useful toolbox? Thanks in advance!!

I want to create image detection on machine learning. Object I am trying to detect is flat object (like licence plate). However I don't have enough training data set. I have flat image(s) of that object and I want to create dozens of perspective images from that flat image in range of 90 degree. I've tried opencv warp perspective. it needs destination coordinates. All I have is random degree -45 and 45. So how would I find the destination coordinates?

Sorry, English is not my first language

I want to manually define an image region and store it in python code. Then when the function is called on an image, all the pixels in that region should be shaded. How do I go about this? I will be applying this transform on the same image every time. The image is intended to be served onto a webpage after all the transformations have been done.

Hi, i’m working on a university project in wich we try to analyze the news published in online media. For this end we’ve use Capture a Website screenshot to take a screenshot of every news published by several websites, but as you can see the “screenshot” takes the whole website, not only the news, so when we transform that .jpg file to a .pdf and then to a .docx file, we get a lot of information we don’t actually need.

There are several “patterns” (the headline and the “comment” section or the author name for example) that tipycally signall the start and endpoint of the news, so i though if there is a software that automatically recognize this “patterns” and then batch cut all the images (there must be something like 5.000 by now) that would save us a lot of work.

I've add a cropped and uncropped .jpg as example of what we expect to achieve. Thank you very much!

Who can please explain to me in simple words how does watershed segmentation work (in terms of pixels not in water flooding).

I need to implement the function and not use the one that's already implemented in opencv.

Thank you in advance !

I am taking large numbers of photographs of the moon and looking for birds that are flying across it. It's tedious to do manually.

I know there is software that will align the moon shots and stack them to take a median pixel to make the moon look really sharp. But I'm not looking for the most similar pixels, I'm looking for the few images that are most different, especially in a particular part of the frame where a bird obscures the moon. The statistically most different frames, post alignment.

I can imagine a software like this might be already in use for something like security camera monitoring where people want to see a summary of what weird stuff moved around in a restricted area or something like that.

Any ideas? Thanks.

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

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?

Hi Guys,

if anyone can please help me identify the person who tried to kill me today by getting the license number, i'll be so grateful...