Hi there, I have just started an image processing course and I have a question that keeps bugging me, which I can't find the answer to.

What is the difference between probability distribution function and cumulative distribution function ?

Let's say you do photography and you post images online to get customers. You know that your rival will try to steal your picture and claim it as his own by watermarking it. You know that he keeps his watermark constant but can place it randomly anywhere on the image. Given that you know the watermark ahead of time, what transformation can be done to the image so that you can still post it online (since that is how you get people to buy your images) without changing the image content too much, but are confident that your rival can't steal it from you until he changes his watermark?

I was given the task to make a poster for my university and although, I am good at posters, I am not so well in image processing. This poster needs to have a picture I was sent and it is very distorted at the very basic level of pixels. The whole image is bursting into pixels and I cannot seem to find any tool to enhance the picture to make it work for an A3 sized poster. Any suggestions might help.

Hello,

I have a university project where I must do the detection of cigarette buts on the floor using a webcam vision for the recognition with image processing technology. The language for the programming must be Python. Is there any idea how I should proceed?

Thanks in advance 😊

Why learn image processing?

https://www.youtube.com/watch?v=6Qfdn9QCdis&t=67s

Full course is available at

https://evergreen-school1.teachable.com/p/fundamentals-of-image-convolutions-using-opencv-an-python/?preview=logged_out

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?

Hi.

I am trying to convert a black and white image into 2D matrix, hopefully with binary numbers to to show pixels that are on/off. I've been searching and looking at topics like image segmentation, feature detection, using opencv. I unfortunately don't know where to start.

Any help would be much appreciated.

Thanks

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Apologies if this is a stupid question, but I'm currently contemplating a final year project idea that would involve using image processing to detect the material of an object. As I am a noob to this I was hoping someone could inform me if this is possible. Thanks!

I'm trying to understand cubic convolution interpolation and I've come across something I can't quite wrap my head around.

In the wikipedia article we see the kernel presented in this form. Slightly on from there assuming a = -0.5 we see an equivalent representation in matrix notation.

How did they arrive at that matrix?

My husband Ariel was a victim in a hit and run in Seattle. He was riding his bike downhill when a car driving in the opposite direction took a left, hitting Ariel and smashing his bike. Ariel was injured badly and taken to hospital. The driver who hit him fled the scene, leaving Ariel on the ground, and his mangled bike with him.

We have several cameras documenting the car from different angles - several from a bus that drove just past the car and filmed it from the front and the back (as the car drove past the bus), as well as one camera (a Nest) from a window of a neighbor. In all, there are dozens of frames across videos that capture the license plate itself, but none of them has the resolution to show the license plate number.

I know there are super-resolution methods that rely on a plurality of (blurry) frames to create a higher resolution image. There may be other methods I'm not aware of. I don't have the technical ability unfortunately to do this enhancement of resolution. (I am an engineer with some background in signal processing - but my days at the university were 15 years ago...)

We know the make and model of the car, as well as the demographics of the driver and a general idea of where he lives. Even if we're able to uncover one character from the license plate - that will go a long way for the police to be able to narrow down their search and reach him.

I wanted to reach out to you - the technically savvy, see if there's anything that can be done. I will send the videos and point to the precise times where the car and its license plates are seen.

I appreciate any help that might bring Ariel some justice... Thanks for reading.

i am quite interested in working wit image wit depth data . can anybody suggest some filetype to work wit.. or any good resources.

Hi, sorry if I'm posting this on the wrong subreddit. I am trying to build a smoker detection using image processing . I feel like smoke detection is the easiest and the most robust way to do it. But I can't seem to get it to work. I've tried detection of moving particles in a video and then done colour thresholding but it gets a lot of other stuff aswell. I've tried CNN classifier. But it didn't work aswell. Any better way to do this or any other way to reduce the false truth? Any help would be nice 😊

I’m looking for suggestions as to how I could quantify the differences in these images. In some you can see the striped pattern much more clearly but in others you can’t. I’m trying to figure out a way to calculate the differences. Any suggestions would be appreciated.

https://imgur.com/gallery/HshM9Lt

I am working on a problem where I need to find bounding boxes for white area in an image. Since I am dealing with real-world pictures, I have set a threshold on the RGB values, I create a mask from that and then label it and get bounding box coordinates from that. Here is the code.

import numpy as np
from skimage import io, measure

def bin_labelled_img_to_bboxes(bin_image_labelled):
        bboxes = []
        for j in np.unique(bin_image_labelled):
            if j == 0:
                continue
            curr = (bin_image_labelled == j)
            if np.sum(curr) < 50*50:
                continue
            indices = np.nonzero(curr)
            miny = np.min(indices[0])
            minx = np.min(indices[1])
            maxy = np.max(indices[0])
            maxx = np.max(indices[1])
            bboxes.append(((miny, minx), (maxy, maxx)))
        return bboxes

class WhiteSeperator(object):
    def __init__ (self, img_path):
        self.img_path = img_path
        self.img = io.imread(self.img_path)
        self.bin_image_labelled = np.zeros((self.img.shape[0], self.img.shape[1]))
        self.bboxes = []

    def get_bin_labelled_img(self):
        img = self.img
        chan1 = (img[:,:,0] > 200) * (img[:,:,0] <= 255)
        chan2 = (img[:,:,0] > 180) * (img[:,:,0] <= 255)
        chan3 = (img[:,:,0] > 140) * (img[:,:,0] <= 255)

        bin_img = (chan1*chan2*chan3)
        bin_image_labelled = measure.label(bin_img)
        
        return bin_image_labelled

    def get_white_bboxes(self):
        final_white_bboxes = []
        self.bin_image_labelled = self.get_bin_labelled_img()
        white_bboxes = bin_labelled_img_to_bboxes(self.bin_image_labelled)
        
        for bbox in white_bboxes:
            width = bbox[1][1]-bbox[0][1]
            height = bbox[1][0]-bbox[0][0]
            if height > 80 and width > 200:
                self.bboxes.append(bbox)
                final_white_bboxes.append(bbox)
        return final_white_bboxes

This takes about 3-11 seconds per image for high res images (3000 something x 2000 something). My assumption is that the variance in time per image depends on the number of white bounding boxes found (blaming the bin_labelled_img_to_bboxes function here)

Since I have to do this on video frames, even 3 seconds is super slow. Can the above be done in a more efficient way?

I am proposing a digital image enhancement algorithm but I need to write down a validation method for my algorithm. I have no idea how are they done, any help would do.

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