I thought that a feature extractor, which called as backbone, draw the areas, but don't.
In the segmentation model, who fills the region?
:What function is responsible for that?
I want to upload example image but I can't. Bear with me.
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I want to process the captured video. I will try to capture the video of handwriting on paper / drawing on paper. But I do not want to show the hand or pen on the paper while live streaming via p5.js.
Can this be done using machine learning?
Any idea how to implement this?
If I understand you right you want to detect where in the image the hand is a draw an overlay on this position right?
If so You can use YOLO more information to detect where the hand is.
There are some trained networks that you can download maybe they are good enough, maybe you have to train your own just for handy.
There are also some libery for yolo and JS https://github.com/ModelDepot/tfjs-yolo-tiny
You may not need to go the full ML object segmentation route.
If the paper's position and illumination are constant (or at least knowable) you could try some simple heuristic comparing the pixels in the current frame with a short history and using the most constant pixel values. There might be some lag as new parts of your drawing 'become constant' so maybe you could try some modification to the accumulation, such as if the pixel was white and is going black.
I want to detect Land mines in satellite Images. Initially I built a model with each image having multiple labels and trained it to classify the images.
However I want to use Image Segmentation technique as mentioned here : https://towardsdatascience.com/dstl-satellite-imagery-contest-on-kaggle-2f3ef7b8ac40
I downloaded the required images through aws s3 bucket. I want to label each pixel of the multispectral image I have generated from Band files.
However I am facing difficulty in how to labelling.
Are there any open source or otherwise tools to do the same.
EDIT : The images are 12 band multispectral satellite images.
You can use AWS Ground Truth to create a job that can label the images you require. AWS also has released which might help https://aws.amazon.com/about-aws/whats-new/2019/12/amazon-sagemaker-ground-truth-adds-auto-segment-feature-for-semantic-segmentation-labeling/
I am using tensorflow object detection API. I have two classes of interest. In the first trial, I got reasonable results, but I found it was easy to get false positive of both classes in the pure background images. These background images (i.e., images without any class bbx) have not been included in the training set.
How can I add them into the training set? It seems not work if I simply add samples without bbx.
Your goal is to add negative images to your training dataset to strength the background class (id 0 in the detection API). You can reach this with the VOC Pascal XML annotation format. In your XML file is the height and width of your image without object. Usually you label objects the coordinates and height and width of your object and object name is in the XML file. If you use labelImg you can generate a XML file corresponded to your negative image with the verify button. Also can Roboflow generates XML files with and without objects.
I'm trying to collect my own training data set for the image detection (Recognition, yet). Right now, I have 4 classes and 750 images for each. Each images are just regular images of the each classes; however, some of images are blur or contain outside objects such as, different background or other factors (but nothing distinguishable stuff). Using that training data set, image recognition is really bad.
My question is,
1. Does the training image set needs to contain the object in various background/setting/environment (I believe not...)?
2. Lets just say training worked fairly accurately and I want to know the location of the object on the image. I figure there is no way I can find the location just using the image recognition, so if I use the bounding box, how/where in the code can I see the location of the bounding box?
Thank you in advance!
It is difficult to know in advance what features your programm will learn for each class. But then again, if your unseen images will be in the same background, the background will play no role. I would suggest data augmentation in training; randomly color distortion, random flipping, random cropping.
You can't see in the code where the bounding box is. You have to label/annotate them yourself first in your collected data, using a tool as LabelMe for example. Then comes learning the object detector.
I am working on an application which something like that I have an image and e.g. there is a glass or a cup or a chair in it. The object can be of any type
My question here is that is there any way that i can apply some image processing filters or something like that which returns me an image that just contain the object and the background is transparent
You can use object detection methods such as
http://opencv.willowgarage.com/documentation/object_detection.html
http://docs.opencv.org/modules/objdetect/doc/latent_svm.html
to detect the object, plot a bounding box around it and extract it from the image.
depends on your application, but you can also use image difference (background subtraction) to get the object...
Actually I have solved the problem
the issue was I do not want to use any advance method that uses template matching or neural networks or anything like that
so in my case the aim was to recognize an object in an image and that object could be anything (e.g. a table,a cellphone, a person, a shirt etc) and the catch was that there could be at most one object in an image
so just using watershed segmentation of opencv I was able to separate the object from the background
but the threshold used for the watershed differs with respect to the frequency of the image and the difference of shades of the object from the background