I recently read a paper where the researchers used a UNet algorithm to localize/detect cyclones using a bounding box. However, my interpretation of a UNet is that it performs semantic segmentation and that bounding boxes were used in localization algorithms instead. Can someone clear this up for me?
Link to mentioned paper:https://repository.library.noaa.gov/view/noaa/31895/noaa_31895_DS1.pdf
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Shown above is a sample image of runway that needs to be localized(a bounding box around runway)
i know how image classification is done in tensorflow, My question is how do I label this image for training?
I want model to output 4 numbers to draw bounding box.
In CS231n they say that we use a classifier and a localization head.
but how does my model knows where are the runnway in 400x400 images?
In short How do I LABEL this image for training? So that after training my model detects and localizes(draw bounding box around this runway) runways from input images.
Please feel free to give me links to lectures, videos, github tutorials from where I can learn about this.
**********Not CS231n********** I already took that lecture and couldnt understand how to solve using their approach.
Thanks
If you want to predict bounding boxes, then the labels are also bounding boxes. This is what most object detection systems use for training. You can just have bounding box labels, or if you want to detect multiple object classes, then also class labels for each bounding box would be required.
Collect data from google or any resources that contains only runway photos (From some closer view). I would suggest you to use a pre-trained image classification network (like VGG, Alexnet etc.) and fine tune this network with downloaded runway data.
After building a good image classifier on runway data set you can use any popular algorithm to generate region of proposal from the image.
Now take all regions of proposal and pass them to classification network one by one and check weather this network is classifying given region of proposal as positive or negative. If it classifying as positively then most probably your object(Runway) is present in that region. Otherwise it's not.
If there are a lot of region of proposal in which object is present according to classifier then you can use non maximal suppression algorithms to reduce number of positive proposals.
I'm trying to use a pretrained VGG16 as an object localizer in Tensorflow on ImageNet data. In their paper, the group mentions that they basically just strip off the softmax layer and either toss on a 4D/4000D fc layer for bounding box regression. I'm not trying to do anything fancy here (sliding windows, RCNN), just get some mediocre results.
I'm sort of new to this and I'm just confused about the preprocessing done here for localization. In the paper, they say that they scale the image to 256 as its shortest side, then take the central 224x224 crop and train on this. I've looked all over and can't find a simple explanation on how to handle localization data.
Questions: How do people usually handle the bounding boxes here?...
Do you use something like the tf.sample_distorted_bounding_box command, and then rescale the image based on that?
Do you just rescale/crop the image itself, and then interpolate the bounding box with the transformed scales? Wouldn't this result in negative box coordinates in some cases?
How are multiple objects per image handled?
Do you just choose a single bounding box from the beginning ,crop to that, then train on this crop?
Or, do you feed it the whole (centrally cropped) image, and then try to predict 1 or more boxes somehow?
Does any of this generalize to the Detection or segmentation (like MS-CoCo) challenges, or is it completely different?
Anything helps...
Thanks
Localization is usually performed as an intersection of sliding windows where the network identifies the presence of the object you want.
Generalizing that to multiple objects works the same.
Segmentation is more complex. You can train your model on a pixel mask with your object filled, and you try to output a pixel mask of the same size
I have read an article regarding Brain tumor segmentation.That article has some methods to segment the brain tumor cells from normal brain cells.Those methods are pre-processing,segmentation and feature extraction.But I couldn't understand,whats the difference between segmentation and Feature extraction.I googled it also,but still I didn't understand.Can anyone please explain the basic concept of this methods?
Segmentation is usually understood as the decomposition of a whole into parts. In particular, decomposing or partitioning an image into homogeneous regions.
Feature extraction is a broader concept, which can be described as finding areas with specific properties, such as corners, but it can also be any set of measurements, be them scalar, vector or other. Those features are commonly used for pattern recognition and classification.
A typical processing scheme could be to segment out cells from the image, then characterizing their shape by means of, say edge smoothness features, and telling normal from ill cells.
Image Segmentation vs. Feature Localization • Image Segmentation: If R is a segmented region,
1. R is usually connected; all pixels in R are connected (8- connected or 4-connected).
2. Ri \ Rj = , i 6= j; regions are disjoint.
3. [ni=1Ri = I, where I is the entire image; the segmentation
is complete.
• Feature Localization: a coarse localization of image fea- tures based on proximity and compactness – more e↵ective than Image Segmentation.
Feature extraction is a prerequisite for image segmentation.
When you face a project for segmenting a particular shape or structure in an image, one of the procedure to be applied is to extract the relevant features for that region so that you can differentiate it from other region.
A simple and basic features which are commonly used in image segmentation could be intensity. So you can make different groups of structure based on the intensity they show in the image.
Feature extraction is used for classification and relevant and significant features are used for labeling different classed inside an image.
I need to prepare training data which I will then use with OpenCV's cascaded classifier. I understand that for training data I'll need to provide rectangular images as samples with aspect ratios that correspond to the -w and -h parameters in OpenCV's training commands.
I was fine with this idea, but then I saw web-based annotation tool LabelMe.
People have labelled in LabelMe using complex polygons!
Can these polygons be somehow used in cascaded training?
Wouldn't using irregular polygons improve the classification results?
If not, then what is the use of the complex polygons that outline objects in LabelMe'd images?
Data sets annotated with LabelMe are used for many different purposes. Some of them, like image segmentation, require tight boundaries, rather than bounding boxes.
On the other hand, the cascade classifier in OpenCV is designed to classify rectangular image regions. It is then used as part of a sliding-window object detector, which also works with bounding boxes.
Whether tight boundaries help improve object detection is an interesting question. There is evidence that the background pixels caught by the bounding box actually help the classification.
I have been given an image with rgb channels. I only want to see the persons face. How would I do that? Are neural nets used for this? If so, are there existing data files from neural nets that have already done the processing?
Since your questions is tagged with OpenCV, I will assume that you are looking for a solution within this library.
The first step is to find the faces. For this, use one of the cascade object detectors that are available: either the Viola-Jones one or the LBP one.
OpenCV comes with cascades trained for face detection for each of these detectors.
Then, it depends if getting a bounding box is enough or not.
If you need something more accurate, then you can:
[coarse face] use a skin color detector inside the face bounding box to get a finer face estimate, binarize the image and finally close the face shape using morphological filtering;
[fine face contour] use something like a grabcut procedure to get a pixel-accurate contour. You can initialize the grabcut with borders of the bounding box as background and center part of the bounding box as foreground.
Not really sure what you want to do, but you can use Haar Classifier for face detection.
From then on, it should be easy to only display the face. While there are available classifiers online, you can try training your own classifier should you have the time. I have done classifiers on hand, face, eyes before and it have an impressive result.
Should you need more help on training classifier, etc, just comment here, I will try my best to assist you.
Face detection functionality is also available in the Computer Vision System Toolbox for MATLAB in the form of the vision.CascadeObjectDetector object.