I'm sorry if this is a simple question...but may I ask how can I use online datasets to obtain segmented object images (and hence be able to generate masks from it)? I'll need both the segmented object and the masks afterwards.
For example, I need to use datasets that will produce results on the right:
Image taken from: https://www.researchgate.net/figure/Example-of-GrabCut-segmentation_fig2_282740894
I don't really want to segment images myself and would rather use categorised datasets instead.So basically, I'll want to be able to search for an object type (e.g. bikes) and have a large set of segmented bikes. (Basically, have large sets of segmented object images grouped and stored by categories.)
I know that COCO dataset has categorised and annotated objects. But I'm not sure how to use it...Or if there's better ways of achieving my goal, what would it be? Would someone please help? Thanks a lot!
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When preforming image co-registration of multiple subjects, how should we select the reference image?
Can a randomly selected image form one dataset could be the reference image for an image from the other dataset?
If we do that, should all the images belonging to the reference image dataset be co-registered with the reference image as well?
I couldn't find any material in this area. Could someone please advice?
I'm not sure exactly what you mean by the term "dataset", but I will assume you are asking about co-registering multiple images from different patients (i.e. multiple 3D images per subject).
To answer your questions:
If there are no obvious choices about which image is best, then a random choice is fine. If you have e.g. a CT and an MRI for each subject, then co-registration using the CT images is likely going to give you better results because of intrinsic image characteristics (e.g. less distortion, image value linked to physical quantity).
I suppose that depends on what you want to do, but if it is important to have all imaging data in the same co-registered reference space then yes.
Another option is to try and generate an average image, and then use that as a reference to register other images to. Without more information about what you are trying to achieve it's hard to give any more specific advice.
I have a bit of a complicated process in Power Automate. I'm trying to parse user uploaded screenshots and categorizing them into different variables. At first, it seemed that an obvious choice would be to build and train the AI Model but the only issue is that the data in the screenshots can vary (i.e. some images will contain more rows, some won't contain the relevant data, and the data can be located in different regions of the screenshot).
Some example of images, which a user can upload, are as follows: (i) Samsung 1 Metrics, (ii) Samsung 2 Metrics (iii) iPhone metrics
My attempt was to perform OCR on the uploaded screenshot and then do string parsing. Therefore, I tried attempting the following flow: Flow Diagram and specifically the substring parsing as:
Substring parsing
Basically, I'm performing OCR on the screenshot and then searching for a substring which corresponds to the values that I'm interested in. I'm unsure if this is the best way to do this as it isn't dynamic (i.e. I have to offset the substring index by a certain amount of characters). Any advice is greatly appreciated.
I believe you should be able to train a custom Form Processing model to extract the information you need. You can two different collections in your training dataset to have the model be able to recognize both Samsung and iPhone layouts.
All you'll need is 5 samples for each collection and you should be good to go.
I have to count the number of checked and unchecked boxes in a paper sheet.The size of the checkbox is very small.Which will be the best object detection algorithms for this or any other approach.I have some images on which I can do customized training.Note my task is only object detection & recognition not localization.One approach is to extract the portion of the image which is containing the check boxes & apply contours to classify which is checked or unchecked.My question how I will extract that portion of an image which is containing scanned document or sheet.
I think you have to use Convolutional Neural Network it is the best object detection algorithm that I have ever used, although the matter of small objects this algorithm is so good at identifying small hidden patterns so, I think its work best for you, just try it.
I am working on a project where I am trying to extract key features of a bicycle from an overall image. I am currently investigating the use of Haar Cascades to train my computer to find certain regions of interest from said bicycles, e.g. the pedal-sprocket, seat, handle-bars. Then I will extract local features from these sub regions accordingly. The purpose is to create an overall descriptor of a particular bicycle so I can try to match it throughout a sample set of images of other bicycles.
My questions are as follows: Can I train a Haar classifier to look for a sub-component of an overall object? For example, say I want to look for the handlebars on a bicycle. How should I design the training? Should I detect the bicycle first, and then detect the handlebars within the overall bicycle region (Similar to detecting the eyes within a face in terms of facial recognition)? Since I know beforehand that all my images will contain a picture of a bicycle, I'm not sure if there is any point in detecting the bicycle to begin with and then looking for sub components.
In terms of training a Haar cascade and creating an XML that I can use (in OpenCV 3.1 and Python 3.6), could I just set up the positive and negative images with pictures of bicycles and no bicycles respectively? With the difference being that I isolate the particular area of interest by cropping the image appropriately each time (e.g. where the handlebars are)?
Also open to any recommendations about how others might solve the general problem of extracting key features for object matching. This is just one approach I am currently investigating. Thanks!
I am very new to java and using ELKI. I have three dimensional objects have information about their uncertainty ( a multivariate gaussian). I would like to use FDBSCAN to cluster my data. I am wondering if it is possible to do this in ELKI using the UncertainiObject class. However, I am not sure how to do this.
Any help or pointers to examples will be very useful.
Yes, you can use, e.g., SimpleGaussianContinuousUncertainObject to model uncertain data with Gaussian uncertainty. But if you want a full multivariate Gaussian, you will have to modify its source code. It is not a very complicated class.
Many of the algorithms assume you can put a bounding box around uncertain objects, in order to prune the search space (otherwise, you will always be in O(n^2)). This is more difficult with rotated Gaussians!
The key difficulty with using all of these is actually data input. There is no standard file format for specifying objects with uncertainty. Apparently, most people that work with uncertain data just use certain data, and add an artificial uncertainty to it. But even that needs a lot of parameters to tune, and I am not convinced by this approach.