deep learning for shape localization and recognition

Question

There is a set of images, each of which contains different shape entities, such as shown in the following figure. I am trying to localize and recognize these different shapes. For instance, adding a bounding box for each different shape and maybe even label it. What are the major research papers/deep learning models that have been able to solve this kind of problem?

Answer 1

Object detection papers such as rcnn, faster rcnn, yolo and ssd would help you solve this if you were bent on using a deep learning approach.

It's easy to say this is a trivial problem that can be solved with tools in OpenCV and deep learning is overkill, but I can see many reasons to use deep learning tools and that does not answer your question.

Answer 2

We assume that your shapes has different scales and rotations. Actually your main image shown above is very large for training process and it needs a lot of training samples to generate a good accuracy at the end on test samples. In this case it is better to train a Convolutional Neural Network on a short images (like 128x128) with only one shape per each image and then use slide trick! This project will have three main steps:

1. Generate test and train samples, each image should have only one shape
2. Train a classifier to recognize a single shape within each input image
3. Use slide trick! Break your original image containing many shapes to overlapping blocks of size 128x128. Pass each block to your model trained in the second step.

In this way at the end you will have label for each shape from your trained model, and also you will have location of each shape using slide trick. For the classifier you can use exactly CNN structure of Tensorflow's MNIST tutorial. Here is a paper with exactly same method applied to finger print images to extract local features. A direct fingerprint minutiae extraction approach based on convolutional neural networks