Tag: Computer vision

Region based CNN(R-CNN)

To know about R-CNN let’s first know why we have to know it which is a very important thing because without a solid reason you can’t have a dedication to learn it.
R-CNN is an algorithm used for object detection in a an image. Object detection is a bit different from object recognition but this little bit difference make it a complex task for machine to tackle.


Object recognition is to check whether an image contains an object or not , it can by solved by various CNN architecture like ResNet, DeepNet, Inception-ResNet e.t.c while Object detection is a task of recognising the class of object as well as its localisation i.e where it is placed in the overall image or we can say to build a bounding box around that object in the image.


So after this you not only tell us that this is the image of a cat but also tell that where the hell it is located by building a bounding box around it.It has been used in various Automated computer vision problem like real time face detection, person tracking,pose detection e.t.c

In this Section we will discuss three types of algorithm regarding this object detection problem.

  • R-CNN
  • Fast R-CNN
  • Faster R-CNN

R-CNN:

Introduction:

In this method we use Image segmentation method(Selective Search algorithm) to extract around 2k regions from image where probability of finding an object is high. We willn’t discuss about the segmentation method used for the region proposal rather will post a separate blog for it. we also build a thin bounding box around each (2k) regions so that to check whether the region we got is a positive example or not using IOU( Intersection over Union) technique by choosing a threshold value. If the IOU value for the proposed region is greater than the threshold with the ground truth bounding box of training example then we say that it is the positive example of that class and all the rest regions are the negative examples.It can be possible that multiple regions can have IOU value greater than threshold , all will be selected as the positive example in that case.
(**We assume here that each training example contains object of a single class only.**)

R-CNN

Now each region will be sent to some deep ConvNet architecture for the feature extraction process and the extracted feature vector will be sent for the SVM classifier to detect whether it contains a class or not. There will be a separate SVM for each class.
Since each proposed region can have different sizes and aspect ratios but for feeding them into ConvNet which has been already trained on the large ImageNet dataset of fixed dimensions(if you are using pretrained network as your feature extractor) then you must have to change the size of each regions into the one on which the network has been trained on. for this we use Image warping method, you can check it out here

AlexNet
VGG-16

Transfer Learning :

There are various Networks which has been trained on large image datasets like VGG , ResNet , AlexNet , Inception-ResNet , Dense-Net e.t.c (shown above). If you are using such type of pre-trained networks in your model for the feature extraction process then you must fine tune it so that it can adapt to your datasets requirement.However the learning rate in this case must be very low (say 0.0001) to not to disturb the learned parameters that much.
You have to change the output layers of such network with your own layer with C+1 nodes in it (C represent no. of classes and 1 representing the background class).Once the network has been fine tuned the extracted feature can be directly fed to the classifier.

Choosing positive and negative examples:

There is a difference in choosing positive examples during fine tuning of ConvNet and SVM classifier.
During Fine tuning we want our network to extract rich features for our proposed regions hence in that case positive features will be selected among the regions proposed by the segmentation process which will be decided by the threshold value of IOU discussed above
Whie in SVM classifier training we want our model to classify the images into their separate class having high accuracy which can be generalised to all types of images containing that class so we use the ground truth example as the positive example for each class.

Bounding Box regression:

The bounding box around the proposed region due to the segmentation isn’t accurate .It can crop out some of the parts of the object , to correct the accuracy of the bounding box we use a linear regression method to learn a function which maps the proposed region bounding box into the ground truth bounding box.
A bounding box is composed by 4 parameters (x,y,w,h) first two represent the coordinate of the centre and the last two represent width and height of the box respectively.

Rich feature hierarchies for accurate object detection and semantic segmentation
Tech report (v5)
Ross Girshick Jeff Donahue Trevor Darrell Jitendra Malik
UC Berkeley

References:

https://arxiv.org/pdf/1311.2524.pdf
http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf

Image Warping(Affine transformation)

What is Image Warping?
It is a technique to manipulate the digital images by changing its shape ,size and dimensions.It can be used to distort or to correct distortness depending upon the need of the task.
So we need a transformation matrix (often called geometric transform) which maps the input image(source image) to a transformed output one(target image). There are various transformation functions present but we will discuss about Affine transformation here.

Warping using different Geometric Transform

Affine Transformation:

It is a Linear transformation function which performs Scaling , Rotation , Shear , Translation or combination of all the above on the source image by preserving its structure and distance ratio.It uses a 2*3 matrix for transformation.

Before applying Affine Transformation pixel coordinates is converted into Homogenous Coordinates for the ease of Transforamtion. By saying Homogenous coordinate it simply means adding a dimension to the raw coordinate of the image. Image works on a 2-D workspace so its homogenous coordinate will be extended to 3 dimensions.

                         X = (x,y)       ⇒     X’ = (x,y,1)

Once coordinates has been converted into Homogenous coordinates now Affine transformation can be applied to it for different types of manipulation on the source image

orange box = Linear transformation matrix : blue box = translation vector: red box = Projection vectors: blue circle = added dimension for converting into homogenous coordinate
Different transformation matrix

If you see deeply in these transformation you will observe that it only maps the coordinate of the source image into a new coordinate based on a particular transformation and the pixel intensity for each previous coordinate will now belong to newer one after the transformation in the target image.

Let’s take an example of scaling an image uniformly with a factor of 3 so an image of (3,3) will now become (9,9)

Uniform Scaling an image with a factor of 3

As you can see that most of the coordinates doesn’t get any value and hence information is lost due to the digitalisation and sampling of the image.
For calculating the values of missing coordinate different interpolation is used like Nearest neighbour, Bilinear, Bicubic e.t.c.

In Nearest neighbour the missing coordinate gets the value of its nearest coordinate while in Bilinear interpolation the value is obtained by the weighted sum of its nearest coordinate depending upon its distance from it.

Calculating the transformation matrix:

Till now we transform the image using the transformation matrix which is already known to us.But here we will try to find out the coefficient of the transformation matrix by observing the target image and the source image.
Let’s suppose that we have a source image and its transformed image and we want to obtain the transformation matrix regarding that. Since for 2-D image transformation matrix has 6 degree of freedom i.e 6 unknown values so to determine it we need 6 equations.
Each point can give us 2 equation (one for x and one for y) so we need 3 points in the source image and same 3 points after transformation in the target image. So by comparing these 3 points in both image we can get all 6 values of the matrix and hence transformation matrix can be calculated.

Comparing points in source and target image

Where does it used in Computer vision problem…….?

In Object Detection algorithm R-CNN , We use a deep ConvNet as a feature extractor for the image.The extracted feature vector for each image get fed to the SVM classifier for the recognition purpose.
However the ConvNet which we are using as a feature extractor is trained on a large databases of images of fixed dimension. For extracting features of the test images(or the proposed region in case of R-CNN which can be of varying dimension) we need to first map it into the size in which that Network has been trained on , so here image warping is used specially affine transformation.

References:

Learning OpenCV — Gary Bradski and Adrian Kaehler
CS6640: Image Processing(Affine Transformation, Landmarks registration, Non linear Warping)
— Arthur Coste: coste.arthur@gmail.com