OpenCV does have reliable lib functions that can handle vision requirements. You can study various segmentation functions available in OpenCV to see separation of foreground from background. You can also look at probably background and foreground. A quick example is to examine GrabCut (see the link below)

http://docs.opencv.org/master/doc/py_tutorials/py_imgproc/py_grabcut/py_grabcut.html?highlight=foreground

In general, Computer Vision used features in the image for segmentation, detection etc. The same feature set can be used to train and test data using methods as simple as RBFs or NNs. Using these techniques you can label an unlabeled test set, or use labeled samples online to train and test.

Yes, of course there are connections. Since a lot of computer visions problems can be solved (or optimized) by classification approaches (i.e, segmentation is basically to label pixels/voxels), machine learning approaches are becoming popular in computer vision field (ANNs, SVMs, RBFs,...). 

Dear Abhinav

both are hot topics of research nowadays, machine vision concerns more of image understanding and identification and it includes several stages such as enhancement, segmentation, and identification, we may need computational intelligence in different stages. selecting the appropriate approach is crucial issue, so first we need to identify the problem we need to use CI in, and then specify the appropriate approach...

best,

Mohammad

Machine learning is a consequence of the development of the idea of artificial intelligence and methods of its implementation in practice. Concerns the development of software used especially in innovative technologies and industry. The corresponding algorithms are designed to enable the software to automate the process of data collection and analysis to improve and develop their own system.

Learning can be considered as a concretization of the algorithm or parameter selection, called knowledge or skill. This is done with many types of methods of acquiring knowledge and ways to represent knowledge.

The acquisition of knowledge can be developed based on the analysis of images in computer vision. Computer image processing is an analogue of the process that takes place in the human eye and optic nerve. Recognition of the image is represented in greater visual perception, which occurs in the human brain.

Created by human models of systems analysis and interpretation of visual information are based on what is known about the human visual system (HVS). In the context of computer vision may be implemented the following tasks:

- Image acquisition,

- Pre-processing the digital image,

- Image analysis,

- Image recognition and analysis of image features