Computer vision techniques for calibration, localization and recognition

This thesis explores several practical applications of computer vision using learning based techniques.

We begin by exploring the task of single image camera calibration. That is, the prediction of a camera's orientation, zoom and distortion from single images. Our solution is more robust than approaches that rely on detecting geometric elements such as straight lines or vanishing points. Our learning-based solution can harness subtle but important cues available in the images.

We then tackle the problems of visual place recognition and visual localization.
These tasks deal with the recognition of a camera's pose (position and orientation) using images captured by the camera.
This is a difficult task, particularly when the appearance of the locations changes.
This technique can complement or replace GPS in situations where it is not precise or robust enough, such as indoors.

Finally, we develop two novel general-purpose modules for convolutional neural architectures.
We propose the CNN-COSFIRE module for the task of image recognition. It explicitly models the relative in-plane arrangement of convolutional neural network responses, and can be used in detection or classification tasks.
We also introduce a new module for convolutional neural networks called 'push-pull' layer that increases their robustness to several types of perturbations of the input images. It is based on a biological phenomenon known as push-pull inhibition.