Digital imaging brings a new set of possibilities to photography. For example, little pictures can be assembled to form a large panorama, and digital cameras are trying to mimic the human visual system to produce better pictures. This manuscript aims at developing the algorithms required to stitch a set of pictures together to obtain a bigger and better image. This thesis explores three important topics of panoramic photography: The alignment of images, the matching of the colours, and the rendering of the resulting panorama. In addition, one chapter is devoted to 3D and constrained estimation. Aligning pictures can be difficult when the scene changes while taking the photographs. A method is proposed to model these changes —or outliers— that appear in image pairs, by computing the outlier distribution from the image histograms and handling the image-to-image correspondence problem as a mixture of inliers versus outliers. Compared to the standard methods, this approach uses the information contained in the image in a better way, and leads to a more reliable result. Digital cameras aim at reproducing the adaptation capabilities of the human eye in capturing the colours of a scene. As a consequence, there is often a large colour mismatch between two pictures. This work exposes a novel way of correcting for colour mismatches by modelling the transformation introduced by the camera, and reversing it to get consistent colours. Finally, this manuscript proposes a method to render high dynamic range images that contain very bright as well as very dark regions. To reproduce this kind of pictures the contrast has to be reduced in order to match the maximum contrast displayable on a screen or on paper. This last method, which is based on a complex model of the human visual system, reduces the contrast of the image while maintaining the little details visible the scene.