Light-field technology heralds one of the biggest changes to imaging since 1826, when Joseph-Nicéphore Niépce made the first permanent photograph of a scene from nature. A single light-field snapshot can provide photos where focus, exposure, and even depth of field are adjustable after the picture is taken. Light-field imaging is far more ambitious. Instead of merely recording the sum of all the light rays falling on each photosite, a light-field camera aims to measure the intensity and direction of every incoming ray. With that information, you can generate not just one but every possible image of whatever is within the camera’s field of view at that moment. The information a light-field camera records is, mathematically speaking, part of something that optics specialists call the plenoptic function. This function describes the totality of light rays filling a given region of space at any one moment. It’s a function of five dimensions, because you need three (x, y, and z) to specify the position of each vantage point, plus two more (often denoted θ and φ) for the angle of every incoming ray.

Computational photography or computational imaging refers to digital image capture and processing techniques that use digital computation instead of optical processes. Computational photography can improve the capabilities of a camera, or introduce features that were not possible at all with film based photography, or reduce the cost or reduce the size of camera elements. Examples of computational photography include in-camera computation of digital panoramas, high-dynamic-range images, and light field cameras. Light field cameras use novel optical elements to capture three dimensional scene information which can then be used to produce 3D images, enhanced of depth-of-field, and selective de-focusing (or "post focus"). Enhanced depth-of-field reduces the need for mechanical focusing systems. All of these features use computational imaging techniques.

3D reconstruction is one of fundmental tasks in computer vision. One can reconstruct geometric structure of real wold scene by estimating the depth and poses of cameras, which provides infrastructural data for object detection, segmentation and background modeling.

Object Detection and Tracking is one of important branches in reseach domain of computer vision.