Camera

Summary

A camera is an optical instrument used to capture and store images or videos, either digitally via an electronic , or chemically via a light-sensitive material such as photographic film. As a pivotal technology in the fields of photography and videography, cameras have played a significant role in the progression of visual arts, media, entertainment, surveillance, and scientific research. The invention of the camera dates back to the 19th century and has since evolved with advancements in technology, leading to a vast array of types and models in the 21st century. Cameras function through a combination of various mechanical components and principles. These include exposure control, which regulates the amount of light reaching the sensor or film; the lens, which focuses the light; the viewfinder, which allows the user to preview the scene; and the film or sensor, which captures the image. Several types of cameras exist, each suited to specific uses and offering unique capabilities.

Official source

https://en.wikipedia.org/wiki/Camera

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Learning Long-Range Perception Using Self-Supervision From Short-Range Sensors and Odometry

Mirko Nava

We introduce a general self-supervised approach to predict the future outputs of a short-range sensor (such as a proximity sensor) given the current outputs of a long-range sensor (such as a camera). We assume that the former is directly related to some piece of information to be perceived (such as the presence of an obstacle in a given position), whereas the latter is information rich but hard to interpret directly. We instantiate and implement the approach on a small mobile robot to detect obstacles at various distances using the video stream of the robot's forward-pointing camera, by training a convolutional neural network on automatically-acquired datasets. We quantitatively evaluate the quality of the predictions on unseen scenarios, qualitatively evaluate robustness to different operating conditions, and demonstrate usage as the sole input of an obstacle-avoidance controller. We additionally instantiate the approach on a different simulated scenario with complementary characteristics, to exemplify the generality of our contribution.

2019

The virtual frame technique: ultrafast imaging with any camera

John Martin Kolinski

Many phenomena of interest in nature and industry occur rapidly and are difficult and cost-prohibitive to visualize properly without specialized cameras. Here we describe in detail the virtual frame technique (VFT), a simple, useful, and accessible mode of imaging that increases the frame acquisition rate of any camera by several orders of magnitude by leveraging its dynamic range. The VFT is a powerful tool for capturing rapid phenomena where the dynamics facilitate a transition between two states, and are thus binary. The advantages of the VFT are demonstrated by examining such dynamics in five physical processes at unprecedented rates and spatial resolution: fracture of an elastic solid, wetting of a solid surface, rapid fingerprint reading, peeling of adhesive tape, and impact of an elastic hemisphere on a hard surface. We show that the performance of the VFT exceeds that of any commercial high-speed camera not only in rate of imaging but also in field of view, achieving a 65MHz frame rate at 4MPx resolution. Finally, we discuss the performance of the VFT with several commercially available conventional and high-speed cameras. In principle, modern cell phones can achieve imaging rates of over a million frames per second using the VFT.

2019

Modeling of 2D+1 texture movies for video coding

Gloria Menegaz, Julien Reichel

We propose a novel model-based coding system for video. Model-based coding aims at improving compression gain by replacing the non-informative image elements with some perceptually equivalent models. Images enclosing large textured regions are ideal candidates. Texture movies are obtained by filming a static texture with a moving camera. The integration of the motion information within the generative texture process allows to replace the “real” texture with a “visually equivalent” synthetic one, while preserving the correct motion perception. Global motion estimation is used to determine the movement of the camera and to identify the overlapping region between two successive frames. Such an information is then exploited for the generation of the texture movies. The proposed method for synthesizing 2D+1 texture movies is able to emulate any piece-wise linear trajectory. Compression performances are very encouraging. On this kind of video sequences, the proposed method improves the compression rate of an MPEG4 state-of-the-art video coder of an order of magnitude while providing a sensibly better perceptual quality. Importantly, the current implementation is real-time on Intel PIII processors.

2003