Visual Illusions | Showcase Template

Monocular Cues: Exercise Take advantage of monocular cues to implement a 2D sketch to trick the eye into perceiving a 3D scene. Introduction # Monocular cues provide depth information when viewing a scene with one eye. There are several effects that are based on this type of illusions, such as: Motion parallax Kinetic depth effect Perspective Relative size In this particular case we will focus on the motion parallax. When an observer moves, the apparent relative motion of several stationary objects against a background gives hints about their relative distance.
Aplications Visual Phenomena: Exercise Study, implement and discuss possible applications of some known visual phenomena and optical illusions. Introduction # In visual perception, an optical illusion (also called a visual illusion) is an illusion caused by the visual system and characterized by a visual percept that arguably appears to differ from reality. Illusions come in a wide variety; their categorization is difficult because the underlying cause is often not clear but a classification proposed by Richard Gregory is useful as an orientation.
Moire Kinegram: Exercise Implement a kinegram and some moiré patterns which are close related visual phenomena to masking. Introduction # In mathematics, physics, and art, moiré patterns or moiré fringes are large-scale interference patterns that can be produced when a partially opaque ruled pattern with transparent gaps is overlaid on another similar pattern. For the moiré interference pattern to appear, the two patterns must not be completely identical, but rather displaced, rotated, or have slightly different pitch.
Masking: Exercise Implement in software an image processing web app supporting different image kernels and supporting: Image histogram visualization. Different lightness (coloring brightness) tools. Introduction # In image processing, a kernel, convolution matrix, or mask is a small matrix used for blurring, sharpening, embossing, edge detection, and more. This is accomplished by doing a convolution between the kernel and an image. Or more simply, when each pixel in the output image is a function of the nearby pixels (including itself) in the input image, the kernel is that function.