Publication
Global and high-level effects in crowding cannot be predicted by either high-dimensional pooling or target cueing
Related concepts (32)
Procedural texture
In computer graphics, a procedural texture is a texture created using a mathematical description (i.e. an algorithm) rather than directly stored data. The advantage of this approach is low storage cost, unlimited texture resolution and easy texture mapping. These kinds of textures are often used to model surface or volumetric representations of natural elements such as wood, marble, granite, metal, stone, and others. Usually, the natural look of the rendered result is achieved by the usage of fractal noise and turbulence functions.
Tiled rendering
Tiled rendering is the process of subdividing a computer graphics image by a regular grid in optical space and rendering each section of the grid, or tile, separately. The advantage to this design is that the amount of memory and bandwidth is reduced compared to immediate mode rendering systems that draw the entire frame at once. This has made tile rendering systems particularly common for low-power handheld device use.
Feature engineering
Feature engineering or feature extraction or feature discovery is the process of extracting features (characteristics, properties, attributes) from raw data. Due to deep learning networks, such as convolutional neural networks, that are able to learn it by itself, domain-specific- based feature engineering has become obsolete for vision and speech processing.
Feature learning
In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.
Feature (machine learning)
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. Choosing informative, discriminating and independent features is a crucial element of effective algorithms in pattern recognition, classification and regression. Features are usually numeric, but structural features such as strings and graphs are used in syntactic pattern recognition. The concept of "feature" is related to that of explanatory variable used in statistical techniques such as linear regression.
Tile-based video game
A tile-based video game is a type of video or video game where the playing area consists of small square (or, much less often, rectangular, parallelogram, or hexagonal) graphic images referred to as tiles laid out in a grid. That the screen is made of such tiles is a technical distinction, and may not be obvious to people playing the game. The complete set of tiles available for use in a playing area is called a tileset. Tile-based games usually simulate a top-down, side view, or 2.
Feature (computer vision)
In computer vision and , a feature is a piece of information about the content of an image; typically about whether a certain region of the image has certain properties. Features may be specific structures in the image such as points, edges or objects. Features may also be the result of a general neighborhood operation or feature detection applied to the image. Other examples of features are related to motion in image sequences, or to shapes defined in terms of curves or boundaries between different image regions.
Hybrid drive
In computing, a hybrid drive (solid state hybrid drive – SSHD) is a logical or physical storage device that combines a faster storage medium such as solid-state drive (SSD) with a higher-capacity hard disk drive (HDD). The intent is adding some of the speed of SSDs to the cost-effective storage capacity of traditional HDDs. The purpose of the SSD in a hybrid drive is to act as a cache for the data stored on the HDD, improving the overall performance by keeping copies of the most frequently used data on the faster SSD drive.
Feature selection
Feature selection is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Stylometry and DNA microarray analysis are two cases where feature selection is used. It should be distinguished from feature extraction. Feature selection techniques are used for several reasons: simplification of models to make them easier to interpret by researchers/users, shorter training times, to avoid the curse of dimensionality, improve data's compatibility with a learning model class, encode inherent symmetries present in the input space.
Optical illusion
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. According to that, there are three main classes: physical, physiological, and cognitive illusions, and in each class there are four kinds: Ambiguities, distortions, paradoxes, and fictions.