Object-oriented Interface
Object-oriented Interface - Related Videos
14:38
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Cited by 12 •
2012
We describe a novel methodology for creating naturalistic 3-D objects and object categories with precisely defined feature variations. We use simulations of the biological processes of morphogenesis and phylogenesis to create novel, naturalistic virtual 3-D objects and object categories that can then be rendered as visual images or haptic objects.
01:30
Oriented Surfaces
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2026
A surface is called orientable if a consistent choice of unit normal vector can be made at every point on the surface. A thin soap film stretched across a wire loop provides a familiar example. The film separates the air on one side from the air on the other, so one side can be selected as positive and the opposite side as negative. Once this choice is made, a unit normal vector can be assigned smoothly across the entire surface.At each point on the soap film, a unit normal vector points...
10:14
Object Substitution Masking
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University Visual masking is a term used by perceptual scientists to refer to a wide range of phenomena in which in an image is presented but not perceived by an observer because of the presentation of a second image. There are several different kinds of masking, many of them relatively intuitive and unsurprising. But one surprising and important type of masking is called Object Substitution Masking. It has been a focus of research in vision...
05:55
Multiple Object Tracking
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University In a staggeringly complex and engaging world, it is crucial to selectively process some stimuli at the expense of others. Experimental psychologists call this ability attention. Specifically, visual attention refers to the ability to selectively process aspects of a visual scene. Many paradigms used to study visual attention involve brief, punctuated, and repeated trials. However, everyday situations often place sustained demands...
02:04
Protein-protein Interfaces
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2020
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
FAQs
Polymorphism lets a statistical program call the same operation on different model classes, even when their algorithms and internal data differ. For example, a fitting or prediction command can be issued through the shared interface while each class supplies its own implementation. This reduces the need for model-specific control logic and allows alternative analytical methods to work within one software design.
Abstraction separates what statistical component users can request from the internal details needed to produce the result. Encapsulation keeps algorithms and data inside their class, so callers interact with approved operations rather than depending on implementation details. This boundary makes components easier to replace or revise while preserving the commands used by surrounding analysis code.
An interface differs from a model implementation because it specifies required operations without prescribing the algorithm behind them. That distinction matters when several statistical approaches must provide fitting, prediction, summaries, or uncertainty evaluation through consistent commands. The interface establishes compatibility at the level of available behavior, while each class remains responsible for its own internal data and computational choices.
When methods or datasets evolve, a stable interface can limit the changes needed in the rest of an analytical system. New or revised classes can implement the established operations, while existing calling code continues to use the same commands. In statistical software, this supports extension and interoperability without requiring every component to understand each model’s internal representation.
First identify the operations that collaborating statistical components must expose, such as fitting, prediction, summarization, or uncertainty evaluation. Next, have each relevant class provide those operations using its own algorithm and data. Client code can then invoke the shared commands, and testing can focus on whether each class satisfies the expected contract.
Statistical software benefits when different model classes can be selected without changing the surrounding analysis workflow. A common set of commands can cover model fitting, prediction, data summaries, and uncertainty evaluation, allowing the same analytical structure to work with multiple implementations. This organization is useful when software must accommodate changing methods or datasets over time.
In a statistics codebase, the interface connects analytical tasks to reusable software components rather than tying them to one model’s representation. That connection can improve interoperability between models and data structures, while abstraction keeps the workflow focused on requested results. The outcome is a system that is easier to organize, test, maintain, and extend as analytical requirements change.