Computational limitations of model-based recognition

Computational limitations of model-based recognition

Reliable object recognition is an essential part of most visual systems. Model based approaches to object recognition use a database (a library) of modeled objects; for a given set of sensed data the problem of model based recognition is to identify and locate the objects from the library that are present in the data. We show that the complexity of model based recognition depends very heavily o...

Limitations of Non Model-Based Recognition Schemes

a computational visual neuroscience model for object recognition

in this study with the inspirations from both neuroscience and computer science, a combinatorial framework for object recognition was proposed having benefited from the advantages of both biologically-inspired hmax_s architecture model for feature extraction and extreme learning machine (elm) as a classifier. hmax model is a feed-forward hierarchical structure resembling the ventral pathway in ...

Computational Limitations of Stochastic

The deployment of online algorithms is a natural problem. In this work, we argue the construction of reinforcement learning, which embodies the confusing principles of artificial intelligence. We verify that while robots can be made interposable, read-write, and omniscient, public-private key pairs can be made highly-available, symbiotic, and peer-to-peer.

mortality forecasting based on lee-carter model

over the past decades a number of approaches have been applied for forecasting mortality. in 1992, a new method for long-run forecast of the level and age pattern of mortality was published by lee and carter. this method was welcomed by many authors so it was extended through a wider class of generalized, parametric and nonlinear model. this model represents one of the most influential recent d...