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The recognition of object categories in images is a challenging problem in computer vision, especially when the number of categories is large. This is due to high intra class variability and the need for generalization across variations of objects within the same category. Objects within one category may look quite different.
BCR – for bar code recognition [6] DLR – for document layer recognition [citation needed] These basic technologies allow extracting information from paper documents for further processing in the enterprise information systems such as ERP, CRM, and others. [citation needed] The documents for data capture can be divided into 3 groups ...
Eight Disciplines Methodology (8D) is a method or model developed at Ford Motor Company used to approach and to resolve problems, typically employed by quality engineers or other professionals. Focused on product and process improvement, its purpose is to identify, correct, and eliminate recurring problems. [ 1 ]
Problem/Need-recognition is the first step in the buying decision. Without knowing what the customer needs, they will not be enticed to purchase the product. The need can be triggered by internal stimuli (e.g. hunger, thirst) or external stimuli (e.g. advertising). [14] Maslow held that needs are arranged in a hierarchy.
PERT is a method of analyzing the tasks involved in completing a project, especially the time needed to complete each task, and to identify the minimum time needed to complete the total project. It incorporates uncertainty by making it possible to schedule a project while not knowing precisely the details and durations of all the activities. It ...
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning.The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
Embedded methods are a catch-all group of techniques which perform feature selection as part of the model construction process. The exemplar of this approach is the LASSO method for constructing a linear model, which penalizes the regression coefficients with an L1 penalty, shrinking many of them to zero.
Problems in need of solutions range from simple personal tasks (e.g. how to turn on an appliance) to complex issues in business and technical fields. The former is an example of simple problem solving (SPS) addressing one issue, whereas the latter is complex problem solving (CPS) with multiple interrelated obstacles. [1]