Object Recognition With an Elastic Net-Regularized Hierarchical MAX Model of the Visual Cortex

Abstract

Cloud computing faces more security threats, requiring better security measures. This paper examines the various classification and categorization schemes for cloud computing security issues, including the widely known CIA trinity (confidentiality, integrity, and availability), by considering critical aspects of the cloud, such as service models, deployment models, and involved parties. A comprehensive comparison of cloud security classifications constructs an exhaustive taxonomy. ISO27005, NIST SP 800-30, CRAMM, CORAS, OCTAVE Allegro, and COBIT 5 are rigorously compared based on their applicability, adaptability, and suitability within a cloud-based hosting methodology. The findings of this research recommend OCTAVE Allegro as the preferred cloud hosting paradigm. With many security models available in management studies, it is imperative to identify those suitable for the rapidly expanding and dynamically evolving cloud environment. This study underscores the significant methods for securing data on cloud-hosting platforms, thereby contributing to establishing a robust cloud security taxonomy and hosting methodology.

Objects and scenes classification with selective use of central and peripheral image content

“…Similarly, the En-HMAX model was developed to mimic basic structures of the ventral visual system; a hierarchy of brain areas mediate object recognition. It model the first 100 ms of the feedforward visual cognition of primates [7]. It differs from deep learning methods in that it is feed-forward, in terms of training/processing data, with no back-propagation or feedback loops.…”

“…Our En-HMAX model ( Figure 1A) [8,7,4,5,9] comprises three layers, each consisting of both simple S and complex C units. We use independent component analysis (ICA) to generate filters that resemble the receptive fields of V1 simple cells.…”

Affine invariant fusion feature extraction based on geometry descriptor and BIT for object recognition

“…Therefore, it is not difficult for human vision to recognise an affine image. Now biologically inspired transformation (BIT) methods for invariant feature extraction are rising [29, 30]. Sountsov proposes a BIT, which constructs the edge detector and interval detector in two stages and then produces the output feature map by logarithmic mapping [31].…”

Biologically-inspired object recognition system for recognizing natural scene categories

“…In this work, we present our biological inspired model for object recognition, that is the En-HMAX model [11], [12], to address the holistic scene understanding problem.…”