Approach of facial surfaces by contour

Ahdid, Rachid; Safi, Saïd; Manaut, B.

doi:10.1109/icmcs.2014.6911284

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…The examples of the HTM application for face recognition are shown in [10], [11], [12], [25], [13], [30]. The works involve the application of AR [33], Yale [34] and ORL databases [35]. The HTM processing face images usually involves the conversion of the RGB images into gray scale and filtering using standard deviation filter.…”

Section: B Htm Applicationsmentioning

confidence: 99%

Hierarchical Temporal Memory Using Memristor Networks: A Survey

Krestinskaya

Dolzhikova

James

2018

IEEE Trans. Emerg. Top. Comput. Intell.

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This paper presents a survey of the currently available hardware designs for implementation of the human cortex inspired algorithm, Hierarchical Temporal Memory (HTM). In this review, we focus on the state of the art advances of memristive HTM implementation and related HTM applications. With the advent of edge computing, HTM can be a potential algorithm to implement on-chip near sensor data processing. The comparison of analog memristive circuit implementations with the digital and mixed-signal solutions are provided. The advantages of memristive HTM over digital implementations against performance metrics such as processing speed, reduced on-chip area and power dissipation are discussed. The limitations and open problems concerning the memristive HTM, such as the design scalability, sneak currents, leakage, parasitic effects, lack of the analog learning circuits implementations and unreliability of the memristive devices integrated with CMOS circuits are also discussed.

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Section: B Htm Applicationsmentioning

confidence: 99%

Hierarchical Temporal Memory Using Memristor Networks: A Survey

Krestinskaya

Dolzhikova

James

2018

IEEE Trans. Emerg. Top. Comput. Intell.

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“…The AR database contains 100 classes of faces with 26 face images per class with various natural variation and occlusions [13]. The ORL database includes 40 classes with 10 image per class with occlusions, scale variations and rotations [1]. The YALE database contains 15 classes with 11 images per class including different facial expressions and natural variabilities [14].…”

Section: System Level Simulationmentioning

confidence: 99%

Feature extraction without learning in an analog spatial pooler memristive-CMOS circuit design of hierarchical temporal memory

Krestinskaya

James

2018

Analog Integr Circ Sig Process

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Hierarchical Temporal Memory (HTM) is a neuromorphic algorithm that emulates sparsity, hierarchy and modularity resembling the working principles of neocortex. Feature encoding is an important step to create sparse binary patterns. This sparsity is introduced by the binary weights and random weight assignment in the initialization stage of the HTM. We propose the alternative deterministic method for the HTM initialization stage, which connects the HTM weights to the input data and preserves natural sparsity of the input information. Further, we introduce the hardware implementation of the deterministic approach and compare it to the traditional HTM and existing hardware implementation. We test the proposed approach on the face recognition problem and show that it outperforms the conventional HTM approach.

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An Overview of the Hierarchical Temporal Memory Accelerators

Zyarah¹,

Kudithipudi²

2023

Artificial Intelligence Applications and Reconfigurable Architectures

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Approach of facial surfaces by contour

Cited by 5 publications

References 4 publications

Hierarchical Temporal Memory Using Memristor Networks: A Survey

Hierarchical Temporal Memory Using Memristor Networks: A Survey

Feature extraction without learning in an analog spatial pooler memristive-CMOS circuit design of hierarchical temporal memory

An Overview of the Hierarchical Temporal Memory Accelerators

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