Methods And Circuits For Focalplane Computation Of Features In Cmos Visual Sensors

• doi: 10.1109/ARVLSI.2001.915564
• title: Methods and circuits for focal-plane computation of features in CMOS visual sensors
• publisher: IEEE
• isbn: 0-7695-1038-8
• issn: 1522-869X
• rank: 2994
• access_type: LOCKED
• content_type: Conferences
• abstract: Feature detection, and tracking is a fundamental problem in computer vision research. By detecting and tracking features in an image sequence it is possible to recover information about both the motion of the viewer and the structure of the environment. The selection of features is a computationally intensive task. We derive two low-complexity algorithms that are suitable for integration in a CMOS sensor with focal-plane processing. We review the two algorithms and the circuits that implement them. We presents results from accurate simulations and experimental results from fabricated CMOS sensors.
• article_number: 915564
• pdf_url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=915564
• html_url: https://ieeexplore.ieee.org/document/915564/
• abstract_url: https://ieeexplore.ieee.org/document/915564/
• publication_title: Proceedings 2001 Conference on Advanced Research in VLSI. ARVLSI 2001
• conference_location: Salt Lake City, UT, USA
• conference_dates: 14-16 March 2001
• publication_number: 7312
• is_number: 19773
• publication_year: 2001
• publication_date: 14-16 March 2001
• start_page: 238
• end_page: 248
• citing_paper_count: 0
• citing_patent_count: 0
• download_count: 34
• insert_date: 20020807
• index_terms:
  • ieee_terms:
    • Sensor phenomena and characterization
    • Computer vision
    • CMOS image sensors
    • CMOS technology
    • Cameras
    • Sensor systems
    • CMOS process
    • Circuit simulation
    • Image sequences
    • Streaming media
  • dynamic_index_terms:
    • Vision Sensors
    • Feature Detection
    • Simulation Accuracy
    • Feature Tracking
    • Computer Vision Research
    • Power Consumption
    • Image Regions
    • Neighboring Pixels
    • Neighborhood Pixels
    • Input Current
    • Minimum Eigenvalue
    • CMOS Technology
    • Global Motion
    • Pixel Array
    • Weak Inverse
• isbn_formats:
  • format: Print ISBN, value: 0-7695-1038-8, isbnType: Historical
• authors:
  • Author Name: A. Pesavento
    Affiliation: Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA
    Author URL: https://ieeexplore.ieee.org/author/37375888700
    ID: 37375888700
    Order: 1
    Author Affiliations:
    • Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA
  • Author Name: C. Koch
    Affiliation: Computation and Neural Systems Program, California Institute of Technology, Pasadena, CA, USA
    Author URL: https://ieeexplore.ieee.org/author/37282902400
    ID: 37282902400
    Order: 2
    Author Affiliations:
    • Computation and Neural Systems Program, California Institute of Technology, Pasadena, CA, USA

Image Sensor

• sensor_type: CMOS
• resolution: 8x8 pixels
• dynamic_range: Not specified
• pixel_size: 189x189µm
• dark_current: Not specified

Optical Data

• focal_length: Not specified
• aperture: Not specified
• field_of_view: Not specified
• distortion: Not specified

Performance Metrics

• power_consumption: < 1 mW (per pixel)

Applications & Benefits

• cell_imaging: Feature detection in visual sensors for potentially tracking motion and structure of the environment.
• benefits: Low power consumption, the ability to integrate sensing and processing on the same chip.

Supporting Organizations

• supported_by: NSF, Center for Neuromorphic Systems Engineering at Caltech

Manuscript Details

• publication_date: 14-16 March 2001

Relevancy Score

• score: 9
• missing_fields:
  • fill factor
  • quantum efficiency
  • readout speed
  • temporal noise
  • fixed-pattern noise
  • analog-to-digital conversion techniques
  • microlenses
  • on-chip colour filters
  • global or rolling shutters
  • backside illumination

Processed JSON Filename

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