Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Upsampling01:22

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
Aliasing01:18

Aliasing

Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original signal...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

OCTA-based AMD Stage Grading Enhancement via Class-Conditioned Style Transfer.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Efficient In-Training Adaptive Compound Loss Function Contribution Control for Medical Image Segmentation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Profile of snakebite cases admitted to the Poison Control Center of Bach Mai Hospital in northern Vietnam from 2008 to 2020.

Transactions of the Royal Society of Tropical Medicine and Hygiene·2025
Same author

Retinal OCT Layer Segmentation via Joint Motion Correction and Graph-Assisted 3D Neural Network.

IEEE access : practical innovations, open solutions·2024
Same author

Enhancing lesion detection in liver and kidney CT scans via lesion mask selection from two models: A main model and a model focused on small lesions.

Computers in biology and medicine·2024
Same author

TigerBase: A DNA registration system to enhance enforcement and compliance testing of captive tiger facilities.

Forensic science international. Genetics·2024
Same journal

Style-Aware Contrastive Test-Time Adaptation: A Dual-Cache Model for Robust Vision-Language Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Semantic Frame Interpolation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Physics-Guided Cross-Modal Decoupling with Test-Time Adaptation for Hyperspectral Image Restoration.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Related Experiment Videos

Scale-aware saliency for application to frame rate upconversion.

Natan Jacobson1, Truong Q Nguyen

  • 1Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA. njacobso@ucsd.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|December 15, 2011
PubMed
Summary
This summary is machine-generated.

A novel scale-aware saliency detection method uses color and texture to identify visual importance. This approach enhances digital video processing and reduces manual parameter tuning for improved performance.

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Image Processing
  • Human Visual Perception

Background:

  • Saliency detection is crucial for digital video processing, drawing from human visual perception principles.
  • Current methods often require manual parameter tuning, limiting their automation potential.

Purpose of the Study:

  • Introduce a new scale-aware saliency detection method.
  • Improve automatic saliency determination in images and video.
  • Reduce the need for manual operator tuning of saliency parameters.

Main Methods:

  • Utilize a scale-space model incorporating color and texture cues for scale determination.
  • Integrate scale information into a discriminant saliency engine via soft weighting of center-surround parameters.
  • Evaluate performance against human fixation data and apply to frame rate upconversion.

Main Results:

  • Achieved excellent performance validated by human fixation data.
  • Demonstrated class-leading objective (PSNR/SSIM) and subjective results in frame rate upconversion.
  • Significantly reduced the necessity for operator tuning of saliency parameters.

Conclusions:

  • The proposed scale-aware method offers robust and automatic saliency detection.
  • This approach advances digital video processing applications, including frame rate upconversion.
  • The method is suitable for diverse applications requiring automatic visual importance determination.