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

Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
Detection of Black Holes01:10

Detection of Black Holes

Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it instrumental in...

You might also read

Related Articles

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

Sort by
Same author

Obliquity disruption and Antarctic ice sheet dynamics over a 2.4-Myr astronomical grand cycle.

Science advances·2025
Same author

Building an Open-Vocabulary Video CLIP Model With Better Architectures, Optimization and Data.

IEEE transactions on pattern analysis and machine intelligence·2024
Same author

Towards Transferable Adversarial Attacks on Image and Video Transformers.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2023
Same author

Climate-controlled submarine landslides on the Antarctic continental margin.

Nature communications·2023
Same author

Is it time to stand united? Goat veterinary society (GVS).

The Veterinary record·2021
Same author

Scale Normalized Image Pyramids With AutoFocus for Object Detection.

IEEE transactions on pattern analysis and machine intelligence·2021
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Videos

Vehicle detection using partial least squares.

Aniruddha Kembhavi1, David Harwood, Larry S Davis

  • 1Microsoft Corporation, aniruddk, City Center/16503,1 Microsoft Way, Redmond, WA 98052, USA. anikem@umd.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|October 6, 2010
PubMed
Summary
This summary is machine-generated.

This study presents an advanced vehicle detection system for aerial images, utilizing novel Color Probability Maps and other descriptors. The enhanced detector achieves superior performance on challenging datasets.

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Image Analysis
  • Machine Learning

Background:

  • Vehicle detection in aerial imagery is crucial for urban planning and surveillance.
  • Existing methods often struggle with complex visual data and require further optimization.

Purpose of the Study:

  • To develop an improved vehicle detection system for aerial images.
  • To enhance detection accuracy by integrating a comprehensive set of image descriptors.

Main Methods:

  • A novel feature set, Color Probability Maps, was developed.
  • Combined Color Probability Maps with Histograms of Oriented Gradients and Pairs of Pixels descriptors.
  • Utilized Partial Least Squares for dimensionality reduction and feature selection.

Main Results:

  • Achieved superior performance compared to previous approaches on two challenging datasets.
  • The integrated feature set significantly improved detection accuracy.
  • Dimensionality reduction and feature selection enhanced efficiency.

Conclusions:

  • The proposed vehicle detection system demonstrates high accuracy and efficiency.
  • The combination of advanced image descriptors and feature selection offers a robust solution for aerial vehicle detection.
  • This method has strong potential for applications in urban planning and visual surveillance.