Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Vector Algebra: Method of Components

Vector Algebra: Method of Components

It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...

Vector or Cross Product

Vector or Cross Product

Vector Components in the Cartesian Coordinate System

Vector Components in the Cartesian Coordinate System

Vectors are usually described in terms of their components in a coordinate system. Even in everyday life, we naturally invoke the concept of orthogonal projections in a rectangular coordinate system. For example, if someone gives you directions for a particular location, you will be told to go a few km in a direction like east, west, north, or south, along with the angle in which you are supposed to move. In a rectangular (Cartesian) xy-coordinate system in a plane, a point in a plane is...

Scalar and Vector Triple Products

Scalar and Vector Triple Products

Two vectors can be multiplied using a scalar product or a vector product. The resultant of a scalar product is scalar, while with vector products, the resultant is a vector. These rules of the scalar or vector product between two vectors can be applied to multiple vectors to obtain meaningful combinations. The scalar triple product is the dot product of a vector with the cross product of two vectors.
The scalar triple product is the dot product of a vector with the cross product of two vectors....

Distance Problem

Distance Problem

When an object's velocity changes over time, the total distance traveled can be determined by summing small displacement intervals over short increments. This approach approximates the true distance through numerical summation and the use of integral calculus. An estimate of the total displacement can be obtained by measuring velocity at regular intervals and multiplying each value by the corresponding time step.If a runner accelerates over the first three seconds of a race, speed measurements...

Vector Product (Cross Product)

Vector Product (Cross Product)

Vector multiplication of two vectors yields a vector product, with the magnitude equal to the product of the individual vectors multiplied by the sine of the angle between both the vectors and the direction perpendicular to both the individual vectors. As there are always two directions perpendicular to a given plane, one on each side, the direction of the vector product is governed by the right-hand thumb rule.
Consider the cross product of two vectors. Imagine rotating the first vector about...

You might also read

Related Articles

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

Sort by

Same author

Recent advances in information security.

TheScientificWorldJournal·2015

Same author

Recent advances in information technology.

TheScientificWorldJournal·2014

Same journal

The Eco-Friendly Preparation of Se, Zn, and Ag MONPs and Their Current Medical Applications and Drug Delivery for AD Diseases.

TheScientificWorldJournal·2026

Same journal

Fear of COVID-19: A Comparative Study Among University Students in Peru.

TheScientificWorldJournal·2026

Same journal

Opportunities and Challenges of Integrating Ethiopian Traditional Medicine System Into Modern Medicine: A Narrative Review.

TheScientificWorldJournal·2026

Same journal

Exploring the Antiparasitic Activity of the Sea Cucumber Isostichopus sp. aff. badionotus From the Northern Coast of Colombia Against Trypanosoma cruzi.

TheScientificWorldJournal·2026

Same journal

Kalanchoe ceratophylla (Crassulaceae): The True Identity of Sidingin, a Medicinal Plant From Sumatra, Based on Morphological and Molecular Evidence.

TheScientificWorldJournal·2026

Same journal

Genetic Variation of Chicken Growth Differentiation Factor-9 Gene and Association With Egg Characteristics: A Systematic Review.

TheScientificWorldJournal·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 28, 2026

Super-resolution Imaging of Neuronal Dense-core Vesicles

Super-resolution Imaging of Neuronal Dense-core Vesicles

Published on: July 2, 2014

Multiscale distance coherence vector algorithm for content-based image retrieval.

Zeng Jiexian¹, Liu Xiupeng², Fei Yu³

¹School of Software, Nanchang Hangkong University, Nanchang 330063, China.

Thescientificworldjournal

|June 3, 2014

Summary

This summary is machine-generated.

A novel multiscale distance coherence vector algorithm enhances content-based image retrieval (CBIR). This method improves accuracy and noise resistance for image search, outperforming existing techniques.

Related Experiment Videos

Last Updated: Apr 28, 2026

Super-resolution Imaging of Neuronal Dense-core Vesicles

Super-resolution Imaging of Neuronal Dense-core Vesicles

Published on: July 2, 2014

Area of Science:

Computer Science
Image Processing
Artificial Intelligence

Background:

Existing distance coherence vector algorithms struggle with shape variations and noise in content-based image retrieval (CBIR).
The need for robust image retrieval methods that are invariant to transformations is critical.

Purpose of the Study:

To propose a Multiscale Distance Coherence Vector (MDCV) algorithm for improved content-based image retrieval (CBIR).
To enhance the antinoise performance and invariance to transformations in image retrieval systems.

Main Methods:

Image contour evolution using a Gaussian function.
Extraction of distance coherence vectors from original and evolved image contours.
Generation of Multiscale Distance Coherence Vectors through weighted distribution.

Main Results:

The proposed MDCV algorithm demonstrates invariance to translation, rotation, and scaling.
The algorithm exhibits significant antinoise performance.
Experimental results show higher recall and precision rates for noisy image retrieval.

Conclusions:

The Multiscale Distance Coherence Vector algorithm offers a robust solution for content-based image retrieval.
This approach effectively addresses limitations of previous methods, particularly in noisy conditions and with shape variations.