Jove
Visualize
Contact Us

Related Concept Videos

Methods of Classification and Identification01:28

Methods of Classification and Identification

Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Functional Classification of Joints01:09

Functional Classification of Joints

Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An immobile...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...

You might also read

Related Articles

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

Sort by
Same author

EuroCity Persons 2.0: A Large and Diverse Dataset of Persons in Traffic.

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

Semantic Scene Completion Using Local Deep Implicit Functions on LiDAR Data.

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

Tree-Structured Models for Efficient Multi-Cue Scene Labeling.

IEEE transactions on pattern analysis and machine intelligence·2016
Same author

Mixture of Switching Linear Dynamics to Discover Behavior Patterns in Object Tracks.

IEEE transactions on pattern analysis and machine intelligence·2016
Same author

Monocular pedestrian detection: survey and experiments.

IEEE transactions on pattern analysis and machine intelligence·2009
Same author

A Bayesian, exemplar-based approach to hierarchical shape matching.

IEEE transactions on pattern analysis and machine intelligence·2007
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
Same journal

GoP-based Quality Enhancement on Video Compression.

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

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

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

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

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

Updated: Jun 2, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

A multilevel Mixture-of-Experts framework for pedestrian classification.

Markus Enzweiler1, Dariu M Gavrila

  • 1Environment Perception Department, Daimler AG Group Research & MCG Development, 89081 Ulm, Germany. markus.enzweiler@daimler.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|April 14, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new pedestrian recognition system using a multilevel Mixture-of-Experts model. This approach enhances pedestrian classification by combining various features and data types for improved accuracy.

More Related Videos

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Related Experiment Videos

Last Updated: Jun 2, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Area of Science:

  • Computer Vision
  • Machine Learning
  • Pattern Recognition

Background:

  • Pedestrian recognition remains a significant challenge in computer vision despite extensive research.
  • Accurate pedestrian detection is crucial for applications like autonomous driving and surveillance.

Purpose of the Study:

  • To develop a novel approach for improved pedestrian classification.
  • To effectively combine diverse features and data modalities for robust recognition.

Main Methods:

  • A multilevel Mixture-of-Experts framework was employed.
  • Integration of pose-level shape cues (Chamfer shape matching) and modality-level data (image intensity, depth, flow).
  • Feature-level fusion using Histograms of Oriented Gradients (HOG) and Local Binary Patterns (LBP) with Multilayer Perceptrons (MLP) and linear Support Vector Machines (linSVM) as expert classifiers.

Main Results:

  • The proposed method demonstrated enhanced pedestrian classification performance.
  • Combining multiple features and data sources proved effective in improving recognition accuracy.
  • The Mixture-of-Experts approach successfully integrated information from different levels of analysis.

Conclusions:

  • The novel multilevel Mixture-of-Experts approach offers a promising solution for robust pedestrian recognition.
  • Effective fusion of diverse cues and features is key to overcoming current limitations in pedestrian detection.
  • This method provides a flexible and powerful framework for complex classification tasks.