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

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:
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,
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...
Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
Associative Learning01:27

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

You might also read

Related Articles

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

Sort by
Same author

CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection.

Proceedings. IEEE International Conference on Computer Vision·2026
Same author

Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data.

Machine intelligence research (Beijing)·2026
Same author

Implementation of an ai-enabled multimodal emergency care system is associated with improved sudden cardiac death rescue outcomes in anyang.

Scientific reports·2026
Same author

Effects of physical therapy modalities for early postoperative pain following total knee arthroplasty: a systematic review and network meta-analysis.

Frontiers in rehabilitation sciences·2026
Same author

Liquid Crystal Elastomers for Adaptive Intelligent Systems: From Molecular Design to Multifunctional Applications.

Angewandte Chemie (International ed. in English)·2026
Same author

Development and validation of the Pulmonary Nodule Malignant Transformation Fear Scale (PN-MTFS) to identify patients at high risk of cancer-related fear: a multicenter study.

Frontiers in psychology·2026

Related Experiment Videos

Unsupervised learning of probabilistic object models (POMs) for object classification, segmentation, and recognition

Yuanhao Chen1, Long Leo Zhu, Alan Yuille

  • 1University of Science and Technology of China, Hefei, People's Republic of China. yhchen4@ustc.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 22, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for learning probabilistic object models (POMs) with minimal supervision, enhancing object classification, segmentation, and recognition tasks through complementary visual cues and knowledge propagation.

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Object recognition and segmentation often require large amounts of labeled data.
  • Existing methods struggle with variations in scale, rotation, and hybrid object classes.

Purpose of the Study:

  • To develop a weakly supervised method for learning probabilistic object models (POMs).
  • To improve performance in object classification, segmentation, and recognition.
  • To reduce the need for extensive manual supervision in training.

Main Methods:

  • Formulated as a structure induction and learning task, combining elementary POMs.
  • Introduced a novel knowledge propagation procedure for inter-POM information sharing.
  • Learned POMs on Interest Points (POM-IP), regional features (POM-mask), and edgelets (POM-edgelets) sequentially.

Main Results:

  • Achieved improved performance in classification and segmentation on large datasets.
  • Demonstrated invariance to scale and rotation for both learning and inference.
  • Successfully learned hybrid object classes and enabled object recognition through inter-object matching.

Conclusions:

  • The proposed method significantly reduces supervision requirements and speeds up inference.
  • The developed POMs offer robust performance across various computer vision tasks.
  • This approach advances weakly supervised learning for complex object modeling.