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Related Concept Videos

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...
Typical Model Studies01:30

Typical Model Studies

Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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...
Modeling and Similitude01:12

Modeling and Similitude

Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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...
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...

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Related Experiment Videos

Identifying Rare and Subtle Behaviors: A Weakly Supervised Joint Topic Model.

Timothy M Hospedales, Jian Li, Shaogang Gong

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 27, 2011
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel weakly supervised model for identifying rare and subtle behaviors in videos. The approach enables real-time detection with minimal data, outperforming existing methods.

    Related Experiment Videos

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Automated video analysis struggles with identifying rare, subtle behaviors crucial for detecting anomalies like dangerous activities.
    • Existing methods require extensive data and supervision, which is often unavailable for rare events.
    • Subtle behaviors are challenging due to small spatio-temporal deviations and presence in cluttered scenes.

    Purpose of the Study:

    • To develop a novel weakly supervised joint topic model for identifying rare and subtle behaviors in video.
    • To enable behavior modeling from very few examples, even without user-defined localization.
    • To achieve real-time classification and localization of subtle behaviors.

    Main Methods:

    • Introduction of a multiclass topic model with partially shared latent structure.
    • Development of associated learning and inference algorithms for the proposed model.
    • Weakly supervised approach requiring minimal user input and localization.

    Main Results:

    • The model can learn behaviors from as few as one example.
    • Effective online and real-time classification and localization of rare and subtle behaviors.
    • Outperformed contemporary alternative methods on two public-space datasets.

    Conclusions:

    • The proposed weakly supervised joint topic model effectively addresses challenges in rare and subtle behavior analysis.
    • This approach significantly advances automated video behavior analysis capabilities.
    • Enables practical applications in security and anomaly detection with limited data.