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Structural Classification of Joints01:20

Structural Classification of Joints

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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...
5.1K
Masking and Demasking Agents01:19

Masking and Demasking Agents

2.9K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
2.9K
Functional Classification of Joints01:09

Functional Classification of Joints

5.5K
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...
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Related Experiment Video

Updated: Oct 19, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Joint Multisource Saliency and Exemplar Mechanism for Weakly Supervised Video Object Segmentation.

Qing En, Lijuan Duan, Zhaoxiang Zhang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |September 22, 2021
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    This study introduces a new framework for weakly supervised video object segmentation (WSVOS) using multisource saliency and an exemplar mechanism. The approach effectively segments objects with category labels, overcoming limitations of existing methods.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence

    Background:

    • Weakly supervised video object segmentation (WSVOS) is challenging due to reliance solely on category labels.
    • Existing WSVOS methods struggle with spatiotemporal understanding and semantic exploration.

    Purpose of the Study:

    • To develop a novel framework for WSVOS that integrates multisource saliency and an exemplar mechanism.
    • To address limitations in spatiotemporal knowledge comprehension and semantic information exploration in WSVOS.

    Main Methods:

    • Proposed a multisource saliency module integrating spatial and temporal saliency as bottom-up cues.
    • Introduced an adaptive exemplar module for top-down cues, providing guidance for object segmentation.
    • Developed a framework to construct correspondences between bottom-up and top-down cues without ground-truth reference frames.

    Main Results:

    • The multisource saliency module effectively identifies salient regions and mitigates disruptions from confusing areas.
    • The adaptive exemplar module offers reliable guidance for segmenting co-occurring objects within intraclass videos.
    • The integrated framework achieved state-of-the-art performance in WSVOS tasks.

    Conclusions:

    • The proposed framework offers a novel approach to WSVOS by effectively combining bottom-up and top-down cues.
    • This method advances the field by enabling accurate pixel-level segmentation with only category labels.
    • The framework demonstrates superior performance, setting a new benchmark for WSVOS.