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

Convolution Properties II01:17

Convolution Properties II

587
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
587
Convolution Properties I01:20

Convolution Properties I

602
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
602
Types of Non-structural Cracks in Concrete01:28

Types of Non-structural Cracks in Concrete

505
Non-structural cracks are primarily of three types: plastic, early-age thermal, and drying shrinkage cracks. Plastic cracks are further classified into plastic shrinkage cracks and plastic settlement cracks.
Plastic shrinkage cracks typically form within hours after the concrete is poured. The concrete's surface dries faster than the bottom, creating tensile stress that the still-plastic concrete cannot withstand, leading to diagonal or randomly patterned cracks on the concrete surface.
505
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.6K
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

947
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
947
Associative Learning01:27

Associative Learning

1.3K
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...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Modelling and spatial discrimination of small mammal assemblages: an example from western Sichuan (China).

Ecological modelling·2010
Same author

Nutritional status alters saccharin intake and sweet receptor mRNA expression in rat taste buds.

Brain research·2010
Same author

Involvement of mitochondria-mediated apoptosis in ethylbenzene-induced renal toxicity in rat.

Toxicological sciences : an official journal of the Society of Toxicology·2010
Same author

Clinical significance of miR-221 and its inverse correlation with p27Kip¹ in hepatocellular carcinoma.

Molecular biology reports·2010
Same author

Cruciate ligament reconstruction using LARS artificial ligament under arthroscopy: 81 cases report.

Chinese medical journal·2010
Same author

[Influence of ethylbenzene on the levels of mandelic acid and phenylglyoxylic acid in urine, ultrastructure and the expressions of Mitochondrial apoptotic-related proteins in the rat nephridial tissues].

Zhonghua lao dong wei sheng zhi ye bing za zhi = Zhonghua laodong weisheng zhiyebing zazhi = Chinese journal of industrial hygiene and occupational diseases·2010
Same journal

Mask-guided Asymmetric Contrastive and Semantic Alignment for Unsupervised Person Re-Identification.

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

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

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

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

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

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

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

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

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

Location Matters: Frequency-Spatial Dual Space Adaptation for Cross-Domain Few-Shot Segmentation.

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

Related Experiment Video

Updated: Feb 3, 2026

Hierarchical and Programmable One-Pot Oligosaccharide Synthesis
09:56

Hierarchical and Programmable One-Pot Oligosaccharide Synthesis

Published on: September 6, 2019

7.3K

DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection.

Qin Zou, Zheng Zhang, Qingquan Li

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |November 3, 2018
    PubMed
    Summary
    This summary is machine-generated.

    DeepCrack, a novel deep convolutional neural network, accurately detects pavement cracks by learning high-level features. This method significantly improves crack detection performance on challenging datasets.

    More Related Videos

    Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
    08:03

    Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

    Published on: December 7, 2021

    2.8K
    Antibody Staining in C. Elegans Using "Freeze-Cracking"
    13:10

    Antibody Staining in C. Elegans Using "Freeze-Cracking"

    Published on: October 14, 2013

    23.8K

    Related Experiment Videos

    Last Updated: Feb 3, 2026

    Hierarchical and Programmable One-Pot Oligosaccharide Synthesis
    09:56

    Hierarchical and Programmable One-Pot Oligosaccharide Synthesis

    Published on: September 6, 2019

    7.3K
    Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
    08:03

    Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

    Published on: December 7, 2021

    2.8K
    Antibody Staining in C. Elegans Using "Freeze-Cracking"
    13:10

    Antibody Staining in C. Elegans Using "Freeze-Cracking"

    Published on: October 14, 2013

    23.8K

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Image Analysis

    Background:

    • Cracks are common in infrastructure, posing challenges for detection due to poor continuity and low contrast.
    • Traditional image-based crack detection methods struggle with low-level features.

    Purpose of the Study:

    • To propose DeepCrack, an end-to-end deep convolutional neural network for automatic crack detection.
    • To leverage high-level features for improved crack representation and detection.

    Main Methods:

    • Developed DeepCrack, an end-to-end trainable deep convolutional neural network.
    • Utilized an encoder-decoder architecture based on SegNet.
    • Fused multi-scale deep convolutional features from hierarchical stages and paired encoder-decoder features.

    Main Results:

    • Achieved an average F-Measure over 0.87 on three challenging crack datasets.
    • Demonstrated superior performance compared to current state-of-the-art methods.
    • Successfully captured line structures using fused multi-scale features.

    Conclusions:

    • DeepCrack effectively addresses the challenges of crack detection in images.
    • The proposed method offers a robust solution for automatic crack detection.
    • DeepCrack represents a significant advancement in computer vision for structural health monitoring.