Deep Convolutional Networks
Deep Convolutional Networks - Related Videos
03:31
End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
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2023
The present protocol describes a novel end-to-end salient object detection algorithm. It leverages deep neural networks to enhance the precision of salient object detection within intricate environmental contexts.
13:19
Deep Neural Networks for Image-Based Dietary Assessment
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Cited by 13 •
2021
The goal of the work presented in this article is to develop technology for automated recognition of food and beverage items from images taken by mobile devices. The technology comprises of two different approaches - the first one performs food image recognition while the second one performs food image segmentation.
06:19
Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
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Cited by 1 •
2024
This article describes a set of methods for measuring the suppressive ability of sniffing alcoholic beverages on the wasabi-elicited stinging sensation.
01:17
Convolution Properties II
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2024
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...
01:20
Convolution Properties I
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2024
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: The associative property suggests that the merged convolution of three functions remains unchanged regardless of the sequence of convolution. For instance, for three functions x(t), h(t), and g(t) is written as, When two LTI systems with impulse...
FAQs
Learned filters respond to local regions and pass their outputs through nonlinear activation functions, allowing successive layers to build increasingly hierarchical representations rather than relying on one fixed transformation. Pooling or strided convolution can then reduce spatial resolution. This combination helps the model represent structured image, signal, or spatial-measurement patterns at multiple levels.
Reducing spatial resolution through pooling or strided convolution changes the representation passed to subsequent layers. This can help a network process progressively more compact spatial information while preserving the learned feature hierarchy. Because the choice is part of architecture design, it must suit the data and task; performance depends on selecting a suitable architecture.
During training, backpropagation uses prediction error to adjust the convolutional filters. The model therefore does not simply apply preset image or signal rules; it changes parameters in response to examples and the selected error objective. Representative training data are essential because the learned filters and final performance depend on the patterns available during optimization.
An engineering workflow begins by presenting structured data such as images, signals, or spatial measurements to the network, then passing it through repeated convolutional layers, nonlinear activations, and, where appropriate, pooling or strided convolution. Training compares predictions with the target outcome and uses backpropagation to update filters. The trained model can then support a specified prediction task.
Deep convolutional networks are useful in engineering when relevant patterns are difficult to capture with manually designed signal-processing rules. Their learned feature extraction can support image classification, object detection, and defect inspection, while the same general approach can analyze sensor data. The practical benefit is potentially improved accuracy, but results still depend on representative data and architecture.
In medical imaging and defect inspection, the network can process image data to support analysis of patterns associated with the task being studied. For sensor-based engineering problems, it can instead learn from structured signal measurements. These applications show why input format and task should guide model design, while computational resources remain a practical condition for achieving useful performance.