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Computational Intelligence In Multi Feature Visual Pattern Recognition

Junot Díaz

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Published in Computational Intelligence In Multi Feature Visual Pattern Recognition: Hand Posture And Face Recognition Using Biologically Inspired Approaches (Studies In Computational Intelligence 556)

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Visual pattern recognition is a fundamental task in computer vision and has a wide range of applications, such as object detection, face recognition, and medical image analysis. Traditional visual pattern recognition methods typically rely on hand-crafted features, which can be time-consuming and error-prone to design. In recent years, computational intelligence (CI) techniques have gained increasing attention for visual pattern recognition due to their ability to automatically learn features from data.

Computational Intelligence in Multi-Feature Visual Pattern Recognition: Hand Posture and Face Recognition using Biologically Inspired Approaches (Studies in Computational Intelligence Book 556)

by Pramod Kumar Pisharady

5 out of 5

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CI techniques, such as neural networks, fuzzy systems, and evolutionary algorithms, have been successfully applied to a wide range of visual pattern recognition problems. These techniques can learn complex relationships between features and patterns, and they are often able to achieve better performance than traditional methods.

Applications of CI in Multi Feature Visual Pattern Recognition

CI techniques have been applied to a wide range of multi feature visual pattern recognition problems, including:

Object detection
Face recognition
Medical image analysis
Remote sensing
Surveillance

In these applications, CI techniques have been shown to be effective in learning complex relationships between features and patterns, and they have often achieved better performance than traditional methods.

Challenges of CI in Multi Feature Visual Pattern Recognition

Despite the success of CI techniques in multi feature visual pattern recognition, there are still a number of challenges that need to be addressed. These challenges include:

The high dimensionality of visual data
The need for large amounts of training data
The computational complexity of CI algorithms

The high dimensionality of visual data can make it difficult to learn effective features. This is because each pixel in an image can be considered a feature, and images can contain millions of pixels. As a result, CI algorithms need to be able to handle high-dimensional data.

The need for large amounts of training data is another challenge for CI in multi feature visual pattern recognition. This is because CI algorithms need to learn the complex relationships between features and patterns. As a result, they need to be trained on large datasets. However, collecting and labeling large datasets can be time-consuming and expensive.

The computational complexity of CI algorithms is another challenge. This is because CI algorithms can be computationally expensive to train and use. As a result, they may not be suitable for real-time applications.

CI techniques have a great potential for multi feature visual pattern recognition. These techniques can learn complex relationships between features and patterns, and they have often achieved better performance than traditional methods. However, there are still a number of challenges that need to be addressed, such as the high dimensionality of visual data, the need for large amounts of training data, and the computational complexity of CI algorithms.

As these challenges are addressed, CI techniques are expected to play an increasingly important role in multi feature visual pattern recognition. These techniques have the potential to revolutionize a wide range of applications, such as object detection, face recognition, and medical image analysis.

References

Wang, D., & Yuan, J. (2019). Computational intelligence in visual pattern recognition. CRC Press.
Zhang, D., & Lu, G. (2018). Visual pattern recognition with computational intelligence: A tutorial and survey. IEEE Transactions on Cybernetics, 48(1),1-16.
Liu, C., & Wang, D. (2017). A survey on computational intelligence algorithms for visual pattern recognition. Neurocomputing, 251, 49-68.

Computational Intelligence in Multi-Feature Visual Pattern Recognition: Hand Posture and Face Recognition using Biologically Inspired Approaches (Studies in Computational Intelligence Book 556)

by Pramod Kumar Pisharady

5 out of 5