Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments

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Publisher ‏ : ‎ Mdpi AG (August 24, 2021)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 454 pages
ISBN-10 ‏ : ‎ 3036512683
ISBN-13 ‏ : ‎ 978-3036512686
Item Weight ‏ : ‎ 2.59 pounds
Dimensions ‏ : ‎ 6.69 x 1.38 x 9.61 inches

With the rapid advancements in technology, smart sensor environments are becoming more prevalent in various industries such as healthcare, transportation, and security. These environments rely on advanced computational intelligence techniques for object detection, feature extraction, and recognition to make informed decisions in real-time.

Object detection is a crucial task in smart sensor environments as it allows the system to identify and track objects of interest. Techniques such as deep learning-based algorithms, like Convolutional Neural Networks (CNNs), are commonly used for object detection due to their ability to learn complex patterns from large datasets.

Feature extraction is another important step in the process of analyzing sensor data. It involves identifying relevant information from raw data to improve the accuracy and efficiency of the system. Feature extraction techniques like Principal Component Analysis (PCA) and Local Binary Patterns (LBP) can help in extracting meaningful features from sensor data for further analysis.

Recognition is the final stage in the process, where the system identifies and categorizes objects based on the extracted features. This can involve tasks like facial recognition, object classification, and gesture recognition. Advanced computational intelligence techniques, such as Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), and Random Forests, are commonly used for recognition tasks in smart sensor environments.

Overall, advanced computational intelligence plays a crucial role in enhancing the capabilities of smart sensor environments for object detection, feature extraction, and recognition. With the ongoing research and development in this field, we can expect to see even more sophisticated and efficient systems in the near future.
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