What is Object Recognition?
Object recognition, also known as object classification, is a subfield of computer vision that focuses on identifying objects within digital images or videos. It involves training machine learning algorithms, such as neural networks, to recognize and categorize objects based on their features and characteristics. Object recognition is a critical component of many applications, including image search, robotics, autonomous vehicles, and virtual reality.
How does Object Recognition work?
Object recognition algorithms typically involve the following steps:
- Image preprocessing: Images are preprocessed to ensure they are of the same size and format before being fed into the object recognition algorithm.
- Feature extraction: Deep learning algorithms, such as CNNs, are used to extract features from the input images. These features help to identify patterns and characteristics that differentiate objects from one another.
- Classification: The extracted features are used to classify the objects into different categories. This is typically done using a fully connected layer in a neural network that outputs class probabilities.
- Post-processing: The final step involves interpreting the classification results and generating the output, such as object labels or confidence scores.