AI - Computer Vision

Key Concepts

Computer vision systems typically involve the following steps:

• Image Acquisition: Capturing images or videos using cameras.
• Image Processing: Enhancing and manipulating images (e.g., noise reduction, color correction).
• Feature Extraction: Identifying and extracting relevant features from images (e.g., edges, corners, textures).
• Object Detection: Identifying and locating objects within an image.
• Image Classification: Categorizing an entire image based on its content.

Related Technologies

Several technologies are closely related to computer vision:

• Deep Learning: A powerful machine learning technique that has revolutionized computer vision.
• Convolutional Neural Networks (CNNs): A type of deep learning network particularly well-suited for image processing.
• Machine Learning: Provides the foundation for many computer vision algorithms.

Example Code (Python)

import tensorflow as tf
import matplotlib.pyplot as plt

# Load a sample image
image = tf.io.read_file("sample_image.jpg")
image = tf.image.decode_jpeg(image, channels=3)

# Convert to a tensor
image_tensor = tf.convert_to_tensor(image)

# Preprocess the image
# (This is a simplified example; real-world preprocessing is more complex)
# ...

# Create a model (placeholder)
# model = ...

# ... (Training and prediction logic) ...

# Visualize the result (placeholder)
# plt.imshow(result_image)
# plt.show()