COMPUTER VISION

Image Acquisition: The process of obtaining images or videos from various sources such as cameras, drones, or sensors.

Preprocessing: Techniques to enhance images by removing noise, adjusting brightness and contrast, or resizing.

Feature Extraction: Identifying relevant features or patterns within images, such as edges, corners, textures, or shapes.

Object Detection: Locating and identifying objects within an image or video stream, often using techniques like Haar cascades, YOLO (You Only Look Once), or R-CNN (Region-based Convolutional Neural Networks).

Object Recognition: Classifying objects into predefined categories or classes, such as recognizing different types of animals, vehicles, or landmarks.

Image Segmentation: Dividing an image into multiple segments or regions to simplify its representation and enable more detailed analysis.

Deep Learning: The use of convolutional neural networks (CNNs) and other deep learning architectures to automatically learn features and patterns from images, enabling more accurate and robust computer vision systems.

Applications: Computer vision has diverse applications across various industries, including healthcare (medical image analysis), automotive (autonomous vehicles), retail (object detection for inventory management), security (surveillance systems), and entertainment (augmented reality filters).

Challenges: Despite significant advancements, computer vision still faces challenges such as occlusion, illumination variations, viewpoint changes, and the need for large annotated datasets for training deep learning models.

Ethical Considerations: As computer vision technology becomes more prevalent, it raises important ethical questions related to privacy, bias, and the potential for misuse or unintended consequences.

#snsinstitutions

#snsdesignthinkers

#designthinking