Computer Vision (CV) is a field of artificial intelligence focused on enabling machines to interpret and understand visual data, such as images or videos. Its goal is to extract meaningful information—like identifying objects, detecting patterns, or understanding scenes—to support decision-making. Image Processing (IP), on the other hand, refers to techniques for manipulating or analyzing images to improve their quality, extract details, or transform them into a more usable form. While both fields work with visual data, CV aims to derive high-level understanding, whereas IP focuses on modifying or enhancing the raw data itself.
The relationship between CV and IP is hierarchical. IP often serves as a preprocessing step for CV tasks. For example, before a computer vision system can detect faces in an image, it might use IP methods like noise reduction (e.g., Gaussian blur) or contrast adjustment to clean up the input. Similarly, edge detection algorithms (a common IP technique) might highlight object boundaries, which CV algorithms then use to identify shapes or track motion. Conversely, CV can guide IP by providing context—such as using object recognition to determine which parts of an image need enhancement. Libraries like OpenCV integrate both domains, offering tools for filtering, transforming, and analyzing images in ways that bridge the two fields.
Developers working on CV projects often rely on IP techniques to improve input quality or reduce computational complexity. For instance, resizing an image (IP) before feeding it into a neural network (CV) speeds up processing without sacrificing accuracy. A practical example is autonomous vehicles: IP algorithms correct lens distortion or adjust lighting in camera feeds, while CV systems interpret the cleaned data to detect pedestrians or traffic signs. Understanding both fields allows developers to optimize pipelines—like combining histogram equalization (IP) with convolutional neural networks (CV) for better medical image analysis. While CV deals with “what” is in an image, IP handles “how” to prepare or refine it, making them complementary tools in solving visual computing problems.