Okay, you've learned what features like edges and corners generally are. But why go through the trouble of finding them? Why not just work with the raw pixel values directly?

Working directly with millions of pixel values presents significant challenges. Imagine trying to find a specific face in a crowd photo by comparing every single pixel value of your target face image to every possible location in the crowd photo. This would be incredibly slow and very sensitive to minor changes. If the lighting is slightly different, or the face is turned a bit, the raw pixel values will change dramatically, likely causing your simple comparison to fail.

Feature detection offers a more effective approach by focusing on the "interesting" parts of an image. Here's why detecting features is so beneficial:

Data Reduction and Efficiency: Images contain a massive amount of data. A 1-megapixel color image has 1 million pixels, each with (typically) 3 color values, totaling 3 million numbers! Features, like corners or edge points, represent this information much more compactly. Instead of millions of pixel values, you might work with just hundreds or thousands of feature points. This drastically reduces the computational load for subsequent processing steps, making algorithms faster and more efficient.
Information Concentration: Features often correspond to points of high information content. Edges typically represent the boundaries of objects, while corners often occur where edges meet or where there's a significant change in shape or texture. These are locations that are often important for understanding the image's content. By focusing on features, we concentrate our analysis on these meaningful areas.
Robustness to Variations: Raw pixel values are highly sensitive to changes in lighting, camera viewpoint, object scale, and rotation. A slight shadow can change pixel intensities significantly. Features, particularly well-chosen ones, tend to be more stable under these variations. For instance, the corner of a building remains a corner whether it's seen on a sunny or cloudy day, or from slightly different angles (within limits). This stability makes feature-based methods more reliable for real-application tasks where conditions aren't perfectly controlled.
Foundation for Complex Tasks: Feature detection is rarely the end goal; it's usually a fundamental first step for many sophisticated computer vision applications:
- Object Matching and Recognition: If you can reliably detect the same features on an object across different images (despite changes in viewpoint or lighting), you can match the object. This is essential for recognizing known objects or finding them in new scenes.
- Image Stitching: To create panoramas, software needs to find corresponding points (features) in overlapping images to align them correctly.
- Motion Tracking: Features can be tracked from one video frame to the next to understand how objects are moving.
- 3D Reconstruction: By finding corresponding features in images taken from different viewpoints, it's possible to infer the 3D structure of the scene.
- Robotics and Navigation: Robots often use features as landmarks to understand their position and navigate their environment.

In essence, detecting features allows us to move from a dense, sensitive representation (pixels) to a sparse, more stable, and semantically richer representation. This abstraction makes it feasible to build systems that can analyze and understand visual information in a way that mirrors, albeit simplistically, how biological vision systems process salient information. It provides a set of reliable anchor points upon which more complex interpretations of the image can be built.