Contours in Computer Vision

According to OpenCV documentation, Contours are said to be a curve that simply joins all the continuous points, along the boundary, having the same color or intensity. It can be represented using chain code, Fourier descriptors, shape context etc.

Contours can be defined as the curves or outlines that represent the boundaries of objects or shapes within an image. These curves join all the continuous points (along the boundary) having the same color or intensity, highlighting the structural properties of objects and providing a way to extract and represent shape information from images.

Contours are useful when working with grayscale or binary images where objects are clearly distinguished from the background based on variations in brightness or color. Even with simple images we transform it into grayscale or binary first to detect contours in a simple way.

Properties of Contours

Contours possess several essential properties that make them invaluable in computer vision:

• Hierarchy: Hierarchical relationships between different contours provide information about the nesting and arrangement of objects within an image.
• Area and Perimeter: Contours enable the calculation of the area and perimeter of objects, facilitating size and shape analysis.
• Orientation: Contours provide information about the orientation and alignment of objects within an image.
• Convexity: Contours help determine the convex or concave properties of objects, aiding in shape classification and analysis.

In computer vision, a contour is like a digital representation of that outline. It can be described as the series of connected points that define the boundary of an object, separating and/or highlighting it from the background. These points tend to share similar color or intensity values, making them distinct from their surroundings. In this article, we are going to explore more on contours in computer vision.