Explain the concept of range searching in Computational Geometry.

Range searching in computational geometry refers to the process of finding all the objects within a specified range or region in a given geometric space. The objects can be points, lines, polygons, or any other geometric entities.

The concept of range searching is widely used in various applications, such as geographic information systems, computer graphics, robotics, and computer vision. It allows us to efficiently retrieve relevant information from large datasets based on their spatial properties.

There are different types of range searching techniques depending on the geometric space and the type of objects being searched. Some common techniques include:

1. Point Range Searching: In this technique, the goal is to find all the points within a specified range. This can be achieved using data structures like kd-trees, range trees, or quad trees. These data structures partition the space into smaller regions, allowing for efficient searching and retrieval of points within a given range.

2. Range Trees: Range trees are a data structure specifically designed for range searching in higher dimensions. They organize the points in a hierarchical structure, allowing for efficient range queries in logarithmic time complexity.

3. Orthogonal Range Searching: This technique is used when searching for objects that are aligned with the coordinate axes, such as rectangles or axis-aligned bounding boxes. Various data structures like range trees, segment trees, or interval trees can be used to efficiently search for objects within a specified range.

4. Convex Hull: Convex hull algorithms are used to find the smallest convex polygon that encloses a set of points. This can be useful in range searching when we want to find all the points within a convex polygon.

Overall, range searching in computational geometry is a fundamental concept that enables efficient retrieval of geometric objects within a specified range. It plays a crucial role in solving various spatial problems and optimizing algorithms in a wide range of applications.