Explain the steps in automatic image analysis and interpretation.

9 b] Explain the steps in automatic image analysis and interpretation.

Steps in Automatic Image Analysis and Interpretation

Automatic image analysis and interpretation involve several steps to accurately process and analyze an image. One of the crucial aspects of this process is handling pixel transformations, which often require interpolation techniques to ensure the final image is accurate and visually coherent.

Why Are Interpolation Techniques Needed?

When performing transformations, such as scaling or rotating an image, pixel values may be mapped to new positions that are non-integer coordinates or fall outside the acceptable range. This often results in gaps, misalignment, or distorted pixel distribution. Interpolation techniques are essential to address these issues by estimating pixel values at non-integer coordinates, ensuring the transformed image maintains visual integrity and quality.

Forward Mapping

Forward mapping involves applying transformations to each pixel of the image iteratively, which results in new coordinate positions for these pixels. The pixel values are then copied to their new positions in the output image. However, since the transformed coordinates may not align perfectly with pixel positions, interpolation is necessary to fill in any gaps and create a smooth, coherent image.

Backward Mapping

Backward mapping, on the other hand, involves examining the output image’s pixels and determining their corresponding positions in the input image. This approach ensures that all pixels in the input image are accounted for in the output. Similar to forward mapping, interpolation is crucial to resolve any discrepancies in pixel placement caused by the transformation.

Example of a Transformation

Consider the rotation of a point (10, 5) by 45°:

Since these new coordinates (3.535, 10.605) are not integers, the rotation process requires interpolation to determine the exact pixel values at these positions. The interpolation method rounds off the coordinates to the nearest integers, in this case, (4, 11), ensuring that the image maintains its structure.

Interpolation Techniques

1. Nearest Neighbour Interpolation (Zero-Order Interpolation)

• This technique assigns the value of the closest pixel to the new position, maintaining brightness equal to the nearest neighbor. However, it may cause pixel blocking and spatial distortions, known as aliasing.

1. Bilinear Interpolation (First-Order Interpolation)

• Bilinear interpolation considers the four closest neighboring pixels, calculating the new pixel value based on their weighted average. This technique reduces aliasing artifacts but may cause edge blurring.

• Here, ( g(x, y) ) is the output image, and ( f(x’, y’) ) represents the pixel values in the original image.

1. Bicubic Interpolation (Second-Order Interpolation)

• This technique considers a larger neighborhood of 16 pixels and uses cubic polynomials to calculate new pixel values. It produces images closer to the original with better visual quality, but at the cost of increased computational complexity.

As the complexity of interpolation increases, the quality of the resulting image improves, though it requires more computational resources. Each method balances accuracy and computational efficiency, depending on the application’s requirements.