A Semi-automated Statistical Algorithm for Object Separation
This work addresses object separation in images, which is useful for computer vision applications, but it appears incremental as it builds on existing statistical methods without claiming major breakthroughs.
The authors tackled the problem of object identification and segregation in grayscale and color images by developing a semi-automated statistical algorithm that uses Gaussian distributions and Mahalanobis distance to subdivide images into variable thresholds, demonstrating its effectiveness across a variety of images.
We explicate a semi-automated statistical algorithm for object identification and segregation in both gray scale and color images. The algorithm makes optimal use of the observation that definite objects in an image are typically represented by pixel values having narrow Gaussian distributions about characteristic mean values. Furthermore, for visually distinct objects, the corresponding Gaussian distributions have negligible overlap with each other and hence the Mahalanobis distance between these distributions are large. These statistical facts enable one to sub-divide images into multiple thresholds of variable sizes, each segregating similar objects. The procedure incorporates the sensitivity of human eye to the gray pixel values into the variable threshold size, while mapping the Gaussian distributions into localized δ-functions, for object separation. The effectiveness of this recursive statistical algorithm is demonstrated using a wide variety of images.