Image Engineering and Related Publications

Abstract

Image engineering is a discipline that includes image processing, image analysis, image understanding, and the applications of these techniques. To promote its development and evolvement, this paper provides a well-regulated explanation of the definition of image engineering, as well as its intention and extension. It also introduces a new classification of the theories of image engineering, and the applications of image technology. A thorough statistical survey on the publications in this discipline is carried out, and an analysis and discussion of the statistics from the classification results are presented. This work shows a general and an up-to-date picture of the status, progress, trends and application areas of image engineering.

A comparative study of Image Region-Based Segmentation Algorithms

“…The sum of the squared Euclidian distances between the samples in a cluster and the cluster center is called within-cluster variation. K-Means are widely used in many applications such as data extraction and image segmentation [21]. The K-Means method is an iterative algorithm that minimizes the sum of distances between each object and its cluster centroid.…”

Quality Assessment on Satellite Images based on Internal Criterion Techniques

“…The clustering approaches can be classified into two general groups: partitional and hierarchical clustering algorithms. Partitional clustering algorithms such as k-means are widely used in many applications such as data mining [11], compression, image segmentation [6], [9] and machine learning. Therefore, the advantage of clustering algorithms is that the classification of objects and easy to implement.…”

“…Therefore, the advantage of clustering algorithms is that the classification of objects and easy to implement. Similarly, the drawbacks in this classification are of how to determine the number of clusters and decrease the numbers of iteration [11]. In this paper, k-means clustering is used to find cluster centers that minimize the intra-class variance, i.e., the sum of squared distances from each data point being clustered to its cluster center.…”

Simultaneous image segmentation and 3D plane fitting for RGB-D sensors — An iterative framework

“…Finally, we choose to initially over-segment the image and merge them into final planes as the refinement iterations proceeds. This makes our approach suitable for general situations, since it waives the requirement that the number of image regions has to be a known prior as in many earlier works [41,28,37].…”