Size matters: saccades during scene perception

We investigated the effect of image size on saccade amplitudes. First, in a meta-analysis, relevant results from previous scene perception studies are summarised, suggesting the possibility of a linear relationship between mean saccade amplitude and image size. Forty-eight observers viewed 96 colour scene images scaled to four different sizes, while their eye movements were recorded. Mean and median saccade amplitudes were found to be directly proportional to image size, while the mode of the distribution lay in the range of very short saccades. However, saccade amplitudes expressed as percentages of image size were not constant over the different image sizes; on smaller stimulus images, the relative saccades were found to be larger, and vice versa. In sum, and as far as mean and median saccade amplitudes are concerned, the size of stimulus images is the dominant factor. Other factors, such as image properties, viewing task, or measurement equipment, are only of subordinate importance. Thus, the role of stimulus size has to be reconsidered, in theoretical as well as methodological terms.

Applied Graph Theory in Computer Vision and Pattern Recognition

This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas. It covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks. The first part of the book presents the application of graph theory to low-level processing of digital images such as a new method for partitioning a given image into a hierarchy of homogeneous areas using graph pyramids, or a study of the relationship between graph theory and digital topology. Part II presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, including a survey of graph based methodologies for pattern recognition and computer vision, a presentation of a series of computationally efficient algorithms for testing graph isomorphism and related graph matching tasks in pattern recognition and a new graph distance measure to be used for solving graph matching problems. Finally, Part III provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks. It includes a critical review of the main graph-based and structural methods for fingerprint classification, a new method to visualize time series of graphs, and potential applications in computer network monitoring and abnormal event detection.