Feature detection from local energy
Pattern Recognition Letters
On the classification of image features
Pattern Recognition Letters
The role of integral features for perceiving image discriminability
Pattern Recognition Letters
The Selection of Natural Scales in 2D Images Using Adaptive Gabor Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using models of feature perception in distortion measure guidance
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Models for Predictiong Visual Target Distinctness
Computational Models for Predictiong Visual Target Distinctness
Genetic algorithm-based relevance feedback for image retrieval using local similarity patterns
Information Processing and Management: an International Journal
Rate control optimization in embedded wavelet coding
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
A critical examination of the assumptions used in dynamic allocation
Journal of Visual Communication and Image Representation
Axiomatic approach to computational attention
Pattern Recognition
Information visibility using transmission methods
Pattern Recognition Letters
Comparative visibility analysis of advertisement images
Image Communication
Sustainable image transmission
Journal of Visual Communication and Image Representation
Hi-index | 0.14 |
It is of great benefit to have advance knowledge of human visual target acquisition performance for targets or other relevant objects. However, search performance inherently shows a large variance and depends strongly on prior knowledge of the perceived scene. A typical search experiment therefore requires a large number of observers to obtain statistically reliable data. Moreover, measuring target acquisition performance in field situations is usually impractical and often very costly or even dangerous. This paper presents a new method for characterizing information of a target relative to its background. The resultant computational measures are then applied to quantify the visual distinctness of targets in complex natural backgrounds from digital imagery. A generalization of the Kullback-Leibler joint information gain of various random variables is shown to correlate strongly with visual target distinctness as estimated by human observers. Bootstrap methods for assessing statistical accuracy were used to produce this inference.