| Preface | ix |
| 1 | Models of feature perception in distortion measure guidance | 1 |
| 1.1 | Introduction | 1 |
| 1.2 | Computational models for feature detection | 3 |
| 1.2.1 | Image features from Laplacian zero-crossings | 3 |
| 1.2.2 | Image features from phase congruency | 3 |
| 1.2.3 | Image features from active sensors | 6 |
| 1.2.3.1 | A data-driven multisensor scheme | 10 |
| 1.2.3.2 | The activated sensors in the multisensor scheme | 11 |
| 1.3 | Error measure guidance | 14 |
| 1.4 | Experimental results | 17 |
| 1.4.1 | Distinctness of targets and their immediate surroundings | 17 |
| 1.5 | The role of integral features for perceiving image discriminability | 18 |
| 1.5.1 | An original image quality model for predicting the visibility of the difference between a pair of images | 19 |
| 1.5.1.1 | The spatial sensitivity function | 21 |
| 1.5.2 | Applications | 22 |
| 1.5.2.1 | Distinctness of targets and their immediate surroundings | 23 |
| 1.6 | Conclusions | 24 |
| 2 | Computational measures based on space-frequency analysis | 25 |
| 2.1 | Introduction | 25 |
| 2.2 | The multichannel organization of images | 27 |
| 2.2.1 | Overview of approach | 27 |
| 2.2.2 | Clumps of energy in the amplitude spectrum | 29 |
| 2.2.2.1 | A spatial to 2D spatial-frequency transformation | 30 |
| 2.2.2.2 | A data-driven multichannel design | 33 |
| 2.2.2.2.1 | The data-driven selection of bands of orientation | 33 |
| 2.2.2.2.2 | The data-driven selection of radial frequency channels | 34 |
| 2.2.2.3 | The selection of the most activated sensors | 37 |
| 2.2.3 | Bank of log-Gabors filters | 37 |
| 2.2.4 | Activated filters in the bank | 40 |
| 2.3 | Filtered response based distinctness measure | 40 |
| 2.3.1 | Selection of fixation points | 41 |
| 2.3.2 | "Filtered-response" (FR) distinctness measure | 41 |
| 2.4 | Integral features based distinctness measure | 44 |
| 2.4.1 | Preattentive stage | 44 |
| 2.4.2 | Integral-feature (IF) distinctness measure | 46 |
| 2.5 | Experimental results | 47 |
| 2.5.1 | Images, apparatus, subjects, and laboratory viewing conditions | 48 |
| 2.5.2 | Predicting visual target distinctness | 49 |
| 2.5.2.1 | Psychophysical target distinctness | 50 |
| 2.5.2.2 | Search experiment | 50 |
| 2.5.2.3 | Results | 51 |
| 2.5.2.3.1 | Psychophysical target distinctness | 56 |
| 2.5.2.3.2 | Computational target distinctness | 56 |
| 2.5.2.3.3 | Experiment 1 | 58 |
| 2.5.2.3.4 | Experiment 2 | 59 |
| 2.5.2.3.5 | Experiment 3 | 59 |
| 2.5.2.3.6 | Experiment 4 | 60 |
| 2.6 | Conclusions | 62 |
| 3 | Defining the notion of visual pattern | 67 |
| 3.1 | Introduction | 67 |
| 3.2 | Material and methods | 68 |
| 3.2.1 | Images | 68 |
| 3.2.2 | The RGFF image representational model | 68 |
| 3.2.2.1 | Selection of strongly responding filters | 71 |
| 3.2.2.2 | Distance between filtered responses | 71 |
| 3.2.2.2.1 | The best definition of integral feature for segregating visual patterns | 72 |
| 3.2.2.2.2 | Congruence in integral features between two filtered responses | 73 |
| 3.2.2.3 | Decomposition of the original reference image into its "visual patterns" | 75 |
| 3.2.2.3.1 | Clustering of activated filters | 76 |
| 3.2.3 | Evaluation function | 82 |
| 3.2.3.1 | Datasets | 83 |
| 3.2.3.2 | Psychophysical target distinctness | 84 |
| 3.2.3.3 | Computational target distinctness metric | 91 |
| 3.3 | Results and discussion | 91 |
| 3.3.1 | Experiment 1 | 93 |
| 3.3.2 | Experiment 2 | 97 |
| 3.3.3 | Experiment 3 | 98 |
| 3.3.4 | Experiment 4 | 102 |
| 3.3.5 | Experiment 5 | 102 |
| 3.4 | Conclusions | 109 |
| 4 | Information theoretic measures | 111 |
| 4.1 | Introduction | 111 |
| 4.2 | Basic axiomatic characterization | 113 |
| 4.3 | Information conservation constraint | 118 |
| 4.3.1 | Selective information gain | 119 |
| 4.3.2 | Properties of the selective information gain | 121 |
| 4.4 | Significance conservation constraint | 123 |
| 4.4.1 | Compound information gain | 124 |
| 4.4.2 | Properties of the compound gain | 125 |
| 4.5 | Comparative study | 128 |
| 4.5.1 | Images and datasets | 129 |
| 4.5.2 | Psychophysical target distinctness | 130 |
| 4.5.3 | Results and discussion | 130 |
| 4.5.3.1 | Experiment 1 | 130 |
| 4.5.3.2 | Experiment 2 | 138 |
| 4.5.3.3 | Experiment 3 | 138 |
| 4.5.3.4 | Statistical Accuracy | 140 |
| 4.6 | Conclusion | 144 |
| Epilogue | 147 |
| A | Comparison with other saliency models | 157 |
| B | Integral opponent-colors features | 161 |
| B.1 | Introduction | 161 |
| B.2 | Preattentive stage | 162 |
| B.2.1 | RGB to opponent-color encoding transform | 164 |
| B.2.2 | 2D bank of log-Gabors design | 165 |
| B.2.3 | Activated filters from the bank | 166 |
| B.2.4 | Fixation points on each filter response | 167 |
| B.3 | Integration stage | 167 |
| B.3.1 | Integral opponent-colors features | 167 |
| B.3.2 | Target distinctness on each activated filter | 174 |
| B.4 | Decision stage | 174 |
| B.5 | Applications | 175 |
| B.5.1 | Distinctness of targets and their immediate surroundings | 175 |
| B.5.2 | Distinctness of targets in noisy environments | 178 |
| B.6 | Conclusions | 181 |
| C | Forms of gain and divergence | 183 |
| D | Calculating derivatives | 185 |
| Bibliography | 187 |
| Index | 201 |
