Human Perceptual Performance With Nonliteral Imagery: Region Recognition and Texture-Based Segmentation
| Autor: | Narayanan Srinivasan, Michael J. Sinai, Edward A. Essock, J. Kevin DeFord, Bruce C. Hansen |
|---|---|
| Rok vydání: | 2004 |
| Předmět: |
Adult
Male Visual perception Computer science Color vision media_common.quotation_subject ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Experimental and Cognitive Psychology False color Texture (music) Task (project management) Perception Humans Computer vision Segmentation ComputingMethodologies_COMPUTERGRAPHICS media_common business.industry Recognition Psychology Perceptual performance Imagination Visual Perception Female Artificial intelligence business Color Perception |
| Zdroj: | Journal of Experimental Psychology: Applied. 10:97-110 |
| ISSN: | 1939-2192 1076-898X |
| Popis: | In this study the authors address the issue of how the perceptual usefulness of nonliteral imagery should be evaluated. Perceptual performance with nonliteral imagery of natural scenes obtained at night from infrared and image-intensified sensors and from multisensor fusion methods was assessed to relate performance on 2 basic perceptual tasks to fundamental characteristics of the imagery. Specifically, single-sensor imagery and fused multisensor imagery (both achromatic and false color) were used to test performance on a region recognition task and a texture segmentation task. Results indicate that the use of color rendering and type of scene content play specific roles in determining perceptual performance allowed by nonliteral imagery. The authors argue that the usefulness of various image-rendering methods should be evaluated with respect to multiple perceptual tasks. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|