Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Images convey multiple meanings that depend on the context in which the vier perceptually organizes the scene. By assuming a stan- dardized natural-scene-perception-taxonomy comprised of a hierarchy of nested spatial-taxons (17) (6) (5), image segmentation is operationalized into a series of two-class inferences. Each inference determines the opti- mal spatial-taxon region, partitioning a scene into a foreground, subject and salient objects and/or sub-objects. I demonstrate the results of a fuzzy-logic-natural-vision-processing engine that implements this novel approach. The engine uses fuzzy-logic inference to simulate low-level vi- sual processes and a few rules of figure-ground perceptual organization. Allod spatial-taxons must conform to a set of "meaningfulness" cues, as specified by a generic scene-type. The engine was tested on 70 real images composed of three "generic scene-types", each of which required a different combination of the perceptual organization rules built into our model. Five human subjects rated image-segmentation quality on a scale from 1 to 5 (5 being the best). The majority of generic-scene-type image segmentations received a score of 4 or 5 (very good, perfect). ROC plots show that this engine performs better than normalized-cut (9) on generic-scene type images.
['Lauren Barghout']
I am writing a research paper on a idea Visual Taxometric Approach to Image Segmentation Using Fuzzy-Spatial Taxon Cut Yields Contextually Relevant Regions
[{"from": "human", "value": "I am writing a research paper on a idea Visual Taxometric Approach to Image Segmentation Using Fuzzy-Spatial Taxon Cut Yields Contextually Relevant Regions"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Images convey multiple meanings that depend on the context in which the vier perceptually organizes the scene. By assuming a stan- dardized natural-scene-perception-taxonomy comprised of a hierarchy of nested spatial-taxons (17) (6) (5), image segmentation is operationalized into a series of two-class inferences. Each inference determines the opti- mal spatial-taxon region, partitioning a scene into a foreground, subject and salient objects and/or sub-objects. I demonstrate the results of a fuzzy-logic-natural-vision-processing engine that implements this novel approach. The engine uses fuzzy-logic inference to simulate low-level vi- sual processes and a few rules of figure-ground perceptual organization. Allod spatial-taxons must conform to a set of \"meaningfulness\" cues, as specified by a generic scene-type. The engine was tested on 70 real images composed of three \"generic scene-types\", each of which required a different combination of the perceptual organization rules built into our model. Five human subjects rated image-segmentation quality on a scale from 1 to 5 (5 being the best). The majority of generic-scene-type image segmentations received a score of 4 or 5 (very good, perfect). ROC plots show that this engine performs better than normalized-cut (9) on generic-scene type images."}]