After each region was assigned a specific frame within an object class, a new image was constructed from the fractal stimuli regions and their paired object images, and a composite of regions and object images was built. Upon classification of a region as an object class, it was passed to a final stage, where it was tested against each of the 100 frames within the object class indicated in the prior step. Each region was then tested against the 15 object classes. These regions were then assigned bounding boxes and extracted to their own image files, where artifacts were cleaned up, and the images were prepared for use as testing data. Our method was to implement code that identified regions in the fractal stimuli with boundaries larger than a given size. The final dataset for this project contained fifteen 3D models, with 100 images for each. We used 3Ds Max to load, animate and render 15 different 3D models. Since the fractal stimuli were black and white, we chose to create greyscale top-lit views of each 3D model, and then convert them to black and white. Our approach to building object classes was to limit classes to a small number (15) and build synthetic datasets from many rotated views of 15 different 3D models. Because we were concerned with identifying the forms of objects, if we had worked with photographs, we would have needed to isolate every object from its background. In this regard, we attempted to build a model of a “pareidolia classifier” and in order to achieve an effective model, we had to combine classification techniques with some kind of generative component.Īt the initial stage, we chose to synthesize data rather than work from an existing dataset. The application of a CNN was motivated by the fact that they are able to learn relevant features from an image at different levels, similar to the different levels of receptive fields in the human visual system. In the current research, our goal was to use Convolutional Neural Networks (CNNs) to mimic the results observed in human subjects' experiments for pareidolia perception. This prior research inspired us to build a deep-learning-based model of pareidolia detection. As the result, fractals with medium to low fractal dimensions tended to produce more noticeable pareidolia. Participants were presented with fractal noise at different fractal dimensions and asked to imagine objects within the shapes. In previous research in 2016, it was asked of subjects to form perception from images of the computer-generated fractal. Fractal patterns are common all over nature such as lightning, clouds, trees, rivers, and mountains. These structures are fractal and display complex patterns that are statistically self-similar at different levels of magnification and show patterns at increasingly fine size scales. Pareidolia is the tendency to perceive a pattern, often a meaningful image in a random or accidental arrangement of shapes and lines such as seeing shapes in clouds, rocks, trees, etc .
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