Human object-selective cortex shows a large-scale organization characterized by the high-level properties of both animacy and object size. To what extent are these neural responses explained by primitive perceptual features that distinguish animals from objects and big objects from small objects? To address this question, we used a texture synthesis algorithm to create a class of stimuli-texforms-which preserve some mid-level texture and form information from objects while rendering them unrecognizable. We found that unrecognizable texforms were sufficient to elicit the large-scale organizations of object-selective cortex along the entire ventral pathway. Further, the structure in the neural patterns elicited by texforms was well predicted by curvature features and by intermediate layers of a deep convolutional neural network, supporting the mid-level nature of the representations. These results provide clear evidence that a substantial portion of ventral stream organization can be accounted for by coarse texture and form information without requiring explicit recognition of intact objects.

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We introduce the proto-object model of visual clutter perception. This unsupervised model segments an image into superpixels, then merges neighboring superpixels that share a common color cluster to obtain proto-objects-defined here as spatially extended regions of coherent features. Clutter is estimated by simply counting the number of proto-objects. We tested this model using 90 images of realistic scenes that were ranked by observers from least to most cluttered. Comparing this behaviorally obtained ranking to a ranking based on the model clutter estimates, we found a significant correlation between the two (Spearman's ρ = 0.814, p < 0.001). We also found that the proto-object model was highly robust to changes in its parameters and was generalizable to unseen images. We compared the proto-object model to six other models of clutter perception and demonstrated that it outperformed each, in some cases dramatically. Importantly, we also showed that the proto-object model was a better predictor of clutter perception than an actual count of the number of objects in the scenes, suggesting that the set size of a scene may be better described by proto-objects than objects. We conclude that the success of the proto-object model is due in part to its use of an intermediate level of visual representation-one between features and objects-and that this is evidence for the potential importance of a proto-object representation in many common visual percepts and tasks.

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A generative model of category representation is introduced that uses computer vision methods to extract category-consistent features (CCFs) directly from images of category exemplars. The model was trained on 4800 images of common objects, and CCFs were obtained for 68 categories spanning subordinate, basic, and superordinate levels in a category hierarchy. Participants searched for these same categories. Targets cued at the subordinate level were preferentially fixated, but fixated targets were verified faster following a basic-level cue. The subordinate-level advantage in guidance is explained by the number of target category CCFs, a measure of category specificity that decreases with movement up the category hierarchy. The basic-level advantage in verification is explained by multiplying CCF number by sibling distance, a measure of category distinctiveness. With this model the visual representations of real-world object categories, each learned from the vast numbers of image exemplars accumulated throughout our everyday experience, can finally be studied.

Automatic detection and segmentation of brain tumors in 3D MR neuroimages can significantly aid early diagnosis, surgical planning, and follow-up assessment. However, due to diverse location and varying size, primary and metastatic tumors present substantial challenges for detection. We present a fully automatic, unsupervised algorithm that can detect single and multiple tumors from 3 to 28,079 mm 3 in volume. Using 20 clinical 3D MR scans containing from 1 to 15 tumors per scan, the proposed approach achieves between 87.84% and 95.30% detection rate and an average end-to-end running time of under 3 minutes. In addition, 5 normal clinical 3D MR scans are evaluated quantitatively to demonstrate that the approach has the potential to discriminate between abnormal and normal brains.Index Terms-brain tumor detection, MRI brain asymmetry, 3D separable Laplacian of Gaussian, 3D blob detection