better results with their 3D SIFT descriptor approach than with other approaches like simple 2D SIFT descriptors and Gradient Magnitude. Analyzing the Human Brain in 3D Magnetic Resonance Images The Feature-based Morphometry (FBM) technique uses extrema in a difference of Gaussian scale-space to analyze and classify 3D magnetic resonance images (MRIs) of the human brain. FBM models the image probabilistically as a collage of independent features, conditional on image geometry and group labels, e.g. healthy subjects and subjects with Alzheimer's disease (AD). Features are first extracted in individual images from a 4D difference of Gaussian scale-space, then modeled in terms of their appearance, geometry and group co-occurrence statistics across a set of images. FBM was validated in the analysis of AD using a set of ~200 volumetric MRIs of the human brain, automatically identifying established indicators of AD in the brain and classifying mild AD in new images with a rate of 80%. Competing methods Competing methods for scale invariant object recognition under clutter / partial occlusion include the following. RIFT is a rotation-invariant generalization of SIFT. The RIFT descriptor is constructed using circular normalized patches divided into concentric rings of equal width and within each ring a gradient orientation histogram is computed. To maintain rotation invariance, the orientation is measured at each point relative to the direction pointing outward from the center. RootSIFT is a variant of SIFT that modifies descriptor normalization. Since SIFT descriptors are histograms (and as such probability distributions), employing Euclidean distance to determine their similarity is not a natural choice. Comparing such descriptors using similarity measures tailored to probability distributions such as Bhattacharyya coefficient (also known as Hellinger kernel) turns out to be more beneficial. For this purpose, the originally normalized descriptor is first normalized and the square root of each element is computed followed by renormalization. After these algebraic manipulations, RootSIFT descriptors can be normally compared using Euclidean distance which is equivalent to using the Hellinger kernel on the original SIFT descriptors. This normalization scheme termed “L1-sqrt” was previously introduced for the block normalization of HOG features whose rectangular block arrangement descriptor variant (R-HOG) is conceptually similar to the SIFT descriptor. G-RIF: Generalized Robust Invariant Feature is a general context descriptor which encodes edge orientation, edge density and hue information in a unified form combining perceptual information with spatial encoding. The object recognition scheme uses neighboring context based voting to estimate object models. "SURF: