Integrating Color, Texture, and Geometry for Image Retrieval

This paper examines the problem of image retrieval from large, heterogeneous image databases. We present a technique that fulfills several needs identified by surveying recent research in the field. This technique fairly integrates a diverse and expandable set of image properties (for example, color, texture, and location) in a retrieval framework, and allows end-users substantial control over their use. We propose a novel set of evaluation methods in addition to applying established tests for image retrieval; our technique proves competitive with state-of-the-art methods in these tests and does better on certain tasks. Furthermore, it improves on many standard image retrieval algorithms by supporting queries based on subsections of images. For certain queries this capability significantly increases the relevance of the images retrieved, and further expands the user’s control over the retrieval process

Adolescent brain maturation and cortical folding: evidence for reductions in gyrification

Evidence from anatomical and functional imaging studies have highlighted major modifications of cortical circuits during adolescence. These include reductions of gray matter (GM), increases in the myelination of cortico-cortical connections and changes in the architecture of large-scale cortical networks. It is currently unclear, however, how the ongoing developmental processes impact upon the folding of the cerebral cortex and how changes in gyrification relate to maturation of GM/WM-volume, thickness and surface area. In the current study, we acquired high-resolution (3 Tesla) magnetic resonance imaging (MRI) data from 79 healthy subjects (34 males and 45 females) between the ages of 12 and 23 years and performed whole brain analysis of cortical folding patterns with the gyrification index (GI). In addition to GI-values, we obtained estimates of cortical thickness, surface area, GM and white matter (WM) volume which permitted correlations with changes in gyrification. Our data show pronounced and widespread reductions in GI-values during adolescence in several cortical regions which include precentral, temporal and frontal areas. Decreases in gyrification overlap only partially with changes in the thickness, volume and surface of GM and were characterized overall by a linear developmental trajectory. Our data suggest that the observed reductions in GI-values represent an additional, important modification of the cerebral cortex during late brain maturation which may be related to cognitive development

Exploiting Sequential Phonetic Constraints in Recognizing Spoken Words

Machine recognition of spoken language requires developing more robust recognition algorithms. The current paper extends the work of Shipman and Zue by investigating the power of partial phonetic descriptions. First we demonstrate that sequences of manner of articulation classes are more reliable and provide more constraint than other classes. Alone these are of limited utility, due to the high degree of variability in natural speech. This variability is not uniform, however, as most modifications and deletions occur in unstressed syllables. The stressed syllables provide substantially more constraint. This indicates that recognition algorithms can be made more robust by exploiting the manner of articulation information in stressed syllables

Recognizing Rigid Objects by Aligning Them with an Image

This paper presents an approach to recognition where an object is first {\\it aligned} with an image using a small number of pairs of model and image features, and then the aligned model is compared directly against the image. To demonstrate the method, we present some examples of recognizing flat rigid objects with arbitrary three-dimensional position, orientation, and scale, from a single two-scale-space segmentation of edge contours. The method is extended to the domain of non-flat objects as well

Learning for stereo vision using the structured support vector machine

We present a random field based model for stereo vision with explicit occlusion labeling in a probabilistic framework. The model employs non-parametric cost functions that can be learnt automatically using the structured support vector machine. The learning algorithm enables the training of models that are steered towards optimizing for a particular desired loss function, such as the metric used to evaluate the quality of the stereo labeling. Experimental results demonstrate that the performance of our method surpasses that of previous learning approaches and is comparable to the state-of-the-art for pixel-based stereo. Moreover, our method achieves good results even when trained on different image sets, in contrast with the common practice of hand tuning to specific benchmark images. In addition, we investigate the impact of graph structure on model performance. Our study shows that random field models with longer-range edges generally outperform the 4-connected grid and that this advantage is especially pronounced for noisy images

Beyond trees: Common factor models for 2D human pose recovery

Tree structured models have been widely used for determining the pose of a human body, from either 2D or 3D data. While such models can effectively represent the kinematic constraints of the skeletal structure, they do not capture additional constraints such as coordination of the limbs. Tree structured models thus miss an important source of information about human body pose, as limb coordination is necessary for balance while standing, walking, or running, as well as being evident in other activities such as dancing and throwing. In this paper we consider the use of undirected graphical models that augment a tree structure with latent variables in order to account for coordination between limbs. We refer to these as common-factor models, since they are constructed by using factor analysis to identify additional correlations in limb position that are not accounted for by the kinematic tree structure. These commonfactor models have an underlying tree structure and thus a variant of the standard Viterbi algorithm for a tree can be applied for efficient estimation. We present some experimental results contrasting common-factor models with tree models, and quantify the improvement in pose estimation for 2D image data. 1

An Object Recognition System for Complex Imagery that Models theProbability of a False Positive

This paper describes an object recognition system for use in complex imagery that can perform recognition adaptively by setting the matching threshold such that the probability of a false positive is low. In order to accurately model small, irregularly shaped objects, we represent the objects using dense sets of edge pixels with associated local orientations. Three-dimensional objects are modeled by a set of two-dimensional views of the object. We allow translation, rotation, and scaling of the views to approximate full three-dimensional motion of the object. We use a version of the Hausdorff measure to determine which positions of an object model are good matches to an image. These positions are determined efficiently through the examination of a hierarchical cell decomposition of the transformation space, which allows large volumes of the space to be pruned quickly. Additional techniques are used to decrease the computation time of the method when matching is performed against a catalog of object models. We then describe a new model of the matching process that allows the probability of a false positive to be estimated efficiently at run-time. Finally, we give results of this system recognizing object in infrared and intensity images

A Multi-Resolution Technique for Comparing Images Using the Hausdorff Distance

The Hausdorff distance measures the extent to which each point of a "model" set lies near some point of an "image" set and vice versa. In this paper we describe an efficient method of computing this distance, based on a multi-resolution tessallation of the space of possible transformations of the model set. We focus on the case in which the model is allowed to translate and scale with respect to the image. This four-dimensional transformation space (two translation and two scale dimensions) is searched rapidly, while guaranteeing that no match will be missed. We present some examples of identifying an object in a cluttered scene, including cases where the object is partially hidden from view