Computer vision and machine learning have great potential to aid in aesthetic judgments and exploration, particularly in the understanding of shapes. This paper presents our work in a well-defined but largely unexplored problem in this field: the automated recognition of apparel silhouette attributes for real-world products. Silhouette attributes, such as v-neck for dresses and open toe for shoes, are very important attributes for understanding the appearance of apparel but difficult to recognize automatically. We propose methods employing multi-modal features and supervised learning to automatically recognize silhouette attributes based on product images and the associated text. These algorithms are extensively tested on a large dataset of dresses, tops, and shoes provided by online retailers. The proposed silhouette recognition approach achieves high recognition accuracy on the nine silhouette categories. Our approach and experiments are also expected to stimulate future research on this topic.

This paper presents an algorithm for measuring hair and face appearance in 2D images. Our approach starts by using learned mixture models of color and location information to suggest the hypotheses of the face, hair, and background regions. In turn, the image gradient information is used to generate the likely suggestions in the neighboring image regions. Either Graph-Cut or Loopy Belief Propagation algorithm is then applied to optimize the resulting Markov network in order to obtain the most likely hair and face segmentation from the background. We demonstrate that our algorithm can precisely identify the hair and face regions from a large dataset of face images automatically detected by the state-of-the-art face detector.

We present an efficient probabilistic method for identity recognition in personal photo albums. Personal photos are usually taken under uncontrolled conditions -the captured faces exhibit significant variations in pose, expression and illumination that limit the success of traditional face recognition algorithms. We show how to improve recognition rates by incorporating additional cues present in personal photo collections, such as clothing appearance and information about when the photo was taken. This is done by constructing a Markov random field (MRF) that effectively combines all available contextual cues in a principled recognition framework. Performing inference in the MRF produces markedly improved recognition results in a challenging dataset consisting of the personal photo collections of multiple people. At the same time, the computational cost of our approach remains comparable to that of standard face recognition approaches.

Colorectal cancer can easily be prevented provided that the precursors to tumors, small colonic polyps, are detected and removed. Currently, the only definitive examination of the colon is fiber-optic colonoscopy, which is invasive and expensive. Computed tomographic colonography (CTC) is potentially a less costly and less invasive alternative to FOC. It would be desirable to have computer-aided detection (CAD) algorithms to examine the large amount of data CTC provides. Most current CAD algorithms have high false positive rates at the required sensitivity levels. We developed and evaluated a postprocessing algorithm to decrease the false positive rate of such a CAD method without sacrificing sensitivity. Our method attempts to model the way a radiologist recognizes a polyp while scrolling a cross-sectional plane through three-dimensional computed tomography data by classification of the changes in the location of the edges in the two-dimensional plane. We performed a tenfold cross-validation study to assess its performance using sensitivity/specificity analysis on data from 48 patients. The mean specificity over all experiments increased from 0.19 (0.35) to 0.47 (0.56) for a sensitivity of 1.00 (0.95).

To utilize CT or MRI images for computer aided diagnosis applications, robust features that represent 3D image data need to be constructed and subsequently used by a classification method. We present a computer aided diagnosis system for early diagnosis of colon cancer. The system extracts features via a new 3D pattern processing method and processes them using a support vector machine classifier. Our 3D pattern processing method, called Random Orthogonal Shape Section (ROSS) mimics the radiologist's way of viewing these images and combines information from many random triples of mutually orthogonal sections going through the volume. Another contribution of the paper is a new feedback framework between the classification algorithm and the definition of the features. This framework, called Distinctive Component Analysis combines support vector samples with linear discriminant analysis to map the features of clustered support vectors to a lower dimensional space where the two classes of objects of interest are optimally separated to obtain better features. We show that the combination of these better features with support vector machine classification provides a good recognition rate.

This paper describes a two-stage system for 3D tracking of pose and deformation of the human face in monocular image sequences without the use of special markers. The first stage of the system learns the space of all possible facial deformations by applying principal component analysis on real stereo tracking data. The resulting model approximates any generic shape as a linear combination of shape basis vectors. The second stage of the system uses this low-complexity deformable model for simultaneous tracking of pose and deformation of the face from a single image sequence. This stage is known as model-based monocular tracking. There are three main contributions of this paper. First we demonstrate that a data-driven approach for model construction is suitable for tracking non rigid objects and offers an elegant and practical alternative to the task of manual construction of models using 3D scanners or CAD modelers. Second, we show that such a method exhibits good tracking accuracy (errors less than 5 mm) and robustness characteristics. Third, we demonstrate that our system exhibits very promising generalization properties in enabling tracking of multiple persons with the same 3D model.

Adenomatous polyps in the colon are believed to be the precursor to colorectal carcinoma, the second leading cause of cancer deaths in United States. In this paper, we propose a new method for computer-aided detection of polyps in computed tomography (CT) colonography (virtual colonoscopy), a technique in which polyps are imaged along the wall of the air-inflated, cleansed colon with X-ray CT. Initial work with computer aided detection has shown high sensitivity, but at a cost of too many false positives. We present a statistical approach that uses support vector machines to distinguish the differentiating characteristics of polyps and healthy tissue, and uses this information for the classification of the new cases. One of the main contributions of the paper is the new three-dimensional pattern processing approach, called random orthogonal shape sections method, which combines the information from many random images to generate reliable signatures of shape. The input to the proposed system is a collection of volume data from candidate polyps obtained by a high-sensitivity, low-specificity system that we developed previously. The results of our ten-fold cross-validation experiments show that, on the average, the system increases the specificity from 0.19 (0.35) to 0.69 (0.74) at a sensitivity level of 1.0 (0.95).