This paper presents a formulation for unsupervised learning of clusters reflecting multiple causal structure in binary data. Unlike the "hard" k-means clustering algorithm and the "soft" mixture model, each of which assumes that a single hidden event generates each data point, a multiple cause model accounts for observed data by combining assertions from many hidden causes, each of which can pertain to varying degree to any subset of the observable dimensions. We employ an objective function and iterative gradient descent learning algorithm resembling the conventional mixture model. A crucial issue is the rnixingfunction for combining beliefs from different cluster centers in order to generate data predictions whose errors are minimized both during recognition and learning. The mixing function constitutes a prior assumption about underlying structural regularities of the data domain; we demonstrate a weakness inherent to the popular weighted sum followed by sigmoid squashing, and offer alternative forms of the nonlinearity for two types of data domain. Results are presented demonstrating the algorithm's ability successfully to discover coherent multiple causal representations in several experimental data sets.
Closed or nearly closed regions are an important form of perceptual structure arising both in natural imagery and in many forms of human-created imagery including sketches, line art, graphics, and formal drawings. This paper presents an effective algorithm especially suited for finding perceptually salient, compact closed region structure in hand-drawn sketches and line art. We start with a graph of curvilinear fragments whose proximal endpoints form junctions. The key problem is to manage the search of possible path continuations through junctions in an effort to find paths satisfying global criteria for closure and figural salience. We identify constraints particular to this domain for ranking path continuations through junctions, based on observations of the ways that junctions arise in line drawings. In particular, we delineate the roles of the principle of good continuation versus maximally turning paths. Best-first bidirectional search checks for the cleanest, most obvious paths first, then reverts to more exhaustive search to find paths cluttered by blind alleys. Results are demonstrated on line drawings from several sources including line art, engineering drawings, sketches on whiteboards, as well as contours from photographic imagery.
We present a user interface design for labeling elements in document images at a pixel level. Labels are represented by overlay color, which might map to such terms as "handwriting", "machine print", "graphics", etc
This extended abstract reprises our UIST '03 paper on "Perceptually-Supported Image Editing of Text and Graphics." We introduce a novel image editing program, called ScanScribe, that emphasizes easy selection and manipulation of material found in informal, casual documents such as sketches, handwritten notes, whiteboard images, screen snapshots, and scanned documents.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.