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We introduce a new, online, stroke-based recognition system for hand-drawn diagrams which belong to a group of documents with an explicit structure obvious to humans but only loosely defined from the machine point of view. We propose a model for recognition by selection of symbol candidates, based on evaluation of relations between candidates using a set of predicates. It is suitable for simpler structures where the relations are explicitly given by symbols, arrows in the case of diagrams. Knowledge of a specific diagram domain is used--the two domains are flowcharts and finite automata. Although the individual pipeline steps are tailored for these, the system can readily be adapted for other domains. Our entire diagram recognition pipeline is outlined. Its core parts are text/non-text separation, symbol segmentation, their classification and structural analysis. Individual parts have been published by the authors previously and so are described briefly and referenced. Thorough evaluation on benchmark databases shows the accuracy of the system reaches the state of the art and is ready for practical use. The paper brings several contributions: (a) the entire system and its state-of-the-art performance; (b) the methodology exploring document structure when it is loosely defined; (c) the thorough experimental evaluation; (d) the new annotated database for online sketched flowcharts and finite automata diagrams.

We address a problem of learning ordinal classifiers from partially annotated examples. We introduce a V-shaped interval-insensitive loss function to measure discrepancy between predictions of an ordinal classifier and a partial annotation provided in the form of intervals of candidate labels. We show that under reasonable assumptions on the annotation process the Bayes risk of the ordinal classifier can be bounded by the expectation of an associated interval-insensitive loss. We propose several convex surrogates of the interval-insensitive loss which are used to formulate convex learning problems. We described a variant of the cutting plane method which can solve large instances of the learning problems. Experiments on a real-life application of human age estimation show that the ordinal classifier learned from cheap partially annotated examples can achieve accuracy matching the results of the so-far used supervised methods which require expensive precisely annotated examples.

We deal with the problem of thin stringi¾?1D or platei¾?2D elastic material folding and its modeling. The examples could be metallic wire, metal, kevlar or rubber sheet, fabric, or as in our case, garment. The simplest scenario attempts to fold rectangular sheet in the middle. The quality of the fold is measured by relative displacement of the sheet edges. We use this scenario to analyse the effect of the inaccurate estimation of the material properties on the fold quality. The same method can be used for accurate placing of the elastic sheet in applications, e.g. the industrial production assembly. In our previous work, we designed a model simulating the behavior of homogeneous rectangular garment during a relatively slow folding by a dual-arm robot. The physics based model consists of a set of differential equations derived from the static forces equilibrium. Each folding phase is specified by a set of boundary conditions. The simulation of the garment behavior is computed by solving the boundary value problem. We have shown that the model depends on a single material parameter, which is a weight to stiffness ratio. For a known weight to stiffness ratio, the model is solved numerically to obtain the folding trajectory executed by the robotic arms later. The weight to stiffness ratio can be estimated in the course of folding or manually in advance. The goal of this contribution is to analyse the effect of the ratio inaccurate estimation on the resulting fold. The analysis is performed by simulation and in a real robotic garment folding using the CloPeMa dual-arm robotic testbed. In addition, we consider a situation, in which the weight to stiffness ratio cannot be measured exactly but the range of the ratio values is known. We demonstrate that the fixed value of the ratio produces acceptable fold quality for a reasonable range of the ratio values. We show that only four weight to stiffness ratio values can be used to fold all typical fabrics varying from a soft e.g. sateen to a stiff e.g. denim material with the reasonable accuracy. Experiments show that for a given range of the weight to stiffness ratio one has to choose the value on the pliable end of the range to achieve acceptable results.