Sketch recognition

About: Sketch recognition is a research topic. Over the lifetime, 1611 publications have been published within this topic receiving 40284 citations.

Efficient Marker based Gesture Recognition Technique

Abstract: Gesture recognition techniques are used in order to achieve spontaneous and natural machine interactions. Normal Hand gesture based recognition techniques using single 2D camera have serious issues in terms of correct tracking; also the false recognition occurrences are higher. In this paper, we present an efficient method of implementing gesture recognition using marker. In this approach we use a single marker for gesture recognition by doing mouse emulation. Tracking is accurate and false recognition occurrences are found to be very low in this method. Also this approach is found to be computationally efficient and better user experience compared to other hand gesture recognition techniques using single 2D camera.

Employing a Restricted Set of Qualitative Relations in Recognizing Plain Sketches

Abstract: In this paper, we employ aspects of machine learning, computer vision, and qualitative representations to build a classifier of plain sketches. The paper proposes a hybrid technique for accurately recognizing hand-drawn sketches, by relying on a set of qualitative relations between the strokes that compose such sketches, and by taking advantage of two major perspectives for processing images. Our implementation shows promising results for recognizing sketches that have been hand-drawn by human participants.

A syntactic pattern recognition approach based on a distortion tolerant adjacency grammar and a spatial indexed parser. Application to sketched document recognition

Abstract: Sketch recognition is a discipline which has gained an increasing interest in the last 20 years. This is due to the appearance of new devices such as PDA, Tablet PC's or digital pen \& paper protocols. From the wide range of sketched documents we focus on those that represent structured documents such as: architectural floor-plans, engineering drawing, UML diagrams, etc. To recognize and understand these kinds of documents, first we have to recognize the different compounding symbols and then we have to identify the relations between these elements. From the way that a sketch is captured, there are two categories: on-line and off-line. On-line input modes refer to draw directly on a PDA or a Tablet PC's while off-line input modes refer to scan a previously drawn sketch. This thesis is an overlapping of three different areas on Computer Science: Pattern Recognition, Document Analysis and Human-Computer Interaction. The aim of this thesis is to interpret sketched documents independently on whether they are captured on-line or off-line. For this reason, the proposed approach should contain the following features. First, as we are working with sketches the elements present in our input contain distortions. Second, as we would work in on-line or off-line input modes, the order in the input of the primitives is indifferent. Finally, the proposed method should be applied in real scenarios, its response time must be slow. To interpret a sketched document we propose a syntactic approach. A syntactic approach is composed of two correlated components: a grammar and a parser. The grammar allows describing the different elements on the document as well as their relations. The parser, given a document checks whether it belongs to the language generated by the grammar or not. Thus, the grammar should be able to cope with the distortions appearing on the instances of the elements. Moreover, it would be necessary to define a symbol independently of the order of their primitives. Concerning to the parser when analyzing 2D sentences, it does not assume an order in the primitives. Then, at each new primitive in the input, the parser searches among the previous analyzed symbols candidates to produce a valid reduction. Taking into account these features, we have proposed a grammar based on Adjacency Grammars. This kind of grammars defines their productions as a multiset of symbols rather than a list. This allows describing a symbol without an order in their components. To cope with distortion we have proposed a distortion model. This distortion model is an attributed estimated over the constraints of the grammar and passed through the productions. This measure gives an idea on how far is the symbol from its ideal model. In addition to the distortion on the constraints other distortions appear when working with sketches. These distortions are: overtracing, overlapping, gaps or spurious strokes. Some grammatical productions have been defined to cope with these errors. Concerning the recognition, we have proposed an incremental parser with an indexation mechanism. Incremental parsers analyze the input symbol by symbol given a response to the user when a primitive is analyzed. This makes incremental parser suitable to work in on-line as well as off-line input modes. The parser has been adapted with an indexation mechanism based on a spatial division. This indexation mechanism allows setting the primitives in the space and reducing the search to a neighbourhood. A third contribution is a grammatical inference algorithm. This method given a set of symbols captures the production describing it. In the field of formal languages, different approaches has been proposed but in the graphical domain not so much work is done in this field. The proposed method is able to capture the production from a set of symbol although they are drawn in different order. A matching step based on the Haussdorff distance and the Hungarian method has been proposed to match the primitives of the different symbols. In addition the proposed approach is able to capture the variability in the parameters of the constraints. From the experimental results, we may conclude that we have proposed a robust approach to describe and recognize sketches. Moreover, the addition of new symbols to the alphabet is not restricted to an expert. Finally, the proposed approach has been used in two real scenarios obtaining a good performance.

EUROGRAPHICS Tutorial on Sketch Recognition.

Abstract: Sketch recognition is the automated understanding of hand-drawn diagrams. Despite the prevalence of keyboards and mice, hand-drawings still pervade in education, design, and other diagrams. This full day tutorial explains why sketch recognition is important, the underlying algorithms, how sketch recognition can be used in traditional interfaces, and the field’s experiences with sketch recognition used in different domains.