Sketch recognition

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

A novel hand gesture recognition based on high-level features

Abstract: Gesture recognition plays an important role in human–computer interaction. However, most existing methods are complex and time-consuming, which limit the use of gesture recognition in real-time environments. In this paper, we propose a static gesture recognition system that combines depth information and skeleton data to classify gestures. Through feature fusion, hand digit gestures of 0–9 can be recognized accurately and efficiently. According to the experimental results, the proposed gesture recognition system is effective and robust, which is invariant to complex background, illumination changes, reversal, structural distortion, rotation, etc. We have tested the system both online and offline which proved that our system is satisfactory to real-time requirements, and therefore it can be applied to gesture recognition in real-world human–computer interaction systems.

Fuzzy sensor for gesture recognition based on motion and shape recognition of hand

Abstract: Gesture recognition needs to take into account very different information. It needs to perform the fusion of hand shape and motion information. This paper presents a solution using a fuzzy sensor approach for the aggregation of such measurement information. Motion recognition is based on a measurement represented by numerical fuzzy subsets, and shape recognition is based on measurements represented by lexical fuzzy subsets.

SketchML a representation language for novel sketch recognition approach

Abstract: Multimodal interfaces can be profitably used to support increasingly complex services in assistive environments. In particular, sketch-based interfaces offer users an effortless and powerful communication way to represent concepts and commands on different devices. Unlike other modalities, sketch-based interaction can be easily fitted according to heterogeneous services. Moreover it can be quickly personalized according to the user needs.Developing a sketch-based interface for a specific service is a time-consuming operation that requires the re-engineering and/or the re-designing of the whole recognizer framework. This paper describes a definitive framework by which the user, simply by using freehand drawing, can define every kind of sketch-based interface. The definition of the interface and its recognition process are performed by using our developed Sketch Modeling Language (SketchML).

Statistical Gesture Recognition Through Modelling of Parameter Trajectories

Abstract: The recognition of human gestures is a challenging problem that can contribute to a natural man-machine interface. In this paper, we present a new technique for gesture recognition. Gestures are modelled as temporal trajectories of parameters. Local sub-sequences of these trajectories are extracted and used to define an orthogonal space using principal component analysis. In this space the probabilistic density function of the training trajectories is represented by a multidimensional histogram, which builds the basis for the recognition. Experiments on three different recognition problems show the general utility of the approach.

Implementation of pattern recognition techniques and overview of its applications in various areas of artificial intelligence

Abstract: A pattern is an entity, vaguely defined, that could be given a name, e.g. fingerprint image, handwritten word, human face, speech signal, DNA sequence. Pattern recognition is the study of how machines can observe the environment, learn to distinguish patterns of interest from their background, and make sound and reasonable decisions about the categories of the patterns. The goal of pattern recognition research is to clarify complicated mechanisms of decision making processes and automatic these function using computers. Pattern recognition systems can be designed using the following main approaches: template matching, statistical methods, syntactic methods and neural networks. This paper reviews Pattern Recognition, Process, Design Cycle, Application, Models etc. This paper focuses on Statistical method of pattern Recognition .