Sketch recognition

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

HAGR-D: A Novel Approach for Gesture Recognition with Depth Maps

Abstract: The hand is an important part of the body used to express information through gestures, and its movements can be used in dynamic gesture recognition systems based on computer vision with practical applications, such as medical, games and sign language. Although depth sensors have led to great progress in gesture recognition, hand gesture recognition still is an open problem because of its complexity, which is due to the large number of small articulations in a hand. This paper proposes a novel approach for hand gesture recognition with depth maps generated by the Microsoft Kinect Sensor (Microsoft, Redmond, WA, USA) using a variation of the CIPBR (convex invariant position based on RANSAC) algorithm and a hybrid classifier composed of dynamic time warping (DTW) and Hidden Markov models (HMM), called the hybrid approach for gesture recognition with depth maps (HAGR-D). The experiments show that the proposed model overcomes other algorithms presented in the literature in hand gesture recognition tasks, achieving a classification rate of 97.49% in the MSRGesture3D dataset and 98.43% in the RPPDI dynamic gesture dataset.

Connector semantics for sketched diagram recognition

Abstract: Comprehensive interpretation of hand-drawn diagrams is a long-standing challenge. Connectors (arrows, edges and lines) are important components of many types of diagram. In this paper we discuss techniques for syntactic and semantic recognition of connectors. Undirected graphs, digraphs and organization charts are presented as exemplars of three broad classes that encompass many types of connected diagram. Generic techniques have been incorporated into the recognition engine of InkKit, an extensible sketch toolkit, thus reducing the development costs for sketch tools.

Sketch-R2CNN: An Attentive Network for Vector Sketch Recognition.

Abstract: Freehand sketching is a dynamic process where points are sequentially sampled and grouped as strokes for sketch acquisition on electronic devices. To recognize a sketched object, most existing methods discard such important temporal ordering and grouping information from human and simply rasterize sketches into binary images for classification. In this paper, we propose a novel single-branch attentive network architecture RNN-Rasterization-CNN (Sketch-R2CNN for short) to fully leverage the dynamics in sketches for recognition. Sketch-R2CNN takes as input only a vector sketch with grouped sequences of points, and uses an RNN for stroke attention estimation in the vector space and a CNN for 2D feature extraction in the pixel space respectively. To bridge the gap between these two spaces in neural networks, we propose a neural line rasterization module to convert the vector sketch along with the attention estimated by RNN into a bitmap image, which is subsequently consumed by CNN. The neural line rasterization module is designed in a differentiable way to yield a unified pipeline for end-to-end learning. We perform experiments on existing large-scale sketch recognition benchmarks and show that by exploiting the sketch dynamics with the attention mechanism, our method is more robust and achieves better performance than the state-of-the-art methods.

A Large-Scale Software-Generated Face Composite Sketch Database

Abstract: Numerous algorithms that can identify suspects depicted in sketches following eyewitness descriptions of criminals are currently being developed because of their potential importance in forensics investigations. Yet, despite the prevalent use of software-generated composite sketches by law enforcement agencies, there still exist few such sketches which can be used by researchers to adequately evaluate face photo- sketch recognition algorithms when using these composites. The main contribution of this paper is the creation of the University of Malta Software- Generated Face Sketch (UoM-SGFS) database that is publicly available and which contains the largest number of viewed software-generated sketches, that also exhibit several deformations and exaggerations to mimic sketches obtained in real- world investigations. Further, in contrast to other databases, all sketches in this new database are represented in colour. {Lastly, state-of-the- art recognition algorithms are found to perform worse on the software-generated composites than on hand-drawn sketches, while recognition accuracies still lag far behind those achieved for traditional photo-to-photo comparisons.

Sketch recognition with continuous feedback based on incremental intention extraction

Abstract: On-line synchronous sketch recognition has the advantages of convenient input and natural interaction. But among the existing algorithms, some are just able to process simple sketches, and some have so high computational complexity as not to satisfy the real-time demand. In order to solve the problem of efficiency and coverage, a sketch recognition algorithm based on incremental intention extraction is presented. By defining the lag window, the algorithm understands the sketch intention of users on the base of incremental intention extraction. Moreover, the algorithm can update the existing intention sections according to the latest information in order that the recognition results are in line with the sketch intention of users. Experiments show that, the algorithm can recognize kinds of sketches in real time.