Topic
Sketch recognition
About: Sketch recognition is a research topic. Over the lifetime, 1611 publications have been published within this topic receiving 40284 citations.
Papers published on a yearly basis
Papers
More filters
01 Jan 2004
TL;DR: This paper characterize sketching as an interactive, incremental process, and argues that sketch recognition algorithms should be tailored to take advantage of these properties of sketches that separate them from images.
Abstract: Sketch understanding has received attention as an enabling technology for natural human-computer interaction (Thomas Stahovich & Randall Davis, 2002). With the widespread availability of pen based PDAs, and more recently with the emergence of Tablet PCs, there is an increasing interest in sketch recognition. Current approaches to sketch recognition treat sketches as static images and apply structural or syntactic recognition techniques commonly used in computer vision. In this paper, we characterize sketching as an interactive, incremental process, and argue that sketch recognition algorithms should be tailored to take advantage of these properties of sketches that separate them from images. We report experimental results showing how the order in which strokes are drawn affects the recognition speed and propose possible approaches for achieving algorithms with better memory and speed requirements.
3 citations
22 Sep 2006
TL;DR: The aim is to obtain a list of characters which contains all, or as much as possible, characters of the container’s registration number in order to recognize them.
Abstract: This paper describes the process of location and recognition of container code characters. The system has to deal with outdoor images. Top hat transformation, segmentation algorithms and filters have been applied in order to locate the container’s registration number. Our aim is to obtain a list of characters which contains all, or as much as possible, characters of the container’s registration number in order to recognize them. This work is part of a higher order project whose aim is the automation of the entrance gate of a port. Key–Words: computer vision, segmentation, character recognition.
3 citations
01 Oct 2017
TL;DR: Experiments show that the accuracy of dynamic gesture recognition method using HMM-CART model is higher than that of traditional single channel HMM.
Abstract: This paper focuses on improving the recognition accuracy of dynamic gesture learning, and proposes dynamic gesture track recognition strategy based on HMM-CART model structure. It integrates the modeling ability of the HMM (Hidden-Markov-Model) for time series data and the interpretability of CART (Classification-And-Regression-Tree) for its ability of fast classification and regression, and use it for dynamic gesture recognition. Time series data of movement gestures collected by LeapMotion sensor will be first divided into four channels: finger shape, palm normal vector, palm ball radius and palm displacement vector, and then HMM in HMM Layer will be built for each channel, finally the likelihood probability of model calculated for each sub-sequence of observation should be classified as the input of the CART model in CART Layer to identify the gesture. Experiments show that the accuracy of dynamic gesture recognition method using HMM-CART model is higher than that of traditional single channel HMM.
3 citations
01 Aug 2019
TL;DR: The experimental results show that EEO significantly outperform SA as well as other well-known meta-heuristic optimization algorithms such as PSO, Harmony, and MVO.
Abstract: The main aim of this work is to develop a component-based face sketch recognition model. The proposed model adopts an enhanced evolutionary optimizer (EEO) to perform the task of face sketched components localization. EEO is applied to an unknown input sketch to make an automatic localization for its components i.e. eyes, nose, and mouth. After that, HOG features are extracted, and cosine similarity measure is computed to find the best components location. EEO integrates Q-learning algorithm with the simulated annealing (SA) algorithm as a single mode. The Q-learning algorithm is used to control the execution of SA parameters i.e. temperature and the mutation rate at run time. The proposed approach was evaluated on three face sketch recognition benchmark problem which are LFW, AR, and CUHK. The experimental results show that EEO significantly outperform SA as well as other well-known meta-heuristic optimization algorithms such as PSO, Harmony, and MVO.
3 citations
22 Apr 2021
TL;DR: In this paper, a web-based sketch recognition algorithm based on Deep Neural Network (DNN), called Marcelle-Sketch, was developed, which end-users can train incrementally.
Abstract: Machine learning systems became pervasive in modern interactive technology but provide users with little, if any, agency with respect to how their models are trained from data. In this paper, we are interested in the way novices handle learning algorithms, what they understand from their behavior and what strategy they may use to "make it work". We developed a web-based sketch recognition algorithm based on Deep Neural Network (DNN), called Marcelle-Sketch, that end-users can train incrementally. We present an experimental study that investigate people's strategies and (mis)understandings in a realistic algorithm-teaching task. Our study involved 12 participants who performed individual teaching sessions using a think-aloud protocol. Our results show that participants adopted heterogeneous strategies in which variability affected the model performances. We highlighted the importance of sketch sequencing, particularly at the early stage of the teaching task. We also found that users' understanding is facilitated by simple operations on drawings, while confusions are caused by certain inherent properties of DNN. From these findings, we propose implications for design of IML systems dedicated to novices and discuss the socio-cultural aspect of this research.
3 citations