Sketched symbol recognition using Latent-Dynamic Conditional Random Fields and distance-based clustering

Deufemia, Vincenzo; Risi, Michele; Tortora, Genoveffa

doi:10.1016/j.patcog.2013.09.016

Cited by 29 publications

(10 citation statements)

References 36 publications

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“…Particular attention deserve the management of stateless protocols, database implementation, and more general aspects of Webapp control°ow management. Finally, we aim to simplify the speci¯cation process, by reducing the manual activities and by introducing sketch-based interaction paradigms [8,11].…”

Section: Discussionmentioning

confidence: 99%

A Tool Supporting End-User Development of Access Control in Web Applications

Caruccio

Deufemia

D’Souza

et al. 2015

Int. J. Soft. Eng. Knowl. Eng.

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End-user development (EUD) is drawing an increasing attention due to the necessity of users to frequently extend and personalize their applications. In particular, EUD in the context of Web (EUDWeb) is focusing on technologies capable of supporting development tasks that the end-user feels more complex. However, although the specification and implementation of access control is perceived as a particularly complex task, little efforts have been made to support it within current EUDWeb environments. Thus, in this paper we propose an EUDWeb framework and tool for the specification and the generation of web applications embedding access control mechanisms. We extended a previous mockup-based EUDWeb approach, by introducing visual assistance mechanisms enabling the specification of role-based access control policies, and their integration within the application logic. The usability of the proposed framework has been evaluated by means of a user study, in which we have shown that a group of heterogeneous end-users could proficiently use the proposed framework to develop meaningful web applications, some of which including access control functionalities

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Section: Discussionmentioning

confidence: 99%

A Tool Supporting End-User Development of Access Control in Web Applications

Caruccio

Deufemia

D’Souza

et al. 2015

Int. J. Soft. Eng. Knowl. Eng.

Self Cite

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“…Though not directly related to circuit components recognition, we come across some works that performs sketch symbol recognition (e.g., Deufemia et al [11]), identifying hand-drawn graphics components from the textual parts (e.g., Avola et al [3,4]) in an online handwritten document using machine learning based approaches. The authors use discriminative features like entropy, band ratio, X scan, intersection and projection and SVM classifier [3] while in [4] they use original features like curvature and linearity along with the 6 methods of the previous paper [3] along with extreme machine learning (EML) classifier to perform classification of drawing symbols and texts.…”

Section: Literature Surveymentioning

confidence: 99%

Offline hand-drawn circuit component recognition using texture and shape-based features

Roy

Bhattacharya

Sarkar

et al. 2020

Multimed Tools Appl

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Circuit diagram is the very foundation of electrical and electronic sciences. A circuit diagram consists of various symbols called circuit components that specify the functionality of that circuit. Every day-today gadgets that we use are made up with a number of electrical/electronic circuits to play out their particular tasks. Till date circuit designers have to physically enter all data from the hand-drawn circuits into computers, and this procedure requires some investment in terms of time and carries mistakes with high likelihood. To this end, in this paper, we propose a method that relaxes this constraint by introducing a method for recognition of hand-drawn electrical and electronic circuit components, with both analog and digital components included. In the proposed method, the pre-processed images of circuit components are used for training and testing a recognition model using a feature set consisting of a texture based feature descriptor, called histogram of oriented gradients (HOG), and shape based features that include centroid distance, tangent angle, and chain code histogram. In addition, the texture based feature, being large in number compared to others is optimized using a feature selection algorithm called ReliefF. Classification of components is done by using sequential minimal optimization (SMO) classifier. The proposed method has been evaluated on a dataset of 20 different circuit components with 150 samples in each class. The experimental outcome shows that the proposed approach provides average 93.83% accuracy on the present database. We also compare our method with some of the state-of-the-art methods and we see that our method outperforms these methods.

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“…In this section, we describe approaches and methodologies defined in the literature concerning the mapping of gesture recognition [3,12,13]. In particular, we focus on different application domains in which the recognition of gestures plays an important role [21].…”

Section: Related Workmentioning

confidence: 99%

Perceiving space through sound: mapping human movements into MIDI

Breve¹,

Cirillo²,

Desiato³

et al. 2020

International Conferences on Distributed Multimedia Systems

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Gestural expressiveness plays a fundamental role in the interaction with people, environments, animals, things, and so on. Thus, several emerging application domains would exploit the interpretation of movements to support their critical designing processes. To this end, new forms to express the people's perceptions could help their interpretation, like in the case of music. This paper presents a novel algorithm for mapping human movements into MIDI music. The algorithm has been implemented in a system that integrates a module for real-time tracking of movements through a sample-based synthesizer using different types of filters to modulate frequencies. The system has been evaluated through a user study, in which several users have participated to a room experience, yielding significant results about their perceptions with respect to the environment they were immersed.

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Sketched symbol recognition using Latent-Dynamic Conditional Random Fields and distance-based clustering

Cited by 29 publications

References 36 publications

A Tool Supporting End-User Development of Access Control in Web Applications

A Tool Supporting End-User Development of Access Control in Web Applications

Offline hand-drawn circuit component recognition using texture and shape-based features

Perceiving space through sound: mapping human movements into MIDI

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