248 research outputs found
Symbol Recognition: Current Advances and Perspectives
Abstract. The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content
Fuzzy Intervals for Designing Structural Signature: An Application to Graphic Symbol Recognition
Revised selected papers from Eighth IAPR International Workshop on Graphics RECognition (GREC) 2009.The motivation behind our work is to present a new methodology for symbol recognition. The proposed method employs a structural approach for representing visual associations in symbols and a statistical classifier for recognition. We vectorize a graphic symbol, encode its topological and geometrical information by an attributed relational graph and compute a signature from this structural graph. We have addressed the sensitivity of structural representations to noise, by using data adapted fuzzy intervals. The joint probability distribution of signatures is encoded by a Bayesian network, which serves as a mechanism for pruning irrelevant features and choosing a subset of interesting features from structural signatures of underlying symbol set. The Bayesian network is deployed in a supervised learning scenario for recognizing query symbols. The method has been evaluated for robustness against degradations & deformations on pre-segmented 2D linear architectural & electronic symbols from GREC databases, and for its recognition abilities on symbols with context noise i.e. cropped symbols
Musings on Symbol Recognition
This paper does not pretend to be yet another survey on symbol recognition methods. It will rather try to take a step back, look at the main efforts done in that area throughout the years and propose some interesting directions to investigate
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Generating 3D product design models in real-time using hand motion and gesture
This thesis was submitted for the degree of Master of Philosophy and awarded by Brunel University.Three dimensional product design models are widely used in conceptual design and in the early stage of prototyping during the design processes. A product design specification often demands a substantial amount of 3D models to be constructed within a short period of time. Current methods begin with designers sketching product concepts in 2D using pencil and paper, which in turn are then translated into 3D models by a design individual with CAD expertise, using a 3D modelling software package such as Pro Engineer, Solid Works, Auto CAD etc. Several novel methods have been used to incorporate hand motion as a way of interacting with computers. There are three main types of technology available to capture motion data, capable of translating human motion into numeric data which can be read by a computer system. The first being, hand gesture glove-based systems such as “Cyberglove”, these systems are generally used to capture hand gesture and joint angle information. The second is full body motion capture systems, optical and non-optical-based, and finally vision based gesture recognition systems which capture full degree of - freedom (DOF) hand motion estimation. There has yet to be a method using any of the above mentioned input devices to rapidly produce 3D product design models in real time, using hand motion and gestures. In this research, a novel method is presented, using a motion capture system to capture hand gestures and motion in real time, to recreate 3D curves and surfaces, which can be translated into 3D product design models. The main aim of this research is to develop a hand motion and gesture-based rapid 3D product modelling method, allowing designers to interactively sketch out 3D concepts in real time using a virtual workspace.
A database of a number of hand signs was built for both architectural hand signs (preliminary study) and Product Design hand signs. A marker set model with a total of eight markers (five on the left hand and three on right hand/marker pen) was designed and used in the capture of hand gestures with the use of an Optical Motion Capture System. A preliminary testing session was successfully completed to determine whether the Motion Capture system would be suitable for a real-time application, by effectively modelling a train station in an offline state using hand motion and gesture. An OpenGL software application was programmed using C++ and the Microsoft Foundation Classes which was used to communicate and pass information of captured motion from the EVaRT system to the user
Synthesizing and Editing Photo-realistic Visual Objects
In this thesis we investigate novel methods of synthesizing new images of a deformable visual object using a collection of images of the object. We investigate both parametric and non-parametric methods as well as a combination of the two methods for the problem of image synthesis. Our main focus are complex visual objects, specifically deformable objects and objects with varying numbers of visible parts. We first introduce sketch-driven image synthesis system, which allows the user to draw ellipses and outlines in order to sketch a rough shape of animals as a constraint to the synthesized image. This system interactively provides feedback in the form of ellipse and contour suggestions to the partial sketch of the user. The user's sketch guides the non-parametric synthesis algorithm that blends patches from two exemplar images in a coarse-to-fine fashion to create a final image. We evaluate the method and synthesized images through two user studies. Instead of non-parametric blending of patches, a parametric model of the appearance is more desirable as its appearance representation is shared between all images of the dataset. Hence, we propose Context-Conditioned Component Analysis, a probabilistic generative parametric model, which described images with a linear combination of basis functions. The basis functions are evaluated for each pixel using a context vector computed from the local shape information. We evaluate C-CCA qualitatively and quantitatively on inpainting, appearance transfer and reconstruction tasks. Drawing samples of C-CCA generates novel, globally-coherent images, which, unfortunately, lack high-frequency details due to dimensionality reduction and misalignment. We develop a non-parametric model that enhances the samples of C-CCA with locally-coherent, high-frequency details. The non-parametric model efficiently finds patches from the dataset that match the C-CCA sample and blends the patches together. We analyze the results of the combined method on the datasets of horse and elephant images
Free-hand Sketch Understanding and Analysis
PhDWith the proliferation of touch screens, sketching input has become popular among many software
products. This phenomenon has stimulated a new round of boom in free-hand sketch research,
covering topics like sketch recognition, sketch-based image retrieval, sketch synthesis
and sketch segmentation. Comparing to previous sketch works, the newly proposed works are
generally employing more complicated sketches and sketches in much larger quantity, thanks
to the advancements in hardware. This thesis thus demonstrates some new works on free-hand
sketches, presenting novel thoughts on aforementioned topics.
On sketch recognition, Eitz et al. [32] are the first explorers, who proposed the large-scale
TU-Berlin sketch dataset [32] that made sketch recognition possible. Following their work, we
continue to analyze the dataset and find that the visual cue sparsity and internal structural complexity
are the two biggest challenges for sketch recognition. Accordingly, we propose multiple
kernel learning [45] to fuse multiple visual cues and star graph representation [12] to encode the
structures of the sketches. With the new schemes, we have achieved significant improvement
on recognition accuracy (from 56% to 65.81%). Experimental study on sketch attributes is performed
to further boost sketch recognition performance and enable novel retrieval-by-attribute
applications.
For sketch-based image retrieval, we start by carefully examining the existing works. After
looking at the big picture of sketch-based image retrieval, we highlight that studying the sketch’s
ability to distinguish intra-category object variations should be the most promising direction to
proceed on, and we define it as the fine-grained sketch-based image retrieval problem. Deformable
part-based model which addresses object part details and object deformations is raised
to tackle this new problem, and graph matching is employed to compute the similarity between
deformable part-based models by matching the parts of different models. To evaluate this new
problem, we combine the TU-Berlin sketch dataset and the PASCAL VOC photo dataset [36] to
form a new challenging cross-domain dataset with pairwise sketch-photo similarity ratings, and
our proposed method has shown promising results on this new dataset. Regarding sketch synthesis, we focus on the generating of real free-hand style sketches for
general categories, as the closest previous work [8] only managed to show efficacy on a single
category: human faces. The difficulties that impede sketch synthesis to reach other categories
include the cluttered edges and diverse object variations due to deformation. To address those
difficulties, we propose a deformable stroke model to form the sketch synthesis into a detection
process, which is directly aiming at the cluttered background and the object variations. To alleviate
the training of such a model, a perceptual grouping algorithm is further proposed that
utilizes stroke length’s relationship to stroke semantics, stroke temporal order and Gestalt principles
[58] to perform part-level sketch segmentation. The perceptual grouping provides semantic
part-level supervision automatically for the deformable stroke model training, and an iterative
learning scheme is introduced to gradually refine the supervision and the model training. With
the learned deformable stroke models, sketches with distinct free-hand style can be generated for
many categories
Analysis of Digital Logic Schematics Using Image Recognition
This thesis presents the results of research in the area of automated recognition of digital logic schematics. The adaptation of a number of existing image processing techniques for use with this kind of image is discussed, and the concept of using sets of tokens to represent the overall drawing i s explained in detail. Methods are given for using tokens to describe schematic component shapes, to represent the connections between components, and to provide sufficient information to a parser so that an equation can be generated. A Microsoft Windows-based test program which runs under Windows 95 or Windows NT has been written to implement the ideas presented. This program accepts either scanned images of digital schematics, or computer-generated images in Microsoft Windows bitmap format as input. It analyzes the input schematic image for content, and produces a corresponding logical equation as output. It also provides the functionality necessary to build and maintain an image token library
Electronic Imaging & the Visual Arts. EVA 2013 Florence
Important Information Technology topics are presented: multimedia systems, data-bases, protection of data, access to the content. Particular reference is reserved to digital images (2D, 3D) regarding Cultural Institutions (Museums, Libraries, Palace – Monuments, Archaeological Sites). The main parts of the Conference Proceedings regard: Strategic Issues, EC Projects and Related Networks & Initiatives, International Forum on “Culture & Technology”, 2D – 3D Technologies & Applications, Virtual Galleries – Museums and Related Initiatives, Access to the Culture Information. Three Workshops are related to: International Cooperation, Innovation and Enterprise, Creative Industries and Cultural Tourism
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