Freeform User Interfaces for Graphical Computing

報告番号: 甲15222 ; 学位授与年月日: 2000-03-29 ; 学位の種別: 課程博士 ; 学位の種類: 博士(工学) ; 学位記番号: 博工第4717号 ; 研究科・専攻: 工学系研究科情報工学専

Human interaction with digital ink : legibility measurement and structural analysis

Literature suggests that it is possible to design and implement pen-based computer interfaces that resemble the use of pen and paper. These interfaces appear to allow users freedom in expressing ideas and seem to be familiar and easy to use. Different ideas have been put forward concerning this type of interface, however despite the commonality of aims and problems faced, there does not appear to be a common approach to their design and implementation. This thesis aims to progress the development of pen-based computer interfaces that resemble the use of pen and paper. To do this, a conceptual model is proposed for interfaces that enable interaction with "digital ink". This conceptual model is used to organize and analyse the broad range of literature related to pen-based interfaces, and to identify topics that are not sufficiently addressed by published research. Two issues highlighted by the model: digital ink legibility and digital ink structuring, are then investigated. In the first investigation, methods are devised to objectively and subjectively measure the legibility of handwritten script. These methods are then piloted in experiments that vary the horizontal rendering resolution of handwritten script displayed on a computer screen. Script legibility is shown to decrease with rendering resolution, after it drops below a threshold value. In the second investigation, the clustering of digital ink strokes into words is addressed. A method of rating the accuracy of clustering algorithms is proposed: the percentage of words spoiled. The clustering error rate is found to vary among different writers, for a clustering algorithm using the geometric features of both ink strokes, and the gaps between them. The work contributes a conceptual interface model, methods of measuring digital ink legibility, and techniques for investigating stroke clustering features, to the field of digital ink interaction research

AutoGraff: towards a computational understanding of graffiti writing and related art forms.

The aim of this thesis is to develop a system that generates letters and pictures with a style that is immediately recognizable as graffiti art or calligraphy. The proposed system can be used similarly to, and in tight integration with, conventional computer-aided geometric design tools and can be used to generate synthetic graffiti content for urban environments in games and in movies, and to guide robotic or fabrication systems that can materialise the output of the system with physical drawing media. The thesis is divided into two main parts. The first part describes a set of stroke primitives, building blocks that can be combined to generate different designs that resemble graffiti or calligraphy. These primitives mimic the process typically used to design graffiti letters and exploit well known principles of motor control to model the way in which an artist moves when incrementally tracing stylised letter forms. The second part demonstrates how these stroke primitives can be automatically recovered from input geometry defined in vector form, such as the digitised traces of writing made by a user, or the glyph outlines in a font. This procedure converts the input geometry into a seed that can be transformed into a variety of calligraphic and graffiti stylisations, which depend on parametric variations of the strokes

Classification of Graphomotor Impressions Using Convolutional Neural Networks: An Application to Automated Neuro-Psychological Screening Tests

Graphomotor impressions are a product of complex cognitive, perceptual and motor skills and are widely used as psychometric tools for the diagnosis of a variety of neuro-psychological disorders. Apparent deformations in these responses are quantified as errors and are used are indicators of various conditions. Contrary to conventional assessment methods where manual analysis of impressions is carried out by trained clinicians, an automated scoring system is marked by several challenges. Prior to analysis, such computerized systems need to extract and recognize individual shapes drawn by subjects on a sheet of paper as an important pre-processing step. The aim of this study is to apply deep learning methods to recognize visual structures of interest produced by subjects. Experiments on figures of Bender Gestalt Test (BGT), a screening test for visuo-spatial and visuo-constructive disorders, produced by 120 subjects, demonstrate that deep feature representation brings significant improvements over classical approaches. The study is intended to be extended to discriminate coherent visual structures between produced figures and expected prototypes

Efficient and Robust Optical Character Recognition Algorithm for Signature Recognition

With the technology development over the past decades, it became necessary to provide secure recognition systems. The Optical Character Recognition (OCR) can be considered as one of the most useful software to offer security. It works on the principal of recognizing the patterns with the use of a computer algorithm. OCR has multiple uses in places that need security verification such as banks, elevators, police departments. Furthermore, it can be used in several categories simultaneously. There are two types of recognition. First is the static approach which is based on the information of the input. Second is the dynamic recognition which is more usable for recognition of speech. In fact, OCR will be one of the most important techniques for human computer interaction in future. However, in this paper we have used OCR as feature to implement our algorithm. We are presenting a new algorithm that is capable of recognizing each signature individually. This makes the system more efficient and robust,especially in banks which need to verify the customer’s signature on a regular basis. A highly efficient C# system was developed to implement the new algorithm

SEGMENTASI KARAKTER TULISAN TANGAN ONLINE MENGGUNAKAN FILTER IIR

Segmentasi karakter merupakan proses yang sangat penting dalam analisa dan pengenalan karakter tulisan tangan. Paper ini adalah mengembangkan suatu metode segmentasi yang dapat menghasilkan segmen karakter tulisan tangan online sesuai dengan segmentasi acuan. Beberapa algoritma segmentasi telah dikembangkan. Sebagian menggunakan pendekatan wavelet dan sebagian lagi menggunakan pendekatan filter. Karakteristik data yang digunakan pada kedua pendekatan tersebut adalah kecepatan linear. Penggunaan karakteristik ini masih menghasilkan derau yang tinggi, sehingga mempersulit proses segmentasi. Hal ini disebabkan karena adanya perbedaan kecepatan menulis dan kecepatan sampling. Sulitnya proses segmentasi terjadi karena adanya lokal maksimum dan minimum yang bukan sebenarnya. Akibatnya, titik potong segmentasi menjadi tidak tepat. Secara keseluruhan proses segmentasi menjadi tidak akurat dan tidak sesuai dengan segmen acuan. Untuk menghilangkan atau memfilter derau tersebut digunakan filter smoothing IIR (infinite impulse response filters). Filter ini memiliki kemampuan yang baik dalam menghilangkan atau memfilter derau. Penghilangan derau pada data karakter tulisan tangan online ini untuk mempermudah proses segmentasi. Selain itu, penggunaan filter IIR ini dapat meningkatkan akurasi posisi pemotongan segmen. Paper ini menggunakan 52 data karakter tulisan tangan online yang terdiri dari dua set data karakter a-z. Hasil eksperimen yang diperoleh menunjukan bahwa filter IIR menghasilkan proses smoothing yang baik. Hal ini dibuktikan dengan sedikitnya lokal maksimum dan minimum yang dihasilkan sehingga memudahkan melakukan pemotongan pada titik segmen dan diperoleh ketepatan jumlah segmen dan posisi pemotongan segmen

A Neural Network Model for Cursive Script Production

This article describes a neural network model, called the VITEWRITE model, for generating handwriting movements. The model consists of a sequential controller, or motor program, that interacts with a trajectory generator to move a. hand with redundant degrees of freedom. The neural trajectory generator is the Vector Integration to Endpoint (VITE) model for synchronous variable-speed control of multijoint movements. VITE properties enable a simple control strategy to generate complex handwritten script if the hand model contains redundant degrees of freedom. The proposed controller launches transient directional commands to independent hand synergies at times when the hand begins to move, or when a velocity peak in a given synergy is achieved. The VITE model translates these temporally disjoint synergy commands into smooth curvilinear trajectories among temporally overlapping synergetic movements. The separate "score" of onset times used in most prior models is hereby replaced by a self-scaling activity-released "motor program" that uses few memory resources, enables each synergy to exhibit a unimodal velocity profile during any stroke, generates letters that are invariant under speed and size rescaling, and enables effortless. connection of letter shapes into words. Speed and size rescaling are achieved by scalar GO and GRO signals that express computationally simple volitional commands. Psychophysical data concerning band movements, such as the isochrony principle, asymmetric velocity profiles, and the two-thirds power law relating movement curvature and velocity arise as emergent properties of model interactions.National Science Foundation (IRI 90-24877, IRI 87-16960); Office of Naval Research (N00014-92-J-1309); Air Force Office of Scientific Research (F49620-92-J-0499); Defense Advanced Research Projects Agency (90-0083

LSTM-CNN: An efficient diagnostic network for Parkinson's disease utilizing dynamic handwriting analysis

Background and objectives: Dynamic handwriting analysis, due to its non-invasive and readily accessible nature, has recently emerged as a vital adjunctive method for the early diagnosis of Parkinson's disease. In this study, we design a compact and efficient network architecture to analyse the distinctive handwriting patterns of patients' dynamic handwriting signals, thereby providing an objective identification for the Parkinson's disease diagnosis. Methods: The proposed network is based on a hybrid deep learning approach that fully leverages the advantages of both long short-term memory (LSTM) and convolutional neural networks (CNNs). Specifically, the LSTM block is adopted to extract the time-varying features, while the CNN-based block is implemented using one-dimensional convolution for low computational cost. Moreover, the hybrid model architecture is continuously refined under ablation studies for superior performance. Finally, we evaluate the proposed method with its generalization under a five-fold cross-validation, which validates its efficiency and robustness. Results: The proposed network demonstrates its versatility by achieving impressive classification accuracies on both our new DraWritePD dataset ($96.2\%$) and the well-established PaHaW dataset ($90.7\%$). Moreover, the network architecture also stands out for its excellent lightweight design, occupying a mere $0.084$M of parameters, with a total of only $0.59$M floating-point operations. It also exhibits near real-time CPU inference performance, with inference times ranging from $0.106$ to $0.220$s. Conclusions: We present a series of experiments with extensive analysis, which systematically demonstrate the effectiveness and efficiency of the proposed hybrid neural network in extracting distinctive handwriting patterns for precise diagnosis of Parkinson's disease