تمثيل الإطار الخارجي للكلمات العربية بكفاءة من خلال الدمج بين نموذج الكنتور النشط وتحديد ونقاط الزوايا

Graphical curves and surfaces fitting are hot areas of research studies and application, such as artistic applications, analysis applications and encoding purposes. Outline capture of digital word images is important in most of the desktop publishing systems. The shapes of the characters are stored in the computer memory in terms of their outlines, and the outlines are expressed as Bezier curves. Existing methods for Arabic font outline description suffer from low fitting accuracy and efficiency. In our research, we developed a new method for outlining shapes using Bezier curves with minimal set of curve points. A distinguishing characteristic of our method is that it combines the active contour method (snake) with corner detection to achieve an initial set of points that is as close to the shape's boundaries as possible. The method links these points (snake + corner) into a compound Bezier curve, and iteratively improves the fitting of the curve over the actual boundaries of the shape. We implemented and tested our method using MATLAB. Test cases included various levels of shape complexity varying from simple, moderate, and high complexity depending on factors, such as: boundary concavities, number of corners. Results show that our method achieved average 86% of accuracy when measured relative to true shape boundary. When compared to other similar methods (Masood & Sarfraz, 2009; Sarfraz & Khan, 2002; Ferdous A Sohel, Karmakar, Dooley, & Bennamoun, 2010), our method performed comparatively well. Keywords: Bezier curves, shape descriptor, curvature, corner points, control points, Active Contour Model.تعتبر المنحنيات والأسطح الرسومية موضوعاً هاماً في الدراسات البحثية وفي التطبيقات البرمجية مثل التطبيقات الفنية، وتطبيقات تحليل وترميز البيانات. ويعتبر تخطيط الحدود الخارجية للكلمات عملية أساسية في غالبية تطبيقات النشر المكتبي. في هذه التطبيقات تخزن أشكال الأحرف في الذاكرة من حيث خطوطها الخارجية، وتمثل الخطوط الخارجية على هيئة منحنيات Bezier. الطرق المستخدمة حالياً لتحديد الخطوط الخارجية للكلمات العربية تنقصها دقة وكفاءة الملاءمة ما بين الحدود الحقيقية والمنحنى الرسومي الذي تقوم بتشكيله. في هذا البحث قمنا بتطوير طريقة جديدة لتخطيط الحدود الخارجية للكلمات تعتمد على منحنيات Bezier بمجموعة أقل من المنحنيات الجزئية. تتميز طريقتنا بخاصية مميزة وهي الدمج بين آلية لاستشعار الزوايا مع آلية نموذج الكنتور النشط (الأفعى). يتم الدمج بين نقاط الزوايا ونقاط الأفعى لتشكيل مجموعة موحدة من النقاط المبدئية قريبة قدر الإمكان من الحدود الحقيقية للشكل المراد تحديده. يتشكل منحنى Bezier من هذه المجموعة المدمجة، وتتم عملية تدريجية على دورات لملاءمة المنحنى على الحدود الحقيقية للشكل. قام الباحث بتنفيذ وتجربة الطريقة الجديدة باستخدام برنامج MATLAB. وتم اختيار أشكال رسومية كعينات اختبار تتصف بمستويات متباينة من التعقيد تتراوح ما بين بسيط إلى متوسط إلى عالي التعقيد على أساس عوامل مثل تقعرات الحدود، عدد نقاط الزوايا، الفتحات الداخلية، إلخ. وقد أظهرت نتائج الاختبار أن طريقتنا الجديدة حققت دقة في الملائمة تصل نسبتها إلى 86% مقارنة بالحدود الحقيقية للشكل المستهدف. وكذلك فقد كان أداء طريقتنا جيداً بالمقارنة مع طرق أخرى مماثلة

Volumetric cloud generation using a Chinese brush calligraphy style

Includes bibliographical references.Clouds are an important feature of any real or simulated environment in which the sky is visible. Their amorphous, ever-changing and illuminated features make the sky vivid and beautiful. However, these features increase both the complexity of real time rendering and modelling. It is difficult to design and build volumetric clouds in an easy and intuitive way, particularly if the interface is intended for artists rather than programmers. We propose a novel modelling system motivated by an ancient painting style, Chinese Landscape Painting, to address this problem. With the use of only one brush and one colour, an artist can paint a vivid and detailed landscape efficiently. In this research, we develop three emulations of a Chinese brush: a skeleton-based brush, a 2D texture footprint and a dynamic 3D footprint, all driven by the motion and pressure of a stylus pen. We propose a hybrid mapping to generate both the body and surface of volumetric clouds from the brush footprints. Our interface integrates these components along with 3D canvas control and GPU-based volumetric rendering into an interactive cloud modelling system. Our cloud modelling system is able to create various types of clouds occurring in nature. User tests indicate that our brush calligraphy approach is preferred to conventional volumetric cloud modelling and that it produces convincing 3D cloud formations in an intuitive and interactive fashion. While traditional modelling systems focus on surface generation of 3D objects, our brush calligraphy technique constructs the interior structure. This forms the basis of a new modelling style for objects with amorphous shape

STEFANN: Scene Text Editor using Font Adaptive Neural Network

Textual information in a captured scene plays an important role in scene interpretation and decision making. Though there exist methods that can successfully detect and interpret complex text regions present in a scene, to the best of our knowledge, there is no significant prior work that aims to modify the textual information in an image. The ability to edit text directly on images has several advantages including error correction, text restoration and image reusability. In this paper, we propose a method to modify text in an image at character-level. We approach the problem in two stages. At first, the unobserved character (target) is generated from an observed character (source) being modified. We propose two different neural network architectures - (a) FANnet to achieve structural consistency with source font and (b) Colornet to preserve source color. Next, we replace the source character with the generated character maintaining both geometric and visual consistency with neighboring characters. Our method works as a unified platform for modifying text in images. We present the effectiveness of our method on COCO-Text and ICDAR datasets both qualitatively and quantitatively.Comment: Accepted in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 202

MCCFNet: multi-channel color fusion network for cognitive classification of traditional Chinese paintings.

The computational modeling and analysis of traditional Chinese painting rely heavily on cognitive classification based on visual perception. This approach is crucial for understanding and identifying artworks created by different artists. However, the effective integration of visual perception into artificial intelligence (AI) models remains largely unexplored. Additionally, the classification research of Chinese painting faces certain challenges, such as insufficient investigation into the specific characteristics of painting images for author classification and recognition. To address these issues, we propose a novel framework called multi-channel color fusion network (MCCFNet), which aims to extract visual features from diverse color perspectives. By considering multiple color channels, MCCFNet enhances the ability of AI models to capture intricate details and nuances present in Chinese painting. To improve the performance of the DenseNet model, we introduce a regional weighted pooling (RWP) strategy specifically designed for the DenseNet169 architecture. This strategy enhances the extraction of highly discriminative features. In our experimental evaluation, we comprehensively compared the performance of our proposed MCCFNet model against six state-of-the-art models. The comparison was conducted on a dataset consisting of 2436 TCP samples, derived from the works of 10 renowned Chinese artists. The evaluation metrics employed for performance assessment were Top-1 Accuracy and the area under the curve (AUC). The experimental results have shown that our proposed MCCFNet model significantly outperform all other benchmarking methods with the highest classification accuracy of 98.68%. Meanwhile, the classification accuracy of any deep learning models on TCP can be much improved when adopting our proposed framework

Stroke trajectory generation for a robotic Chinese calligrapher.

Lam, Hiu Man.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 84-89).Abstracts in English and Chinese.Chapter Chapter 1: --- Introduction --- p.1Chapter 1.1. --- Overview on Robotics --- p.1Chapter 1.2. --- Literture Review on Art-Robot --- p.1Chapter 1.3. --- Robot artist for Chinese Calligraphy and Paintings --- p.3Chapter 1.4. --- Motivation and Research Objective --- p.4Chapter 1.5. --- Thesis Outline --- p.5Chapter Chapter 2: --- Intelligent Robotic Art System --- p.6Chapter 2.1. --- Previous Configuration --- p.6Chapter 2.1.1. --- 3 DOF Manipulator --- p.7Chapter 2.1.2. --- Digital Image Input System --- p.7Chapter 2.2. --- Hardware Modification --- p.8Chapter 2.2.1. --- Additional Degree of Freedoms --- p.8Chapter 2.2.2. --- Infra-red Sensing System for Manipulator Positioning --- p.9Chapter 2.2.3. --- Axial-rotary Brush --- p.11Chapter 2.2.4. --- Interface program --- p.13Chapter 2.2.5. --- Vibration Reduction --- p.16Chapter Chapter 3: --- Skeletonization Based on Delaunay Triangulation and Bezier Interpolation --- p.18Chapter 3.1. --- Background Theory --- p.20Chapter 3.1.1. --- Smoothed Local Symmetry --- p.20Chapter 3.1.2. --- Delaunay Triangulation --- p.21Chapter 3.1.3. --- Bezier Curve --- p.23Chapter 3.2. --- Algorithm --- p.24Chapter 3.2.1. --- Edge Sampling --- p.24Chapter 3.2.2. --- Triangle Modification --- p.26Chapter 3.2.3. --- Triangle Filtering and Replacement --- p.28Chapter 3.2.4. --- Internal Edge Refinement --- p.30Chapter 3.2.5. --- Skeletal Interpolation --- p.31Chapter 3.3. --- Experiments --- p.32Chapter 3.4. --- Chapter Summary --- p.36Chapter Chapter 4: --- Stroke Segmentation for Chinese Words --- p.37Chapter 4.1. --- Rule-based Spurious Branches Removal --- p.38Chapter 4.1.1. --- Spurious Branch in Stroke Terminal --- p.40Chapter 4.1.2. --- Spurious Branch Caused by Turning Stroke --- p.42Chapter 4.2. --- Stroke Connectivity Determination --- p.44Chapter 4.2.1. --- Gradient of Medial Axis --- p.45Chapter 4.2.2. --- Gradient of Branch Boundary --- p.47Chapter 4.2.3. --- Branch Width --- p.49Chapter 4.2.4. --- Combined Objective Function --- p.50Chapter 4.3. --- Stroke Generation --- p.51Chapter 4.3.1. --- Stroke Connection between Branches --- p.52Chapter 4.3.2. --- Stroke Generation in Stroke Terminal --- p.53Chapter 4.4. --- Experiment Using Intelligent Robotic Art System --- p.54Chapter 4.5. --- Discussion --- p.59Chapter Chapter 5: --- Experimental Acquisition of Brush Footprints --- p.61Chapter 5.1. --- Brush Footprint Extraction --- p.62Chapter 5.2. --- Graphical Interface for Inputting Sample Points of Brush Footprints --- p.64Chapter 5.3. --- Curve Fitting for Brush Footprint Sample Points --- p.70Chapter 5.3.1. --- Curve Fitting Using Genetic Algorithm --- p.70Chapter 5.3.2. --- Curve Fitting by Least Squares Regression --- p.72Chapter 5.4. --- Discussion --- p.74Chapter Chapter 6: --- Trajectory Generation for Robotic Chinese Calligraphy --- p.75Chapter 6.1. --- Stroke Trajectory Searching with According Stroke Width --- p.75Chapter 6.2. --- Improvement in Stroke Trajectory --- p.77Chapter 6.3. --- Experiment --- p.80Conclusion and Future Work --- p.82References --- p.84Appendix --- p.90Chapter 9.1. --- Segmented Strokes of Bada Shanren's Calligraphy --- p.9

Emotion Detection and Classification using Hybrid Feature Selection and Deep Learning Techniques

Image sentiment analysis has gained significant attention due to the increasing availability of user-generated content on various platforms such as social media, e-commerce websites, and online reviews. The core of our approach lies in the deep learning model, which combines the strengths of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. The CNN component captures local dependencies and learns high-level features, while the LSTM component captures long-term dependencies and maintains contextual information. By fusing these two components, our model effectively captures both local and global context, leading to improved sentiment analysis performance. During the execution first select the context and generate visual feature vector for generation of captions. The EfficientNetB7 model is applied in order to construct the image description for every individual picture. The Attention-based LSTM as well as Gated Recurrent Unit (GRU) greedy method are the two approaches that are utilized in the process of classifying sentiment labels. The proposed research has been categorized into three different phases. In Phase 1 describe various data preprocessing and normalization techniques. It also demonstrates training using RESNET-101 deep learning-based CNN classification algorithm. In Phase 2 extract the various features from the selected context of input image. The context has been selected based on detected objects from the image and generates a visual caption for the entire dataset. The      generated captions are dynamically used for model training as well as testing to both datasets. The EfficientNet module has used for generation of visual context from selected contexts. Finally in phase 3 classification model has built using a Deep Convolutional Neural Network (DCNN). The proposed algorithm classified the entire train and test dataset with different cross- validations such as 5-fold, 10-fold and 15-fold etc. The numerous activation functions are also used for evaluation of the proposed algorithm in different ways. The higher accuracy of the proposed model is 96.20% sigmoid function for 15-fold cross validation

Wholetoning: Synthesizing Abstract Black-and-White Illustrations

Black-and-white imagery is a popular and interesting depiction technique in the visual arts, in which varying tints and shades of a single colour are used. Within the realm of black-and-white images, there is a set of black-and-white illustrations that only depict salient features by ignoring details, and reduce colour to pure black and white, with no intermediate tones. These illustrations hold tremendous potential to enrich decoration, human communication and entertainment. Producing abstract black-and-white illustrations by hand relies on a time consuming and difficult process that requires both artistic talent and technical expertise. Previous work has not explored this style of illustration in much depth, and simple approaches such as thresholding are insufficient for stylization and artistic control. I use the word wholetoning to refer to illustrations that feature a high degree of shape and tone abstraction. In this thesis, I explore computer algorithms for generating wholetoned illustrations. First, I offer a general-purpose framework, “artistic thresholding”, to control the generation of wholetoned illustrations in an intuitive way. The basic artistic thresholding algorithm is an optimization framework based on simulated annealing to get the final bi-level result. I design an extensible objective function from our observations of a lot of wholetoned images. The objective function is a weighted sum over terms that encode features common to wholetoned illustrations. Based on the framework, I then explore two specific wholetoned styles: papercutting and representational calligraphy. I define a paper-cut design as a wholetoned image with connectivity constraints that ensure that it can be cut out from only one piece of paper. My computer generated papercutting technique can convert an original wholetoned image into a paper-cut design. It can also synthesize stylized and geometric patterns often found in traditional designs. Representational calligraphy is defined as a wholetoned image with the constraint that all depiction elements must be letters. The procedure of generating representational calligraphy designs is formalized as a “calligraphic packing” problem. I provide a semi-automatic technique that can warp a sequence of letters to fit a shape while preserving their readability

Artificial neural network (ANN) enabled internet of things (IoT) architecture for music therapy

Alternative medicine techniques such as music therapy have been a recent interest of medical practitioners and researchers. Significant clinical evidence suggests that music has a positive influence over pain, stress and anxiety for the patients of cancer, pre and post surgery, insomnia, child birth, end of life care, etc. Similarly, the technologies of Internet of Things (IoT), Body Area Networks (BAN) and Artificial Neural Networks (ANN) have been playing a vital role to improve the health and safety of the population through offering continuous remote monitoring facilities and immediate medical response. In this article, we propose a novel ANN enabled IoT architecture to integrate music therapy with BAN and ANN for providing immediate assistance to patients by automating the process of music therapy. The proposed architecture comprises of monitoring the body parameters of patients using BAN, categorizing the disease using ANN and playing music of the most appropriate type over the patient’s handheld device, when required. In addition, the ANN will also exploit Music Analytics such as the type and duration of music played and its impact over patient’s body parameters to iteratively improve the process of automated music therapy. We detail development of a prototype Android app which builds a playlist and plays music according to the emotional state of the user, in real time. Data for pulse rate, blood pressure and breath rate has been generated using Node-Red, and ANN has been created using Google Colaboratory (Colab). MQTT broker has been used to send generated data to Android device. The ANN uses binary and categorical cross-entropy loss functions, Adam optimiser and ReLU activation function to predict the mood of patient and suggest the most appropriate type of music