Готові списки джерел за темами / Sketch object recognition

Добірка наукової літератури з теми "Sketch object recognition"

Автор: Grafiati

Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Sketch object recognition"

Chen, Kezhen, Irina Rabkina, Matthew D. McLure, and Kenneth D. Forbus. "Human-Like Sketch Object Recognition via Analogical Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1336–43. http://dx.doi.org/10.1609/aaai.v33i01.33011336.

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Deep learning systems can perform well on some image recognition tasks. However, they have serious limitations, including requiring far more training data than humans do and being fooled by adversarial examples. By contrast, analogical learning over relational representations tends to be far more data-efficient, requiring only human-like amounts of training data. This paper introduces an approach that combines automatically constructed qualitative visual representations with analogical learning to tackle a hard computer vision problem, object recognition from sketches. Results from the MNIST dataset and a novel dataset, the Coloring Book Objects dataset, are provided. Comparison to existing approaches indicates that analogical generalization can be used to identify sketched objects from these datasets with several orders of magnitude fewer examples than deep learning systems require.

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Ali, Safdar, Nouraiz Aslam, DoHyeun Kim, Asad Abbas, Sania Tufail, and Beenish Azhar. "Context awareness based Sketch-DeepNet architecture for hand-drawn sketches classification and recognition in AIoT." PeerJ Computer Science 9 (April 27, 2023): e1186. http://dx.doi.org/10.7717/peerj-cs.1186.

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A sketch is a black-and-white, 2-D graphical representation of an object and contains fewer visual details as compared to a colored image. Despite fewer details, humans can recognize a sketch and its context very efficiently and consistently across languages, cultures, and age groups, but it is a difficult task for computers to recognize such low-detail sketches and get context out of them. With the tremendous increase in popularity of IoT devices such as smartphones and smart cameras, etc., it has become more critical to recognize free hand-drawn sketches in computer vision and human-computer interaction in order to build a successful artificial intelligence of things (AIoT) system that can first recognize the sketches and then understand the context of multiple drawings. Earlier models which addressed this problem are scale-invariant feature transform (SIFT) and bag-of-words (BoW). Both SIFT and BoW used hand-crafted features and scale-invariant algorithms to address this issue. But these models are complex and time-consuming due to the manual process of features setup. The deep neural networks (DNNs) performed well with object recognition on many large-scale datasets such as ImageNet and CIFAR-10. However, the DDN approach cannot be carried out for hand-drawn sketches problems. The reason is that the data source is images, and all sketches in the images are, for example, ‘birds’ instead of their specific category (e.g., ‘sparrow’). Some deep learning approaches for sketch recognition problems exist in the literature, but the results are not promising because there is still room for improvement. This article proposed a convolutional neural network (CNN) architecture called Sketch-DeepNet for the sketch recognition task. The proposed Sketch-DeepNet architecture used the TU-Berlin dataset for classification. The experimental results show that the proposed method beats the performance of the state-of-the-art sketch classification methods. The proposed model achieved 95.05% accuracy as compared to existing models DeformNet (62.6%), Sketch-DNN (72.2%), Sketch-a-Net (77.95%), SketchNet (80.42%), Thinning-DNN (74.3%), CNN-PCA-SVM (72.5%), Hybrid-CNN (84.42%), and human recognition accuracy of 73% on the TU-Berlin dataset.

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Meghana, K. S. "Face Sketch Recognition Using Computer Vision." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 25, 2021): 2005–9. http://dx.doi.org/10.22214/ijraset.2021.36806.

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Now-a-days need for technologies for identification, detection and recognition of suspects has increased. One of the most common biometric techniques is face recognition, since face is the convenient way used by the people to identify each-other. Understanding how humans recognize face sketches drawn by artists is of significant value to both criminal investigators and forensic researchers in Computer Vision. However, studies say that hand-drawn face sketches are still very limited in terms of artists and number of sketches because after any incident a forensic artist prepares a victim’s sketches on behalf of the description provided by an eyewitness. Sometimes suspect uses special mask to hide some common features of faces like nose, eyes, lips, face-color etc. but the outliner features of face biometrics one could never hide. Here we concentrate on some specific facial geometric feature which could be used to calculate some ratio of similarities from the template photograph database against the forensic sketches. The project describes the design of a system for face sketch recognition by a computer vision approach like Discrete Cosine Transform (DCT), Local Binary Pattern Histogram (LBPH) algorithm and a supervised machine learning model called Support Vector Machine (SVM) for face recognition. Tkinter is the standard GUI library for Python. Python when combined with Tkinter provides a fast and easy way to create GUI applications. Tkinter provides a powerful object-oriented interface to the Tk GUI toolkit.

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Yoon, Gang-Joon, and Sang Min Yoon. "Sketch-based 3D object recognition from locally optimized sparse features." Neurocomputing 267 (December 2017): 556–63. http://dx.doi.org/10.1016/j.neucom.2017.06.034.

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Zhang, Hua, Peng She, Yong Liu, Jianhou Gan, Xiaochun Cao, and Hassan Foroosh. "Learning Structural Representations via Dynamic Object Landmarks Discovery for Sketch Recognition and Retrieval." IEEE Transactions on Image Processing 28, no. 9 (September 2019): 4486–99. http://dx.doi.org/10.1109/tip.2019.2910398.

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Dhaigude, Santosh. "Computer Vision Based Virtual Sketch Using Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 264–68. http://dx.doi.org/10.22214/ijraset.2022.39814.

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Abstract: In todays world during this pandemic situation Online Learning is the only source where one could learn. Online learning makes students more curious about the knowledge and so they decide their learning path . But considering the academics as they have to pass the course or exam given, they need to take time to study, and have to be disciplined about their dedication. And there are many barriers for Online learning as well. Students are lowering their grasping power the reason for this is that each and every student was used to rely on their teacher and offline classes. Virtual writing and controlling system is challenging research areas in field of image processing and pattern recognition in the recent years. It contributes extremely to the advancement of an automation process and can improve the interface between man and machine in numerous applications. Several research works have been focusing on new techniques and methods that would reduce the processing time while providing higher recognition accuracy. Given the real time webcam data, this jambord like python application uses OpenCV library to track an object-of-interest (a human palm/finger in this case) and allows the user to draw bymoving the finger, which makes it both awesome and interesting to draw simple thing. Keyword: Detection, Handlandmark , Keypoints, Computer vision, OpenCV

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Ding, Xue, Shun Han, Hong Hong Yang, Xiao Feng Wang, and Kun Wu Yang. "Human Head Portrait Feature Extraction Based on SIFT." Applied Mechanics and Materials 644-650 (September 2014): 4322–24. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.4322.

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As the number of students who attend the arts exams has been growing, each admission institutions need to score the human head portraits of art sketch with the number of ten thousand and even 100 thousand. Thus it is an innovative research on how to conduct image recognition with the help of advanced computer technology. Image Recognition Technology is to give the computer the intelligence of human vision, so that the computer can quickly and accurately recognize the object on the input images. However, in the recognition process such factors as light, rotation and shield increase the difficulty of identifying the human head portrait images. In order to get better recognition performance, this paper studies the feature extraction of human head portrait based on SIFT (Scale Invariant Feature Transform). From the practical application, it can be seen that the approach proposed in this paper is feasible and is of good recognition performance.

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Yousefi, Bardia, and Chu Kiong Loo. "Development of Biological Movement Recognition by Interaction between Active Basis Model and Fuzzy Optical Flow Division." Scientific World Journal 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/238234.

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Following the study on computational neuroscience through functional magnetic resonance imaging claimed that human action recognition in the brain of mammalian pursues two separated streams, that is, dorsal and ventral streams. It follows up by two pathways in the bioinspired model, which are specialized for motion and form information analysis (Giese and Poggio 2003). Active basis model is used to form information which is different from orientations and scales of Gabor wavelets to form a dictionary regarding object recognition (human). Also biologically movement optic-flow patterns utilized. As motion information guides share sketch algorithm in form pathway for adjustment plus it helps to prevent wrong recognition. A synergetic neural network is utilized to generate prototype templates, representing general characteristic form of every class. Having predefined templates, classifying performs based on multitemplate matching. As every human action has one action prototype, there are some overlapping and consistency among these templates. Using fuzzy optical flow division scoring can prevent motivation for misrecognition. We successfully apply proposed model on the human action video obtained from KTH human action database. Proposed approach follows the interaction between dorsal and ventral processing streams in the original model of the biological movement recognition. The attained results indicate promising outcome and improvement in robustness using proposed approach.

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MIKHALEVSKYI, Vitalii. "THE FEATURES OF BASIC MODELING TOOLS OF 3D OBJECTS IN SKETCHUP." Herald of Khmelnytskyi National University 305, no. 1 (February 23, 2022): 53–58. http://dx.doi.org/10.31891/2307-5732-2022-305-1-53-58.

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Recently, the intuitive-oriented program for designers and architects, which is used to quickly create three-dimensional models of objects, structures, buildings and interiors – SketchUp, has gained widespread recognition in the world of 3D. So now almost all other developers include in their software products or direct support for models (files) SketchUp, or data exchange with it through special plug-ins. SketchUp is designed primarily for sketch, searchable 3D modeling in three-dimensional space. However, SketchUp is successfully used to develop a variety of projects in all genres of design, advertising, engineering design, film and game production. The saltation in the increasing popularity of SketchUp has occurred since the program was “tied” to the new owner’s Internet projects – Google 3D models and Google Earth. The reason for choosing and acquiring SketchUp by Google, apparently, was its simplicity and accessibility. By creating a model of an architectural structure or any other object in SketchUp, users could place their creations in the public online collections of Google. So, in particular, the selection of collections “Cities under development” contains several thousand models of real architectural buildings of the world. At the same time, Google accepted 3D models only with the requirement that they were textured and equipped with the correct geographical reference. Compared to many other popular packages, SketchUp has a number of features that are positioned as advantages. The main feature of this program is the almost complete absence of pre-configuration windows. All geometric characteristics during or immediately after the end of the tool are set from the keyboard in the Value Control Box. Another key feature is the Push/Pull tool, which allows any plane to be “pulled out” to the side, creating new side walls as it moves. You can move the plane against the predetermined curve, using a special tool Follow Me. The program is also characterized by extreme accuracy of calculations and measurements.

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Zembylas, Michalinos. "The therapisation of social justice as an emotional regime: implications for critical education." Journal of Professional Capital and Community 1, no. 4 (October 10, 2016): 286–301. http://dx.doi.org/10.1108/jpcc-05-2016-0015.

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Purpose The purpose of this paper is to sketch out what one can see as the emerging “therapeutic turn” in a wide range of areas of contemporary social life including education, especially in relation to understandings of vulnerability and social justice, and then poses the question of what emotional regime has accompanied the emergence of this “therapization” movement, making emotional life in schools the “object-target” for specific technologies of power. Design/methodology/approach The psychologization of social problems has been very much in evidence in the development of educational policies and practices – an approach which not only pathologizes social problems as individual psychological deficiencies or traits, but also obscures the recognition of serious structural inequalities and ideological commitments that perpetuate social injustices through educational policy and practice. In the present paper, the author adopts a different perspective, that of the history, sociology and politics of emotions and affects to show how and why the therapization of social justice is part of the conditions for the birth of particular forms of biopower in schools. Findings There is an urgent need to expose how psychologized approaches that present social justice as an individualizing responsibility are essentially depoliticizing vulnerability by silencing the shared complicities. It is argued, then, that it is crucial to pay attention to the political and structural dimensions of vulnerability. Originality/value Attending to the emotional regime of therapization of social justice has important implications to counter forms of biopower that work through processes of normalization.

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Дисертації з теми "Sketch object recognition"

Eitz, Mathias [Verfasser], and Marc [Akademischer Betreuer] Alexa. "Human Object Sketches: Datasets, Descriptors, Computational Recognition and 3d Shape Retrieval / Mathias Eitz. Betreuer: Marc Alexa." Berlin : Universitätsbibliothek der Technischen Universität Berlin, 2012. http://d-nb.info/1029192820/34.

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Kaelbling, Leslie P., and Tomás Lozano-Pérez. "Learning Three-Dimensional Shape Models for Sketch Recognition." 2004. http://hdl.handle.net/1721.1/7424.

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Artifacts made by humans, such as items of furniture and houses, exhibit an enormous amount of variability in shape. In this paper, we concentrate on models of the shapes of objects that are made up of fixed collections of sub-parts whose dimensions and spatial arrangement exhibit variation. Our goals are: to learn these models from data and to use them for recognition. Our emphasis is on learning and recognition from three-dimensional data, to test the basic shape-modeling methodology. In this paper we also demonstrate how to use models learned in three dimensions for recognition of two-dimensional sketches of objects.
Singapore-MIT Alliance (SMA)

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Частини книг з теми "Sketch object recognition"

Wang, Zhuoying, Qingkai Fang, and Yongtao Wang. "Geometric Object 3D Reconstruction from Single Line Drawing Image Based on a Network for Classification and Sketch Extraction." In Document Analysis and Recognition – ICDAR 2021, 598–613. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86549-8_38.

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Mendola, Joseph. "Introduction." In Experience and Possibility, 1–8. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198869764.003.0001.

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This is an introductory chapter. It sketches the project of the book, which is to understand the ontology of a central class of particulars, and of their most basic and central properties and relations. This central class encompasses the commonsense entities that our experience seems naïvely to reveal. First of all, there are ordinary visible objects like balls and cars. The book investigates the kind of particularity they present in experience. Second, there are locations in space and time that such ordinary things occupy, and which have a somewhat different sort of particularity. Third, there are the material bits that make up the balls and cars. The proper understanding of both the particularity and the concrete properties and relations of ordinary concrete objects like these demands certain metaphysical novelties. It requires a return to the ancient conception that there is a difference between different ways of being, specifically between the existence of actual tables and chairs with their evident colors and shapes on one hand, and what might be called “the subsistence” of certain merely possible beings on the other. But it also requires the recognition of various sorts of unity relations less than strict identity, which for instance relate determinable and relevantly determinate properties. All these novelties involve distinctive forms of modal structure.

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Grenander, Ulf, and Michael I. Miller. "Introduction." In Pattern Theory. Oxford University Press, 2006. http://dx.doi.org/10.1093/oso/9780198505709.003.0002.

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This book is to be an accessible book on patterns, their representation, and inference. There are a small number of ideas and techniques that, when mastered, make the subject more accessible. This book has arisen from ten years of a research program which the authors have embarked upon, building on the more abstract developments of metric pattern theory developed by one of the authors during the 1970s and 1980s. The material has been taught over multiple semesters as part of a second year graduate-level course in pattern theory, essentially an introduction for students interested in the representation of patterns which are observed in the natural world. The course has attracted students studying biomedical engineering, computer science, electrical engineering, and applied mathematics interested in speech recognition and computational linguistics, as well as areas of image analysis, and computer vision. Now the concept of patterns pervades the history of intellectual endeavor; it is one of the eternal followers in human thought. It appears again and again in science, taking on different forms in the various disciplines, and made rigorous through mathematical formalization. But the concept also lives in a less stringent form in the humanities, in novels and plays, even in everyday language. We use it all the time without attributing a formal meaning to it and yet with little risk of misunderstanding. So, what do we really mean by a pattern? Can we define it in strictly logical terms? And if we can, what use can we make of such a definition? These questions were answered by General Pattern Theory, a discipline initiated by Ulf Grenander in the late 1960s [1–5]. It has been an ambitious effort with the only original sketchy program having few if any practical applications, growing in mathematical maturity with a multitude of applications having appeared in biology/medicine and in computer vision, in language theory and object recognition, to mention but a few. Pattern theory attempts to provide an algebraic framework for describing patterns as structures regulated by rules, essentially a finite number of both local and global combinatory operations. Pattern theory takes a compositional view of the world, building more and more complex structures starting from simple ones. The basic rules for combining and building complex patterns from simpler ones are encoded via graphs and rules on transformation of these graphs.

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Тези доповідей конференцій з теми "Sketch object recognition"

Chen, Kezhen, Ken Forbus, Balaji Vasan Srinivasan, Niyati Chhaya, and Madeline Usher. "Sketch Recognition via Part-based Hierarchical Analogical Learning." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/331.

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Sketch recognition has been studied for decades, but it is far from solved. Drawing styles are highly variable across people and adapting to idiosyncratic visual expressions requires data-efficient learning. Explainability also matters, so that users can see why a system got confused about something. This paper introduces a novel part-based approach for sketch recognition, based on hierarchical analogical learning, a new method to apply analogical learning to qualitative representations. Given a sketched object, our system automatically segments it into parts and constructs multi-level qualitative representations of them. Our approach performs analogical generalization at multiple levels of part descriptions and uses coarse-grained results to guide interpretation at finer levels. Experiments on the Berlin TU dataset and the Coloring Book Objects dataset show that the system can learn explainable models in a data-efficient manner.

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Cruz-Lozano, Ricardo, Fisseha M. Alemayehu, Stephen Ekwaro-Osire, and Haileyesus Endeshaw. "Determining Probability of Importance of Features in a Sketch." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-52807.

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Sketches are the main tools for the communication of concepts among design team’s members during the ideation phase of the design process. Imprecisely defined sketches lead to uncertainty in communication during the design process. Thus, as a contribution to reduce the uncertainty in design communication, an initial framework for the quantification of uncertainty associated to sketches was presented in previous work. In that initial framework, the probabilities of the features in a sketch were determined based on the assessment of an experienced designer. This approach reduced the usability of the framework by professionals with limited experience e.g. entry-level engineers. Thus, this posed the need of an improved framework and brought the following research question: Can a probabilistic method be used to improve the quantification of uncertainty in sketches? Accordingly, to answer this research question the following specific aims were established: 1) Ranking of features in a sketch, 2) Determination of the probability of importance of features in a sketch, and 3) Quantification of uncertainty in a sketch. The first aim focused on determining and classifying the features in a sketch, based on a hierarchical approach. The second aim focused on determining the probability of importance of the features in a sketch, by assessing its probability of likeliness using an object recognition approach, and by applying a probability transformation. The third aim focused on the quantification of the uncertainty in a sketch, based on the calculation and normalization of the sketch’s entropy. This resulted in the development of an improved framework for the quantification of uncertainty in sketches, which can be used by design practitioners with limited experience, and whose application is presented and detailed in a case study.

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Lim, Joseph J., C. Lawrence Zitnick, and Piotr Dollar. "Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection." In 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2013. http://dx.doi.org/10.1109/cvpr.2013.406.

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Hongwei Li, Liang Lin, Tianfu Wu, Xiaobai Liu, and Lanfang Dong. "Object-of-interest extraction by integrating stochastic inference with learnt active shape sketch." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761329.

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Nurizada, Anar, and Anurag Purwar. "Sketch-Based Mechanism Simulation Using Machine Learning." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-72149.

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Abstract This paper presents a machine learning approach for building an object detector for interactive simulation of planar linkages from handmade sketches and drawings found in patents and texts. Touch- and pen-input devices and interfaces have made sketching a more natural way for designers to express their ideas, especially during early design stages, but sketching existing complex mechanisms can be tedious and error-prone. While there are software applications available to help users make drawings, including that of a linkage mechanism, it is both educational and instructive to see existing sketches come to life via automated simulation. However, texts and patents present rich and diverse styles of mechanism drawings, which makes automated recognition difficult. Modern machine learning algorithms for object recognition require an extensive number of training images. However, there are no data sets of planar linkages available online. Therefore, our first goal was to generate images of sketches similar to hand-drawn ones and use state-of-the-art deep generation models, such as β-VAE, to produce more training data from a limited set of images. The latent space of β-VAE was explored by linear and spherical interpolations between sub-spaces and by varying latent space’s dimensions. This served two-fold objectives — 1) examine the possibility of generating new synthesized images via interpolation and 2) develop insights in the dependence of latent space dimension on bar linkage parameters. t-SNE dimensionality reduction technique was implemented to visualize the latent space of a β-VAE in a 2D space. Training images produced by animation rendering were used for fine-tuning a real-time object detection system — YOLOv3.

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Dey, Sounak, Anjan Dutta, Suman K. Ghosh, Ernest Valveny, Josep Llados, and Umapada Pal. "Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch." In 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018. http://dx.doi.org/10.1109/icpr.2018.8545452.

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Guo, Ting, Yongtao Wang, Yafeng Zhou, Zheqi He, and Zhi Tang. "Geometric Object 3D Reconstruction from Single Line Drawing Image with Bottom-Up and Top-Down Classification and Sketch Generation." In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2017. http://dx.doi.org/10.1109/icdar.2017.115.

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Glasser, Adrian, and Howard C. Howland. "Artistic transformations in image processing." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1986. http://dx.doi.org/10.1364/oam.1986.thn9.

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Анотація:

We have examined certain transformations, linear and nonlinear, which transform frame-grabbed black-and-white photographic images into artistic renditions of the original. Such transforms resemble sketches, sketch and wash renditions, or mosaics. Generally these transforms involve a large reduction in information of the original photographic image, and we have attempted to measure this reduction using coding algorithms. The reverse, increase in information when an artist creates a painting from a sketch, was also evaluated. Lastly, we also studied the importance of the information retained and discarded in the recognition of faces in a series of artistically transformed, frequency filtered images. We conclude that information necessary for recognizing faces (and many other objects) is broadly and redundantly distributed throughout the spatial frequency spectrum.

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Fu, Luoting, and Levent Burak Kara. "Recognizing Network-Like Hand-Drawn Sketches: A Convolutional Neural Network Approach." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87402.

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Hand-drawn sketches are powerful cognitive devices for the efficient exploration, visualization and communication of emerging ideas in engineering design. It is desirable that CAD/CAE tools be able to recognize the back-of-the-envelope sketches and extract the intended engineering models. Yet this is a nontrivial task for freehand sketches. Here we present a novel, neural network-based approach designed for the recognition of network-like sketches. Our approach leverages a trainable, detector/recognizer and an autonomous procedure for the generation of training samples. Prior to deployment, a Convolutional Neural Network is trained on a few labeled prototypical sketches and learns the definitions of the visual objects. When deployed, the trained network scans the input sketch at different resolutions with a fixed-size sliding window, detects instances of defined symbols and outputs an engineering model. We demonstrate the effectiveness of the proposed approach in different engineering domains with different types of sketching inputs.

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Purwar, Anurag, and Anar Nurizada. "Transforming Hand-Drawn Sketches of Linkage Mechanisms Into Their Digital Representation." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-90495.

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Abstract This paper presents a deep neural network based approach for interactive digital transformation and simulation of n-bar planar linkages consisting of both revolute and prismatic joints from hand-drawn sketches. Instead of taking a pure computer vision approach, we combine the output of a convolutional deep neural network with the topological knowledge of linkage mechanisms to create a framework for recognition of hand-drawn sketches. To accomplish this, we first synthetically generate a dataset of images of linkage mechanism sketches similar to hand-drawn ones and then fine-tune a state of the art deep neural network capable of detecting discrete objects. While the network had previously been exposed to only a general class of images of every-day objects, it was for the first time trained with a set of building blocks of linkage mechanisms, viz. joints and links. Thereafter, we present a novel algorithm, which performs topological analysis on the set of detected objects to create a kinematic model of the sketched mechanisms. The results show that this algorithm performs well on hand-drawn sketches and could help with conversions of such sketches to their digital representation for effective communication, analysis, cataloging, and classification.

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