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Journal articles on the topic 'Sketches'
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Dr Abdul Aziz Malik and Araf Saddique. "Sketch Writing of Irfan Javed." Tasdiqتصدیق۔ 2, no. 1 (January 29, 2021): 91–105. http://dx.doi.org/10.56276/tasdiq.v2i1.34.
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Irfan Javed is a renowned sketch writer. He has sketched various literary and social personalities of his time. His sketches reflect his era.“Darwazy” is the first book of his sketches. The sketches of this book reflect the personalities as well as the social and political situation of this era.An effort has been made through this article to present a critical overview of Irfan Javed's sketching in the context of "Darwazy”
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Kudrowitz, Barry, Paula Te, and David Wallace. "The influence of sketch quality on perception of product-idea creativity." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 26, no. 3 (August 2012): 267–79. http://dx.doi.org/10.1017/s0890060412000145.
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AbstractThis paper explores the relationship between the quality of a sketch and how others perceive the creativity of the idea portrayed by the sketch. In this study, sketch quality is characterized through its line work, perspective, and proportions. Four different toaster ideas were each sketched by four people with different backgrounds and levels of sketching proficiency. Then, 360 reviewers ranked the toasters for idea creativity, referring to a set of four sketches: one sketch for each toaster concept. The level of sketch quality for each toaster concept was varied among one of four quality levels. Higher quality idea sketches were found to correlate with higher creativity rank (p> 0.005), and lower quality sketches correlated with a lower creativity rank (p> 0.0005). A toaster idea portrayed with the highest quality level of sketch was 2.3 times more likely to be ranked as the most creative idea within the given set of idea sketches. The results underscore the importance of how an idea is presented, and support the need for sketching instruction in engineering and design curriculum.
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Haas, Peter J. "Sketches get sketchier." Communications of the ACM 54, no. 8 (August 2011): 100. http://dx.doi.org/10.1145/1978542.1978565.
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Cao, Nan, Xin Yan, Yang Shi, and Chaoran Chen. "AI-Sketcher : A Deep Generative Model for Producing High-Quality Sketches." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2564–71. http://dx.doi.org/10.1609/aaai.v33i01.33012564.
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Sketch drawings play an important role in assisting humans in communication and creative design since ancient period. This situation has motivated the development of artificial intelligence (AI) techniques for automatically generating sketches based on user input. Sketch-RNN, a sequence-to-sequence variational autoencoder (VAE) model, was developed for this purpose and known as a state-of-the-art technique. However, it suffers from limitations, including the generation of lowquality results and its incapability to support multi-class generations. To address these issues, we introduced AI-Sketcher, a deep generative model for generating high-quality multiclass sketches. Our model improves drawing quality by employing a CNN-based autoencoder to capture the positional information of each stroke at the pixel level. It also introduces an influence layer to more precisely guide the generation of each stroke by directly referring to the training data. To support multi-class sketch generation, we provided a conditional vector that can help differentiate sketches under various classes. The proposed technique was evaluated based on two large-scale sketch datasets, and results demonstrated its power in generating high-quality sketches.
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Power, A. J., and Charles Wells. "A formalism for the specification of essentially-algebraic structures in 2-categories." Mathematical Structures in Computer Science 2, no. 1 (March 1992): 1–28. http://dx.doi.org/10.1017/s0960129500001110.
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A type of higher-order two-dimensional sketch is defined which has models in suitable 2-categories. It has as special cases the ordinary sketches of Ehresmann and certain previously defined generalizations of one-dimensional sketches. These sketches allow the specification of constructions in 2-categories such as weighted limits, as well as higher-order constructions such as exponential objects and subobject classifiers, that cannot be sketched by limits and colimits. These sketches are designed to be the basis of a category-based methodology for the description of functional programming languages, complete with rewrite rules giving the operational semantics, that is independent of the usual specification methods based on formal languages and symbolic logic. A definition of ‘path grammar’, generalizing the usual notion of grammar, is given as a step towards this goal.
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Yu, Emilie, Rahul Arora, J. Andreas Bærentzen, Karan Singh, and Adrien Bousseau. "Piecewise-smooth surface fitting onto unstructured 3D sketches." ACM Transactions on Graphics 41, no. 4 (July 2022): 1–16. http://dx.doi.org/10.1145/3528223.3530100.
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We propose a method to transform unstructured 3D sketches into piecewise smooth surfaces that preserve sketched geometric features. Immersive 3D drawing and sketch-based 3D modeling applications increasingly produce imperfect and unstructured collections of 3D strokes as design output. These 3D sketches are readily perceived as piecewise smooth surfaces by viewers, but are poorly handled by existing 3D surface techniques tailored to well-connected curve networks or sparse point sets. Our algorithm is aligned with human tendency to imagine the strokes as a small set of simple smooth surfaces joined along stroke boundaries. Starting with an initial proxy surface, we iteratively segment the surface into smooth patches joined sharply along some strokes, and optimize these patches to fit surrounding strokes. Our evaluation is fourfold: we demonstrate the impact of various algorithmic parameters, we evaluate our method on synthetic sketches with known ground truth surfaces, we compare to prior art, and we show compelling results on more than 50 designs from a diverse set of 3D sketch sources.
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Hu, Qinheping, Rishabh Singh, and Loris D’Antoni. "Solving Program Sketches with Large Integer Values." ACM Transactions on Programming Languages and Systems 44, no. 2 (June 30, 2022): 1–28. http://dx.doi.org/10.1145/3532849.
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Program sketching is a program synthesis paradigm in which the programmer provides a partial program with holes and assertions. The goal of the synthesizer is to automatically find integer values for the holes so that the resulting program satisfies the assertions. The most popular sketching tool, Sketch , can efficiently solve complex program sketches but uses an integer encoding that often performs poorly if the sketched program manipulates large integer values. In this article, we propose a new solving technique that allows Sketch to handle large integer values while retaining its integer encoding. Our technique uses a result from number theory, the Chinese Remainder Theorem, to rewrite program sketches to only track the remainders of certain variable values with respect to several prime numbers. We prove that our transformation is sound and the encoding of the resulting programs are exponentially more succinct than existing Sketch encodings. We evaluate our technique on a variety of benchmarks manipulating large integer values. Our technique provides speedups against both existing Sketch solvers and can solve benchmarks that existing Sketch solvers cannot handle.
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Shankar, Sree, and Rahul Rai. "Sketching in three dimensions: A beautification scheme." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 31, no. 3 (December 5, 2016): 376–92. http://dx.doi.org/10.1017/s0890060416000512.
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AbstractPrimary among all the activities involved in conceptual design is freehand sketching. There have been significant efforts in recent years to enable digital design methods that leverage humans’ sketching skills. Conventional sketch-based digital interfaces are built on two-dimensional touch-based devices like sketchers and drawing pads. The transition from two-dimensional to three-dimensional (3-D) digital sketch interfaces represents the latest trend in developing new interfaces that embody intuitiveness and human–human interaction characteristics. In this paper, we outline a novel screenless 3-D sketching system. The system uses a noncontact depth-sensing RGB-D camera for user input. Only depth information (no RGB information) is used in the framework. The system tracks the user's palm during the sketching process and converts the data into a 3-D sketch. As the generated data is noisy, making sense of what is sketched is facilitated through a beautification process that is suited to 3-D sketches. To evaluate the performance of the system and the beautification scheme, user studies were performed on multiple participants for both single-stroke and multistroke sketching scenarios.
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Purwandari, Endina Putri, Aan Erlansari, Andang Wijanarko, and Erich Adinal Adrian. "Face sketch recognition using principal component analysis for forensics application." Jurnal Teknologi dan Sistem Komputer 8, no. 3 (April 24, 2020): 178–84. http://dx.doi.org/10.14710/jtsiskom.2020.13422.
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Recognition of human faces in forensics applications can be identified through the Sketch recognition method by matching sketches and photos. The system gives five criminal candidates who have similarities to the sketch given. This study aims to perform facial recognition on photographs and sketches using Principal Component Analysis (PCA) as feature extraction and Euclidean distance as a calculation of the distance of test images to training images. The PCA method was used to recognize facial images from pencil sketch drawings. The system dataset is in the form of photos and sketches in the CUHK Face Sketch database consists of 93 photos and 93 sketches, and personal documentation consists of five photos and five sketches. The sketch matching application to training data produces an accuracy of 76.14 %, precision of 91.04 %, and recall of 80.26 %, while testing with sketch modifications produces accuracy and recall of 95 % and precision of 100 %.
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Raghatwan, Jyoti S., and Sandhya Arora. "Improved Sketch-to-Photo Generation Using Filter Aided Generative Adversarial Network." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 9 (September 30, 2022): 104–11. http://dx.doi.org/10.17762/ijritcc.v10i9.5713.
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Generating a photographic face image from given input sketch is most challenging task in computer vision. Mainly the sketches drawn by sketch artist used in human identification. Sketch to photo synthesis is very important applications in law enforcement as well as character design, educational training. In recent years Generative Adversarial Network (GAN) shows excellent performance on sketch to photo synthesis problem. Quality of hand drawn sketches affects the quality generated photo. It might be possible that while handling the hand drawn sketches, accidently by touching the user hand on pencil sketch or similar activities causes noise in given sketch. Likewise different styles like shading, darkness of pencil used by sketch artist may cause unnecessary noise in sketches. In recent year many sketches to photo synthesis methods are proposed, but they are mainly focused on network architecture to get better performance. In this paper we proposed Filter-aided GAN framework to remove such noise while synthesizing photo images from hand drawn sketches. Here we implement and compare different filtering methods with GAN. Quantitative and qualitative result shows that proposed Filter-aided GAN generate the photo images which are visually pleasant and closer to ground truth image.
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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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Kim, Gahyeon, and Mankyu Sung. "CoR-SketchAR: Cooperative Sketch-Based Real-Time Augmented Reality Authoring Tool for Crowd Simulation." Applied Sciences 12, no. 15 (July 23, 2022): 7416. http://dx.doi.org/10.3390/app12157416.
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In this study, we propose CoR-SketchAR, an augmented reality (AR) environment authoring tool that uses dry-erase markers for real-time collaboration among users. The most important requirement for multi-user collaboration in an AR environment is that the 3D virtual object must be placed at a specific location and can be easily changed by multiple users. Because marker-based registration techniques, which are widely used for matching virtual objects with real ones, require a marker for each object, and creating a crowd simulation environment with objects of various shapes and sizes requires the use of a large number of markers, which is time consuming and expensive. CoR-SketchAR, instead, creates an urban AR environment by drawing sketches with easily altered dry-erase marker. Then, system recognizes the shapes and colors of the sketches automatically. Those recognized shapes and colors provide the exact positions for overlaying the 3D virtual objects, which are the environment factors, on the real environment in augmented reality manner. We can even specify the path the crowd are moving along with a simple sketch stroke. We apply the computer vision technique to recognize the colors and shapes of sketches. By altering the size, shape and color sketches, the system is able to create a wide variety of dynamic urban environments. To validate the proposed techniques, we built two stand-alone software systems to check the usability of the proposed system (a 2D screen-based environmental authoring tool and a sketch-based environmental authoring tool) and conducted experiments in which two users collaborated with each other to create an environment with a specific authoring tool and then report surveys. In the experiments, users collaborated in pairs to create environmental elements, such as highways, buildings, trees, and the starting and goal positions of crowds. After recognizing them, the system then automatically creates a 3D environment, and crowds are animated accordingly. Based on a user survey, we observed that participants who used sketch-based environmental authoring tools were more active and accessible than those who used 2D screen-based authoring tools. The results of the study show that CoR-SketchAR can be further used to create a dynamical crowd simulation on a large scale using beam projectors or portable devices by simply adding sketches based on different scenarios.
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Mohammed, Amal Ahmed Hasan, and Jiazhou Chen. "Cleanup Sketched Drawings: Deep Learning-Based Model." Applied Bionics and Biomechanics 2022 (May 6, 2022): 1–17. http://dx.doi.org/10.1155/2022/2238077.
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Rough drawings provide artists with a simple and efficient way to express shapes and ideas. Artists frequently use sketches to highlight their envisioned curves, using several groups’ raw strokes. These rough sketches need enhancement to remove some subtle impurities and completely simplify curves over the sketched images. This research paper proposes using a fully convolutional network (FCNN) model to simplify rough raster drawings using deep learning. As input, the FCNN takes a sketch image of any size and automatically generates a high-quality simplified sketch image as output. Our model intuitively addresses the shortcomings in the rough sketch image, such as noises and unwanted background, as well as the low resolution of the rough sketch image. The FCNN model is trained by three raster image datasets, which are publicly available online. This paper demonstrates the efficiency and effectiveness of using deep learning in cleaning and improving the roughly drawn image in an automatic way. For evaluating the results, the mean squared error (MSE) metric was used. From experimental results, it was observed that an enhanced FCNN model reported better accuracy, reducing the prediction error by 0.08 percent for simplifying the rough sketch compared to the existing methods.
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Wan, Weiguo, and Hyo Jong Lee. "A Joint Training Model for Face Sketch Synthesis." Applied Sciences 9, no. 9 (April 26, 2019): 1731. http://dx.doi.org/10.3390/app9091731.
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The exemplar-based method is most frequently used in face sketch synthesis because of its efficiency in representing the nonlinear mapping between face photos and sketches. However, the sketches synthesized by existing exemplar-based methods suffer from block artifacts and blur effects. In addition, most exemplar-based methods ignore the training sketches in the weight representation process. To improve synthesis performance, a novel joint training model is proposed in this paper, taking sketches into consideration. First, we construct the joint training photo and sketch by concatenating the original photo and its sketch with a high-pass filtered image of their corresponding sketch. Then, an offline random sampling strategy is adopted for each test photo patch to select the joint training photo and sketch patches in the neighboring region. Finally, a novel locality constraint is designed to calculate the reconstruction weight, allowing the synthesized sketches to have more detailed information. Extensive experimental results on public datasets show the superiority of the proposed joint training model, both from subjective perceptual and the FaceNet-based face recognition objective evaluation, compared to existing state-of-the-art sketch synthesis methods.
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Seo, Chang Wook, Amirsaman Ashtari, and Junyong Noh. "Semi-supervised reference-based sketch extraction using a contrastive learning framework." ACM Transactions on Graphics 42, no. 4 (July 26, 2023): 1–12. http://dx.doi.org/10.1145/3592392.
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Sketches reflect the drawing style of individual artists; therefore, it is important to consider their unique styles when extracting sketches from color images for various applications. Unfortunately, most existing sketch extraction methods are designed to extract sketches of a single style. Although there have been some attempts to generate various style sketches, the methods generally suffer from two limitations: low quality results and difficulty in training the model due to the requirement of a paired dataset. In this paper, we propose a novel multi-modal sketch extraction method that can imitate the style of a given reference sketch with unpaired data training in a semi-supervised manner. Our method outperforms state-of-the-art sketch extraction methods and unpaired image translation methods in both quantitative and qualitative evaluations.
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Wang, Shouxia, Shuxia Wang, and Weiping He. "Multistroke Grouping of Online Freehand Axonometric Sketches for Mechanical Models." Mathematical Problems in Engineering 2020 (May 7, 2020): 1–13. http://dx.doi.org/10.1155/2020/7439341.
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Multistroke drawing occurs frequently in conceptual design sketches; however, it is almost unsupported by the current sketch-based user interfaces. We proposed a sketch recognition system based on the multistroke primitive grouping method. Based on grouping the strokes that lie within the mutual boundaries between adjacent regions, we create line drawings from online freehand axonometric sketches of mechanical models. First, closed regions and their boundary bands of the sketch were extracted. Then, the strokes that cross the boundary bands of two or more closed regions are segmented, and the strokes that lie within the intersection of two adjacent boundary bands are grouped. Finally, grouped strokes are simplified into a new single stroke and then fitted as a geometric primitive; thus, the input sketches are recognized to the line drawings. We developed a prototype of the sketch recognition system to evaluate the proposed method. The results showed that the input sketches are simplified into the accurate line drawings efficiently. The proposed method can be applied to both multistroke overtracing and nonovertracing sketches.
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Bai, Jing, Mengjie Wang, and Dexin Kong. "Deep Common Semantic Space Embedding for Sketch-Based 3D Model Retrieval." Entropy 21, no. 4 (April 4, 2019): 369. http://dx.doi.org/10.3390/e21040369.
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Sketch-based 3D model retrieval has become an important research topic in many applications, such as computer graphics and computer-aided design. Although sketches and 3D models have huge interdomain visual perception discrepancies, and sketches of the same object have remarkable intradomain visual perception diversity, the 3D models and sketches of the same class share common semantic content. Motivated by these findings, we propose a novel approach for sketch-based 3D model retrieval by constructing a deep common semantic space embedding using triplet network. First, a common data space is constructed by representing every 3D model as a group of views. Second, a common modality space is generated by translating views to sketches according to cross entropy evaluation. Third, a common semantic space embedding for two domains is learned based on a triplet network. Finally, based on the learned features of sketches and 3D models, four kinds of distance metrics between sketches and 3D models are designed, and sketch-based 3D model retrieval results are achieved. The experimental results using the Shape Retrieval Contest (SHREC) 2013 and SHREC 2014 datasets reveal the superiority of our proposed method over state-of-the-art methods.
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Zang, Sicong, Shikui Tu, and Lei Xu. "Linking Sketch Patches by Learning Synonymous Proximity for Graphic Sketch Representation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (June 26, 2023): 11096–103. http://dx.doi.org/10.1609/aaai.v37i9.26314.
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Graphic sketch representations are effective for representing sketches. Existing methods take the patches cropped from sketches as the graph nodes, and construct the edges based on sketch's drawing order or Euclidean distances on the canvas. However, the drawing order of a sketch may not be unique, while the patches from semantically related parts of a sketch may be far away from each other on the canvas. In this paper, we propose an order-invariant, semantics-aware method for graphic sketch representations. The cropped sketch patches are linked according to their global semantics or local geometric shapes, namely the synonymous proximity, by computing the cosine similarity between the captured patch embeddings. Such constructed edges are learnable to adapt to the variation of sketch drawings, which enable the message passing among synonymous patches. Aggregating the messages from synonymous patches by graph convolutional networks plays a role of denoising, which is beneficial to produce robust patch embeddings and accurate sketch representations. Furthermore, we enforce a clustering constraint over the embeddings jointly with the network learning. The synonymous patches are self-organized as compact clusters, and their embeddings are guided to move towards their assigned cluster centroids. It raises the accuracy of the computed synonymous proximity. Experimental results show that our method significantly improves the performance on both controllable sketch synthesis and sketch healing.
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Kim, Hyungbum, Junho Kim, and Heekyung Yang. "Portrait Sketch Generative Model for Misaligned Photo-to-Sketch Dataset." Mathematics 11, no. 17 (September 1, 2023): 3761. http://dx.doi.org/10.3390/math11173761.
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A deep-learning-based model for generating line-based portrait sketches from portrait photos is proposed in this paper. The misalignment problem is addressed by the introduction of a novel loss term, designed to tolerate misalignments between Ground Truth sketches and generated sketches. Artists’ sketching strategies are mimicked by dividing the portrait into face and hair regions, with separate models trained for each region, and the outcomes subsequently combined. Our contributions include the resolution of misalignment between photos and artist-created sketches, and high-quality sketch results via region-based model training. The experimental results show the effectiveness of our approach in generating convincing portrait sketches, with both quantitative and visual comparisons to State-of-the-Art techniques. The quantitative comparisons demonstrate that our method preserves the identity of the input portrait photos, while applying the style of Ground Truth sketch.
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Zeng, Y., A. Pardasani ,, J. Dickinson ,, Z. Li ,, H. Antunes , and, V. Gupta, and D. Baulier. "Mathematical Foundation for Modeling Conceptual Design Sketches1." Journal of Computing and Information Science in Engineering 4, no. 2 (May 28, 2004): 150–59. http://dx.doi.org/10.1115/1.1683825.
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This paper aims to establish a mathematical foundation for modeling free-hand design sketches throughout the conceptual design process. Both experimental and theoretical approaches are used. In using the experimental approach, one case study from a book and one case study from an automobile assembly system manufacturer are used to illustrate the characteristics of design sketches. These characteristics provide the requirements for models of sketch representation. In using the theoretical approach, a mathematical structure of design sketches is established. This mathematical structure can naturally and logically model the evolving sketches generated in the design process, through integrating the strengths of set theory and mereology. In addition to the formal representation of design sketches, the mathematical model provides a logical foundation for formally designing sketch interpretation algorithms. An algorithmic structure of three-dimension reconstruction from two-dimension line drawing is derived using this mathematical model.
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Myronova, Mariia, William Neveu, and Mikhail Bessmeltsev. "Differential Operators on Sketches via Alpha Contours." ACM Transactions on Graphics 42, no. 4 (July 26, 2023): 1–15. http://dx.doi.org/10.1145/3592420.
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A vector sketch is a popular and natural geometry representation depicting a 2D shape. When viewed from afar, the disconnected vector strokes of a sketch and the empty space around them visually merge into positive space and negative space , respectively. Positive and negative spaces are the key elements in the composition of a sketch and define what we perceive as the shape. Nevertheless, the notion of positive or negative space is mathematically ambiguous: While the strokes unambiguously indicate the interior or boundary of a 2D shape, the empty space may or may not belong to the shape's exterior. For standard discrete geometry representations, such as meshes or point clouds, some of the most robust pipelines rely on discretizations of differential operators, such as Laplace-Beltrami. Such discretizations are not available for vector sketches; defining them may enable numerous applications of classical methods on vector sketches. However, to do so, one needs to define the positive space of a vector sketch, or the sketch shape. Even though extracting this 2D sketch shape is mathematically ambiguous, we propose a robust algorithm, Alpha Contours , constructing its conservative estimate: a 2D shape containing all the input strokes, which lie in its interior or on its boundary, and aligning tightly to a sketch. This allows us to define popular differential operators on vector sketches, such as Laplacian and Steklov operators. We demonstrate that our construction enables robust tools for vector sketches, such as As-Rigid-As-Possible sketch deformation and functional maps between sketches, as well as solving partial differential equations on a vector sketch.
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Yi-Luen Do, Ellen. "Design sketches and sketch design tools." Knowledge-Based Systems 18, no. 8 (December 2005): 383–405. http://dx.doi.org/10.1016/j.knosys.2005.07.001.
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Mittal, Shristi, Rhutuja Satpute, Shubhamm Mohitte, Leena Ragha, and Dhanashri Bhosale. "Freehand to Digital Circuit Reconstruction Using HOG and SVM." ITM Web of Conferences 40 (2021): 03009. http://dx.doi.org/10.1051/itmconf/20214003009.
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Sketches are commonly used in the fields of engineering and architecture, especially for the early design phases. Engineers spend considerable time setting up initial designs using pencil and paper, and then redrawing them to any software. This problem can be solved by using the idea to scan the circuit sketch with android device which is drawn on the paper and translate it into standard layouts and run circuit simulations. The scanned image will be pre-processed and further segmented. The segmented image will be used to extract the features which are in turn given for classification. Recognizing sketches may seem so quick and intuitive to humans but it is really a big challenge for the machine. In this proposed work the aim is to achieve high precision trainable electronic circuit component recognizer for sketched circuits with fast response time and simple extensibility to new components.
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Drexler, Dominik, Jendrik Seipp, and Hector Geffner. "Learning Sketches for Decomposing Planning Problems into Subproblems of Bounded Width." Proceedings of the International Conference on Automated Planning and Scheduling 32 (June 13, 2022): 62–70. http://dx.doi.org/10.1609/icaps.v32i1.19786.
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Recently, sketches have been introduced as a general language for representing the subgoal structure of instances drawn from the same domain. Sketches are collections of rules of the form C -> E over a given set of features where C expresses Boolean conditions and E expresses qualitative changes. Each sketch rule defines a subproblem: going from a state that satisfies C to a state that achieves the change expressed by E or a goal state. Sketches can encode simple goal serializations, general policies, or decompositions of bounded width that can be solved greedily, in polynomial time, by the SIW_R variant of the SIW algorithm. Previous work has shown the computational value of sketches over benchmark domains that, while tractable, are challenging for domain-independent planners. In this work, we address the problem of learning sketches automatically given a planning domain, some instances of the target class of problems, and the desired bound on the sketch width. We present a logical formulation of the problem, an implementation using the ASP solver Clingo, and experimental results. The sketch learner and the SIW_R planner yield a domain-independent planner that learns and exploits domain structure in a crisp and explicit form.
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Wolter, Uwe. "Logics of Statements in Context-Category Independent Basics." Mathematics 10, no. 7 (March 28, 2022): 1085. http://dx.doi.org/10.3390/math10071085.
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Based on a formalization of open formulas as statements in context, the paper presents a freshly new and abstract view of logics and specification formalisms. Generalizing concepts like sets of generators in Group Theory, underlying graph of a sketch in Category Theory, sets of individual names in Description Logic and underlying graph-based structure of a software model in Software Engineering, we coin an abstract concept of context. We show how to define, in a category independent way, arbitrary first-order statements in arbitrary contexts. Examples of those statements are defining relations in Group Theory, commutative, limit and colimit diagrams in Category Theory, assertional axioms in Description Logic and constraints in Software Engineering. To validate the appropriateness of the newly proposed abstract framework, we prove that our category independent definitions and constructions give us a very broad spectrum of Institutions of Statements at hand. For any Institution of Statements, a specification (presentation) is given by a context together with a set of first-order statements in that context. Since many of our motivating examples are variants of sketches, we will simply use the term sketch for those specifications. We investigate exhaustively different kinds of arrows between sketches and their interrelations. To pave the way for a future development of category independent deduction calculi for sketches, we define arbitrary first-order sketch conditions and corresponding sketch constraints as a generalization of graph conditions and graph constraints, respectively. Sketch constraints are the crucial conceptual tool to describe and reason about the structure of sketches. We close the paper with some vital observations, insights and ideas related to future deduction calculi for sketches. Moreover, we outline that our universal method to define sketch constraints enables us to establish and to work with conceptual hierarchies of sketches.
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Ashtari, Amirsaman, Chang Wook Seo, Cholmin Kang, Sihun Cha, and Junyong Noh. "Reference Based Sketch Extraction via Attention Mechanism." ACM Transactions on Graphics 41, no. 6 (November 30, 2022): 1–16. http://dx.doi.org/10.1145/3550454.3555504.
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We propose a model that extracts a sketch from a colorized image in such a way that the extracted sketch has a line style similar to a given reference sketch while preserving the visual content identically to the colorized image. Authentic sketches drawn by artists have various sketch styles to add visual interest and contribute feeling to the sketch. However, existing sketch-extraction methods generate sketches with only one style. Moreover, existing style transfer models fail to transfer sketch styles because they are mostly designed to transfer textures of a source style image instead of transferring the sparse line styles from a reference sketch. Lacking the necessary volumes of data for standard training of translation systems, at the core of our GAN-based solution is a self-reference sketch style generator that produces various reference sketches with a similar style but different spatial layouts. We use independent attention modules to detect the edges of a colorized image and reference sketch as well as the visual correspondences between them. We apply several loss terms to imitate the style and enforce sparsity in the extracted sketches. Our sketch-extraction method results in a close imitation of a reference sketch style drawn by an artist and outperforms all baseline methods. Using our method, we produce a synthetic dataset representing various sketch styles and improve the performance of auto-colorization models, in high demand in comics. The validity of our approach is confirmed via qualitative and quantitative evaluations.
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SRDANOVIĆ, Irena, Naomi IDA, Chikako SHIGEMORI BUČAR, Adam KILGARRIFF, and Vojtěch KOVÁŘ. "Japanese Word Sketches: Advances and Problems." Acta Linguistica Asiatica 1, no. 2 (October 20, 2011): 63–82. http://dx.doi.org/10.4312/ala.1.2.63-82.
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In this paper, we present results of an evaluation of Japanese word sketches and address in detail issues that were observed by the evaluators. A word sketch presents a list of salient collocates of a word, organized by the grammatical relations holding between the word and its collocate. The word sketch functionality is incorporated into the Sketch Engine corpus query system and has been created for more than twenty languages so far, including Japanese. The issues that have been discovered in the evaluation of word sketches in Japanese are to be addressed for further enhancement of the word sketch functionality. Other tools and resources which are combined for use and influence the performance of the word sketches should also be looked over. We divide the issues into the following: 1) the lemmatizer and tagger in use, 2) the sketch grammar that is specifically written for Japanese, and 3) the corpus and statistical methods.
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Cao, Yukun, Yuan Feng, and Xike Xie. "Meta-Sketch: A Neural Data Structure for Estimating Item Frequencies of Data Streams." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (June 26, 2023): 6916–24. http://dx.doi.org/10.1609/aaai.v37i6.25846.
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To estimate item frequencies of data streams with limited space, sketches are widely used in real applications, including real-time web analytics, network monitoring, and self-driving. Sketches can be viewed as a model which maps the identifier of a stream item to the corresponding frequency domain. Starting from the premise, we envision a neural data structure, which we term the meta-sketch, to go beyond the basic structure of conventional sketches. The meta-sketch learns basic sketching abilities from meta-tasks constituted with synthetic datasets following Zipf distributions in the pre-training phase, and can be fast adapted to real (skewed) distributions in the adaption phase. Extensive experiments demonstrate the performance gains of the meta-sketch and offer insights into our proposals.
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Chethana H. T. and Trisiladevi C. Nagavi. "A New Framework for Matching Forensic Composite Sketches With Digital Images." International Journal of Digital Crime and Forensics 13, no. 5 (September 2021): 1–19. http://dx.doi.org/10.4018/ijdcf.20210901.oa1.
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Face sketch recognition is considered as a sub-problem of face recognition. Matching composite sketches with its corresponding digital image is one of the challenging tasks. A new convolution neural network (CNN) framework for matching composite sketches with digital images is proposed in this work. The framework consists of a base CNN model that uses swish activation function in the hidden layers. Both composite sketches and digital images are trained separately in the network by providing matching pairs and mismatching pairs. The final output resulted from the network's final layer is compared with the threshold value, and then the pair is assigned to the same or different class. The proposed framework is evaluated on two datasets, and it exhibits an accuracy of 78.26% with extended-PRIP (E-PRIP) and 69.57% with composite sketches with age variations (CSA) respectively. Experimental analysis shows the improved results compared to state-of-the-art composite sketch matching systems.
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Alan Blum. "Sketches." Literature and Medicine 11, no. 2 (1992): 223–36. http://dx.doi.org/10.1353/lm.2011.0202.
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Kinoshita, Yoshiki, John Power, and Makoto Takeyama. "Sketches." Journal of Pure and Applied Algebra 143, no. 1-3 (November 1999): 275–91. http://dx.doi.org/10.1016/s0022-4049(98)00114-5.
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Liu, Bingchen, Yizhe Zhu, Kunpeng Song, and Ahmed Elgammal. "Self-Supervised Sketch-to-Image Synthesis." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (May 18, 2021): 2073–81. http://dx.doi.org/10.1609/aaai.v35i3.16304.
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Imagining a colored realistic image from an arbitrary-drawn sketch is one of human capabilities that we eager machines to mimic. Unlike previous methods that either require the sketch-image pairs or utilize low-quantity detected edges as sketches, we study the exemplar-based sketch-to-image (s2i) synthesis task in a self-supervised learning manner, eliminating the necessity of the paired sketch data. To this end, we first propose an unsupervised method to efficiently synthesize line-sketches for general RGB-only datasets. With the synthetic paired-data, we then present a self-supervised Auto-Encoder (AE) to decouple the content/style features from sketches and RGB-images, and synthesize images both content-faithful to the sketches and style-consistent to the RGB-images. While prior works employ either the cycle-consistence loss or dedicated attentional modules to enforce the content/style fidelity, we show AE's superior performance with pure self-supervisions. To further improve the synthesis quality in high resolution, we also leverage an adversarial network to refine the details of synthetic images. Extensive experiments on $1024^2$ resolution demonstrate a new state-of-art-art performance of the proposed model on CelebA-HQ and Wiki-Art datasets. Moreover, with the proposed sketch generator, the model shows a promising performance on style mixing and style transfer, which the synthesized images are not only style-consistent but also semantically meaningful.
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Naushad Ali Khan and Dr.Tahseen Bibi. "Sketch By Sajjad Hussain Sarmad." Dareecha-e-Tahqeeq 4, no. 2 (July 5, 2023): 27–31. http://dx.doi.org/10.58760/dareechaetahqeeq.v4i2.115.
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In Urdu, sketching begins with Farhat Ullah Baig's writing "Nazir Ahmed's story, some of my words are their oral". Al thought Muhammad Hussain Azad also tried to show him moving pictures by describing the style of his dress and dialogue in detail for different poets in his book "Aab e Hayat" but first these pictures are blurred and incomplete and all the attention of 2nd Muhammad Hussain Azad has been focused on the heels and clothing .The term Urdu literature is the Urdu translation of the sketch English word sketch "For the gender of literature, the word sketch pin portrait is used in English, it is called sketch in Urdu”, Sketch in Urdu is also called personality and metaphorical Sajjad Hussain Sarmad is the first sketcher of Camal Pur District Attock whose collection of sketches has been published as "Nishant e Sarmadi"
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Wang, Fei, Yu Yang, Baoquan Zhao, Dazhi Jiang, Siwei Chen, and Jianqiang Sheng. "Reconstructing 3D Model from Single-View Sketch with Deep Neural Network." Wireless Communications and Mobile Computing 2021 (April 19, 2021): 1–9. http://dx.doi.org/10.1155/2021/5577530.
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In this paper, we introduce a novel 3D shape reconstruction method from a single-view sketch image based on a deep neural network. The proposed pipeline is mainly composed of three modules. The first module is sketch component segmentation based on multimodal DNN fusion and is used to segment a given sketch into a series of basic units and build a transformation template by the knots between them. The second module is a nonlinear transformation network for multifarious sketch generation with the obtained transformation template. It creates the transformation representation of a sketch by extracting the shape features of an input sketch and transformation template samples. The third module is deep 3D shape reconstruction using multifarious sketches, which takes the obtained sketches as input to reconstruct 3D shapes with a generative model. It fuses and optimizes features of multiple views and thus is more likely to generate high-quality 3D shapes. To evaluate the effectiveness of the proposed method, we conduct extensive experiments on a public 3D reconstruction dataset. The results demonstrate that our model can achieve better reconstruction performance than peer methods. Specifically, compared to the state-of-the-art method, the proposed model achieves a performance gain in terms of the five evaluation metrics by an average of 25.5% on the man-made model dataset and 23.4% on the character object dataset using synthetic sketches and by an average of 31.8% and 29.5% on the two datasets, respectively, using human drawing sketches.
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Hähnlein, Felix, Changjian Li, Niloy J. Mitra, and Adrien Bousseau. "CAD2Sketch." ACM Transactions on Graphics 41, no. 6 (November 30, 2022): 1–18. http://dx.doi.org/10.1145/3550454.3555488.
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Concept sketches are ubiquitous in industrial design, as they allow designers to quickly depict imaginary 3D objects. To construct their sketches with accurate perspective, designers rely on longstanding drawing techniques, including the use of auxiliary construction lines to identify midpoints of perspective planes, to align points vertically and horizontally, and to project planar curves from one perspective plane to another. We present a method to synthesize such construction lines from CAD sequences. Importantly, our method balances the presence of construction lines with overall clutter, such that the resulting sketch is both well-constructed and readable, as professional designers are trained to do. In addition to generating sketches that are visually similar to real ones, we apply our method to synthesize a large quantity of paired sketches and normal maps, and show that the resulting dataset can be used to train a neural network to infer normals from concept sketches. 1
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Akbari Farjad, Shahrzad, and Karim Faez. "Matching Forensic Sketches to Mug Shot Photos Using a Population of Sketches Generated by Combining Geometrical Facial Changes and Genetic Algorithms." Oriental journal of computer science and technology 11, no. 2 (April 20, 2018): 78–87. http://dx.doi.org/10.13005/ojcst11.02.03.
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Matching mug shot photos to forensic sketches drawn according to verbal descriptions of eyewitnesses is a decisive point for criminal investigations. However, the incapability of a witness to precisely describe the appearance of a suspect and his/her reliance on a subjective aspect of the description often lead to imprecise and inadequate sketches. This necessitates the development of robust automated matching methods such that least dependency exists on the quality of original sketches. The focus of the paper is on enhancing the preprocessing phase, before the matching phase is applied, by generating a population of sketches out of each initial sketch via applying geometrical changes in facial areas. The population is then optimized using Genetic Algorithms (GA) by adopting the Structural SIMilarity (SSIM) index as the fitness function. The matching is finally applied to the best sketch produced by GA by employing the Local Feature-based Discriminant Analysis (LFDA) framework. The efficiency of the proposed hybrid approach in achieving correct matchings is evaluated against 88 sketch/photo pairs provided by the Michigan State Police Department and Forensic Art Essentials, and 100 sketch/black-and-white photo pairs from FERET database. The experimental results indicate that our proposed approach obtains fairly better results relative to the LFDA framework. Furthermore, we notice a significant improvement in the retrieval rate if sketch/photo pairs are first cropped to central facial areas before a matching technique is applied.
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Li, Haoyu, Qizhi Chen, Yixin Zhang, Tong Yang, and Bin Cui. "Stingy sketch." Proceedings of the VLDB Endowment 15, no. 7 (March 2022): 1426–38. http://dx.doi.org/10.14778/3523210.3523220.
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Recording the frequency of items in highly skewed data streams is a fundamental and hot problem in recent years. The literature demonstrates that sketch is the most promising solution. The typical metrics to measure a sketch are accuracy and speed, but existing sketches make only trade-offs between the two dimensions. Our proposed solution is a new sketch framework called Stingy sketch with two key techniques: Bit-pinching Counter Tree ( BCTree ) and Prophet Queue ( PQueue ) which optimizes both the accuracy and speed. The key idea of BCTree is to split a large fixed-size counter into many small nodes of a tree structure, and to use a precise encoding to perform carry-in operations with low processing overhead. The key idea of PQueue is to use pipelined prefetch technique to make most memory accesses happen in L2 cache without losing precision. Importantly, the two techniques are cooperative so that Stingy sketch can improve accuracy and speed simultaneously. Extensive experimental results show that Stingy sketch is up to 50% more accurate than the SOTA of accuracy-oriented sketches and is up to 33% faster than the SOTA of speed-oriented sketches.
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Jacquemyn, Carl, Margaret E. H. Pataki, Gary J. Hampson, Matthew D. Jackson, Dmytro Petrovskyy, Sebastian Geiger, Clarissa C. Marques, et al. "Sketch-based interface and modelling of stratigraphy and structure in three dimensions." Journal of the Geological Society 178, no. 4 (February 22, 2021): jgs2020–187. http://dx.doi.org/10.1144/jgs2020-187.
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Geological modelling is widely used to predict resource potential in subsurface reservoirs. However, modelling is often slow, requires use of mathematical methods that are unfamiliar to many geoscientists, and is implemented in expert software. We demonstrate here an alternative approach using sketch-based interface and modelling, which allows rapid creation of complex three-dimensional (3D) models from 2D sketches. Sketches, either on vertical cross-sections or in map-view, are converted to 3D surfaces that outline geological interpretations. We propose a suite of geological operators that handle interactions between the surfaces to form a geologically realistic 3D model. These operators deliver the flexibility to sketch a geological model in any order and provide an intuitive framework for geoscientists to rapidly create 3D models. Two case studies are presented, demonstrating scenarios in which different approaches to model sketching are used depending on the geological setting and available data. These case studies show the strengths of sketching with geological operators. Sketched 3D models can be queried visually or quantitatively to provide insights into heterogeneity distribution, facies connectivity or dynamic model behaviour; this information cannot be obtained by sketching in 2D or on paper.Supplementary material: Rapid Reservoir Modelling prototype (executable and source code) is available at: https://bitbucket.org/rapidreservoirmodelling/rrm. Supplementary screen recordings for the different case studies showing sketch-based modelling in action are available at https://doi.org/10.6084/m9.figshare.c.5084141 and supplementary figure S1-S4 are available at https://doi.org/10.6084/m9.figshare.c.5303043
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Li, Changjian, Hao Pan, Adrien Bousseau, and Niloy J. Mitra. "Free2CAD." ACM Transactions on Graphics 41, no. 4 (July 2022): 1–16. http://dx.doi.org/10.1145/3528223.3530133.
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CAD modeling, despite being the industry-standard, remains restricted to usage by skilled practitioners due to two key barriers. First, the user must be able to mentally parse a final shape into a valid sequence of supported CAD commands; and second, the user must be sufficiently conversant with CAD software packages to be able to execute the corresponding CAD commands. As a step towards addressing both these challenges, we present Free2CAD wherein the user can simply sketch the final shape and our system parses the input strokes into a sequence of commands expressed in a simplified CAD language. When executed, these commands reproduce the sketched object. Technically, we cast sketch-based CAD modeling as a sequence-to-sequence translation problem, for which we leverage the powerful Transformers neural network architecture. Given the sequence of pen strokes as input, we introduce the new task of grouping strokes that correspond to individual CAD operations. We combine stroke grouping with geometric fitting of the operation parameters, such that intermediate groups are geometrically corrected before being reused, as context, for subsequent steps in the sequence inference. Although trained on synthetically-generated data, we demonstrate that Free2CAD generalizes to sketches created from real-world CAD models as well as to sketches drawn by novice users. Code and data are at https://github.com/Enigma-li/Free2CAD.
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Berrahal, Mohammed, and Mostafa Azizi. "Augmented binary multi-labeled CNN for practical facial attribute classification." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 2 (August 1, 2021): 973. http://dx.doi.org/10.11591/ijeecs.v23.i2.pp973-979.
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Both human face recognition and generation by machines are currently an active area of computer vision, drawing curiosity of researchers, capable of performing amazing image analysis, and producing applications in multiple domains. In this paper, we propose a new approach for face attributes classification (FAC) taking advantage from both binary classification and data augmentation. With binary classification we can reach high prediction scores, while augmented data prevent overfitting and overcome the lack of data for sketched photos. Our approach, named Augmented binary multilabel CNN (ABM-CNN), consists of three steps: i) splitting data; ii) transformed-it to sketch (simplification process); iii) train separately each attribute with two convolutional neural networks; the whole process includes two networks: the first (resp. the second) one is to predict attributes on real images (resp. sketches) as inputs. Through experimentation, we figure out that some attributes give high prediction rates with sketches rather than with real images. On the other hand, we build a new face dataset, more consistent and complete, by generating images using Style-GAN model, to which we apply our method for extracting face attributes. As results, our proposal demonstrates more performances compared to those of related works.
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Gao, Lin, Feng-Lin Liu, Shu-Yu Chen, Kaiwen Jiang, Chun-Peng Li, Yu-Kun Lai, and Hongbo Fu. "SketchFaceNeRF: Sketch-based Facial Generation and Editing in Neural Radiance Fields." ACM Transactions on Graphics 42, no. 4 (July 26, 2023): 1–17. http://dx.doi.org/10.1145/3592100.
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Realistic 3D facial generation based on Neural Radiance Fields (NeRFs) from 2D sketches benefits various applications. Despite the high realism of free-view rendering results of NeRFs, it is tedious and difficult for artists to achieve detailed 3D control and manipulation. Meanwhile, due to its conciseness and expressiveness, sketching has been widely used for 2D facial image generation and editing. Applying sketching to NeRFs is challenging due to the inherent uncertainty for 3D generation with 2D constraints, a significant gap in content richness when generating faces from sparse sketches, and potential inconsistencies for sequential multi-view editing given only 2D sketch inputs. To address these challenges, we present SketchFaceNeRF, a novel sketch-based 3D facial NeRF generation and editing method, to produce free-view photo-realistic images. To solve the challenge of sketch sparsity, we introduce a Sketch Tri-plane Prediction net to first inject the appearance into sketches, thus generating features given reference images to allow color and texture control. Such features are then lifted into compact 3D tri-planes to supplement the absent 3D information, which is important for improving robustness and faithfulness. However, during editing, consistency for unseen or unedited 3D regions is difficult to maintain due to limited spatial hints in sketches. We thus adopt a Mask Fusion module to transform free-view 2D masks (inferred from sketch editing operations) into the tri-plane space as 3D masks, which guide the fusion of the original and sketch-based generated faces to synthesize edited faces. We further design an optimization approach with a novel space loss to improve identity retention and editing faithfulness. Our pipeline enables users to flexibly manipulate faces from different viewpoints in 3D space, easily designing desirable facial models. Extensive experiments validate that our approach is superior to the state-of-the-art 2D sketch-based image generation and editing approaches in realism and faithfulness.
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Reddi, Surya Prakasa Rao, Madhusudhana Rao T.V., Srinivasa Rao P., and Prakash Bethapudi. "An Efficient Method for Facial Sketches Synthesization Using Generative Adversarial Networks." Webology 19, no. 1 (January 20, 2022): 3119–29. http://dx.doi.org/10.14704/web/v19i1/web19206.
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The synthesis of facial sketches is an important technique in digital entertainment and law enforcement agencies. Recent advancements in deep learning have shown its possibility in generating images/sketches using attribute guided features. Facial features are important attributes because they determine human faces' detailed description and appearance during sketch generation. Traditionally, the forensic or composite artist has to sketch by interviewing witnesses manually. To automate this process of face sketch generation, a deep learning-based generative adversarial network incorporated with multiple activation functions is proposed for its efficiency improvement. The proposed model is extensively tested using different evaluation metrics such as RMSE, PSNR, SSIM, SRE, SAM, UIQ & BRISQUE.
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Ebrahim, Ali. "High-Level Design Optimizations for Implementing Data Stream Sketch Frequency Estimators on FPGAs." Electronics 11, no. 15 (July 31, 2022): 2399. http://dx.doi.org/10.3390/electronics11152399.
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This paper presents simple yet effective optimizations for implementing data stream frequency estimation sketch kernels using High-Level Synthesis (HLS). The paper addresses design issues common to sketches utilizing large portions of the embedded RAM resources in a Field Programmable Gate Array (FPGA). First, a solution based on Load-Store Queue (LSQ) architecture is proposed for resolving the memory dependencies associated with the hash tables in a frequency estimation sketch. Second, performance fine-tuning through high-level pragmas is explored to achieve the best possible throughput. Finally, a technique based on pre-processing the data stream in a small cache memory prior to updating the sketch is evaluated to reduce the dynamic power consumption. Using an Intel HLS compiler, a proposed optimized hardware version of the popular Count-Min sketch utilizing 80% of the embedded RAM in an Intel Arria 10 FPGA, achieved more than 3x the throughput of an unoptimized baseline implementation. Furthermore, the sketch update rate is significantly reduced when the input stream is skewed. This, in turn, minimizes the effect of high throughput on dynamic power consumption. Compared to FPGA sketches in the published literature, the presented sketch is the most well-rounded sketch in terms of features and versatility. In terms of throughput, the presented sketch is on a par with the fastest sketches fine-tuned at the Register Transfer Level (RTL).
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Mogoşan, Iulia. "Schubert’s “Tenth”: an Interpretation Between Construction and Restitution." Artes. Journal of Musicology 25, no. 1 (April 1, 2022): 58–81. http://dx.doi.org/10.2478/ajm-2022-0005.
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Abstract In Franz Schubert’s creation the fragment takes on various forms of manifestation, ranging from the fragmentary reception of an already constituted piece to the fragmentary notation, in the form of a sketch, of a work that has not yet been completed. A special place belongs to the Tenth Symphony in D major, D 936A, by Schubert, left unfinished; we received it as a sketch, in a convolute, together with two other unfinished symphonies in the same key: D 615 and D 708A. The present study aims to expose three artistic interpretations of these sketches, materialized in completed musical works, with a distinct approach. The intention of the British composer and conductor Brian Newbould was to finish the symphony in the way that Schubert himself would have done, anchoring the musical ideas from the sketches in the composer’s style. Peter Gülke approached the sketches through the eyes of the researcher and the analyst, with the intention of obtaining their most accurate and authentic reproduction, emphasizing the materialization of some of Schubert’s possible intentions. Finally, Luciano Berio manages in Rendering to musically render the sketches per se, imagining a musical fresco where the concrete musical ideas, written by Schubert, are deliberately mixed with the provisional character of the manuscript.
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PETROVČIČ, Mateja. "Word Sketches of Separable Words Liheci in Chinese." Acta Linguistica Asiatica 6, no. 1 (June 29, 2016): 47–57. http://dx.doi.org/10.4312/ala.6.1.47-57.
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Separable words (liheci) are a special type of Chinese verbs with unique syntactical features in a sense that some elements come in between the two morphemes of a verb for a sentence to be grammatically acceptable. Not all separable words are extendable to the same degree. To understand the behaviour of words, it is generally advised to check word sketches, because they are based on large text corpora. This article examines how Chinese separable words are treated in Sketch Engine and discusses on the approprietness of the available Chinese corpora for word sketches. It further stresses the importance of including information on inserted elements in word sketches and gives suggestions on how to include them.
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Duval, Dominique, and Jean-Claude Reynaud. "Sketches and computation – I: basic definitions and static evaluation." Mathematical Structures in Computer Science 4, no. 2 (June 1994): 185–238. http://dx.doi.org/10.1017/s0960129500000438.
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We define a categorical framework, based on the notion of sketch, for specification and evaluation in the senses of algebraic specifications and algebraic programming. This framework goes far beyond our initial motivation, which was to specify computation with algebraic numbers. We begin by redefining sketches in order to deal explicitly with programs. Expressions and terms are carefully defined and studied, then quasi-projective sketches are introduced. We describe static evaluation in these sketches: we propose a rigorous basis for evalution in the corresponding structures. These structures admit an initial model, but are not necessarily equational. In Part II (Duval and Reynaud 1994), we study a more general process, called dynamic evaluation, for structures that may have no initial model.
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Adámek, Jiří, and Jiří Rosický. "Finitary sketches and finitely accessible categories." Mathematical Structures in Computer Science 5, no. 3 (September 1995): 315–22. http://dx.doi.org/10.1017/s0960129500000773.
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Every accessible category is proved to be sketchable by a sketch with finite colimits. In contrast, a finitely accessible category is presented that cannot be sketched by a finitary sketch, i.e., a sketch with finite limits and finite colimits. Also, a category sketchable by a finitary sketch is found that is not finitely accessible.
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Tang, Ming, Zoe Falomir, and Yehua Sheng. "A Multilevel Road Alignment Model for Spatial-Query-by-Sketch." Applied Sciences 10, no. 21 (October 30, 2020): 7685. http://dx.doi.org/10.3390/app10217685.
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A sketch map represents an individual’s perception of a specific location. However, the information in sketch maps is often distorted and incomplete. Nevertheless, the main roads of a given location often exhibit considerable similarities between the sketch maps and metric maps. In this work, a shape-based approach was outlined to align roads in the sketch maps and metric maps. Specifically, the shapes of main roads were compared and analyzed quantitatively and qualitatively in three levels pertaining to an individual road, composite road, and road scene. An experiment was performed in which for eight out of nine maps sketched by our participants, accurate road maps could be obtained automatically taking as input the sketch and the metric map. The experimental results indicate that accurate matches can be obtained when the proposed road alignment approach Shape-based Spatial-Query-by-Sketch (SSQbS) is applied to incomplete or distorted roads present in sketch maps and even to roads with an inconsistent spatial relationship with the roads in the metric maps. Moreover, highly similar matches can be obtained for sketches involving fewer roads.
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Carruth, Hayden. "Three Sketches." Hudson Review 56, no. 1 (2003): 133. http://dx.doi.org/10.2307/3852928.
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Adámek, J., P. T. Johnstone, J. A. Makowsky, and J. Rosický. "Finitary sketches." Journal of Symbolic Logic 62, no. 3 (September 1997): 699–707. http://dx.doi.org/10.2307/2275568.
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AbstractFinitary sketches, i.e., sketches with finite-limit and finite-colimit specifications, are proved to be as strong as geometric sketches, i.e., sketches with finite-limit and arbitrary colimit specifications. Categories sketchable by such sketches are fully characterized in the infinitary first-order logic: they are axiomatizable by σ-coherent theories, i.e., basic theories using finite conjunctions, countable disjunctions, and finite quantifications. The latter result is absolute; the equivalence of geometric and finitary sketches requires (in fact, is equivalent to) the non-existence of measurable cardinals.