SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis

Synthesizing realistic images from human drawn sketches is a challenging problem in computer graphics and vision. Existing approaches either need exact edge maps, or rely on retrieval of existing photographs. In this work, we propose a novel Generative Adversarial Network (GAN) approach that synthesizes plausible images from 50 categories including motorcycles, horses and couches. We demonstrate a data augmentation technique for sketches which is fully automatic, and we show that the augmented data is helpful to our task. We introduce a new network building block suitable for both the generator and discriminator which improves the information flow by injecting the input image at multiple scales. Compared to state-of-the-art image translation methods, our approach generates more realistic images and achieves significantly higher Inception Scores.Comment: Accepted to CVPR 201

Deep Shape Matching

We cast shape matching as metric learning with convolutional networks. We break the end-to-end process of image representation into two parts. Firstly, well established efficient methods are chosen to turn the images into edge maps. Secondly, the network is trained with edge maps of landmark images, which are automatically obtained by a structure-from-motion pipeline. The learned representation is evaluated on a range of different tasks, providing improvements on challenging cases of domain generalization, generic sketch-based image retrieval or its fine-grained counterpart. In contrast to other methods that learn a different model per task, object category, or domain, we use the same network throughout all our experiments, achieving state-of-the-art results in multiple benchmarks.Comment: ECCV 201

Component Segmentation of Engineering Drawings Using Graph Convolutional Networks

We present a data-driven framework to automate the vectorization and machine interpretation of 2D engineering part drawings. In industrial settings, most manufacturing engineers still rely on manual reads to identify the topological and manufacturing requirements from drawings submitted by designers. The interpretation process is laborious and time-consuming, which severely inhibits the efficiency of part quotation and manufacturing tasks. While recent advances in image-based computer vision methods have demonstrated great potential in interpreting natural images through semantic segmentation approaches, the application of such methods in parsing engineering technical drawings into semantically accurate components remains a significant challenge. The severe pixel sparsity in engineering drawings also restricts the effective featurization of image-based data-driven methods. To overcome these challenges, we propose a deep learning based framework that predicts the semantic type of each vectorized component. Taking a raster image as input, we vectorize all components through thinning, stroke tracing, and cubic bezier fitting. Then a graph of such components is generated based on the connectivity between the components. Finally, a graph convolutional neural network is trained on this graph data to identify the semantic type of each component. We test our framework in the context of semantic segmentation of text, dimension and, contour components in engineering drawings. Results show that our method yields the best performance compared to recent image, and graph-based segmentation methods.Comment: Preprint accepted to Computers in Industr

Approaches Used to Recognise and Decipher Ancient Inscriptions: A Review

Inscriptions play a vital role in historical studies. In order to boost tourism and academic necessities, archaeological experts, epigraphers and researchers recognised and deciphered a great number of inscriptions using numerous approaches. Due to the technological revolution and inefficiencies of manual methods, humans tend to use automated systems. Hence, computational archaeology plays an important role in the current era. Even though different types of research are conducted in this domain, it still poses a big challenge and needs more accurate and efficient methods. This paper presents a review of manual and computational approaches used to recognise and decipher ancient inscriptions.Keywords: ancient inscriptions, computational archaeology, decipher, script

Seismic Facies Classification of an Intraslope Minibasin in The Keathley Canyon, Northern Gulf of Mexico

This work examines several volume attributes extracted from 3D seismic data with the goal of seismic facies classification and lithology prediction in intraslope minibasins. The study area is in the Keathley Canyon protraction (KC), within the middle slope of the Northern Gulf of Mexico (GOM). It lays within the tabular salt and minibasins province downdip of the main Pliocene and Pleistocene deltaic depocenters. Interaction between sedimentation and mobile salt substrate lead to the emergence of many stratigraphic patterns in the intraslope minibasins. Interest in subsalt formations left above salt formations poorly logged. Facies classification using Artificial Neural Network (ANN) was applied in those poorly logged areas. The resultant facies classes were calibrated and used to predict the lithology of the recognized facies patterns in an intraslope minibasin, away from well control. Three types of facies classes were identified: Convergent thinning, convergent baselaping and bypassing. The convergent baselaping are found to be the most sand rich among all other facies

Seismic Facies Classification of an Intraslope Minibasin in The Keathley Canyon, Northern Gulf of Mexico

This work examines several volume attributes extracted from 3D seismic data with the goal of seismic facies classification and lithology prediction in intraslope minibasins. The study area is in the Keathley Canyon protraction (KC), within the middle slope of the Northern Gulf of Mexico (GOM). It lays within the tabular salt and minibasins province downdip of the main Pliocene and Pleistocene deltaic depocenters. Interaction between sedimentation and mobile salt substrate lead to the emergence of many stratigraphic patterns in the intraslope minibasins. Interest in subsalt formations left above salt formations poorly logged. Facies classification using Artificial Neural Network (ANN) was applied in those poorly logged areas. The resultant facies classes were calibrated and used to predict the lithology of the recognized facies patterns in an intraslope minibasin, away from well control. Three types of facies classes were identified: Convergent thinning, convergent baselaping and bypassing. The convergent baselaping are found to be the most sand rich among all other facies