Topic
Sketch recognition
About: Sketch recognition is a research topic. Over the lifetime, 1611 publications have been published within this topic receiving 40284 citations.
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TL;DR: In this paper , a two-fold methodology of creating fabric designs and patterns, using both traditional object detection and deep learning methodologies, is introduced, which augments a given partial sketch, which is taken as an input from the user.
Abstract: This paper introduces a two-fold methodology of creating fabric designs and patterns, using both traditional object detection and Deep Learning methodologies. The proposed methodology first augments a given partial sketch, which is taken as an input from the user. This sketch augmentation is performed through a combination of object detection, canvas quilting, and seamless tiling, to achieve a repeatable block of a pattern. This augmented pattern is then carried forward as an input to our variation of the pix2pix GAN, which outputs a styled and colored pattern using the sketch as a baseline. This design pipeline is an overall overhaul of the creative process of a textile designer, and is intended to provide assistance in the design of modern textiles in the industry by reducing the time from going to a sketch to a pattern in under a minute.
10 Feb 2023
TL;DR: Wang et al. as mentioned in this paper proposed a new task named sketch less face image retrieval (SLFIR), in which the retrieval was carried out at each stroke and aim to retrieve the target face photo using a partial sketch with as few strokes as possible.
Abstract: In some specific scenarios, face sketch was used to identify a person. However, drawing a complete face sketch often needs skills and takes time, which hinder its widespread applicability in the practice. In this study, we proposed a new task named sketch less face image retrieval (SLFIR), in which the retrieval was carried out at each stroke and aim to retrieve the target face photo using a partial sketch with as few strokes as possible (see Fig.1). Firstly, we developed a method to generate the data of sketch with drawing process, and opened such dataset; Secondly, we proposed a two-stage method as the baseline for SLFIR that (1) A triplet network, was first adopt to learn the joint embedding space shared between the complete sketch and its target face photo; (2) Regarding the sketch drawing episode as a sequence, we designed a LSTM module to optimize the representation of the incomplete face sketch. Experiments indicate that the new framework can finish the retrieval using a partial or pool drawing sketch.
05 Apr 2023
TL;DR: Zhang et al. as mentioned in this paper proposed a fine-grained triple-branch CNN architecture based on hybrid attention mechanism for instance-level logo sketch retrieval, which can capture the key query-specific information from the limited visual cues in the logo sketches.
Abstract: Sketch-based image retrieval, which aims to use sketches as queries to retrieve images containing the same query instance, receives increasing attention in recent years. Although dramatic progress has been made in sketch retrieval, few efforts are devoted to logo sketch retrieval which is still hindered by the following challenges: Firstly, logo sketch retrieval is more difficult than typical sketch retrieval problem, since a logo sketch usually contains much less visual contents with only irregular strokes and lines. Secondly, instance-specific sketches demonstrate dramatic appearance variances, making them less identifiable when querying the same logo instance. Thirdly, there exist several sketch retrieval benchmarking datasets nowadays, whereas an instance-level logo sketch dataset is still publicly unavailable. To address the above-mentioned limitations, we make twofold contributions in this study for instance-level logo sketch retrieval. To begin with, we construct an instance-level logo sketch dataset containing 2k logo instances and exceeding 9k sketches. To our knowledge, this is the first publicly available instance-level logo sketch dataset. Next, we develop a fine-grained triple-branch CNN architecture based on hybrid attention mechanism termed LogoNet for accurate logo sketch retrieval. More specifically, we embed the hybrid attention mechanism into the triple-branch architecture for capturing the key query-specific information from the limited visual cues in the logo sketches. Experimental evaluations both on our assembled dataset and public benchmark datasets demonstrate the effectiveness of our proposed network.
19 Sep 2022
TL;DR: In this article , the authors present an application that visualizes a subset from 114 digital pen features in real-time while drawing and provides an easy-to-use interface that allows application developers and machine learning practitioners to learn how pen features encode their inputs, helps in the feature selection process, and enables rapid prototyping of sketch and gesture classifiers.
Abstract: Many features have been proposed for encoding the input signal from digital pens and touch-based interaction. They are widely used for analyzing and classifying handwritten texts, sketches, or gestures. Although they are well defined mathematically, many features are non-trivial and therefore difficult to understand for a human. In this paper, we present an application that visualizes a subset from 114 digital pen features in real-time while drawing. It provides an easy-to-use interface that allows application developers and machine learning practitioners to learn how digital pen features encode their inputs, helps in the feature selection process, and enables rapid prototyping of sketch and gesture classifiers.
29 Aug 2011
TL;DR: A novel technique using Mutual Information to classify gestures in a recognition system is presented, based on well-known information theory metrics, which allows for this technique to be easily implemented.
Abstract: Proliferation of gestural interfaces necessitates the creation of robust gesture recognition systems. A novel technique using Mutual Information to classify gestures in a recognition system is presented. As this technique is based on well-known information theory metrics the underlying operation is not as complex as many other techniques which allows for this technique to be easily implemented. A high recognition rate of 98.55% was achieved, with recognition occurring in under 10ms.