Sketch recognition

About: Sketch recognition is a research topic. Over the lifetime, 1611 publications have been published within this topic receiving 40284 citations.

Face Photo-Sketch Recognition Using Bidirectional Collaborative Synthesis Network.

Abstract: This research features a deep-learning based framework to address the problem of matching a given face sketch image against a face photo database. The problem of photo-sketch matching is challenging because 1) there is large modality gap between photo and sketch, and 2) the number of paired training samples is insufficient to train deep learning based networks. To circumvent the problem of large modality gap, our approach is to use an intermediate latent space between the two modalities. We effectively align the distributions of the two modalities in this latent space by employing a bidirectional (photo -> sketch and sketch -> photo) collaborative synthesis network. A StyleGAN-like architecture is utilized to make the intermediate latent space be equipped with rich representation power. To resolve the problem of insufficient training samples, we introduce a three-step training scheme. Extensive evaluation on public composite face sketch database confirms superior performance of our method compared to existing state-of-the-art methods. The proposed methodology can be employed in matching other modality pairs.

Horizontal flip-invariant sketch recognition via local patch hashing

Abstract: This paper introduces a flip aware patch matching frame-work that facilitates scalable sketch recognition. An overlapping spatial grid is utilized to generate an ensemble of patches for each sketch. We rank similarities between freely drawn sketches via a spatial voting process where similar patches in terms of shape and structure arbitrate for the result. Patch similarity is efficiently estimated via the min-hash algorithm. A novel spatial aware reverse index structure ensures the scalability of our scheme. We show the benefits of horizontal flip invariance and structural information in sketch recognition and demonstrate state-of-the-art results in two challenging sketch datasets.

PPTLens: Create Digital Objects with Sketch Images

Abstract: In this work, we introduce the PPTLens system to convert sketch images captured by smart phones to digital flowcharts in PowerPoint. Different from existing sketch recognition system, which is based on hand-drawn strokes, PPTLens enables users to use sketch images as inputs directly. It's more challenging since strokes extracted from sketch images might not only be very messy, but also without temporal information of the drawings. To implement the 'Image to Object' (I2O) scenario, we propose a novel sketch image recognition framework, including an effective stroke extraction strategy and a novel offline sketch parsing algorithm. By enabling sketch images as inputs, our system makes flowchart/diagram production much more convenient and easier.

Aprendiendo un nuevo idioma

Abstract: In the followed article is presented a program able to make gesture image recognition, it is capable to identify each one letter of alphabet. The developments objective is make possible any person can be able to understand and by self-learning to get acknowledge the signal language.

4Y model: A novel approach for finger identification using KINECT

Abstract: In Human Computer Interaction (HCI), one of the recent research areas is Hand Gesture Recognition (HGR). In hand gesture recognition, finger identification and fingertip detection is a challenging work. Because of the enormous applications like sign language, human robot interaction, gesture based applications this area is gaining researchers' attention. In this paper, a novel approach of finger identification named as 4Y model, is proposed. This model is based on geometric calculations and general biometric features. The experimental result for the model gives up to 92% accuracy based on its inputs.