Sketch recognition

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

Attentive gesture recognition

Abstract: This paper presents a novel method for static gesture recognition based on visual attention Our proposed method makes use of a visual attention model to automatically select points that correspond to fixation points of the human eye Gesture recognition is then performed using the determined visual attention fixation points For this purpose, shape context descriptors are used to compare the sparse fixation points of gestures for classification Simulation results are presented in order to illustrate the performance of the proposed perceptual-based attentive gesture recognition method The proposed method not only helps in the development of more natural user-centric interactive interfaces but is also able to achieve a 9642% classification accuracy on the Triesch database of hand postures, which is superior to other methods presented in the literature

Survey on Dynamic Hand Gesture Recognition with Computer Vision

Abstract: According to classification of system model,hand segmentation,hand modeling and hand recognition,the paper surveyed these technologies with computer vision in last several years in detail.Existing problems are analyzed and issues of future researches are presented.In terms of analysis results,simple wearable device,depth vision sensor and integration of multi method will be developing trends in hand recognition in the future.

A Review of Product Sketching in Early Phases of Design

Abstract: Sketching has been the chosen method of expression of product ideas in the initial phase of product design.Existing CAD modeling systems do not support the early phases of design effectively. There has been growing interest to integrate sketching and 3D modeling mainly because of recent developments in interfaces likemotion trackers and haptic devices. The paper reviews the state of the art literature in the areas of conceptual design and sketching, 3D shape creation and visualization, sketch based modeling and sketch recognition to understand the scope of 3D sketching in the design process. It is observed that the majority of the literatures focus on creation of 3D models by interpreting 2D sketch strokes. Sketching behavior of the designer, while creating sketches directly in 3D needs detailed study

Multi-touch Interaction with Gesture Recognition

Abstract: This master's thesis explores the world of multi-touch interaction with gesture recognition. The focus is on camera based multi-touch techniques, as these provide a new dimension to multi-touch with its ability to recognize objects. During the project, a multi-touch table based on the technology Diffused Surface Illumination has been built. In addition to building a table, a complete gesture recognition system has been implemented, and different gesture recognition algorithms have been successfully tested in a multi-touch environment. The goal with this table, and the accompanying gesture recognition system, is to create an open and affordable multi-touch solution, with the purpose of bringing multi-touch out to the masses. By doing this, more people will be able to enjoy the benefits of a more natural interaction with computers. In a larger perspective, multi-touch is just the beginning, and by adding additional modalities to our applications, such as speech recognition and full body tracking, a whole new level of computer interaction will be possible.

Face Recognition - Algorithmic Approach For Large Datasets And 3D Based Point Clouds

Abstract: This work proposes solutions for two different scenarios in face recognition and verification. The first scenario involves large scale unconstrained unsupervised face recognition. The proposed system for this scenario is a complete face recognition framework. The proposed system first studies the performance of unsupervised face recognition for frontalized captured faces in the wild under the effect of a single image super-resolution algorithm. The system also introduces new high dimensional features based on LBP and SURF that perform better than the state-of-the-art features for unconstrained unsupervised face recognition. To solve the large scale recognition process, a new algorithm has been designed to manipulate face images in the dataset. This new algorithm represents all training face images as a fully connected graph. The algorithm then divides the fully connected graph into simpler sub-graphs to enhance the overall recognition rate. The sub-graphs are generated dynamically, and a comparison between different sub-graph selection techniques including minimizing edge weight sums, random selection, and maximizing sum of edge weights inside the sub-graph is provided. Results show that the optimized hierarchical dynamic technique developed with sub-graphs selection increases the recognition rate in large benchmark image dataset by more than 40% for rank 1 recognition rate compared to the original single large graph method. The approach developed in this research is tested on different datasets, especially if the number of images per person in the