Sketch recognition

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

A self-learning visual pattern explorer and recognizer using a higher order neural network

Abstract: A proposal by M. B. Reid et al. (1989) to improve the efficiency of higher-order neural networks was built into a pattern recognition system that autonomously learns to categorize and recognize patterns independently of their position in an input image. It does this by combining higher-order with first-order networks and the mechanisms known from ART. Its recognition is based on a 16*16 pixel input which contains a section of the image found by a separate centering mechanism. With this system position invariant recognition can be implemented efficiently, while combining all the advantages of the subsystems. >

Automated Hand-drawn sketches retrieval and recognition using regularized Particle Swarm Optimization based deep convolutional neural network

Abstract: One of the most popular and rising research area of image processing is free hand-drawn sketch recognition and its retrieval Enlarger number of methods is introduced to retrieve the sketch images but it made few complexity issues and their performance often degraded So, in this paper, we proposed an effective method of Regularized Particle Swarm Optimization Based Deep Convolutional Neural Network (RPSO-DCNN) algorithm to retrieve the performance of free hand-drawn sketches In feature extraction, the Regularized Particle Swarm Optimization (RPSO) model that aim is to produce an optimal evolutionary deep learning result Therefore, the free hand-drawn sketch image classification and its retrieval are performed by Support Vector Machine and Levenshtein distance-based fuzzy k-nearest neighbour (L-FkNN) algorithms Hence, this work can bring in communication between human and computer Experimentally, the simulation work of the proposed RPSO-DCNN model is implemented in the running software of MATLAB The sketch images are chosen from the TU-Berlin dataset, Sketch dataset, SHREC13 dataset, Flickr dataset and Sketchy dataset Aiming is to facilitate the performance of the proposed RPSO-DCNN model with various kinds of state of art methods such as H-CNN, Fuzzy, CNN, MARQS and TCVD The experimental result demonstrates that, the proposed RPSO-DCNN accomplish the optimal accuracy with different state-of-art methods

A 3D Gesture Recognition System for Multimodal Dialog Systems

Abstract: We present a framework for integrating dynamic gestures as a new input modality into arbitrary applications. The framework allows training new gestures and recognizing them as user input with the help of machine learning algorithms. The precision of the gesture recognition is evaluated with special attention to the elderly. We show how this functionality is implemented into our dialogue system and present an example application which allows the system to learn and recognize gestures in a speech based dialogue.

One shot learning gesture recognition with Kinect sensor

Abstract: Gestures are both natural and intuitive for Human-Computer-Interaction (HCI) and the one-shot learning scenario is one of the real world situations in terms of gesture recognition problems. In this demo, we present a hand gesture recognition system using the Kinect sensor, which addresses the problem of one-shot learning gesture recognition with a user-defined training and testing system. Such a system can behave like a remote control where the user can allocate a specific function using a prefered gesture by performing it only once. To adopt the gesture recognition framework, the system first automatically segments an action sequence into atomic tokens, and then adopts the Extended-Motion-History-Image (Extended-MHI) for motion feature representation. We evaluate the performance of our system quantitatively in Chalearn Gesture Challenge, and apply it to a virtual one shot learning gesture recognition system.

Dimensionality reduction-based building recognition

Abstract: Object recognition is being paid more and more attention in computer vision research and a variety of algorithms have been put forward to enhance the recognition performance. However, building recognition, a relatively specific recognition task, is still at a preliminary stage of development, because the challenging task includes rotation, scaling, illumination changes, occlusion, etc. A building recognition scheme is proposed in this paper, which integrates biologically-inspired feature extraction and dimensionality reduction. Experiments undertaken on our own constructed building database demonstrate that our proposed scheme can achieve satisfactory results.