Sketch recognition

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

Hand gesture recognition using different algorithms based on artificial neural network

Abstract: Gesture is one of the most natural and expressive ways of communications between human and computer in a real system. We naturally use various gestures to express our own intentions in everyday life. Hand gesture is one of the important methods of non-verbal communication for human beings. Hand gesture recognition based man-machine interface is being developed vigorously in recent years. This paper gives an overview of different methods for recognizing the hand gestures using MATLAB. It also gives the working details of recognition process using Edge detection and Skin detection algorithms.

Using an Active Shape Structural Model for Biometric Sketch Recognition

Abstract: A deformable shape model called Active Shape Structural Model (ASSM) is used within a biometric framework to define a biometric sketch recognition algorithm. Experimental results show that mainly structural relations rather than statistical features can be used to recognize sketches of different users with high accuracy.

Transfer Learning Based Evolutionary Algorithm for Composite Face Sketch Recognition

Abstract: Matching facial sketches to digital face images has widespread application in law enforcement scenarios. Recent advancements in technology have led to the availability of sketch generation tools, minimizing the requirement of a sketch artist. While these sketches have helped in manual authentication, matching composite sketches with digital mugshot photos automatically show high modality gap. This research aims to address the task of matching a composite face sketch image to digital images by proposing a transfer learning based evolutionary algorithm. A new feature descriptor, Histogram of Image Moments, has also been presented for encoding features across modalities. Moreover, IIITD Composite Face Sketch Database of 150 subjects is presented to fill the gap due to limited availability of databases in this problem domain. Experimental evaluation and analysis on the proposed dataset show the effectiveness of the transfer learning approach for performing cross-modality recognition.

Toward natural interaction in the real world: real-time gesture recognition

Abstract: Using a new hand tracking technology capable of tracking 3D hand postures in real-time, we developed a recognition system for continuous natural gestures. By natural gestures, we mean those encountered in spontaneous interaction, rather than a set of artificial gestures chosen to simplify recognition. To date we have achieved 95.6% accuracy on isolated gesture recognition, and 73% recognition rate on continuous gesture recognition, with data from 3 users and twelve gesture classes. We connected our gesture recognition system to Google Earth, enabling real time gestural control of a 3D map. We describe the challenges of signal accuracy and signal interpretation presented by working in a real-world environment, and detail how we overcame them.

Soft computing, fuzzy logic and recognition technology

Abstract: Recognition systems of one kind or another have been around for a long time. But what we are beginning to see today are recognition systems that are capable of performing tasks that could not be done in the past. The quantum jump in the capabilities of today's recognition systems reflect three converging developments: (a) major advances in sensor technology; (b) major advances in sensor data processing technology; and (c) the use of soft computing techniques to infer a conclusion from observed data.