Computer vision based unistroke keyboards

Erdem, A
Erdem, E
Atalay, Mehmet Volkan
Cetin, AE
In this paper we present a unistroke keyboard based on computer vision. The keyboard can be made of paper containing an image of the keyboard which has an upside down U-shape. Each character is represented by a nonoverlapping rectangular region. The user enters a character to the computer by covering the character region with a stylus. The actions of the user are captured by a camera and the covered key is recognized. During the text entry process the user need not have to raise the stylus from the keyboard and this leads to faster data entry rates. In a companion system the user imitates writing on a surface using a pointer or a stylus. In this case the trace of the pointer is analyzed and the characters are recognized. The character set of the continuous hand writing system is based on the Graffiti alphabet to achieve very high recognition rates.


Computer vision based unistroke keyboard system and mouse for the handicapped
Erdem, ME; Erdem, IA; Atalay, Mehmet Volkan; Cetin, AE (2003-07-09)
In this paper, a unistroke keyboard based on computer vision is described for the handicapped. The keyboard can be made of paper or fabric containing an image of a keyboard, which has an upside down U-shape. It can even be displayed on a computer screen. Each character is represented by a non-overlapping rectangular region on the keyboard image and the user enters a character by illuminating a character region with a laser pointer. The keyboard image is monitored by a camera and illuminated key locations ar...
Blind channel identification methods and channel order estimation
Tuncer, Temel Engin (2006-04-19)
n this paper, we consider three blind channel estimation methods. Cross-relation (CR), subspace (SS) and least squares smoothing (J-LSS) methods are compared for single-input multi-output (SIMO) systems. In contrast to the previous works, we evaluate the practical MSE performances of these methods for short data lengths and random channels when the number of channels is greater than two. Some previously unknown characteristics of these methods are presented. A novel method for blind channel order estimation...
Temporal watermarking of digital video
Koz, A; Alatan, Abdullah Aydın (2003-04-11)
A video watermarking method is presented, based on the temporal sensitivity of Human Visual System (HVS). The method exploits the temporal contrast thresholds of HVS to determine the spatio-temporal locations, where the watermark should be embedded, and the maximum strength of watermark, which still gives imperceptible distortion after watermark insertion. The robustness results indicate that the proposed scheme survives video distortions, such as additive Gaussian noise, ITU H.263+ coding at medium bit rat...
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Erdem, Aykut; Erdem, Erkut; Yardimci, Yasemin; Atalay, Mehmet Volkan; Cetin, A. Enis (2002-01-01)
We describe a computer vision based mouse, which can control and command the cursor of a computer or a computerized system using a camera. In order to move the cursor on the computer screen the user simply moves the mouse shaped passive device placed on a surface within the viewing area of the camera. The video generated by the camera is analyzed using computer vision techniques and the computer moves the cursor according to mouse movements. The computer vision based mouse has regions corresponding to butto...
Face detection in active robot vision
Önder, Murat; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2004)
The main task in this thesis is to design a robot vision system with face detection and tracking capability. Hence there are two main works in the thesis: Firstly, the detection of the face on an image that is taken from the camera on the robot must be achieved. Hence this is a serious real time image processing task and time constraints are very important because of this reason. A processing rate of 1 frame/second is tried to be achieved and hence a fast face detection algorithm had to be used. The Eigenfa...
Citation Formats
A. Erdem, E. Erdem, M. V. Atalay, and A. Cetin, “Computer vision based unistroke keyboards,” 2002, Accessed: 00, 2020. [Online]. Available: