Pdfpen smooth text5/19/2023 ![]() Moreover, instead of LBP and SIFT features, another verification method with the deep features obtained with a CNN-based model, was also proposed and comparatively analyzed. Classification is performed with OC-SVM algorithm. Thus, the dynamic data of the signature sound signal became static data and as in the static image of the signature, feature extraction was performed with the LBP and SIFT algorithms. For dynamic data, spectral flux onset envelop and spectral centroid of audio signals are plotted and converted to image files. From the static data, the features are extracted by the LBP and SIFT algorithms. It was aimed to increase verification success by fusing dynamic and static features. A dataset was built containing static data from the signature image and dynamic data from the signature sound by taking samples from 75 participants according to different combinations of pen, paper types and mobile phone models for recording the sounds of the signatures with their internal microphones. This study assesses the impact of the sound arising from the friction of pen and paper on handwritten signature verification. However, any increase in this performance is and will be highly valuable in terms of fraud detection. The success rate in offline signature verification studies has reached high and limiting levels, recently. Modified MFCC feature based dynamic time warping (MDTW) and spectral feature based Gauss-Bayesian classifier (SGBC) are used and compared. The extraction of features in frequency space is done by using modified MFCC and spectral features extraction. The acoustic signal pre-processing is performed with the help of a SNR algorithm. Based on the experimental analysis, saw scratching acoustic signal is found with appropriate for tree cutting detection. An acoustic sensor experimental setup is established for capturing the acoustic signal generated due to cross cut sawing with varying distances. Detecting the acoustic signal due to saw scratching power level in presence of ambiance noise and the other choral noise sources is a major issue in a forest environment. ![]() The sensing and classification of acoustic signal emitted during tree cutting, is used to extract information of tree cutting events using sensors. This paper deals with tree cutting real-world problem, causing significant damages to forests. In future work, this approach can be used in applications such as writer identification, handwriting and gesture-based computer input technology, emotion recogni- tion, and temporal analysis of sketches. These prelimi- nary results demonstrate that acoustic emissions are a rich source of information, usable - on their own or in conjunc- tion with image-based featuresi - to solve pattern recogni- tion problems. We also present qualitative results for recognizing gestures such as circling, scratch-out, check-marks, and hatching. Recognition rates of over 70% (alphabet) and 90% (26 words) are achieved, based solely on acoustic emissions, with samples provided by a single writer. Test results are presented for isolated lowercase cursive characters and for whole words. Our recognizer uses a template match- ing approach, with templates and similarity measures de- rived variously from: raw power signal with fixed resolu- tion, discrete sequence of magnitudes obtained from peaks in the power signal, and ordered tree obtained from a scale space signal representation. ![]() We examine the feasibility of recognition of handwritten cursive text, exclusively through an analysis of acoustic emissions. The sounds generated by a writing instrument provide a rich and under-utilized source of information for pattern recognition.
0 Comments
Leave a Reply. |