In this paper a feature vector, which has been used for curve coding, is evaluated in case of a signature verification scheme using a real time digital signal processor. The feature extraction method is based on morphologically processing the vertical projections of prescaled signature images. Coding of the curve profiles is carried out using morphological opening which explores the pixel alloc...

The study performs feature extraction for isolated word recognition using Mel-Frequency Cepstral Coefficient (MFCC) for Gujarati language. It explains feature extraction methods MFCC and Linear Predictive Coding (LPC) in brief. The paper compares the performances of MFCC and LPC features under Vector Quantization (VQ) method. The dataset comprising of males and females voices were trained and t...

In this paper, we present a novel method to extract noise-robust speech feature representation in speech recognition. This method employs the algorithm of linear predictive coding (LPC) on the feature time series of mel-frequency cepstral coefficients (MFCC). The resulting linear predictive version of the feature time 國立暨南國際大學電機工程學系 Department of Electrical Engineering, National Chi Nan Univers...

Representing images by compact codes has proven beneficial for many visual recognition tasks. Most existing techniques, however, perform this coding step directly in image feature space, where the distributions of the different classes are typically entangled. In contrast, here, we study the problem of performing coding in a high-dimensional Hilbert space, where the classes are expected to be m...

hyperspectral sensors provide a large number of spectral bands. this massive and complex data structure of hyperspectral images presents a challenge to traditional data processing techniques. therefore, reducing the dimensionality of hyperspectral images without losing important information is a very important issue for the remote sensing community. we propose to use overlap-based feature weigh...

Two classes of relatively simple algorithms have been found to be very effective for unsupervised feature learning: 1) sparse coding that minimizes the reconstruction error, and 2) clustering that captures the data distribution. Coates et al. (2011) analyzed the performance of several off-the-shelf feature learning algorithms, such as, sparse auto-encoders, sparse RBMs, k-means clustering, and ...

Multicamera vehicle tracking is a necessary part of any video-based intelligent transportation system for extracting different traffic parameters such as link travel times and origin/destination counts. In many applications, it is needed to locate traffic cameras disjoint from each other to cover a wide area. This paper presents a method for tracking moving vehicles in such camera networks. The...

The face is a rich source of information about human behavior. Available methods for coding facial displays, however, are human-observer dependent, labor intensive, and difficult to standardize. To enable rigorous and efficient quantitative measurement of facial displays, we have developed an automated method of facial display analysis. In this report, we compare the results with this automated...