The number and nature of the mechanisms for the detection of colored stimuli are still unclear. We use the paradigm of classification images to investigate the detection of a signal of homogeneous color added to a noisy texture. Both signal and noise colors were chosen from the isoluminant plane of the Derrington-Krauskopf-Lennie (DKL) color space. The signal consisted of a square of homogeneou...

One of the most important topics in the sonar sound data processing is proposing a powerful classifier to detect the sound source. In this paper we propose a classifier with proper accuracy. First, proper features should be extracted from sound data; Features could extract from time or frequency domains. Whenever fastness is important, time features are most effective. Otherwise, frequency doma...

This paper addresses the problem of feature extraction for signal classification. It proposes to build features by designing a data-driven filter bank and by pooling the time-frequency representation to provide time-invariant features. For this purpose, our work tackles the problem of jointly learning the filters of a filter bank with a support vector machine. It is shown that, in a restrictive...

This paper reports a new signal classification tool, a modified wavelet network called Thresholding Wavelet Networks (TWN). The network is designed for the purposes of classifying signals. The philosophy of the technique is that often the difference between signals may not lie in the spectral or temporal region where the signal strength is high. Unlike other wavelet networks, this network does ...