André Borges Cavalcante

Measuring Streetscape Complexity Based on the Statistics of Local Contrast and Spatial Frequency

Streetscapes are basic urban elements which play a major role in the livability of a city. The visual complexity of streetscapes is known to influence how people behave in such built spaces. However, how and which characteristics of a visual scene influence our perception of complexity have yet to be fully understood. This study proposes a method to evaluate the complexity perceived in streetscapes based on the statistics of local contrast and spatial frequency. Here, 74 streetscape images from four cities, including daytime and nighttime scenes, were ranked for complexity by 40 participants. Image processing was then used to locally segment contrast and spatial frequency in the streetscapes. The statistics of these characteristics were extracted and later combined to form a single objective measure. The direct use of statistics revealed structural or morphological patterns in streetscapes related to the perception of complexity. Furthermore, in comparison to conventional measures of visual complexity, the proposed objective measure exhibits a higher correlation with the opinion of the participants. Also, the performance of this method is more robust regarding different time scenarios.

Wavelet entropy measure based on matching pursuit decomposition and its analysis to heartbeat intervals

Any natural or biological signal can be seen as a linear combination of meaningful and non-meaningful structures. According to the theory of multiresolution wavelet expansions, one can quantify the degree of information those structures using entropy and then select the most meaningful ones. Herein we propose to use adaptive time and frequency transform (ATFT) to measure wavelet entropy, where one line of approach to ATFT is to use a matching pursuit (MP) framework. The proposed method is tested on a set of heartbeat intervals whose population is composed of healthy and pathological subjects. Our results show that wavelet entropy measure based on MP decomposition can capture significant differences between the analyzed cardiac states that are intrinsically related to the structure of the signal.

Image compression by redundancy reduction

Image compression is achieved by reducing redundancy between neighboring pixels but preserving features such as edges and contours of the original image. Deterministic and statistical models are usually employed to reduce redundancy. Compression methods that use statistics have heavily been influenced by neuroscience research. In this work, we propose an image compression system based on the efficient coding concept derived from neural information processing models. The system performance is compared with principal component analysis (PCA) and the discrete cosine transform (DCT) at several compression ratios (CR). Evaluation through both visual inspection and objective measurements showed that the proposed system is more robust to distortions such as ringing and block artifacts than PCA and DCT.

Effects of second-order statistics on independent component filters

It is known that independent component analysis (ICA) generates filters that are similar to the receptive fields of primary visual cortex (V1) cells. However, ICA fails to yield the frequency tuning exhibited by V1 receptive fields. This work analysis how the shape of IC filters depend on second-order statistics of the input data. Specifically, we show theoretically and through experimentation how the structure of IC filters change with second-order statistics and different types of data preprocessing. Here, we preprocess natural scenes according to four conditions: whitening, pseudo-whitening, local-whitening and high-passfiltering. As results, we show that the filter structure is strongly modulated by the inverse of the covariance of the input signal. However, the distribution of size in frequency domain are similarly biased for all preprocessing conditions.

Speech enhancement based on the response features of facilitated EI neurons

A real-time approach for the enhancement of speech at zero degree azimuth is proposed. This is achieved inspired by the response features of the “Facilitated EI neurons”. This way, frequency segregation through a bandpass filter bank is followed by “supression analysis” which inhibits sources that are not at “facilitated” positions. Unlike with the existing approaches for the solution of cocktail party problem, where the performance under low SNR (signal-to-noise ratio) reverberation conditions is severely limited, the proposed approach has the capability to circumvent these problems. This is quantified through both objective and subjective performance measures and supported by real world simulation examples.