Moving objects forecast in image sequences using autoregressive algorithms

The objective of this paper is to present an overall approach to forecasting the future position of the moving objects of an image sequence after processing the images previous to it. The proposed method makes use of classical techniques such as optical flow to extract objects’ trajectories and velocities, and autoregressive algorithms to build the predictive model. Our method can be used in a variety of applications, where videos with stationary cameras are used, moving objects are not deformed and change their position with time. One of these applications is traffic control, which is used in this paper as a case study with different meteorological conditions to compare with.

Subspace sums for extracting non-random data from massive noise

An algorithm is introduced that distinguishes relevant data points from randomly distributed noise. The algorithm is related to subspace clustering based on axis-parallel projections, but considers membership in any projected cluster of a given side length, as opposed to a particular cluster. An aggregate measure is introduced that is based on the total number of points that are close to the given point in all possible 2 ^d projections of a d-dimensional hypercube. No explicit summation over subspaces is required for evaluating this measure. Attribute values are normalized based on rank order to avoid making assumptions on the distribution of random data. Effectiveness of the algorithm is demonstrated through comparison with conventional outlier detection on a real microarray data set as well as on time series subsequence data.

高分辨率灰度图像的快速多分辨率着色

灰度图像的计算机快速着色有着广泛的应用前景,尤其足对老照片或者旧影片的彩色化处理.基于最优化的着色方法利用少量的人工标色,就能够得到好的着色效果,但是该方法在处理较高分辨率图像时需要消耗大量的存储和计算资源,有时甚至不能得到最后的结果.在最优化着色方法的基础上,文中利用图像着色的特征,提出了使用最优化着色的金字塔模型进行多分辨率着色处理的新方法,始终控制矩阵计算的规模,能够很快地得到视觉效果一致甚至更好的着色效果.实验结果也证明新方法无论在计算时间、存储需求还是最后着色的质量上,尤其是针对高分辨率图像,比原方法有了很大的改进.     

Image universal steganalysis based on best wavelet packet decomposition

Based on the best wavelet packet decomposition of images, a new universal steganalysis method with high detection correct ratio is proposed. First, the best wavelet packet decomposition of image based on the Shannon entropy information cost function is made. Second, high order absolute characteristic function moments of histogram extracted from the coefficient subbands obtained by best wavelet packet decomposition are regarded as features. Finally, these features are processed and a back-propagation (BP) ne...     

Pattern analysis of dermoscopic images based on Markov random fields

In this paper a method for detecting different patterns in dermoscopic images is presented. In order to diagnose a possible skin cancer, physicians assess the lesion based on different rules. While the most famous one is the ABCD rule (asymmetry, border, colour, diameter), the new tendency in dermatology is to classify the lesion performing a pattern analysis. Due to the colour textured appearance of these patterns, this paper presents a novel method based on Markov random field (MRF) extended for colour images that classifies images representing different dermatologic patterns. First, each image plane in L^*a^*b^* colour space is modelled as a MRF following a finite symmetric conditional model (FSCM). Coupling of colour components is taken into account by supposing that features of the MRF in the three colour planes follow a multivariate Normal distribution. Performance is analysed in different colour spaces. The best classification rate is 86% on average.     

Discrete data clustering using finite mixture models

Finite mixture models have been applied for different computer vision, image processing and pattern recognition tasks. The majority of the work done concerning finite mixture models has focused on mixtures for continuous data. However, many applications involve and generate discrete data for which discrete mixtures are better suited. In this paper, we investigate the problem of discrete data modeling using finite mixture models. We propose a novel, well motivated mixture that we call the multinomial generalized Dirichlet mixture. The novel model is compared with other discrete mixtures. We designed experiments involving spatial color image databases modeling and summarization, and text classification to show the robustness, flexibility and merits of our approach.     

Latent classification models for binary data

One of the simplest, and yet most consistently well-performing set of classifiers is the naïve Bayes models (a special class of Bayesian network models). However, these models rely on the (naïve) assumption that all the attributes used to describe an instance are conditionally independent given the class of that instance. To relax this independence assumption, we have in previous work proposed a family of models, called latent classification models (LCMs). LCMs are defined for continuous domains and generalize the naïve Bayes model by using latent variables to model class-conditional dependencies between the attributes. In addition to providing good classification accuracy, the LCM has several appealing properties, including a relatively small parameter space making it less susceptible to over-fitting. In this paper we take a first step towards generalizing LCMs to hybrid domains, by proposing an LCM for domains with binary attributes. We present algorithms for learning the proposed model, and we describe a variational approximation-based inference procedure. Finally, we empirically compare the accuracy of the proposed model to the accuracy of other classifiers for a number of different domains, including the problem of recognizing symbols in black and white images.     

The role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas

Remote sensing hyperspectral sensors are important and powerful instruments for addressing classification problems in complex forest scenarios, as they allow one a detailed characterization of the spectral behavior of the considered information classes. However, the processing of hyperspectral data is particularly complex both from a theoretical viewpoint [e.g. problems related to the Hughes phenomenon (Hughes, 1968) and from a computational perspective. Despite many previous investigations that have been presented in the literature on feature reduction and feature extraction in hyperspectral data, only a few studies have analyzed the role of spectral resolution on the classification accuracy in different application domains. In this paper, we present an empirical study aimed at understanding the relationship among spectral resolution, classifier complexity, and classification accuracy obtained with hyperspectral sensors for the classification of forest areas. We considered two different test sets characterized by images acquired by an AISA Eagle sensor over 126 bands with a spectral resolution of 4.6 nm, and we subsequently degraded its spectral resolution to 9.2, 13.8, 18.4, 23, 27.6, 32.2 and 36.8 nm. A series of classification experiments were carried out with bands at each of the degraded spectral resolutions, and bands selected with a feature selection algorithm at the highest spectral resolution (4.6 nm). The classification experiments were carried out with three different classifiers: Support Vector Machine, Gaussian Maximum Likelihood with Leave-One-Out-Covariance estimator, and Linear Discriminant Analysis. From the experimental results, important conclusions can be made about the choice of the spectral resolution of hyperspectral sensors as applied to forest areas, also in relation to the complexity of the adopted classification methodology. The outcome of these experiments are also applicable in terms of directing the user towards a more efficient use of the current instruments (e.g. programming of the spectral channels to be acquired) and classification techniques in forest applications, as well as in the design of future hyperspectral sensors.     

Development of a pit filling algorithm for LiDAR canopy height models

LiDAR canopy height models (CHMs) can exhibit unnatural looking holes or pits, i.e., pixels with a much lower digital number than their immediate neighbors. These artifacts may be caused by a combination of factors, from data acquisition to post-processing, that not only result in a noisy appearance to the CHM but may also limit semi-automated tree-crown delineation and lead to errors in biomass estimates. We present a highly effective semi-automated pit filling algorithm that interactively detects data pits based on a simple user-defined threshold, and then fills them with a value derived from their neighborhood. We briefly describe this algorithm and its graphical user interface, and show its result in a LiDAR CHM populated with data pits. This method can be rapidly applied to any CHM with minimal user interaction. Visualization confirms that our method effectively and quickly removes data pits.     

Properties of variations in the periods of ring neural oscillators with noise

Effects of additive noise on a series of the periods of oscillations in unidirectionally coupled ring neural networks of ring oscillator type are studied. Kinematical models of the traveling waves of an inconsistency, i.e. the successive same signs in the states of adjacent neurons in the network, are derived. A series of the half periods in the network of N neuron is then expressed by the sum of N sequences of the N-first order autoregressive process, the process with the spectrum of exponential type and the first-order autoregressive process. Noise and the interaction of the inconsistency cause characteristic positive correlations in a series of the half periods of the oscillations. Further, an experiment on an analog circuit of the ring neural oscillator was done and it is shown that correlations in the obtained periods of the oscillations agree with the derived three expressions.