Vasile Buzuloiu

Intensity-driven adaptive-neighborhood technique for polarimetric and interferometric SAR parameters estimation

In this paper, a new method to filter coherency matrices of polarimetric or interferometric data is presented. For each pixel, an adaptive neighborhood (AN) is determined by a region growing technique driven exclusively by the intensity image information. All the available intensity images of the polarimetric and interferometric terms are fused in the region growing process to ensure the validity of the stationarity assumption. Afterward, all the pixels within the obtained AN are used to yield the filtered values of the polarimetric and interferometric coherency matrices, which can be derived either by direct complex multilooking or from the locally linear minimum mean-squared error (LLMMSE) estimator. The entropy/alpha/anisotropy decomposition is then applied to the estimated polarimetric coherency matrices, and coherence optimization is performed on the estimated polarimetric and interferometric coherency matrices. Using this decomposition, unsupervised classification for land applications by an iterative algorithm based on a complex Wishart density function is also applied. The method has been tested on airborne high-resolution polarimetric interferometric synthetic aperture radar (POL-InSAR) images (Oberpfaffenhofen area-German Space Agency). For comparison purposes, the two estimation techniques (complex multilooking and LLMMSE) were tested using three different spatial supports: a fix-sized symmetric neighborhood (boxcar filter), directional nonsymmetric windows, and the proposed AN. Subjective and objective performance analysis, including coherence edge detection, receiver operating characteristics plots, and bias reduction tables, recommends the proposed algorithm as an effective POL-InSAR postprocessing technique.

Image processing techniques to detect and filter objectionable images based on skin tone and shape recognition

In this paper we describe image-processing techniques to detect and categorize potentially objectionable images based solely on the image content. The techniques include a novel method of matching skin tones based on a fuzzy classification scheme and shape recognition techniques to match faces and other elements of the human anatomy. In particular the color matching technique is significantly more accurate than existing methods documented in literature. These techniques offer a means of filtering images, based solely on graphical content, for a new generation of home Internet appliances.

Dynamic hand gesture recognition using the skeleton of the hand

This paper discusses the use of the computer vision in the interpretation of human gestures. Hand gestures would be an intuitive and ideal way of exchanging information with other people in a virtual space, guiding some robots to perform certain tasks in a hostile environment, or interacting with computers. Hand gestures can be divided into two main categories: static gestures and dynamic gestures. In this paper, a novel dynamic hand gesture recognition technique is proposed. It is based on the 2D skeleton representation of the hand. For each gesture, the hand skeletons of each posture are superposed providing a single image which is the dynamic signature of the gesture. The recognition is performed by comparing this signature with the ones from a gesture alphabet, using Baddeleys distance as a measure of dissimilarities between model parameters.

Adaptive-neighborhood histogram equalization of color images

Histogram equalization (HE) is one of the simplest and most effective techniques for enhancing gray-level images. For color images, HE becomes a more difficult task, due to the vectorial nature of the data. We propose a new method for color image enhancement that uses two hierarchical levels of HE: global and local. In order to preserve the hue, equalization is only applied to intensities. For each pixel (called the ‘‘seed’’ when being processed) a variable-sized, variable-shaped neighborhood is determined to contain pixels that are ‘‘similar’’ to the seed. Then, the histogram of the region is stretched to a range that is computed with respect to the statistical parameters of the region (mean and variance) and to the global HE function (of intensities), and only the seed pixel is given a new intensity value. We applied the proposed color HE method to various images and observed the results to be subjectively ‘‘pleasant to the human eye,’’ with emphasized details, preserved colors, and with the histogram of intensities close to the ideal uniform one. The results compared favorably with those of three other methods (histogram explosion, histogram decimation, and three-dimensional histogram equalization) in terms of subjective visual quality.

Measurements with ToF Cameras and Their Necessary Corrections

The most important characteristic of time-of-flight (ToF) cameras is the ability to measure the distance to each image pixel. Thus, for each pixel, information on both its amplitude and distance to the camera are available. However, technological problems inherent to the acquisition principle lead to inaccuracies in estimating both characteristics: on one hand, there are errors in estimating the distance, especially for far-distance pixels. On the other hand, the detected amplitude decreases with the distance. Part of these inaccuracies are corrected with special camera-calibration software. In this paper, we propose two methods that attempt to further correct each information based on the other one. First, the amplitude image is enhanced by using distance information: a pixel-wise, distance-based correction of the amplitude brings to light details otherwise unnoticeable. Secondly, an amplitude-based distance modification corrects some of the distance estimation errors for far-distance pixels.

Improved Cut Detection for the Segmentation of Animation Movies

In this paper an improved cut detection algorithm, adapted to the segmentation of animation movies, is proposed. As color is a major feature of animation movies (each movie has its own particular color distribution) the proposed algorithm applies second order derivatives on Euclidean distances between color histograms of frames quadrants in order to improve the cut detection. For the frame classification, an automatic threshold estimation is proposed. Also, in order to reduce false detections, we propose an algorithm to detect an effect specific to animation movies, named short color change (i.e. thunders, lightening). The resulting method achieved better results compared to the classical histogram-based and motion-discontinuity based approaches, as shown by tests conducted on several animation movies

General adaptive-neighborhood technique for improving synthetic aperture radar interferometric coherence estimation

A new method for filtering the coherence map issued from synthetic aperture radar (SAR) interferometric data is presented. For each pixel of the interferogram, an adaptive neighborhood is determined by a region-growing technique driven by the information provided by the amplitude images. Then pixels in the derived adaptive neighborhood are complex averaged to yield the filtered value of the coherence, after a phase-compensation step is performed. An extension of the algorithm is proposed for polarimetric interferometric SAR images. The proposed method has been applied to both European Remote Sensing (ERS) satellite SAR images and airborne high-resolution polarimetric interferometric SAR images. Both subjective and objective performance analysis, including coherence edge detection, shows that the proposed method provides better results than the standard phase-compensated fixed multilook filter and the Lee adaptive coherence filter.

COLOR IMAGE ENHANCEMENT IN THE FRAMEWORK OF LOGARITHMIC MODELS

The logarithmic model offers new tools for image processing. An efficient method for image enhancement, is to use an affine transformation with the logarithmic operations: addition and scalar multiplication. By adding a fuzzy setting to our model we gain flexibility and better results are possible. We define some criteria for automatically determining the parameters of the processing and this is done via the fuzzy mean and fuzzy variance computed by logarithmic operations.

Filtering noise in color images using adaptive-neighborhood statistics

Various nonlinear, fixed-neighborhood techniques based on local statistics have been proposed in the literature for filtering noise in color images. We present adaptive-neighborhood filtering (ANF) techniques for noise removal in color images. The main idea is to find for each pixel (called the ‘‘seed’’ when being processed) a variable-shaped, variable-sized neighborhood that contains only pixels that are similar to the seed. Then, statistics computed within the adaptive neighborhood are used to derive the filter output. Results of the ANF techniques are compared with those given by a few multivariate fixed-neighborhood filters: the double-window modified trimmed-mean filter, the generalized vector directional filter— double-window—?-trimmed mean filter, the adaptive hybrid multivariate filter, and the adaptive nonparametric filter with Gaussian kernel. It is shown that the ANF techniques provide better visual results, effectively suppressing noise while not blurring the edges; the results are also better in terms of objective measures (such as normalized mean-squared error and normalized color difference) than the results of the other methods.

Fuzzy color-based approach for understanding animated movies content in the indexing task

This paper proposes a method for detecting and analyzing the color techniques used in the animated movies. Each animated movie uses a specific color palette which makes its color distribution one major feature in analyzing the movie content. The color palette is specially tuned by the author in order to convey certain feelings or to express artistic concepts. Deriving semantic or symbolic information from the color concepts or the visual impression induced by the movie should be an ideal way of accessing its content in a content-based retrieval system. The proposed approach is carried out in two steps. The first processing step is the low-level analysis. The movie color content gets represented with several global statistical parameters computed from the movie global weighted color histogram. The second step is the symbolic representation of the movie content. The numerical parameters obtained from the first step are converted into meaningful linguistic concepts through a fuzzy system. They concern mainly the predominant hues of the movie, some of Ittens color contrasts and harmony schemes, color relationships and color richness. We use the proposed linguistic concepts to link to given animated movies according to their color techniques. In order to make the retrieval task easier, we also propose to represent color properties in a graphical manner which is similar to the color gamut representation. Several tests have been conducted on an animated movie database.