Roberto de Alencar Lotufo

Automatic estimation of crowd density using texture

This paper considers the role of automatic estimation of crowd density and its importance for the automatic monitoring of areas where crowds are expected to be present. A new technique is proposed which is able to estimate densities ranging from very low to very high concentration of people, which is a difficult problem because in a crowd only parts of peoples body appear. The new technique is based on the differences of texture patterns of the images of crowds. Images of low density crowds tend to present coarse textures, while images of dense crowds tend to present fine textures. The image pixels are classified in different texture classes and statistics of such classes are used to estimate the number of people. The texture classification and the estimation of people density are carried out by means of self organising neural networks. Results obtained respectively to the estimation of the number of people in a specific area of Liverpool Street Railway Station in London (UK) are presented.

Estimating crowd density with Minkowski fractal dimension

The estimation of the number of people in an area under surveillance is very important for the problem of crowd monitoring. When an area reaches an occupation level greater than the projected one, peoples safety can be in danger. This paper describes a new technique for crowd density estimation based on Minkowski fractal dimension. The fractal dimension has been widely used to characterize data texture in a large number of physical and biological sciences. The results of our experiments show that fractal dimension can also be used to characterize levels of people congestion in images of crowds. The proposed technique is compared with a statistical and a spectral technique, in a test study of nearly 300 images of a specific area of the Liverpool Street Railway Station, London, UK. Results obtained in this test study are presented.

The Ordered Queue and the Optimality of the Watershed Approaches

This work reviews the watershed in the graph framework of a shortest-path forest problem using a lexicographic path cost formulation. This formulation reflects the behavior of the ordered queue-based watershed algorithm. This algorithm is compared with our proposed shortest-path forest (IFT-Image Foresting Transform), concluding that the watershed is a special case of that. Recently many different watershed approaches are being used. We point out that in some cases the watershed algorithm does not keep the optimality of the shortest-path forest solution unless the IFT algorithm is used. The main difference between the algorithms is related to permanently labeling a pixel when inserting or removing it from the queue. The watershed based on the pixel dissimilarity using IFT can segment one-pixel width regions while keeping the optimality of the shortest-path forest solution.

On the efficacy of texture analysis for crowd monitoring

The goal of this work is to assess the efficacy of texture measures for estimating levels of crowd densities in images. This estimation is crucial for the problem of crowd monitoring and control. The assessment is carried out on a set of nearly 300 real images captured from Liverpool Street Train Station, London, UK, using texture measures extracted from the images through the following four different methods: gray level dependence matrices, straight line segments, Fourier analysis, and fractal dimensions. The estimations of crowd densities are given in terms of the classification of the input images in five classes of densities (very low, low, moderate, high and very high). Three types of classifiers are used: neural (implemented according to the Kohonen model), Bayesian, and an approach based on fitting functions. The results obtained by these three classifiers, using the four texture measures, allowed the conclusion that, for the problem of crowd density estimation, texture analysis is very effective.

Morphological Image Processing

Morphological processing (MP) has applications in diverse areas of image processing as filtering, segmentation, and pattern recognition, to both binary and grayscale images. One of the most important operations in morphological image processing is reconstruction from markers. The basic idea is to mark certain image components and then to reconstruct that portion of the image that contains the marked components. The basic fitting operation of morphology is the erosion of an image by a structuring element. Erosion is done by scanning the image with the structuring element. When the structuring element fits completely inside the object, the probe position is marked. The erosion result consists of all scanning locations, where the structuring element fits inside the object. The eroded image is usually a shrunken version of the image, and the shrinking effect is controlled by the structuring element size and shape. As an extension of the binary case, grayscale opening (closing) can be achieved simply by threshold decomposition, followed by binary opening (closing) and stack reconstruction. Grayscale opening and closing have the same properties as their binary equivalents.

IFT-Watershed from gray-scale marker

The watershed transform and the morphological reconstruction are two of the most important operators for image segmentation in the framework of mathematical morphology. In many situations, the segmentation requires the classical watershed transform of a reconstructed image. In this paper, we introduce the IFT-watershed from gray scale marker-a method to compute at same time, the reconstruction and the classical watershed transform of the reconstructed image, without explicit computation of any regional minima. The method is based on the Image Foresting Transform (IFT)-a unified and efficient approach to reduce image processing problems to a minimum-cost path forest problem in a graph. As additional contributions, we demonstrate that (i) the cost map of the IFT-watershed from markers is identical to the output of the superior gray scale reconstruction; (ii) other reconstruction algorithms are not watersheds; and (iii) the proposed method achieves competitive advantages as compared to the current classical watershed approach.

New Tensorial Representation of Color Images: Tensorial Morphological Gradient Applied to Color Image Segmentation

This paper proposes a new Tensorial Representation of HSI color images, where each pixel is a 2 times 2 second order tensor, that can be represented by an ellipse. A proposed tensorial morphological gradient (TMG) is defined as the maximum dissimilarity over the neighborhood determined by a structuring element, and is used in the watershed segmentation framework. Many tensor dissimilarity functions are tested and other color gradients are compared. The comparison uses a new methodology for qualitative evaluation of color image segmentation by watershed, where the watershed lines of the n most significant regions are overlaid on the original image for visual comparison. Experiments show that the TMG using Frobenius norm dissimilarity function presents superior segmentation results, in comparison to other tested gradients.

Design of connected operators using the image foresting transform

The Image Foresting Transform (IFT) reduces optimal image partition problems from seed pixels into a shortest-path forest problem in a graph, whose solution can be obtained in linear time. It has allowed a unified and efficient approach to edge tracking, region growing, watershed transforms, multiscale skeletonization, and Euclidean distance transform. In this paper, we extend the IFT to introduce two connected operators: cutting-off-domes and filling-up-basins. The former simplifies grayscale images by reducing the height of its domes, while the latter reduces the depth of its basins. By automatically or interactively specifying seed pixels in the image and computing a shortest-path forest, whose trees are rooted at these seeds, the IFT creates a simplified image where the brightness of each pixel is associated with the length of the corresponding shortest-path. A label assigned to each seed is propagated, resulting a labeled image that corresponds to the watershed partitioning from markers. The proposed operators may also be used to provide regional image filtering and labeling of connected components. We combine the cutting-off-domes and filling-up-basins to implement regional minima/maxima, h-domes/basins, opening/closing by reconstruction, leveling, area opening/closing, closing of holes, and removal of pikes. Their applications are illustrated with respect to medical image segmentation.

Fingerprint Liveness Detection Using Convolutional Neural Networks

With the growing use of biometric authentication systems in the recent years, spoof fingerprint detection has become increasingly important. In this paper, we use convolutional neural networks (CNNs) for fingerprint liveness detection. Our system is evaluated on the data sets used in the liveness detection competition of the years 2009, 2011, and 2013, which comprises almost 50 000 real and fake fingerprints images. We compare four different models: two CNNs pretrained on natural images and fine-tuned with the fingerprint images, CNN with random weights, and a classical local binary pattern approach. We show that pretrained CNNs can yield the state-of-the-art results with no need for architecture or hyperparameter selection. Data set augmentation is used to increase the classifiers performance, not only for deep architectures but also for shallow ones. We also report good accuracy on very small training sets (400 samples) using these large pretrained networks. Our best model achieves an overall rate of 97.1% of correctly classified samples-a relative improvement of 16% in test error when compared with the best previously published results. This model won the first prize in the fingerprint liveness detection competition 2015 with an overall accuracy of 95.5%.

MMach: a mathematical morphology toolbox for the KHOROS system

o isrs. ept Abstract. Mathematical morphology is a general theory that studies the decomposition of operators between complete lattices in terms of some families of simple operators: dilations, erosions, antidilations, and antierosions. Nowadays, this theory is largely used in image processing and computer vision to extract information from images. The KHOROS system is an open and general environment for image processing and visualization that has become very popular. One of the main characteristics of KHOROS is its flexibility, since it runs on standard machines, supports several standard data formats, uses a visual programming language, and has tools to help the users to build in and install their own programs. A set of new programs can be organized as a subsystem called a toolbox. We present MMach, a fast and comprehensive mathematical morphology toolbox for the KHOROS system dealing with 1-D and 2-D grayscale and binary images. Each program that is applicable to grayscale and binary images has specialized algorithms for each of these data types, and these algorithms are chosen automatically according to the input data. Several examples illustrate applications of the toolbox in image analysis.