Extended set of superpixel features

Как правило, при решении прикладных задач анализа и обработки изображений на основе суперпиксельного представления используется малый набор признаков суперпикселей. Расширение описания суперпикселей может повысить качество обрабатывающих алгоритмов. В статье предлагается набор из 25 базовых признаков суперпикселей, характеризующих их форму, яркость, геометрические параметры и положение на плоскости. Предлагаемые признаки отвечают требованиям низкой сложности вычисления в процессе сегментации изображения и достаточности для решения широкого класса прикладных задач. На их основе представлена модификация известного подхода к формированию суперпикселей, которая заключается в быстрой первичной суперпиксельной сегментации изображения со строгим предикатом однородности, обеспечивающим получение суперпикселей, с высокой точностью сохраняющих информацию исходного растрового изображения, и последующем укрупнении суперпикселей при более мягких предикатах однородности. Экспериментально показано, что такой подход позволяет существенно сократить число элементов изображения, что способствует снижению сложности обрабатывающих алгоритмов, а расширенные суперпиксели более точно соответствуют содержательным областям изображения. Superpixel-based image processing and analysis methods usually use a small set of superpixel features. Expanding the description of superpixels can improve the quality of processing algorithms. In the paper, a set of 25 basic superpixel features of shape, intensity, geometry, and location is proposed. The features meet the requirements of low computational complexity in the process of image superpixel segmentation and sufficiency for solving a wide class of application tasks. Applying the set, we present a modification of the well-known approach to the superpixel generation. It consists of fast primary superpixel segmentation of the image with a strict homogeneity predicate, which provides superpixels preserving the intensity information of the original image with high accuracy, and the subsequent enlargement of the superpixels with softer homogeneity predicates. The experiments show that the approach can significantly reduce the number of image elements, which helps to reduce the complexity of processing algorithms, meanwhile the expanded superpixels more accurately correspond to the image objects.Работа выполнена при поддержке гранта РФФИ (№ 19-37-90116), а также Министерства науки и высшего образования РФ в рамках выполнения работ по Государственному заданию ФНИЦ «Кристаллография и фотоника» РАН (соглашение № 007-ГЗ/Ч3363/26)

Spectral clustering and fuzzy similarity measure for images segmentation

In image segmentation algorithms using spectral clustering, due to the size of the images, the computational load for the construction of the similarity matrix and the solution to the eigenvalue problem for the Laplacian matrix is high. Furthermore, the Gaussian kernel similarity measure is the most used, but it presents problems with irregular data distributions. This work proposes to perform a pre-segmentation or decimation by superpixels with the Simple Linear Iterative Clustering algorithm to reduce the computational cost, and to build the similarity matrix with a fuzzy measure based on the Fuzzy C-Means classifier, providing the algorithm a greater robustness against images with complex distributions and by spectral clustering the final segmentation is determined. Experimentally, it was found that the proposed approach obtains adequate segmentations, good clustering results and a comparable precision with respect to five algorithms; measuring performance under four determined validation metrics.En los algoritmos de segmentación de imágenes mediante agrupamiento espectral, debido al tamaño de las imágenes, la carga computacional para la construcción de la matriz de similitud y la solución al problema de valores propios para la matriz laplaciana son altos. Además, la medida de similitud más utilizada es el kernel gaussiano, el cual presenta problemas con distribuciones de datos irregulares. Este trabajo propone realizar una presegmentación o diezmado mediante superpíxeles con el algoritmo Simple Linear Iterative Clustering, para disminuir el costo computacional y construir la matriz de similaridad con una medida difusa basada en el clasificador Fuzzy C-Means, que proporciona al algoritmo una mayor robustez frente a imágenes con distribuciones complejas; mediante agrupamiento espectral se determina la segmentación final. Experimentalmente, se comprobó que el enfoque propuesto obtiene segmentaciones adecuadas, buenos resultados de agrupamiento y una precisión comparable respecto a cinco algoritmos, midiendo el desempeño bajo cuatro métricas de validación

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing

Human Pose Estimation with Supervoxels

This thesis investigates how segmentation as a preprocessing step can reduce both the search space as well as complexity of human pose estimation in the context of smart environments. A 3D reconstruction is computed with a voxel carving algorithm. Based on a superpixel algorithm, these voxels are segmented into supervoxels that are then applied to pictorial structures in 3D to efficiently estimate the human pose. Both static and dynamic gesture recognition applications were developed