The state-of-the-art progress in cloud detection, identification, and tracking approaches: a systematic review

A cloud is a mass of water vapor floating in the atmosphere. It is visible from the ground and can remain at a variable height for some time. Clouds are very important because their interaction with the rest of the atmosphere has a decisive influence on weather, for instance by sunlight occlusion or by bringing rain. Weather denotes atmosphere behavior and is determinant in several human activities, such as agriculture or energy capture. Therefore, cloud detection is an important process about which several methods have been investigated and published in the literature. The aim of this paper is to review some of such proposals and the papers that have been analyzed and discussed can be, in general, classified into three types. The first one is devoted to the analysis and explanation of clouds and their types, and about existing imaging systems. Regarding cloud detection, dealt with in a second part, diverse methods have been analyzed, i.e., those based on the analysis of satellite images and those based on the analysis of images from cameras located on Earth. The last part is devoted to cloud forecast and tracking. Cloud detection from both systems rely on thresholding techniques and a few machine-learning algorithms. To compute the cloud motion vectors for cloud tracking, correlation-based methods are commonly used. A few machine-learning methods are also available in the literature for cloud tracking, and have been discussed in this paper too

Análise de Movimento Não Rígido em Visão por Computador

Neste artigo são apresentadas várias metodologias actualmente existentes, no domínio da Visão por Computador, para a análise de movimento não rígido e são indicados diversos exemplos de aplicações. Assim o movimento não rígido é classificado e, para cada classe resultante, são indicadas as restrições e as condições inerentes e verificados alguns trabalhos realizados no seu âmbito. Como as questões de análise de movimento e modelização da forma se tornam inseparáveis quando se considera o movimento do tipo não rígido, a modelização sugere uma classificação possível da forma não rígida e do movimento. Assim são também apresentados modelos de forma para objectos deformáveis e indicados vários exemplos de aplicações. Com este estudo, de certo modo aprofundado, das várias metodologias, e suas aplicações, existentes no domínio da análise de movimento não rígido, espera-se contribuir para o seu desenvolvimento, dada a actual carência de boas revisões do estado da arte neste domínio.In this article several methodologies actually existent, in the Computer Vision domain, for non-rigid movement analysis are presented and several examples of applications are indicated. Thus the non-rigid movement is classified and, for each resulting class, the restrictions and the inherent conditions are presented and some works accomplished in its ambit are verified. As the questions of movement and shape analysis becomes non-separable when its considered the movement of the non-rigid type, the shape models also suggests a possible classification of the non-rigid shape and of the movement. Thus shape models for deformable objects will be presented and some examples of applications indicated. With this study, in certain way deep, of several methodologies, and its applications, existent in the domain of the non-rigid movement analysis, the authors hope to contribute for its development, given the actual lack of good state of the art revisions in this domain

Análise de Movimento Não Rígido em Visão por Computador

Neste artigo são apresentadas várias metodologias actualmente existentes, no domínio da Visão por Computador, para a análise de movimento não rígido e são indicados diversos exemplos de aplicações. Assim o movimento não rígido é classificado e, para cada classe resultante, são indicadas as restrições e as condições inerentes e verificados alguns trabalhos realizados no seu âmbito. Como as questões de análise de movimento e modelização da forma se tornam inseparáveis quando se considera o movimento do tipo não rígido, a modelização sugere uma classificação possível da forma não rígida e do movimento. Assim são também apresentados modelos de forma para objectos deformáveis e indicados vários exemplos de aplicações. Com este estudo, de certo modo aprofundado, das várias metodologias, e suas aplicações, existentes no domínio da análise de movimento não rígido, espera-se contribuir para o seu desenvolvimento, dada a actual carência de boas revisões do estado da arte neste domínio

DEFORM'06 - Proceedings of the Workshop on Image Registration in Deformable Environments

Preface These are the proceedings of DEFORM'06, the Workshop on Image Registration in Deformable Environments, associated to BMVC'06, the 17th British Machine Vision Conference, held in Edinburgh, UK, in September 2006. The goal of DEFORM'06 was to bring together people from different domains having interests in deformable image registration. In response to our Call for Papers, we received 17 submissions and selected 8 for oral presentation at the workshop. In addition to the regular papers, Andrew Fitzgibbon from Microsoft Research Cambridge gave an invited talk at the workshop. The conference website including online proceedings remains open, see http://comsee.univ-bpclermont.fr/events/DEFORM06. We would like to thank the BMVC'06 co-chairs, Mike Chantler, Manuel Trucco and especially Bob Fisher for is great help in the local arrangements, Andrew Fitzgibbon, and the Programme Committee members who provided insightful reviews of the submitted papers. Special thanks go to Marc Richetin, head of the CNRS Research Federation TIMS, which sponsored the workshop. August 2006 Adrien Bartoli Nassir Navab Vincent Lepeti

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Image enhancement techniques applied to solar feature detection

This dissertation presents the development of automatic image enhancement techniques for solar feature detection. The new method allows for detection and tracking of the evolution of filaments in solar images. Series of H-alpha full-disk images are taken in regular time intervals to observe the changes of the solar disk features. In each picture, the solar chromosphere filaments are identified for further evolution examination. The initial preprocessing step involves local thresholding to convert grayscale images into black-and-white pictures with chromosphere granularity enhanced. An alternative preprocessing method, based on image normalization and global thresholding is presented. The next step employs morphological closing operations with multi-directional linear structuring elements to extract elongated shapes in the image. After logical union of directional filtering results, the remaining noise is removed from the final outcome using morphological dilation and erosion with a circular structuring element. Experimental results show that the developed techniques can achieve excellent results in detecting large filaments and good detection rates for small filaments. The final chapter discusses proposed directions of the future research and applications to other areas of solar image processing, in particular to detection of solar flares, plages and sunspots

Exploring Motion Signatures for Vision-Based Tracking, Recognition and Navigation

As cameras become more and more popular in intelligent systems, algorithms and systems for understanding video data become more and more important. There is a broad range of applications, including object detection, tracking, scene understanding, and robot navigation. Besides the stationary information, video data contains rich motion information of the environment. Biological visual systems, like human and animal eyes, are very sensitive to the motion information. This inspires active research on vision-based motion analysis in recent years. The main focus of motion analysis has been on low level motion representations of pixels and image regions. However, the motion signatures can benefit a broader range of applications if further in-depth analysis techniques are developed. In this dissertation, we mainly discuss how to exploit motion signatures to solve problems in two applications: object recognition and robot navigation. First, we use bird species recognition as the application to explore motion signatures for object recognition. We begin with study of the periodic wingbeat motion of flying birds. To analyze the wing motion of a flying bird, we establish kinematics models for bird wings, and obtain wingbeat periodicity in image frames after the perspective projection. Time series of salient extremities on bird images are extracted, and the wingbeat frequency is acquired for species classification. Physical experiments show that the frequency based recognition method is robust to segmentation errors and measurement lost up to 30%. In addition to the wing motion, the body motion of the bird is also analyzed to extract the flying velocity in 3D space. An interacting multi-model approach is then designed to capture the combined object motion patterns and different environment conditions. The proposed systems and algorithms are tested in physical experiments, and the results show a false positive rate of around 20% with a low false negative rate close to zero. Second, we explore motion signatures for vision-based vehicle navigation. We discover that motion vectors (MVs) encoded in Moving Picture Experts Group (MPEG) videos provide rich information of the motion in the environment, which can be used to reconstruct the vehicle ego-motion and the structure of the scene. However, MVs suffer from high noise level. To handle the challenge, an error propagation model for MVs is first proposed. Several steps, including MV merging, plane-at-infinity elimination, and planar region extraction, are designed to further reduce noises. The extracted planes are used as landmarks in an extended Kalman filter (EKF) for simultaneous localization and mapping. Results show that the algorithm performs localization and plane mapping with a relative trajectory error below 5:1%. Exploiting the fact that MVs encodes both environment information and moving obstacles, we further propose to track moving objects at the same time of localization and mapping. This enables the two critical navigation functionalities, localization and obstacle avoidance, to be performed in a single framework. MVs are labeled as stationary or moving according to their consistency to geometric constraints. Therefore, the extracted planes are separated into moving objects and the stationary scene. Multiple EKFs are used to track the static scene and the moving objects simultaneously. In physical experiments, we show a detection rate of moving objects at 96:6% and a mean absolute localization error below 3:5 meters