An entropy-based approach to automatic image segmentation of satellite images

An entropy-based image segmentation approach is introduced and applied to color images obtained from Google Earth. Segmentation refers to the process of partitioning a digital image in order to locate different objects and regions of interest. The application to satellite images paves the way to automated monitoring of ecological catastrophes, urban growth, agricultural activity, maritime pollution, climate changing and general surveillance. Regions representing aquatic, rural and urban areas are identified and the accuracy of the proposed segmentation methodology is evaluated. The comparison with gray level images revealed that the color information is fundamental to obtain an accurate segmentation. (C) 2010 Elsevier B.V. All rights reserved.

Image to text translation by multi-label classification

This paper presents an image to text translation platform consisting of image segmentation, region features extraction, region blobs clustering, and translation components. A multi-label learning method is suggested for realizing the translation component. Empirical studies show that the predictive performance of the translation component is better than its counterparts when employed a dual-random ensemble multi-label classification algorithm.

Task-based image annotation and retrieval

In order to address problems of information overload in digital imagery task domains we have developed an interactive approach to the capture and reuse of image context information. Our framework models different aspects of the relationship between images and domain tasks they support by monitoring the interactive manipulation and annotation of task-relevant imagery. The approach allows us to gauge a measure of a user's intentions as they complete goal-directed image tasks. As users analyze retrieved imagery their interactions are captured and an expert task context is dynamically constructed. This human expertise, proficiency, and knowledge can then be leveraged to support other users in carrying out similar domain tasks. We have applied our techniques to two multimedia retrieval applications for two different image domains, namely the geo-spatial and medical imagery domains. © Springer-Verlag Berlin Heidelberg 2007.

Deep Corals, Deep Learning: Moving the Deep Net Towards Real-Time Image Annotation

The mismatch between human capacity and the acquisition of Big Data such as Earth imagery undermines commitments to Convention on Biological Diversity (CBD) and Aichi targets. Artificial intelligence (AI) solutions to Big Data issues are urgently needed as these could prove to be faster, more accurate, and cheaper. Reducing costs of managing protected areas in remote deep waters and in the High Seas is of great importance, and this is a realm where autonomous technology will be transformative.

Diseño e implementación de un módulo para la anotación de imágenes médicas

Se comenzó el trabajo recabando información sobre los distintos enfoques que se le había dado a la anotación a lo largo del tiempo, desde anotación de imágenes a mano, pasando por anotación de imágenes utilizando características de bajo nivel, como color y textura, hasta la anotación automática. Tras entrar en materia, se procedió a estudiar artículos relativos a los diferentes algoritmos utilizados para la anotación automática de imágenes. Dado que la anotación automática es un campo bastante abierto, hay un gran numero de enfoques. Teniendo las características de las imágenes en particular en las que se iba a centrar el proyecto, se fueron descartando los poco idoneos, bien por un coste computacional elevado, o porque estaba centrado en un tipo diferente de imágenes, entre otras cosas. Finalmente, se encontró un algoritmo basado en formas (Active Shape Model) que se consideró que podría funcionar adecuadamente. Básicamente, los diferentes objetos de la imagen son identicados a partir de un contorno base generado a partir de imágenes de muestra, siendo modicado automáticamente para cubrir la zona deseada. Dado que las imágenes usadas son todas muy similares en composición, se cree que puede funcionar bien. Se partió de una implementación del algoritmo programada en MATLAB. Para empezar, se obtuvieron una serie de radiografías del tórax ya anotadas. Las imágenes contenían datos de contorno para ambos pulmones, las dos clavículas y el corazón. El primer paso fue la creación de una serie de scripts en MATLAB que permitieran: - Leer y transformar las imágenes recibidas en RAW, para adaptarlas al tamaño y la posición de los contornos anotados - Leer los archivos de texto con los datos de los puntos del contorno y transformarlos en variables de MATLAB - Unir la imagen transformada con los puntos y guardarla en un formato que la implementación del algoritmo entendiera. Tras conseguir los ficheros necesarios, se procedió a crear un modelo para cada órgano utilizando para el entrenamiento una pequeña parte de las imágenes. El modelo obtenido se probó con varias imágenes de las restantes. Sin embargo, se encontro bastante variación dependiendo de la imagen utilizada y el órgano detectado. ---ABSTRACT---The project was started by procuring information about the diferent approaches to image annotation over time, from manual image anotation to automatic annotation. The next step was to study several articles about the diferent algorithms used for automatic image annotation. Given that automatic annotation is an open field, there is a great number of approaches. Taking into account the features of the images that would be used, the less suitable algorithms were rejected. Eventually, a shape-based algorithm (Active Shape Model) was found. Basically, the diferent objects in the image are identified from a base contour, which is generated from training images. Then this contour is automatically modified to cover the desired area. Given that all the images that would be used are similar in object placement, the algorithm would probably work nicely. The work started from a MATLAB implementation of the algorithm. To begin with, a set of chest radiographs already annotated were obtained. These images came with contour data for both lungs, both clavicles and the heart. The first step was the creation of a series of MATLAB scripts to join the RAW images with the annotation data and transform them into a format that the algorithm could read. After obtaining the necessary files, a model for each organ was created using part of the images for training. The trained model was tested on several of the reimaining images. However, there was much variation in the results from one image to another. Generally, lungs were detected pretty accurately, whereas clavicles and the heart gave more problems. To improve the method, a new model was trained using half of the available images. With this model, a significant inprovement of the results can be seen.

High-level concept annotation using ontology and probabilistic inference

Image annotation is a significant step towards semantic based image retrieval. Ontology is a popular approach for semantic representation and has been intensively studied for multimedia analysis. However, relations among concepts are seldom used to extract higher-level semantics. Moreover, the ontology inference is often crisp. This paper aims to enable sophisticated semantic querying of images, and thus contributes to 1) an ontology framework to contain both visual and contextual knowledge, and 2) a probabilistic inference approach to reason the high-level concepts based on different sources of information. The experiment on a natural scene database from LabelMe database shows encouraging results.

Towards robust automatic affective classification of images using facial expressions for practical applications

Affect is an important feature of multimedia content and conveys valuable information for multimedia indexing and retrieval. Most existing studies for affective content analysis are limited to low-level features or mid-level representations, and are generally criticized for their incapacity to address the gap between low-level features and high-level human affective perception. The facial expressions of subjects in images carry important semantic information that can substantially influence human affective perception, but have been seldom investigated for affective classification of facial images towards practical applications. This paper presents an automatic image emotion detector (IED) for affective classification of practical (or non-laboratory) data using facial expressions, where a lot of “real-world” challenges are present, including pose, illumination, and size variations etc. The proposed method is novel, with its framework designed specifically to overcome these challenges using multi-view versions of face and fiducial point detectors, and a combination of point-based texture and geometry. Performance comparisons of several key parameters of relevant algorithms are conducted to explore the optimum parameters for high accuracy and fast computation speed. A comprehensive set of experiments with existing and new datasets, shows that the method is effective despite pose variations, fast, and appropriate for large-scale data, and as accurate as the method with state-of-the-art performance on laboratory-based data. The proposed method was also applied to affective classification of images from the British Broadcast Corporation (BBC) in a task typical for a practical application providing some valuable insights.

Automatic wildfire detection and simulation using optical information from unmanned aerial systems

In many parts of the world, uncontrolled fires in sparsely populated areas are a major concern as they can quickly grow into large and destructive conflagrations in short time spans. Detecting these fires has traditionally been a job for trained humans on the ground, or in the air. In many cases, these manned solutions are simply not able to survey the amount of area necessary to maintain sufficient vigilance and coverage. This paper investigates the use of unmanned aerial systems (UAS) for automated wildfire detection. The proposed system uses low-cost, consumer-grade electronics and sensors combined with various airframes to create a system suitable for automatic detection of wildfires. The system employs automatic image processing techniques to analyze captured images and autonomously detect fire-related features such as fire lines, burnt regions, and flammable material. This image recognition algorithm is designed to cope with environmental occlusions such as shadows, smoke and obstructions. Once the fire is identified and classified, it is used to initialize a spatial/temporal fire simulation. This simulation is based on occupancy maps whose fidelity can be varied to include stochastic elements, various types of vegetation, weather conditions, and unique terrain. The simulations can be used to predict the effects of optimized firefighting methods to prevent the future propagation of the fires and greatly reduce time to detection of wildfires, thereby greatly minimizing the ensuing damage. This paper also documents experimental flight tests using a SenseFly Swinglet UAS conducted in Brisbane, Australia as well as modifications for custom UAS.

Image-based delineation and classification of built heritage masonry

[EN]Fundación Zain is developing new built heritage assessment protocols. The goal is to objectivize and standardize the analysis and decision process that leads to determining the degree of protection of built heritage in the Basque Country. The ultimate step in this objectivization and standardization effort will be the development of an information and communication technology (ICT) tool for the assessment of built heritage. This paper presents the ground work carried out to make this tool possible: the automatic, image-based delineation of stone masonry. This is a necessary first step in the development of the tool, as the built heritage that will be assessed consists of stone masonry construction, and many of the features analyzed can be characterized according to the geometry and arrangement of the stones. Much of the assessment is carried out through visual inspection. Thus, this process will be automated by applying image processing on digital images of the elements under inspection. The principal contribution of this paper is the automatic delineation the framework proposed. The other contribution is the performance evaluation of this delineation as the input to a classifier for a geometrically characterized feature of a built heritage object. The element chosen to perform this evaluation is the stone arrangement of masonry walls. The validity of the proposed framework is assessed on real images of masonry walls.

Image-Based Delineation and Classification of Built Heritage Masonry

Fundacion Zain is developing new built heritage assessment protocols. The goal is to objectivize and standardize the analysis and decision process that leads to determining the degree of protection of built heritage in the Basque Country. The ultimate step in this objectivization and standardization effort will be the development of an information and communication technology (ICT) tool for the assessment of built heritage. This paper presents the ground work carried out to make this tool possible: the automatic, image-based delineation of stone masonry. This is a necessary first step in the development of the tool, as the built heritage that will be assessed consists of stone masonry construction, and many of the features analyzed can be characterized according to the geometry and arrangement of the stones. Much of the assessment is carried out through visual inspection. Thus, this process will be automated by applying image processing on digital images of the elements under inspection. The principal contribution of this paper is the automatic delineation the framework proposed. The other contribution is the performance evaluation of this delineation as the input to a classifier for a geometrically characterized feature of a built heritage object. The element chosen to perform this evaluation is the stone arrangement of masonry walls. The validity of the proposed framework is assessed on real images of masonry walls.

Quantitative characterization of multi-phase materials by digital image processing

An automatic image processing and analysis technique has been developed for quantitative characterization of multi-phase materials. For the development of this technique is used the Khoros system that offers the basic morphological tools and a flexible, visual programming language. These techniques are implemented in a highly user oriented image processing environment that allows the user to adapt each step of the processing to his special requirements.To illustrate the implementation and performance of this technique, images of two different materials are processed for microstructure characterization. The result is presented through the determination of volume fraction of the different phases or precipitates.

Automatic segmentation and classification of human intestinal parasites from microscopy images

Human intestinal parasites constitute a problem in most tropical countries, causing death or physical and mental disorders. Their diagnosis usually relies on the visual analysis of microscopy images, with error rates that may range from moderate to high. The problem has been addressed via computational image analysis, but only for a few species and images free of fecal impurities. In routine, fecal impurities are a real challenge for automatic image analysis. We have circumvented this problem by a method that can segment and classify, from bright field microscopy images with fecal impurities, the 15 most common species of protozoan cysts, helminth eggs, and larvae in Brazil. Our approach exploits ellipse matching and image foresting transform for image segmentation, multiple object descriptors and their optimum combination by genetic programming for object representation, and the optimum-path forest classifier for object recognition. The results indicate that our method is a promising approach toward the fully automation of the enteroparasitosis diagnosis. © 2012 IEEE.

A Fully Automatic Calibration Framework for Navigated Ultrasound Imaging

Navigated ultrasound (US) imaging is used for the intra-operative acquisition of 3D image data during imageguided surgery. The presented approach includes the design of a compact and easy to use US calibration device and its integration into a software application for navigated liver surgery. User interaction during the calibration process is minimized through automatic detection of the calibration process followed by automatic image segmentation, calculation of the calibration transform and validation of the obtained result. This leads to a fast, interaction-free and fully automatic calibration procedure enabling intra-operative

Fully Automatic CT Segmentation for Computer-Assisted Pre-operative Planning of Hip Arthroscopy

Extraction of both pelvic and femoral surface models of a hip joint from CT data for computer-assisted pre-operative planning of hip arthroscopy is addressed. We present a method for a fully automatic image segmentation of a hip joint. Our method works by combining fast random forest (RF) regression based landmark detection, atlas-based segmentation, with articulated statistical shape model (aSSM) based hip joint reconstruction. The two fundamental contributions of our method are: (1) An improved fast Gaussian transform (IFGT) is used within the RF regression framework for a fast and accurate landmark detection, which then allows for a fully automatic initialization of the atlas-based segmentation; and (2) aSSM based fitting is used to preserve hip joint structure and to avoid penetration between the pelvic and femoral models. Validation on 30 hip CT images show that our method achieves high performance in segmenting pelvis, left proximal femur, and right proximal femur surfaces with an average accuracy of 0.59 mm, 0.62 mm, and 0.58 mm, respectively.

Improving parameters selection of a seeded region growing method for multiband image segmentation

In the last decade, Object Based Image Analysis (OBIA) has been accepted as an effective method for processing high spatial resolution multiband images. This image analysis method is an approach that starts with the segmentation of the image. Image segmentation in general is a procedure to partition an image into homogenous groups (segments). In practice, visual interpretation is often used to assess the quality of segmentation and the analysis relies on the experience of an analyst. In an effort to address the issue, in this study, we evaluate several seed selection strategies for an automatic image segmentation methodology based on a seeded region growing-merging approach. In order to evaluate the segmentation quality, segments were subjected to spatial autocorrelation analysis using Moran's I index and intra-segment variance analysis. We apply the algorithm to image segmentation using an aerial multiband image.