Weakly supervised conditional random fields model for semantic segmentation with image patches.

Image semantic segmentation (ISS) is used to segment an image into regions with differently labeled semantic category. Most of the existing ISS methods are based on fully supervised learning, which requires pixel-level labeling for training the model. As a result, it is often very time-consuming and labor-intensive, yet still subject to manual errors and subjective inconsistency. To tackle such difficulties, a weakly supervised ISS approach is proposed, in which the challenging problem of label inference from image-level to pixel-level will be particularly addressed, using image patches and conditional random fields (CRF). An improved simple linear iterative cluster (SLIC) algorithm is employed to extract superpixels. for image segmentation. Specifically, it generates various numbers of superpixels according to different images, which can be used to guide the process of image patch extraction based on the image-level labeled information. Based on the extracted image patches, the CRF model is constructed for inferring semantic class labels, which uses the potential energy function to map from the image-level to pixel-level image labels. Finally, patch based CRF (PBCRF) model is used to accomplish the weakly supervised ISS. Experiments conducted on two publicly available benchmark datasets, MSRC and PASCAL VOC 2012, have demonstrated that our proposed algorithm can yield very promising results compared to quite a few state-of-the-art ISS methods, including some deep learning-based models

Combining local features and region segmentation: methods and applications

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones. Fecha de lectura: 23-01-2020Esta tesis tiene embargado el acceso al texto completo hasta el 23-07-2021Muchas y muy diferentes son las propuestas que se han desarrollado en el área de la visión artificial para la extracción de información de las imágenes y su posterior uso. Entra las más destacadas se encuentran las conocidas como características locales, del inglés local features, que detectan puntos o áreas de la imagen con ciertas características de interés, y las describen usando información de su entorno (local). También destacan las regiones en este área, y en especial este trabajo se ha centrado en los segmentadores en regiones, cuyo objetivo es agrupar la información de la imagen atendiendo a diversos criterios. Pese al enorme potencial de estas técnicas, y su probado éxito en diversas aplicaciones, su definición lleva implícita una serie de limitaciones funcionales que les han impedido exportar sus capacidades a otras áreas de aplicación. Se pretende impulsar el uso de estas herramientas en dichas aplicaciones, y por tanto mejorar los resultados del estado del arte, mediante la propuesta de un marco de desarrollo de nuevas soluciones. En concreto, la hipótesis principal del proyecto es que las capacidades de las características locales y los segmentadores en regiones son complementarias, y que su combinación, realizada de la forma adecuada, las maximiza a la vez que minimiza sus limitaciones. El principal objetivo, y por tanto la principal contribución del proyecto, es validar dicha hipótesis mediante la propuesta de un marco de desarrollo de nuevas soluciones combinando características locales y segmentadores para técnicas con capacidades mejoradas. Al tratarse de un marco de combinación de dos técnicas, el proceso de validación se ha llevado a cabo en dos pasos. En primer lugar se ha planteado el caso del uso de segmentadores en regiones para mejorar las características locales. Para verificar la viabilidad y el éxito de esta combinación se ha desarrollado una propuesta específica, SP-SIFT, que se ha validado tanto a nivel experimental como a nivel de aplicación real, en concreto como técnica principal de algoritmos de seguimiento de objetos. En segundo lugar, se ha planteado el caso de uso de características locales para mejorar los segmentadores en regiones. Para verificar la viabilidad y el éxito de esta combinación se ha desarrollado una propuesta específica, LF-SLIC, que se ha validado tanto a nivel experimental como a nivel de aplicación real, en concreto como técnica principal de un algoritmo de segmentación de lesiones pigmentadas de la piel. Los resultados conceptuales han probado que las técnicas mejoran a nivel de capacidades. Los resultados aplicados han probado que estas mejoras permiten el uso de estas técnicas en aplicaciones donde antes no tenían éxito. Con ello, se ha considerado la hipótesis validada, y por tanto exitosa la definición de un marco para el desarrollo de nuevas técnicas específicas con capacidades mejoradas. En conclusión, la principal aportación de la tesis es el marco de combinación de técnicas, plasmada en sus dos propuestas específicas: características locales mejoradas con segmentadores y segmentadores mejorados con características locales, y en el éxito conseguido en sus aplicaciones.A huge number of proposals have been developed in the area of computer vision for information extraction from images, and its further use. One of the most prevalent solutions are those known as local features. They detect points or areas of the image with certain characteristics of interest, and describe them using information from their (local) environment. The regions also stand out in the area, and especially this work has focused on the region segmentation algorithms, whose objective is to group the information of the image according to di erent criteria. Despite the enormous potential of these techniques, and their proven success in a number of applications, their de nition implies a series of functional limitations that have prevented them from exporting their capabilities to other application areas. In this thesis, it is intended to promote the use of these tools in these applications, and therefore improve the results of the state of the art, by proposing a framework for developing new solutions. Speci cally, the main hypothesis of the project is that the capacities of the local features and the region segmentation algorithms are complementary, and thus their combination, carried out in the right way, maximizes them while minimizing their limitations. The main objective, and therefore the main contribution of the thesis, is to validate this hypothesis by proposing a framework for developing new solutions combining local features and region segmentation algorithms, obtaining solutions with improved capabilities. As the hypothesis is proposing to combine two techniques, the validation process has been carried out in two steps. First, the use case of region segmentation algorithms enhancing local features. In order to verify the viability and success of this combination, a speci c proposal, SP-SIFT, was been developed. This proposal was validated both experimentally and in a real application scenario, speci cally as the main technique of object tracking algorithms. Second, the use case of enhancing region segmentation algorithm with local features. In order to verify the viability and success of this combination, a speci c proposal, LF-SLIC, was developed. The proposal was validated both experimentally and in a real application scenario, speci cally as the main technique of a pigmented skin lesions segmentation algorithm. The conceptual results proved that the techniques improve at the capabilities level. The application results proved that these improvements allow the use of this techniques in applications where they were previously unsuccessful. Thus, the hypothesis can be considered validated, and therefore the de nition of a framework for the development of new techniques with improved capabilities can be considered successful. In conclusion, the main contribution of the thesis is the framework for the combination of techniques, embodied in the two speci c proposals: enhanced local features with region segmentation algorithms, and region segmentation algorithms enhanced with local features; and in the success achieved in their applications.The work described in this Thesis was carried out within the Video Processing and Understanding Lab at the Department of Tecnología Electrónica y de las Comunicaciones, Escuela Politécnica Superior, Universidad Autónoma de Madrid (from 2014 to 2019). It was partially supported by the Spanish Government (TEC2014-53176-R, HAVideo)

Benchmark evaluation of object segmentation proposal

Abstract. In this research, we provide an in depth analysis and evaluation of four recent segmentation proposals algorithms on PASCAL VOC benchmark. The principal goal of this study is to investigate these object detection proposal methods in an un-biased evaluation framework. Despite having a widespread application, the strengths and weaknesses of different segmentation proposal methods with respect to each other are mostly not completely clear in the previous works. This thesis provides additional insights to the segmentation proposal methods. In order to evaluate the quality of proposals we plot the recall as a function of average number of regions per image. PASCAL VOC 2012 Object categories, where the methodologies show high performance and instances where these algorithms suffer low recall is also discussed in this work. Experimental evaluation reveals that, despite being different in the operational nature, generally all segmentation proposal methods share similar strengths and weaknesses. The analysis also show how one could select a proposal generation method based on object attributes. Finally we show that, improvement in recall can be obtained by merging the proposals of different algorithms together. Experimental evaluation shows that this merging approach outperforms individual algorithms both in terms of precision and recall

Target classification in multimodal video

The presented thesis focuses on enhancing scene segmentation and target recognition methodologies via the mobilisation of contextual information. The algorithms developed to achieve this goal utilise multi-modal sensor information collected across varying scenarios, from controlled indoor sequences to challenging rural locations. Sensors are chiefly colour band and long wave infrared (LWIR), enabling persistent surveillance capabilities across all environments. In the drive to develop effectual algorithms towards the outlined goals, key obstacles are identified and examined: the recovery of background scene structure from foreground object ’clutter’, employing contextual foreground knowledge to circumvent training a classifier when labeled data is not readily available, creating a labeled LWIR dataset to train a convolutional neural network (CNN) based object classifier and the viability of spatial context to address long range target classification when big data solutions are not enough. For an environment displaying frequent foreground clutter, such as a busy train station, we propose an algorithm exploiting foreground object presence to segment underlying scene structure that is not often visible. If such a location is outdoors and surveyed by an infra-red (IR) and visible band camera set-up, scene context and contextual knowledge transfer allows reasonable class predictions for thermal signatures within the scene to be determined. Furthermore, a labeled LWIR image corpus is created to train an infrared object classifier, using a CNN approach. The trained network demonstrates effective classification accuracy of 95% over 6 object classes. However, performance is not sustainable for IR targets acquired at long range due to low signal quality and classification accuracy drops. This is addressed by mobilising spatial context to affect network class scores, restoring robust classification capability

A comparative study of algorithms for automatic segmentation of dermoscopic images

Melanoma is the most common as well as the most dangerous type of skin cancer. Nevertheless, it can be effectively treated if detected early. Dermoscopy is one of the major non-invasive imaging techniques for the diagnosis of skin lesions. The computer-aided diagnosis based on the processing of dermoscopic images aims to reduce the subjectivity and time-consuming analysis related to traditional diagnosis. The first step of automatic diagnosis is image segmentation. In this project, the implementation and evaluation of several methods were proposed for the automatic segmentation of lesion regions in dermoscopic images, along with the corresponding implemented phases for image preprocessing and postprocessing. The developed algorithms include methods based on different state of the art techniques. The main groups of techniques which have been selected to be studied and implemented are thresholding-based methods, region-based methods, segmentation based on deformable models, as well as a new proposed approach based on the bag-of-words model. The implemented methods incorporate modifications for a better adaptation to features associated with dermoscopic images. Each implemented method was applied to a database constituted by 724 dermoscopic images. The output of the automatic segmentation procedure for each image was compared with the corresponding manual segmentation in order to evaluate the performance. The comparison between algorithms was carried out regarding the obtained evaluation metrics. The best results were achieved by the combination of region-based segmentation based on the multi-region adaptation of the k-means algorithm and the subIngeniería de Sistemas Audiovisuale

Artificial Intelligence Based Classification for Urban Surface Water Modelling

Estimations and predictions of surface water runoff can provide very useful insights, regarding flood risks in urban areas. To automatically predict the flow behaviour of the rainfall-runoff water, in real-world satellite images, it is important to precisely identify permeable and impermeable areas. This identification indicates and helps to calculate the amount of surface water, by taking into account the amount of water being absorbed in a permeable area and what remains on the impermeable area. In this research, a model of surface water has been established, to predict the behavioural flow of rainfall-runoff water. This study employs a combination of image processing, artificial intelligence and machine learning techniques, for automatic segmentation and classification of permeable and impermeable areas, in satellite images. These techniques investigate the image classification approaches for classifying three land-use categories (roofs, roads, and pervious areas), commonly found in satellite images of the earth’s surface. Three different classification scenarios are investigated, to select the best classification model. The first scenario involves pixel by pixel classification of images, using Classification Tree and Random Forest classification techniques, in 2 different settings of sequential and parallel execution of algorithms. In the second classification scenario, the image is divided into objects, by using Superpixels (SLIC) segmentation method, while three kinds of feature sets are extracted from the segmented objects. The performance of eight different supervised machine learning classifiers is probed, using 5-fold cross-validation, for multiple SLIC values, while detailed performance comparisons lead to conclusions about the classification into different classes, regarding Object-based and Pixel-based classification schemes. Pareto analysis and Knee point selection are used to select SLIC value and the suitable type of classification, among the aforementioned two. Furthermore, a new diversity and weighted sum-based ensemble classification model, called ParetoEnsemble, is proposed, in this classification scenario. The weights are applied to selected component classifiers of an ensemble, creating a strong classifier, where classification is done based on multiple votes from candidate classifiers of the ensemble, as opposed to individual classifiers, where classification is done based on a single vote, from only one classifier. Unbalanced and balanced data-based classification results are also evaluated, to determine the most suitable mode, for satellite image classifications, in this study. Convolutional Neural Networks, based on semantic segmentation, are also employed in the classification phase, as a third scenario, to evaluate the strength of deep learning model SegNet, in the classification of satellite imaging. The best results, from the three classification scenarios, are compared and the best classification method, among the three scenarios, is used in the next phase of water modelling, with the InfoWorks ICM software, to explore the potential of modelling process, regarding a partially automated surface water network. By using the parameter settings, with a specified amount of simulated rain falling, onto the imaged area, the amount of surface water flow is estimated, to get predictions about runoff situations in urban areas, since runoff, in such a situation, can be high enough to pose a dangerous flood risk. The area of Feock, in Cornwall, is used as a simulation area of study, in this research, where some promising results have been derived, regarding classification and modelling of runoff. The correlation coefficient estimation, between classification and runoff accuracy, provides useful insight, regarding the dependence of runoff performance on classification performance. The trained system was tested on some unknown area images as well, demonstrating a reasonable performance, considering the training and classification limitations and conditions. Furthermore, in these unknown area images, reasonable estimations were derived, regarding surface water runoff. An analysis of unbalanced and balanced data-based classification and runoff estimations, for multiple parameter configurations, provides aid to the selection of classification and modelling parameter values, to be used in future unknown data predictions. This research is founded on the incorporation of satellite imaging into water modelling, using selective images for analysis and assessment of results. This system can be further improved, and runoff predictions of high precision can be better achieved, by adding more high-resolution images to the classifiers training. The added variety, to the trained model, can lead to an even better classification of any unknown image, which could eventually provide better modelling and better insights into surface water modelling. Moreover, the modelling phase can be extended, in future research, to deal with real-time parameters, by calibrating the model, after the classification phase, in order to observe the impact of classification on the actual calibration