Mètode d'extracció multiparamètrica de característiques de textura orientat a la segmentació d'imatges

Tal com es veurà en el següent capítol d'antecedents, existeixen formes molt variades d'afrontar l'anàlisi de textures però cap d'elles està orientada al càlcul en temps real (video rate). Degut a la manca de mètodes que posin tant d'èmfasi en el temps de processat, l'objectiu d'aquesta tesi és definir i desenvolupar un nou mètode d'extracció de característiques de textura que treballi en temps real. Per aconseguir aquesta alta velocitat d'operació, un altre objectiu és presentar el disseny d'una arquitectura específica per implementar l'algorisme de càlcul dels paràmetres de textura definits, així com també l'algorisme de classificació dels paràmetres i la segmentació de la imatge en regions de textura semblant.En el capítol 2 s'expliquen els diversos mètodes més rellevants dins la caracterització de textures. Es veuran els mètodes més importants tant pel que fa als enfocaments estadístics com als estructurals. També en el mateix capítol se situa el nou mètode presentat en aquesta tesi dins els diferents enfocaments principals que existeixen. De la mateixa manera es fa una breu ressenya a la síntesi de textures, una manera d'avaluar quantitativament la caracterització de la textura d'una imatge. Ens centrarem principalment, en el capítol 3, en l'explicació del mètode presentat en aquest treball: s'introduiran els paràmetres de textura proposats, la seva necessitat i definicions. Al ser paràmetres altament perceptius i no seguir cap model matemàtic, en aquest mateix capítol s'utilitza una tècnica estadística anomenada anàlisi discriminant per demostrar que tots els paràmetres introdueixen suficient informació per a la separabilitat de regions de textura i veure que tots ells són necessaris en la discriminació de les textures.Dins el capítol 4 veurem com es tracta la informació subministrada pel sistema d'extracció de característiques per tal de classificar les dades i segmentar la imatge en funció de les seves textures. L'etapa de reconeixement de patrons es durà a terme en dues fases: aprenentatge i treball. També es presenta un estudi comparatiu entre diversos mètodes de classificació de textures i el mètode presentat en aquesta tesi; en ell es veu la bona funcionalitat del mètode en un temps de càlcul realment reduït. S'acaba el capítol amb una anàlisi de la robustesa del mètode introduint imatges amb diferents nivells de soroll aleatori. En el capítol 5 es presentaran els resultats obtinguts mitjançant l'extracció de característiques de textura a partir de diverses aplicacions reals. S'aplica el nostre mètode en aplicacions d'imatges aèries i en entorns agrícoles i sobre situacions que requereixen el processament en temps real com són la segmentació d'imatges de carreteres i una aplicació industrial d'inspecció i control de qualitat en l'estampació de teixits. Al final del capítol fem unes consideracions sobre dos efectes que poden influenciar en l'obtenció correcta dels resultats: zoom i canvis de perspectiva en les imatges de textura.En el capítol 6 es mostrarà l'arquitectura que s'ha dissenyat expressament per al càlcul dels paràmetres de textura en temps real. Dins el capítol es presentarà l'algorisme per a l'assignació de grups de textura i es demostrarà la seva velocitat d'operació a video rate.Finalment, en el capítol 7 es presentaran les conclusions i les línies de treball futures que es deriven d'aquesta tesi, així com els articles que hem publicat en relació a aquest treball i a l'anàlisi de textures. Les referències bibliogràfiques i els apèndixs conclouen el treball

Multi-scale texture segmentation of synthetic aperture radar images

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Local and deep texture features for classification of natural and biomedical images

Developing efficient feature descriptors is very important in many computer vision applications including biomedical image analysis. In the past two decades and before the popularity of deep learning approaches in image classification, texture features proved to be very effective to capture the gradient variation in the image. Following the success of the Local Binary Pattern (LBP) descriptor, many variations of this descriptor were introduced to further improve the ability of obtaining good classification results. However, the problem of image classification gets more complicated when the number of images increases as well as the number of classes. In this case, more robust approaches must be used to address this problem. In this thesis, we address the problem of analyzing biomedical images by using a combination of local and deep features. First, we propose a novel descriptor that is based on the motif Peano scan concept called Joint Motif Labels (JML). After that, we combine the features extracted from the JML descriptor with two other descriptors called Rotation Invariant Co-occurrence among Local Binary Patterns (RIC-LBP) and Joint Adaptive Medina Binary Patterns (JAMBP). In addition, we construct another descriptor called Motif Patterns encoded by RIC-LBP and use it in our classification framework. We enrich the performance of our framework by combining these local descriptors with features extracted from a pre-trained deep network called VGG-19. Hence, the 4096 features of the Fully Connected 'fc7' layer are extracted and combined with the proposed local descriptors. Finally, we show that Random Forests (RF) classifier can be used to obtain superior performance in the field of biomedical image analysis. Testing was performed on two standard biomedical datasets and another three standard texture datasets. Results show that our framework can beat state-of-the-art accuracy on the biomedical image analysis and the combination of local features produce promising results on the standard texture datasets.Includes bibliographical reference

Scene Image Classification and Retrieval

Scene image classification and retrieval not only have a great impact on scene image management, but also they can offer immeasurable assistance to other computer vision problems, such as image completion, human activity analysis, object recognition etc. Intuitively scene identification is correlated to recognition of objects or image regions, which prompts the notion to apply local features to scene categorization applications. Even though the adoption of local features in these tasks has yielded promising results, a global perception on scene images is also well-conditioned in cognitive science studies. Since the global description of a scene imposes less computational burden, it is favoured by some scholars despite its less discriminative capacity. Recent studies on global scene descriptors have even yielded classification performance that rivals results obtained by local approaches. The primary objective of this work is to tackle two of the limitations of existing global scene features: representation ineffectiveness and computational complexity. The thesis proposes two global scene features that seek to represent finer scene structures and reduce the dimensionality of feature vectors. Experimental results show that the proposed scene features exceed the performance of existing methods. The thesis is roughly divided into two parts. The first three chapters give an overview on the topic of scene image classification and retrieval methods, with a special attention to the most effective global scene features. In chapter 4, a novel scene descriptor, called ARP-GIST, is proposed and evaluated against the existing methods to show its ability to detect finer scene structures. In chapter 5, a low-dimensional scene feature, GIST-LBP, is proposed. In conjunction with a block ranking approach, the GIST-LBP feature is tested on a standard scene dataset to demonstrate its state-of-the-art performance

Study on Co-occurrence-based Image Feature Analysis and Texture Recognition Employing Diagonal-Crisscross Local Binary Pattern

In this thesis, we focus on several important fields on real-world image texture analysis and recognition. We survey various important features that are suitable for texture analysis. Apart from the issue of variety of features, different types of texture datasets are also discussed in-depth. There is no thorough work covering the important databases and analyzing them in various viewpoints. We persuasively categorize texture databases ? based on many references. In this survey, we put a categorization to split these texture datasets into few basic groups and later put related datasets. Next, we exhaustively analyze eleven second-order statistical features or cues based on co-occurrence matrices to understand image texture surface. These features are exploited to analyze properties of image texture. The features are also categorized based on their angular orientations and their applicability. Finally, we propose a method called diagonal-crisscross local binary pattern (DCLBP) for texture recognition. We also propose two other extensions of the local binary pattern. Compare to the local binary pattern and few other extensions, we achieve that our proposed method performs satisfactorily well in two very challenging benchmark datasets, called the KTH-TIPS (Textures under varying Illumination, Pose and Scale) database, and the USC-SIPI (University of Southern California ? Signal and Image Processing Institute) Rotations Texture dataset.九州工業大学博士学位論文 学位記番号:工博甲第354号　学位授与年月日:平成25年9月27日CHAPTER 1 INTRODUCTION|CHAPTER 2 FEATURES FOR TEXTURE ANALYSIS|CHAPTER 3 IN-DEPTH ANALYSIS OF TEXTURE DATABASES|CHAPTER 4 ANALYSIS OF FEATURES BASED ON CO-OCCURRENCE IMAGE MATRIX|CHAPTER 5 CATEGORIZATION OF FEATURES BASED ON CO-OCCURRENCE IMAGE MATRIX|CHAPTER 6 TEXTURE RECOGNITION BASED ON DIAGONAL-CRISSCROSS LOCAL BINARY PATTERN|CHAPTER 7 CONCLUSIONS AND FUTURE WORK九州工業大学平成25年

Visual Processing and Latent Representations in Biological and Artificial Neural Networks

The human visual system performs the impressive task of converting light arriving at the retina into a useful representation that allows us to make sense of the visual environment. We can navigate easily in the three-dimensional world and recognize objects and their properties, even if they appear from different angles and under different lighting conditions. Artificial systems can also perform well on a variety of complex visual tasks. While they may not be as robust and versatile as their biological counterpart, they have surprising capabilities that are rapidly improving. Studying the two types of systems can help us understand what computations enable the transformation of low-level sensory data into an abstract representation. To this end, this dissertation follows three different pathways. First, we analyze aspects of human perception. The focus is on the perception in the peripheral visual field and the relation to texture perception. Our work builds on a texture model that is based on the features of a deep neural network. We start by expanding the model to the temporal domain to capture dynamic textures such as flames or water. Next, we use psychophysical methods to investigate quantitatively whether humans can distinguish natural textures from samples that were generated by a texture model. Finally, we study images that cover the entire visual field and test whether matching the local summary statistics can produce metameric images independent of the image content. Second, we compare the visual perception of humans and machines. We conduct three case studies that focus on the capabilities of artificial neural networks and the potential occurrence of biological phenomena in machine vision. We find that comparative studies are not always straightforward and propose a checklist on how to improve the robustness of the conclusions that we draw from such studies. Third, we address a fundamental discrepancy between human and machine vision. One major strength of biological vision is its robustness to changes in the appearance of image content. For example, for unusual scenarios, such as a cow on a beach, the recognition performance of humans remains high. This ability is lacking in many artificial systems. We discuss on a conceptual level how to robustly disentangle attributes that are correlated during training, and test this on a number of datasets

Morphological approaches to understanding Antarctic Sea ice thickness

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Oceanographic Engineering at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2020.Sea ice thickness has long been an under-measured quantity, even in the satellite era. The snow surface elevation, which is far easier to measure, cannot be directly converted into sea ice thickness estimates without knowledge or assumption of what proportion of the snow surface consists of snow and ice. We do not fully understand how snow is distributed upon sea ice, in particular around areas with surface deformation. Here, we show that deep learning methods can be used to directly predict snow depth, as well as sea ice thickness, from measurements of surface topography obtained from laser altimetry. We also show that snow surfaces can be texturally distinguished, and that texturally-similar segments have similar snow depths. This can be used to predict snow depth at both local (sub-kilometer) and satellite (25 km) scales with much lower error and bias, and with greater ability to distinguish inter-annual and regional variability than current methods using linear regressions. We find that sea ice thickness can be estimated to ∼20% error at the kilometer scale. The success of deep learning methods to predict snow depth and sea ice thickness suggests that such methods may be also applied to temporally/spatially larger datasets like ICESat-2.This research was funded by National Aeronautics and Space Administration grant numbers NNX15AC69G and 80NSSC20K0972, the US National Science Foundation grant numbers ANT-1341513, ANT-1341606, ANT-1142075 and ANT-1341717, and the WHOI Academic Programs Office