Stereo Matching Using a Modified Efficient Belief Propagation in a Level Set Framework

Stereo matching determines correspondence between pixels in two or more images of the same scene taken from different angles; this can be handled either locally or globally. The two most common global approaches are belief propagation (BP) and graph cuts. Efficient belief propagation (EBP), which is the most widely used BP approach, uses a multi-scale message passing strategy, an O(k) smoothness cost algorithm, and a bipartite message passing strategy to speed up the convergence of the standard BP approach. As in standard belief propagation, every pixel sends messages to and receives messages from its four neighboring pixels in EBP. Each outgoing message is the sum of the data cost, incoming messages from all the neighbors except the intended receiver, and the smoothness cost. Upon convergence, the location of the minimum of the final belief vector is defined as the current pixel’s disparity. The present effort makes three main contributions: (a) it incorporates level set concepts, (b) it develops a modified data cost to encourage matching of intervals, (c) it adjusts the location of the minimum of outgoing messages for select pixels that is consistent with the level set method. When comparing the results of the current work with that of standard EBP, the disparity results are very similar, as they should be

Artificial Intelligence in the Creative Industries: A Review

This paper reviews the current state of the art in Artificial Intelligence (AI) technologies and applications in the context of the creative industries. A brief background of AI, and specifically Machine Learning (ML) algorithms, is provided including Convolutional Neural Network (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement Learning (DRL). We categorise creative applications into five groups related to how AI technologies are used: i) content creation, ii) information analysis, iii) content enhancement and post production workflows, iv) information extraction and enhancement, and v) data compression. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We foresee that, in the near future, machine learning-based AI will be adopted widely as a tool or collaborative assistant for creativity. In contrast, we observe that the successes of machine learning in domains with fewer constraints, where AI is the `creator', remain modest. The potential of AI (or its developers) to win awards for its original creations in competition with human creatives is also limited, based on contemporary technologies. We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human centric -- where it is designed to augment, rather than replace, human creativity

Depth-Assisted Semantic Segmentation, Image Enhancement and Parametric Modeling

This dissertation addresses the problem of employing 3D depth information on solving a number of traditional challenging computer vision/graphics problems. Humans have the abilities of perceiving the depth information in 3D world, which enable humans to reconstruct layouts, recognize objects and understand the geometric space and semantic meanings of the visual world. Therefore it is significant to explore how the 3D depth information can be utilized by computer vision systems to mimic such abilities of humans. This dissertation aims at employing 3D depth information to solve vision/graphics problems in the following aspects: scene understanding, image enhancements and 3D reconstruction and modeling. In addressing scene understanding problem, we present a framework for semantic segmentation and object recognition on urban video sequence only using dense depth maps recovered from the video. Five view-independent 3D features that vary with object class are extracted from dense depth maps and used for segmenting and recognizing different object classes in street scene images. We demonstrate a scene parsing algorithm that uses only dense 3D depth information to outperform using sparse 3D or 2D appearance features. In addressing image enhancement problem, we present a framework to overcome the imperfections of personal photographs of tourist sites using the rich information provided by large-scale internet photo collections (IPCs). By augmenting personal 2D images with 3D information reconstructed from IPCs, we address a number of traditionally challenging image enhancement techniques and achieve high-quality results using simple and robust algorithms. In addressing 3D reconstruction and modeling problem, we focus on parametric modeling of flower petals, the most distinctive part of a plant. The complex structure, severe occlusions and wide variations make the reconstruction of their 3D models a challenging task. We overcome these challenges by combining data driven modeling techniques with domain knowledge from botany. Taking a 3D point cloud of an input flower scanned from a single view, each segmented petal is fitted with a scale-invariant morphable petal shape model, which is constructed from individually scanned 3D exemplar petals. Novel constraints based on botany studies are incorporated into the fitting process for realistically reconstructing occluded regions and maintaining correct 3D spatial relations. The main contribution of the dissertation is in the intelligent usage of 3D depth information on solving traditional challenging vision/graphics problems. By developing some advanced algorithms either automatically or with minimum user interaction, the goal of this dissertation is to demonstrate that computed 3D depth behind the multiple images contains rich information of the visual world and therefore can be intelligently utilized to recognize/ understand semantic meanings of scenes, efficiently enhance and augment single 2D images, and reconstruct high-quality 3D models

HYperbolic Self-Paced Learning for Self-Supervised Skeleton-based Action Representations

Self-paced learning has been beneficial for tasks where some initial knowledge is available, such as weakly supervised learning and domain adaptation, to select and order the training sample sequence, from easy to complex. However its applicability remains unexplored in unsupervised learning, whereby the knowledge of the task matures during training. We propose a novel HYperbolic Self-Paced model (HYSP) for learning skeleton-based action representations. HYSP adopts self-supervision: it uses data augmentations to generate two views of the same sample, and it learns by matching one (named online) to the other (the target). We propose to use hyperbolic uncertainty to determine the algorithmic learning pace, under the assumption that less uncertain samples should be more strongly driving the training, with a larger weight and pace. Hyperbolic uncertainty is a by-product of the adopted hyperbolic neural networks, it matures during training and it comes with no extra cost, compared to the established Euclidean SSL framework counterparts. When tested on three established skeleton-based action recognition datasets, HYSP outperforms the state-of-the-art on PKU-MMD I, as well as on 2 out of 3 downstream tasks on NTU-60 and NTU-120. Additionally, HYSP only uses positive pairs and bypasses therefore the complex and computationally-demanding mining procedures required for the negatives in contrastive techniques. Code is available at https://github.com/paolomandica/HYSP.Comment: Accepted at ICLR 202

An evaluation of partial differential equations based digital inpainting algorithms

Partial Differential equations (PDEs) have been used to model various phenomena/tasks in different scientific and engineering endeavours. This thesis is devoted to modelling image inpainting by numerical implementations of certain PDEs. The main objectives of image inpainting include reconstructing damaged parts and filling-in regions in which data/colour information are missing. Different automatic and semi-automatic approaches to image inpainting have been developed including PDE-based, texture synthesis-based, exemplar-based, and hybrid approaches. Various challenges remain unresolved in reconstructing large size missing regions and/or missing areas with highly textured surroundings. Our main aim is to address such challenges by developing new advanced schemes with particular focus on using PDEs of different orders to preserve continuity of textural and geometric information in the surrounding of missing regions. We first investigated the problem of partial colour restoration in an image region whose greyscale channel is intact. A PDE-based solution is known that is modelled as minimising total variation of gradients in the different colour channels. We extend the applicability of this model to partial inpainting in other 3-channels colour spaces (such as RGB where information is missing in any of the two colours), simply by exploiting the known linear/affine relationships between different colouring models in the derivation of a modified PDE solution obtained by using the Euler-Lagrange minimisation of the corresponding gradient Total Variation (TV). We also developed two TV models on the relations between greyscale and colour channels using the Laplacian operator and the directional derivatives of gradients. The corresponding Euler-Lagrange minimisation yields two new PDEs of different orders for partial colourisation. We implemented these solutions in both spatial and frequency domains. We measure the success of these models by evaluating known image quality measures in inpainted regions for sufficiently large datasets and scenarios. The results reveal that our schemes compare well with existing algorithms, but inpainting large regions remains a challenge. Secondly, we investigate the Total Inpainting (TI) problem where all colour channels are missing in an image region. Reviewing and implementing existing PDE-based total inpainting methods reveal that high order PDEs, applied to each colour channel separately, perform well but are influenced by the size of the region and the quantity of texture surrounding it. Here we developed a TI scheme that benefits from our partial inpainting approach and apply two PDE methods to recover the missing regions in the image. First, we extract the (Y, Cb, Cr) of the image outside the missing region, apply the above PDE methods for reconstructing the missing regions in the luminance channel (Y), and then use the colourisation method to recover the missing (Cb, Cr) colours in the region. We shall demonstrate that compared to existing TI algorithms, our proposed method (using 2 PDE methods) performs well when tested on large datasets of natural and face images. Furthermore, this helps understanding of the impact of the texture in the surrounding areas on inpainting and opens new research directions. Thirdly, we investigate existing Exemplar-Based Inpainting (EBI) methods that do not use PDEs but simultaneously propagate the texture and structure into the missing region by finding similar patches within the rest of image and copying them into the boundary of the missing region. The order of patch propagation is determined by a priority function, and the similarity is determined by matching criteria. We shall exploit recently emerging Topological Data Analysis (TDA) tools to create innovative EBI schemes, referred to as TEBI. TDA studies shapes of data/objects to quantify image texture in terms of connectivity and closeness properties of certain data landmarks. Such quantifications help determine the appropriate size of patch propagation and will be used to modify the patch propagation priority function using the geometrical properties of curvature of isophotes, and to improve the matching criteria of patches by calculating the correlation coefficients from the spatial, gradient and Laplacian domains. The performance of this TEBI method will be tested by applying it to natural dataset images, resulting in improved inpainting when compared with other EBI methods. Fourthly, the recent hybrid-based inpainting techniques are reviewed and a number of highly performing innovative hybrid techniques that combine the use of high order PDE methods with the TEBI method for the simultaneous rebuilding of the missing texture and structure regions in an image are proposed. Such a hybrid scheme first decomposes the image into texture and structure components, and then the missing regions in these components are recovered by TEBI and PDE based methods respectively. The performance of our hybrid schemes will be compared with two existing hybrid algorithms. Fifthly, we turn our attention to inpainting large missing regions, and develop an innovative inpainting scheme that uses the concept of seam carving to reduce this problem to that of inpainting a smaller size missing region that can be dealt with efficiently using the inpainting schemes developed above. Seam carving resizes images based on content-awareness of the image for both reduction and expansion without affecting those image regions that have rich information. The missing region of the seam-carved version will be recovered by the TEBI method, original image size is restored by adding the removed seams and the missing parts of the added seams are then repaired using a high order PDE inpainting scheme. The benefits of this approach in dealing with large missing regions are demonstrated. The extensive performance testing of the developed inpainting methods shows that these methods significantly outperform existing inpainting methods for such a challenging task. However, the performance is still not acceptable in recovering large missing regions in high texture and structure images, and hence we shall identify remaining challenges to be investigated in the future. We shall also extend our work by investigating recently developed deep learning based image/video colourisation, with the aim of overcoming its limitations and shortcoming. Finally, we should also describe our on-going research into using TDA to detect recently growing serious “malicious” use of inpainting to create Fake images/videos

Bayesian Optimization for Image Segmentation, Texture Flow Estimation and Image Deblurring

Ph.DDOCTOR OF PHILOSOPH

Scene understanding for interactive applications

Para interactuar con el entorno, es necesario entender que está ocurriendo en la escena donde se desarrolla la acción. Décadas de investigación en el campo de la visión por computador han contribuido a conseguir sistemas que permiten interpretar de manera automática el contenido en una escena a partir de información visual. Se podría decir el objetivo principal de estos sistemas es replicar la capacidad humana para extraer toda la información a partir solo de datos visuales. Por ejemplo, uno de sus objetivos es entender como percibimosel mundo en tres dimensiones o como podemos reconocer sitios y objetos a pesar de la gran variación en su apariencia. Una de las tareas básicas para entender una escena es asignar un significado semántico a cada elemento (píxel) de una imagen. Esta tarea se puede formular como un problema de etiquetado denso el cual especifica valores (etiquetas) a cada pixel o región de una imagen. Dependiendo de la aplicación, estas etiquetas puedenrepresentar conceptos muy diferentes, desde magnitudes físicas como la información de profundidad, hasta información semántica, como la categoría de un objeto. El objetivo general en esta tesis es investigar y desarrollar nuevas técnicas para incorporar automáticamente una retroalimentación por parte del usuario, o un conocimiento previo en sistemas inteligente para conseguir analizar automáticamente el contenido de una escena. en particular,esta tesis explora dos fuentes comunes de información previa proporcionado por los usuario: interacción humana y etiquetado manual de datos de ejemplo.La primera parte de esta tesis esta dedicada a aprendizaje de información de una escena a partir de información proporcionada de manera interactiva por un usuario. Las soluciones que involucran a un usuario imponen limitaciones en el rendimiento, ya que la respuesta que se le da al usuario debe obtenerse en un tiempo interactivo. Esta tesis presenta un paradigma eficiente que aproxima cualquier magnitud por píxel a partir de unos pocos trazos del usuario. Este sistema propaga los escasos datos de entrada proporcionados por el usuario a cada píxel de la imagen. El paradigma propuesto se ha validado a través detres aplicaciones interactivas para editar imágenes, las cuales requieren un conocimiento por píxel de una cierta magnitud, con el objetivo de simular distintos efectos.Otra estrategia común para aprender a partir de información de usuarios es diseñar sistemas supervisados de aprendizaje automático. En los últimos años, las redes neuronales convolucionales han superado el estado del arte de gran variedad de problemas de reconocimiento visual. Sin embargo, para nuevas tareas, los datos necesarios de entrenamiento pueden no estar disponibles y recopilar suficientes no es siempre posible. La segunda parte de esta tesis explora como mejorar los sistema que aprenden etiquetado denso semántico a partir de imágenes previamente etiquetadas por los usuarios. En particular, se presenta y validan estrategias, basadas en los dos principales enfoques para transferir modelos basados en deep learning, para segmentación semántica, con el objetivo de poder aprender nuevas clases cuando los datos de entrenamiento no son suficientes en cantidad o precisión.Estas estrategias se han validado en varios entornos realistas muy diferentes, incluyendo entornos urbanos, imágenes aereas y imágenes submarinas.In order to interact with the environment, it is necessary to understand what is happening on it, on the scene where the action is ocurring. Decades of research in the computer vision field have contributed towards automatically achieving this scene understanding from visual information. Scene understanding is a very broad area of research within the computer vision field. We could say that it tries to replicate the human capability of extracting plenty of information from visual data. For example, we would like to understand how the people perceive the world in three dimensions or can quickly recognize places or objects despite substantial appearance variation. One of the basic tasks in scene understanding from visual data is to assign a semantic meaning to every element of the image, i.e., assign a concept or object label to every pixel in the image. This problem can be formulated as a dense image labeling problem which assigns specific values (labels) to each pixel or region in the image. Depending on the application, the labels can represent very different concepts, from a physical magnitude, such as depth information, to high level semantic information, such as an object category. The general goal in this thesis is to investigate and develop new ways to automatically incorporate human feedback or prior knowledge in intelligent systems that require scene understanding capabilities. In particular, this thesis explores two common sources of prior information from users: human interactions and human labeling of sample data. The first part of this thesis is focused on learning complex scene information from interactive human knowledge. Interactive user solutions impose limitations on the performance where the feedback to the user must be at interactive rates. This thesis presents an efficient interaction paradigm that approximates any per-pixel magnitude from a few user strokes. It propagates the sparse user input to each pixel of the image. We demonstrate the suitability of the proposed paradigm through three interactive image editing applications which require per-pixel knowledge of certain magnitude: simulate the effect of depth of field, dehazing and HDR tone mapping. Other common strategy to learn from user prior knowledge is to design supervised machine-learning approaches. In the last years, Convolutional Neural Networks (CNNs) have pushed the state-of-the-art on a broad variety of visual recognition problems. However, for new tasks, enough training data is not always available and therefore, training from scratch is not always feasible. The second part of this thesis investigates how to improve systems that learn dense semantic labeling of images from user labeled examples. In particular, we present and validate strategies, based on common transfer learning approaches, for semantic segmentation. The goal of these strategies is to learn new specific classes when there is not enough labeled data to train from scratch. We evaluate these strategies across different environments, such as autonomous driving scenes, aerial images or underwater ones.<br /

Exploring new representations and applications for motion analysis

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 153-164).The focus of motion analysis has been on estimating a flow vector for every pixel by matching intensities. In my thesis, I will explore motion representations beyond the pixel level and new applications to which these representations lead. I first focus on analyzing motion from video sequences. Traditional motion analysis suffers from the inappropriate modeling of the grouping relationship of pixels and from a lack of ground-truth data. Using layers as the interface for humans to interact with videos, we build a human-assisted motion annotation system to obtain ground-truth motion, missing in the literature, for natural video sequences. Furthermore, we show that with the layer representation, we can detect and magnify small motions to make them visible to human eyes. Then we move to a contour presentation to analyze the motion for textureless objects under occlusion. We demonstrate that simultaneous boundary grouping and motion analysis can solve challenging data, where the traditional pixel-wise motion analysis fails. In the second part of my thesis, I will show the benefits of matching local image structures instead of intensity values. We propose SIFT flow that establishes dense, semantically meaningful correspondence between two images across scenes by matching pixel-wise SIFT features. Using SIFT flow, we develop a new framework for image parsing by transferring the metadata information, such as annotation, motion and depth, from the images in a large database to an unknown query image. We demonstrate this framework using new applications such as predicting motion from a single image and motion synthesis via object transfer.(cont.) Based on SIFT flow, we introduce a nonparametric scene parsing system using label transfer, with very promising experimental results suggesting that our system outperforms state-of-the-art techniques based on training classifiers.by Ce Liu.Ph.D

Learning Object Recognition and Object Class Segmentation with Deep Neural Networks on GPU

As cameras are becoming ubiquitous and internet storage abundant, the need for computers to understand images is growing rapidly. This thesis is concerned with two computer vision tasks, recognizing objects and their location, and segmenting images according to object classes. We focus on deep learning approaches, which in recent years had a tremendous influence on machine learning in general and computer vision in particular. The thesis presents our research into deep learning models and algorithms. It is divided into three parts. The first part describes our GPU deep learning framework. Its hierarchical structure allows transparent use of GPU, facilitates specification of complex models, model inspection, and constitutes the implementation basis of the later chapters. Components of this framework were used in a real-time GPU library for random forests, which we present and evaluate. In the second part, we investigate greedy learning techniques for semi-supervised object recognition. We improve the feature learning capabilities of restricted Boltzmann machines (RBM) with lateral interactions and auto-encoders with additional hidden layers, and offer empirical insight into the evaluation of RBM learning algorithms. The third part of this thesis focuses on object class segmentation. Here, we incrementally introduce novel neural network models and training algorithms, successively improving the state of the art on multiple datasets. Our novel methods include supervised pre-training, histogram of oriented gradient DNN inputs, depth normalization and recurrence. All contribute towards improving segmentation performance beyond what is possible with competitive baseline methods. We further demonstrate that pixelwise labeling combined with a structured loss function can be utilized to localize objects. Finally, we show how transfer learning in combination with object-centered depth colorization can be used to identify objects. We evaluate our proposed methods on the publicly available MNIST, MSRC, INRIA Graz-02, NYU-Depth, Pascal VOC, and Washington RGB-D Objects datasets.Allgegenwärtige Kameras und preiswerter Internetspeicher erzeugen einen großen Bedarf an Algorithmen für maschinelles Sehen. Die vorliegende Dissertation adressiert zwei Teilbereiche dieses Forschungsfeldes: Erkennung von Objekten und Objektklassensegmentierung. Der methodische Schwerpunkt liegt auf dem Lernen von tiefen Modellen (”Deep Learning“). Diese haben in den vergangenen Jahren einen enormen Einfluss auf maschinelles Lernen allgemein und speziell maschinelles Sehen gewonnen. Dabei behandeln wir behandeln wir drei Themenfelder. Der erste Teil der Arbeit beschreibt ein GPU-basiertes Softwaresystem für Deep Learning. Dessen hierarchische Struktur erlaubt schnelle GPU-Berechnungen, einfache Spezifikation komplexer Modelle und interaktive Modellanalyse. Damit liefert es das Fundament für die folgenden Kapitel. Teile des Systems finden Verwendung in einer Echtzeit-GPU-Bibliothek für Random Forests, die wir ebenfalls vorstellen und evaluieren. Der zweite Teil der Arbeit beleuchtet Greedy-Lernalgorithmen für halb überwachtes Lernen. Hier werden hierarchische Modelle schrittweise aus Modulen wie Autokodierern oder restricted Boltzmann Machines (RBM ) aufgebaut. Wir verbessern die Repräsentationsfähigkeiten von RBM auf Bildern durch Einführung lokaler und lateraler Verknüpfungen und liefern empirische Erkenntnisse zur Bewertung von RBM-Lernalgorithmen. Wir zeigen zudem, dass die in Autokodierern verwendeten einschichtigen Kodierer komplexe Zusammenhänge ihrer Eingaben nicht erkennen können und schlagen stattdessen einen hybriden Kodierer vor, der sowohl komplexe Zusammenhänge erkennen, als auch weiterhin einfache Zusammenhänge einfach repräsentieren kann. Im dritten Teil der Arbeit stellen wir neue neuronale Netzarchitekturen und Trainingsmethoden für die Objektklassensegmentierung vor. Wir zeigen, dass neuronale Netze mit überwachtem Vortrainieren, wiederverwendeten Ausgaben und Histogrammen Orientierter Gradienten (HOG) als Eingabe den aktuellen Stand der Technik auf mehreren RGB-Datenmengen erreichen können. Anschließend erweitern wir unsere Methoden in zwei Dimensionen, sodass sie mit Tiefendaten (RGB-D) und Videos verarbeiten können. Dazu führen wir zunächst Tiefennormalisierung für Objektklassensegmentierung ein um die Skala zu fixieren, und erlauben expliziten Zugriff auf die Höhe in einem Bildausschnitt. Schließlich stellen wir ein rekurrentes konvolutionales neuronales Netz vor, das einen großen räumlichen Kontext einbezieht, hochaufgelöste Ausgaben produziert und Videosequenzen verarbeiten kann. Dadurch verbessert sich die Bildsegmentierung relativ zu vergleichbaren Methoden, etwa auf der Basis von Random Forests oder CRF . Wir zeigen dann, dass pixelbasierte Ausgaben in neuronalen Netzen auch benutzt werden können um die Position von Objekten zu detektieren. Dazu kombinieren wir Techniken des strukturierten Lernens mit Konvolutionsnetzen. Schließlich schlagen wir eine objektzentrierte Einfärbungsmethode vor, die es ermöglicht auf RGB-Bildern trainierte neuronale Netze auf RGB-D-Bildern einzusetzen. Dieser Transferlernansatz erlaubt es uns auch mit stark reduzierten Trainingsmengen noch bessere Ergebnisse beim Schätzen von Objektklassen, -instanzen und -orientierungen zu erzielen. Wir werten die von uns vorgeschlagenen Methoden auf den öffentlich zugänglichen MNIST, MSRC, INRIA Graz-02, NYU-Depth, Pascal VOC, und Washington RGB-D Objects Datenmengen aus