Multiple Texture Boltzmann Machines

We assess the generative power of the mPoTmodel of [10] with tiled-convolutional weight sharing as a model for visual textures by specifically training on this task, evaluating model performance on texture synthesis and inpainting tasks using quantitative metrics. We also analyze the relative importance of the mean and covariance parts of the mPoT model by comparing its performance to those of its subcomponents, tiled-convolutional versions of the PoT/FoE and Gaussian-Bernoulli restricted Boltzmann machine (GB-RBM). Our results suggest that while state-of-the-art or better performance can be achieved using the mPoT, similar performance can be achieved with the mean-only model. We then develop a model for multiple textures based on the GB-RBM, using a shared set of weights but texturespecific hidden unit biases. We show comparable performance of the multiple texture model to individually trained texture models.

Statistical models for natural scene data

This thesis considers statistical modelling of natural image data. Obtaining advances in this field can have significant impact for both engineering applications, and for the understanding of the human visual system. Several recent advances in natural image modelling have been obtained with the use of unsupervised feature learning. We consider a class of such models, restricted Boltzmann machines (RBMs), used in many recent state-of-the-art image models. We develop extensions of these stochastic artificial neural networks, and use them as a basis for building more effective image models, and tools for computational vision. We first develop a novel framework for obtaining Boltzmann machines, in which the hidden unit activations co-transform with transformed input stimuli in a stable and predictable way throughout the network. We define such models to be transformation equivariant. Such properties have been shown useful for computer vision systems, and have been motivational for example in the development of steerable filters, a widely used classical feature extraction technique. Translation equivariant feature sharing has been the standard method for scaling image models beyond patch-sized data to large images. In our framework we extend shallow and deep models to account for other kinds of transformations as well, focusing on in-plane rotations. Motivated by the unsatisfactory results of current generative natural image models, we take a step back, and evaluate whether they are able to model a subclass of the data, natural image textures. This is a necessary subcomponent of any credible model for visual scenes. We assess the performance of a state- of-the-art model of natural images for texture generation, using a dataset and evaluation techniques from in prior work. We also perform a dissection of the model architecture, uncovering the properties important for good performance. Building on this, we develop structured extensions for more complicated data comprised of textures from multiple classes, using the single-texture model architecture as a basis. These models are shown to be able to produce state-of-the-art texture synthesis results quantitatively, and are also effective qualitatively. It is demonstrated empirically that the developed multiple-texture framework provides a means to generate images of differently textured regions, more generic globally varying textures, and can also be used for texture interpolation, where the approach is radically dfferent from the others in the area. Finally we consider visual boundary prediction from natural images. The work aims to improve understanding of Boltzmann machines in the generation of image segment boundaries, and to investigate deep neural network architectures for learning the boundary detection problem. The developed networks (which avoid several hand-crafted model and feature designs commonly used for the problem), produce the fastest reported inference times in the literature, combined with state-of-the-art performance

Suomen kansantalouden materiaalivirrat ja niiden vaikutukset : Toteutunut kehitys ja kiertotalouden skenaariot vuodelle 2035

Tutkimuksen tavoitteena oli lisätä ymmärrystä kiertotalouden potentiaalista vaikuttaa Suomen luonnonvarojen käyttöön ja niistä aiheutuviin ympäristö- ja talousvaikutuksiin. Viimeaikaisen kehityksen lisäksi arvioitiin kolmea luonnonvarojen käytön skenaariota vuodelle 2035. Niihin lisättiin kiertotaloustoimenpiteitä vaiheittain siten, että kunniahimoisin skenaario pyrki saavuttamaan Suomen kiertotalouden strategisen ohjelman luonnonvaratavoitteiden lisäksi myös Suomen hiilineutraalisuustavoitteen. Tulosten perusteella kiertotalouden strategisessa ohjelmassa asetut luonnonvaratavoitteet ovat osin saavutettavissa. Tällöin Suomen raaka-aineiden kokonaiskulutus vuonna 2035 ei ylitä vuoden 2015 tasoa, ja materiaalien kiertotalousaste kaksinkertaistuu vuodesta 2015. Myös hiilineutraalisuus voidaan saavuttaa vuoteen 2035 mennessä työssä hahmoteltujen oletuksien ja lisätoimien toteutuessa. Puhdas energiasiirtymä vähentää Suomen päästöjä ja luonnonvarojen käyttöä merkittävästi jo nykyisten päätösten toteutuessa perusskenaariossa. Kiertotaloustoimenpiteet edistävät edelleen päästöjen laskua ja vahvistavat nieluja. Suomen raaka-aineiden kulutus asukasta kohden säilyy kiertotaloustoimenpiteistä huolimatta globaalisti erittäin korkealla tasolla ja resurssituottavuudessa jäädään kauas EU-maiden keskiarvosta. Kiertotalouden toteutukseen tarvitaan lisää kunnianhimoa ja toimintaa tukevia ohjauskeinoja. Julkaisu on päivitetty 22.3.2024, s. 21, 23, 43, 89

Image denoising with nonparametric hidden Markov trees

We develop a hierarchical, nonparametric statistical model for wavelet representations of natural images. Extending previous work on Gaussian scale mixtures, wavelet coefficients are marginally distributed according to infinite, Dirichlet process mixtures. A hidden Markov tree is then used to couple the mixture assignments at neighboring nodes. Via a Monte Carlo learning algorithm, the resulting hierarchical Dirichlet process hidden Markov tree (HDP-HMT) model automatically adapts to the complexity of different images and wavelet bases. Image denoising results demonstrate the effectiveness of this learning process. Index Terms — hidden Markov trees, hierarchical Dirichlet processes, nonparametric Bayesian methods, wavelet transforms, image denoising. 1

N.: Visual boundary prediction: A deep neural prediction network and quality dissection

Abstract This paper investigates visual boundary detection, i.e. prediction of the presence of a boundary at a given image location. We develop a novel neurally-inspired deep architecture for the task. Notable aspects of our work are (i) the use of "covariance features" which depend on the squared response of a filter to the input image, and (ii) the integration of image information from multiple scales and semantic levels via multiple streams of interlinked, layered, and non-linear "deep" processing. Our results on the Berkeley Segmentation Data Set 500 (BSDS500) show comparable or better performance to the topperforming method

Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading

Diabetes is a globally prevalent disease that can cause visible microvascular complications such as diabetic retinopathy and macular edema in the human eye retina, the images of which are today used for manual disease screening and diagnosis. This labor-intensive task could greatly benefit from automatic detection using deep learning technique. Here we present a deep learning system that identifies referable diabetic retinopathy comparably or better than presented in the previous studies, although we use only a small fraction of images (<1/4) in training but are aided with higher image resolutions. We also provide novel results for five different screening and clinical grading systems for diabetic retinopathy and macular edema classification, including state-of-the-art results for accurately classifying images according to clinical five-grade diabetic retinopathy and for the first time for the four-grade diabetic macular edema scales. These results suggest, that a deep learning system could increase the cost-effectiveness of screening and diagnosis, while attaining higher than recommended performance, and that the system could be applied in clinical examinations requiring finer grading.Peer reviewe