Videos tagged with Image Analysis

Many visual perception tasks are fundamentally NP-hard computational problems. Solving these problems robustly requires thinking through combinatorially many hypothesis. Despite this, our human visual system performs these tasks effortlessly. How is this done? I would like to make two points on this topic. First, formulating visual thinking as NP-hard computation tasks has an important advantag...

With the advent of the Internet, billions of images are now freely available online and constitute a dense sampling of the visual world. Using a variety of non?parametric methods, we explore this world with the aid of a large dataset of 79,302,017 images collected from the Web. Motivated by psychophysical results showing the remarkable tolerance of the human visual system to degradations in ima...

Questions in cognitive neuroscience are often framed in terms of correspondences between known types: How is brain state X related to cognitive state Y? What are the correlations or mappings between particular structures and functions? Such framings are well suited for confirmatory testing of coarse-grained hypotheses. They are not necessarily informative, however, for the purpose of exploring ...

Lecture slides: Graphs Regularization for Data Sets and Images: Filtering and Semi-Supervised Classification Outline What are the Main Ideas? Graphs and Regularization Framework What is a Weighted Graph? Why Use Graph Representation? Operators? Weighted Graph Based Regularization? Graph Based Regularization is Not New. . . Applications Filtering by Regularization Image Filtering: Classical Exam...

Every material has a distinctive spectrum. The spectrum of a material tells us about its chemistry. Hyperspectral images produce a spectrum (represented as several hundred numbers) at each pixel in an image. So hyperspectral images enable us to map variations in chemistry. The first hyperspectral scanners, built in the 1980's and 1990's, were designed for airborne applications, primarily for mi...

Lecture slides: Generative Models for Visual Objects and Object Recognition via Bayesian Inference Plato said… How many object categories are there? So what does object recognition involve? Verification: is that bus? Detection: are there cars? Identification: is this a picture of Mao? Object categorization Scene and context categorization Challenges 1: view point variation Challenges 2: ...

The Gaussian Na¨?ve Bayes (GNB) [2] classifier has been successfully applied to fMRI data. However, it is not specifically designed to account for data from multiple subjects and is usually applied to data from a single subject (referred to as GNB-indiv). An extension to the GNB classifier has been proposed ([4], referred to as GNB-pooled), in which the data from all the subjects are combin...

In this paper, a new way to express complex spatial relations is proposed in order to integrate them in a Constraint Satisfaction Problem with bilevel constraints. These constraints allow to build semantic graphs, which can describe more precisely the spatial relations between subparts of a composite object that we look for in an image. For example, it allows to express complex spatial relation...

Lecture slides: Systems Biology and Location Proteomics Automated Interpretation Supervised Learning of High-Resolution Subcellular Location Patterns The Challenge Feature-based, Supervised learning approach Boland & Murphy 2001 Machine Learning Methods Evaluating Classifiers 2D Classification Results Human Classification Results Computer vs. Human 3D HeLa cell images Unsupervised Learning ...

The conditions under which natural vision systems evolved show statistical regularities determined both by the environment and by the actions of the organism. Many aspects of biological vision can be understood as evolutionary adaptations to these regularities. This is demonstrated by the recent sucess in explaining properties of retinal and cortical neurons from the statistics of natural image...