Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

http://library.utia.cas.cz/prace/20030125.pdf

Image registration methods: a survey

The PASCAL Visual Object Classes Challenge

Fast Fourier Transform

Edge detection is a critical component of many vision systems, including object detectors and image segmentation algorithms. Patches of edges exhibit well-known forms of local structure, such as straight lines or T-junctions. In this paper we take advantage of the structure present in local image patches to learn both an accurate and computationally efficient edge detector. We formulate the problem of predicting local edge masks in a structured learning framework applied to random decision forests. Our novel approach to learning decision trees robustly maps the structured labels to a discrete space on which standard information gain measures may be evaluated. The result is an approach that obtains real time performance that is orders of magnitude faster than many competing state-of-the-art approaches, while also achieving state-of-the-art edge detection results on the BSDS500 Segmentation dataset and NYU Depth dataset. Finally, we show the potential of our approach as a general purpose edge detector by showing our learned edge models generalize well across datasets.

/pdf/structured-forests-for-fast-edge-detection-jkd4cdcfz4.pdf

Structured Forests for Fast Edge Detection

In computer vision and image processing, edge detection concerns the localization of significant variations of the grey level image and the identification of the physical phenomena that originated them. This information is very useful for applications in 3D reconstruction, motion, recognition, image enhancement and restoration, image registration, image compression, and so on. Usually, edge detection requires smoothing and differentiation of the image. Differentiation is an ill-conditioned problem and smoothing results in a loss of information. It is difficult to design a general edge detection algorithm which performs well in many contexts and captures the requirements of subsequent processing stages. Consequently, over the history of digital image processing a variety of edge detectors have been devised which differ in their mathematical and algorithmic properties. This paper is an account of the current state of our understanding of edge detection. We propose an overview of research in edge detection: edge definition, properties of detectors, the methodology of edge detection, the mutual influence between edges and detectors, and existing edge detectors and their implementation.

Edge Detection Techniques-An Overview

Text/graphics separation aims at segmenting the document into two layers: a layer assumed to contain text and a layer containing graphical objects. In this paper, we present a consolidation of a method proposed by Fletcher and Kasturi, with a number of improvements to make it more suitable for graphics-rich documents. We discuss the right choice of thresholds for this method, and their stability. We also propose a post-processing step for retrieving text components touching the graphics, through local segmentation of the distance skeleton.

/pdf/text-graphics-separation-revisited-tytday9bg9.pdf

Text/Graphics Separation Revisited

In this article, a system to detect rooms in architectural floor plan images is described. We first present a primitive extraction algorithm for line detection. It is based on an original coupling of classical Hough transform with image vectorization in order to perform robust and efficient line detection. We show how the lines that satisfy some graphical arrangements are combined into walls. We also present the way we detect some door hypothesis thanks to the extraction of arcs. Walls and door hypothesis are then used by our room segmentation strategy; it consists in recursively decomposing the image until getting nearly convex regions. The notion of convexity is difficult to quantify, and the selection of separation lines between regions can also be rough. We take advantage of knowledge associated to architectural floor plans in order to obtain mostly rectangular rooms. Qualitative and quantitative evaluations performed on a corpus of real documents show promising results.

Salvatore Tabbone

Papers

Edge Detection Techniques-An Overview

Text/Graphics Separation Revisited

A system to detect rooms in architectural floor plan images

Deep neural networks-based relevant latent representation learning for hyperspectral image classification

Graph-based bag-of-words for classification