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Open AccessJournal ArticleDOI

Efficient Graph-Based Image Segmentation

TLDR
An efficient segmentation algorithm is developed based on a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image and it is shown that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties.
Abstract
This paper addresses the problem of segmenting an image into regions. We define a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image. We then develop an efficient segmentation algorithm based on this predicate, and show that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties. We apply the algorithm to image segmentation using two different kinds of local neighborhoods in constructing the graph, and illustrate the results with both real and synthetic images. The algorithm runs in time nearly linear in the number of graph edges and is also fast in practice. An important characteristic of the method is its ability to preserve detail in low-variability image regions while ignoring detail in high-variability regions.

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Proceedings Article

Human-seeded attacks and exploiting hot-spots in graphical passwords

TL;DR: The results suggest that these graphical password schemes appear to be at least as susceptible to offline attack as the traditional text passwords they were proposed to replace.
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Towards Automatic Concept-based Explanations

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Book ChapterDOI

Spatio-Temporal Object Detection Proposals

TL;DR: This paper extends a recent 2D object proposal method, to produce spatio-temporal proposals by a randomized supervoxel merging process, and proposes a new efficient supervoxe method that leads to more accurate proposals when compared to existing state-of-the-art supervoxels.
Journal ArticleDOI

Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey

TL;DR: In this article, a comprehensive overview of various approaches of video processing for object detection and tracking in the maritime environment is presented, where the authors follow an approach-based taxonomy wherein the advantages and limitations of each approach are compared.
Posted Content

Semantic Instance Segmentation via Deep Metric Learning

TL;DR: A new method for semantic instance segmentation is proposed, by first computing how likely two pixels are to belong to the same object, and then by grouping similar pixels together, based on a deep, fully convolutional embedding model.
References
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Book

Introduction to Algorithms

TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
Proceedings ArticleDOI

Normalized cuts and image segmentation

TL;DR: This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.
Journal ArticleDOI

Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters

TL;DR: A family of graph-theoretical algorithms based on the minimal spanning tree are capable of detecting several kinds of cluster structure in arbitrary point sets; description of the detected clusters is possible in some cases by extensions of the method.
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