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101 Result(s)
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Chapter
Survey of Image Co-segmentation
This chapter provides a review of the literature related to image co-segmentation and datasets used for evaluation.
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Chapter
Conditional Siamese Convolutional Network
This chapter describes a deep convolutional neural network-based co-segmentation model through an end-to-end training of a conditional siamese encoder-decoder network. The network is composed of a pair of VGG-...
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Chapter
Conclusions
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Chapter
Introduction
If one is given several images sourced from various places and under different contexts, but having at least ‘something’ in common, then the extraction of this common object in all these images is known as ima...
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Chapter
Maximum Common Subgraph Matching
This chapter describes an unsupervised and computationally efficient image co-segmentation algorithm for an image pair. The method is based on subgraph matching. First, the images are represented as region adj...
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Chapter
Co-segmentation Using a Classification Framework
This chapter describes a robust solution for the multi-image co-segmentation problem under a classification problem setup, with the classes being the common foreground and the remaining regions (backgrounds) i...
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Chapter
Mathematical Background
This chapter describes some concepts that will be instrumental in developing the co-segmentation algorithms of this monograph. They are superpixel segmentation, label propagation, maximum common subgraph compu...
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Chapter
Maximally Occurring Common Subgraph Matching
This chapter describes a robust framework to solve image co-segmentation where the common object is not present in all the images in the set. The co-segmentation problem for N images considers the very general se...
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Chapter
Co-segmentation Using Graph Convolutional Network
This chapter describes a supervised method for solving the co-segmentation problem for an image pair by training a graph convolutional neural network (GCNN)-based classifier. First each image pair is represent...
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Book
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Chapter
Few-shot Learning for Co-segmentation
This chapter describes a few-shot learning approach to tackle the small sample size problem encountered in learning co-segmentation models with small datasets like iCoseg and MSRC. The multi-image co-segmentat...
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Chapter and Conference Paper
Zero-Shot Remote Sensing Image Super-Resolution Based on Image Continuity and Self Tessellations
The goal of zero-shot image super-resolution (SR) is to generate high-resolution (HR) images from never-before-seen image distributions. This is challenging, especially, because it is difficult to model the st...
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Chapter and Conference Paper
A Unified Batch Selection Policy for Active Metric Learning
Active metric learning is the problem of incrementally selecting high-utility batches of training data (typically, ordered triplets) to annotate, in order to progressively improve a learned model of a metric o...
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Chapter and Conference Paper
Perceptually Compressive Communication of Interactive Telehaptic Signal
During telehaptic applications over a shared communication medium, Weber’s law of perception based adaptive sampling scheme can be applied to reduce the data rate without degrading the perceptual quality of th...
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Chapter and Conference Paper
Multi-source Open-Set Deep Adversarial Domain Adaptation
We introduce a novel learning paradigm of multi-source open-set unsupervised domain adaptation (MS-OSDA). Recently, the notion of single-source open-set domain adaptation (SS-OSDA) which considers the presence...
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Chapter and Conference Paper
Onboard Hyperspectral Image Compression Using Compressed Sensing and Deep Learning
We propose a real-time onboard compression scheme for hyperspectral datacube which consists of a very low complexity encoder and a deep learning based parallel decoder architecture for fast decompression. The ...
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Chapter and Conference Paper
Unsupervised Multi-source Domain Adaptation Driven by Deep Adversarial Ensemble Learning
We address the problem of multi-source unsupervised domain adaptation (MS-UDA) for the purpose of visual recognition. As opposed to single source UDA, MS-UDA deals with multiple labeled source domains and a si...
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Chapter
Haptic Rendering of Oriented Point Cloud of Heritage Objects Using Proxy Projection
In this paper, a novel method is proposed for rendering of a heritage object represented by an oriented, dense point cloud data without pre-computing a mesh structure. Collision of haptic interaction point (HI...
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Chapter
Task Dependence of Perceptual Deadzone
In this chapter, we study whether the perceptual deadzone depends on the task to be performed during the psychophysical experiments. In order to study this, we design a psychophysical experiment where we defin...
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Chapter
Conclusions
In this chapter we summarize the entire contributions in this monograph and argue in favor of commissioning several other studies on kinesthetic perception.