38,571 research outputs found
Structure Preserving Large Imagery Reconstruction
With the explosive growth of web-based cameras and mobile devices, billions
of photographs are uploaded to the internet. We can trivially collect a huge
number of photo streams for various goals, such as image clustering, 3D scene
reconstruction, and other big data applications. However, such tasks are not
easy due to the fact the retrieved photos can have large variations in their
view perspectives, resolutions, lighting, noises, and distortions.
Fur-thermore, with the occlusion of unexpected objects like people, vehicles,
it is even more challenging to find feature correspondences and reconstruct
re-alistic scenes. In this paper, we propose a structure-based image completion
algorithm for object removal that produces visually plausible content with
consistent structure and scene texture. We use an edge matching technique to
infer the potential structure of the unknown region. Driven by the estimated
structure, texture synthesis is performed automatically along the estimated
curves. We evaluate the proposed method on different types of images: from
highly structured indoor environment to natural scenes. Our experimental
results demonstrate satisfactory performance that can be potentially used for
subsequent big data processing, such as image localization, object retrieval,
and scene reconstruction. Our experiments show that this approach achieves
favorable results that outperform existing state-of-the-art techniques
Recommended from our members
Use of colour for hand-filled form analysis and recognition
Colour information in form analysis is currently under utilised. As technology has advanced and computing costs have reduced, the processing of forms in colour has now become practicable. This paper describes a novel colour-based approach to the extraction of filled data from colour form images. Images are first quantised to reduce the colour complexity and data is extracted by examining the colour characteristics of the images. The improved performance of the proposed method has been verified by comparing the processing time, recognition rate, extraction precision and recall rate to that of an equivalent black and white system
Unsupervised learning of generative topic saliency for person re-identification
(c) 2014. The copyright of this document resides with its authors.
It may be distributed unchanged freely in print or electronic forms.© 2014. The copyright of this document resides with its authors. Existing approaches to person re-identification (re-id) are dominated by supervised learning based methods which focus on learning optimal similarity distance metrics. However, supervised learning based models require a large number of manually labelled pairs of person images across every pair of camera views. This thus limits their ability to scale to large camera networks. To overcome this problem, this paper proposes a novel unsupervised re-id modelling approach by exploring generative probabilistic topic modelling. Given abundant unlabelled data, our topic model learns to simultaneously both (1) discover localised person foreground appearance saliency (salient image patches) that are more informative for re-id matching, and (2) remove busy background clutters surrounding a person. Extensive experiments are carried out to demonstrate that the proposed model outperforms existing unsupervised learning re-id methods with significantly simplified model complexity. In the meantime, it still retains comparable re-id accuracy when compared to the state-of-the-art supervised re-id methods but without any need for pair-wise labelled training data
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise
In this paper, we study the problem of learning image classification models
with label noise. Existing approaches depending on human supervision are
generally not scalable as manually identifying correct or incorrect labels is
time-consuming, whereas approaches not relying on human supervision are
scalable but less effective. To reduce the amount of human supervision for
label noise cleaning, we introduce CleanNet, a joint neural embedding network,
which only requires a fraction of the classes being manually verified to
provide the knowledge of label noise that can be transferred to other classes.
We further integrate CleanNet and conventional convolutional neural network
classifier into one framework for image classification learning. We demonstrate
the effectiveness of the proposed algorithm on both of the label noise
detection task and the image classification on noisy data task on several
large-scale datasets. Experimental results show that CleanNet can reduce label
noise detection error rate on held-out classes where no human supervision
available by 41.5% compared to current weakly supervised methods. It also
achieves 47% of the performance gain of verifying all images with only 3.2%
images verified on an image classification task. Source code and dataset will
be available at kuanghuei.github.io/CleanNetProject.Comment: Accepted to CVPR 201
- …