Learned Multi-patch Similarity. - Internet Archive Scholar

Experiments on several multi-view datasets demonstrate that this approach has advantages over methods based on pairwise patch similarity. ... Encouraged by the success of machine learning, and in particular convolutional neural networks, we propose to learn a matching function which directly maps multiple image patches to a scalar similarity ... Patch Similarity Learning. With the rise of machine learning for computer vision problems, it has also been proposed to learn the similarity measure for (two-view) stereo. ...

arXiv:1703.08836v2 fatcat:b5nfngmdarhibfnw3eb72qagmq

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To capture the features from the pre-processed image, this project offers a new technique for retrieving features called Patch Based Deep Local Feature Learning (PBDLFL). ... Among several supervised segmentation scheme this works employs proposed approach named Self Similarity Multi Level Clustering (SSMLC). ... From the various machine learning approaches this work takes only Self Similarity Multi Level Clustering. ...

doi:10.35940/ijrte.d9579.118419 fatcat:hr4g54higbdxhe65s3fpjgkhyy

Open Access

Experiments on several multi-view datasets demonstrate that this approach has advantages over methods based on pairwise patch similarity. ... Encouraged by the success of machine learning, and in particular convolutional neural networks, we propose to learn a matching function which directly maps multiple image patches to a scalar similarity ... Plane sweeping using three different patch similarity measures. Proposed learned multi-view similarity vs. pairwise ZNCC and pairwise LIFT. Reference images (a-d).Ground truth (e-h). ...

doi:10.3929/ethz-b-000215952 fatcat:vgjh3dp3traivaxvpm7sjrsz2q

In this work, we propose a novel deep similarity learning method that trains a binary classifier to learn the correspondence of two image patches. ... Therefore, approaches of learning a similarity metric are proposed in recent years. ... Methods Similarity Metric Learning We want to learn a function f (x 1 , x 2 ) to compute a similarity score between a pair of patches x 1 and x 2 from different image modalities. ...

doi:10.1080/21681163.2015.1135299 fatcat:uzc4rnc4xbdmfdrgvqbilbrdsu

To this end, we develop an algorithm derived from max-margin structured output learning, and employ the learned similarity measure within a standard rigid registration algorithm. ... Instead of using a universal, but a priori fixed similarity criterion such as mutual information, we propose learning a similarity measure in a discriminative manner such that the reference and correctly ... Validating the learned similarity measure separately from multi-modal registration In this section, we first examine the learned local similarity measure between patches (2), and then we show some properties ...

doi:10.1109/cvpr.2009.5206840 dblp:conf/cvpr/LeeHSACS09 fatcat:4dftlgdxg5duderssc73ef2al4

In particular, we employ a multi-channel random forest to learn the nonlinear relationship between these hybrid features and the target labels (i.e., corresponding to certain anatomical structures). ... Moreover, to accommodate the high inter-subject variations, we further extend our learning-based label fusion to a multi-atlas scenario, i.e., we train a random forest for each atlas and then obtain the ... Instead of pair-wisely estimating the patch-based similarity, Wu et al. ...

doi:10.1007/978-3-319-24574-4_86 pmid:26942235 pmcid:PMC4773030 fatcat:2ghorxv6frgmre3gfvar3im4um

To reduce over-fitting of the FCN model on the training data, we adopted an ensemble strategy in the learning procedure. ... One major limitation of existing state-of-the-art 3D FCN segmentation models is that they often apply image patches of fixed size throughout training and testing, which may miss some complex tissue appearance ... In the multi-encoding part, the training image patch and the most similar atlas patches are separately learned. ...

arXiv:1901.01381v1 fatcat:fbuq7fbsjfcqtm4d5ld7oxq5ey

Our algorithm detects corners by Harris and matches them by a patch-metric learned on top of CIFAR-10 network descriptor. ... Multi-modal images of the same scene capture different signals and therefore their registration is challenging and it is not solved by classic approaches. ... Given a VIS channel patch P v and a NIR patch P n we offer to learn a metric that measures the similarity distance between them. ...

arXiv:1801.05171v6 fatcat:4aqaegtcgbcotf7wdyeom6e4zu

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The first approach uses a two-level learning method which consists of simple clustering of interest patches extracted from a set of training images for each posture, in addition to learning the nonparametric ... This paper primarily investigates the possibility of using multi-level learning of sparse parts-based representations of US Marine postures in an outside and often crowded environment for training exercises ... The first classifier presented in this paper is a multi-class version similar in learning, but different in classification to the pedestrian detector in [5] . ...

doi:10.1007/978-3-642-10268-4_59 fatcat:5o4ntduu3zfldowte6ri7ncv6u

further refined by high-level inter-patch perceptions resorting to the observation that similar patches should share their choices. ... Specifically, the multi-choice supervision for the masked image patches is formed by the soft probability vectors of the discrete token ids, which are predicted by the off-the-shelf image tokenizer and ... similarities and form ensembled learning targets for masked image patches (see Fig. 2 ). ...

arXiv:2203.15371v2 fatcat:kype74wae5esbfg2oc32wu7pxa

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The learned distance metric model is then used to compute the similarity measure between image patches in the label fusion. ... In this paper, we proposed a novel metric learning method to fuse segmentation labels in multi-atlas based image segmentation. ... Conclusion In the paper, we propose a novel nonlocal patch based weighted voting label fusion method with a learned distance metric for measuring similarity between image patches. ...

doi:10.1007/s12021-016-9312-y pmid:27638650 pmcid:PMC5438876 fatcat:ihxneblsjzacjdh67qbfraqe3q

We introduce joint-patch and multi-label learning (JPML) to address these issues. JPML leverages group sparsity by selecting a sparse subset of facial patches while learning a multi-label classifier. ... These are patch and multi-label learning. ... Given such AU relations, we develop joint patch and multi-label learning (JPML) to simultaneously select a discriminative subset of patches and learn multi-AU classifiers. ...

doi:10.1109/cvpr.2015.7298833 pmid:27382243 pmcid:PMC4930865 dblp:conf/cvpr/ZhaoCTCZ15 fatcat:oc27rkscsjclfcyxib5eipbodu

For that purpose, we present different binary and multi-task classification strategies that include two distinct attention mechanisms based on PageRank. ... . − → w i (7) Learning Framework In order to perform binary and multi-task classification, we define two distinct learning input representations, X p and X s , which combine patches representations, ... So, multi-task learning strategies have been proposed [1] , which concurrently learn different semantic relations with the assumption that the learning process of a given semantic relation may be improved ...

doi:10.1007/978-3-030-45439-5_9 fatcat:novvomhhevhhzb53vgns7i4qs4

Specifically, multi-atlas based guidance is incorporated during the network learning. Based on this, the discriminative of the FCN is boosted, which eventually contribute to accurate prediction. ... Alternatively, the patch-based methods have been proposed to relax the requirement of image registration, but the labeling is often determined independently by the target image information, without getting ... those similar atlas patches. ...

doi:10.1007/978-3-319-67434-6_2 pmid:29104969 pmcid:PMC5669261 fatcat:mdat4oofqzghnjhkinevbjpp2y

Finally, we also extend our approach by introducing a multi-structure scheme, which learns a set of local correspondence structures to capture the spatial correspondence sub-patterns between a camera pair ... We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair. ... ) can achieve similar performances to the one under manually divided pose group pairs (proposed+multi-manu). ...

arXiv:1703.06931v3 fatcat:honmcoh67va5fozfl6op6scknu

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Learned Multi-Patch Similarity [article]

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Patch Based Deep Local Feature Learning and Self Similarity Multi Level Clustering for Neonatal Brain Segmentation in MR Images

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