varunr14/newsummary huggingface dataset:
In order to realize the patient privacy protection in medical image opposite to traditional reversible data hiding methods which prior to embed message into the smooth area for pursuing high PSNR value the proposed method priors to embed message into the texture area of the medical images for improving the quality of the details information and helping accurate diagnosis . Furthermore in order to decrease the embedding distortion while enhancing the contrast of the texture area this paper also proposes a message sparse representation method . Experiments implemented on medical images showed that the proposed method enhances the contrast of texture area when compared with previous methods . 


Summary: Explain the reason for PSNR metric can t consistent with human visual system. Embed the message into texture area preferentially for helping accurate diagnosis. Propose message sparse representation method for decreasing embedding distortion.
Organ shape plays an important role in clinical diagnosis surgical planning and treatment evaluation . Shape modeling is a critical factor affecting the performance of deformable model based segmentation methods for organ shape extraction . In most existing works shape modeling is completed in the original shape space with the presence of outliers . In addition the specificity of the patient was not taken into account . This paper proposes a novel target oriented shape prior model to deal with these two problems in a unified framework . The proposed method measures the intrinsic similarity between the target shape and the training shapes on an embedded manifold by manifold learning techniques . With this approach shapes in the training set can be selected according to their intrinsic similarity to the target image . With more accurate shape guidance an optimized search is performed by a deformable model to minimize an energy functional for image segmentation which is efficiently achieved by using dynamic programming . Our method has been validated on 2D prostate localization and 3D prostate segmentation in MRI scans . Compared to other existing methods our proposed method exhibits better performance in both studies . 


Summary: Propose a novel target oriented shape prior modeling method. Measure the intrinsic similarity between the target shape and the training shapes. Incorporate the shape model into an optimized search based segmentation method. Exhibit better performance than other existing methods.
Baseline correction is an important pre processing technique used to separate true spectra from interference effects or remove baseline effects . In this paper an adaptive iteratively reweighted genetic programming based on excellent community information is proposed to model baselines from spectra . Excellent community information which is abstracted from the present excellent community includes an automatic common threshold normal global and local slope information . Significant peaks can be firstly detected by an automatic common threshold . Then based on the characteristic that a baseline varies slowly with respect to wavelength normal global and local slope information are used to further confirm whether a point is in peak regions . Moreover the slope information is also used to determine the range of baseline curve fluctuation in peak regions . The proposed algorithm is more robust for different kinds of baselines and its curvature and slope can be automatically adjusted without prior knowledge . Experimental results in both simulated data and real data demonstrate the effectiveness of the algorithm . 


Summary: Excellent community provides threshold global and local slope information. Global and local slope information further confirm peak distribution information. An adaptive iteratively reweighted genetic programming model the baseline.
We present a novel method that evaluates the geometric consistency of putative point matches in weakly calibrated settings i.e . when the epipolar geometry but not the camera calibration is known using only the point coordinates as information . The main idea behind our approach is the fact that each point correspondence in our data belongs to one of two classes . The classification of each point match relies on the histogram of a quantity representing the difference between cross ratios derived from a construction involving 6 tuples of point matches . Neither constraints nor scenario dependent parameters thresholds are needed . Even for few candidate point matches the ensemble of 6 tuples containing each of them turns to provide statistically reliable histograms that prove to discriminate between inliers and outliers . In fact in most cases a random sampling among this population is sufficient . Nevertheless the accuracy of the method is positively correlated to its sampling density leading to an accuracy versus resulting computational complexity trade off . Theoretical analysis and experiments are given that show the consistent performance of the proposed classification method when applied in inlier outlier discrimination . The achieved accuracy is favourably evaluated against established methods that employ geometric only information i.e . those relying on the Sampson the algebraic and the symmetric epipolar distances . Finally we also present an application of our scheme in uncalibrated stereo inside a RANSAC framework and compare it to the same as above methods . 


Summary: We propose a method for the indexing of point matches based on cross ratios. Matches are characterised by the distribution of a scalar measure derived from multiple point relations. Our consistency measure is processed statistically using random sampling. We propose applications both in weekly calibrated and uncalibrated scenarios. Our method exhibits low false positive rates without the use of thresholds.
This article presents a unified framework for detecting segmenting and tracking unknown objects in everyday scenes allowing for inspection of object hypotheses during interaction over time . A heterogeneous scene representation is proposed with background regions modeled as a combinations of planar surfaces and uniform clutter and foreground objects as 3D ellipsoids . Recent energy minimization methods based on loopy belief propagation tree reweighted message passing and graph cuts are studied for the purpose of multi object segmentation and benchmarked in terms of segmentation quality as well as computational speed and how easily methods can be adapted for parallel processing . One conclusion is that the choice of energy minimization method is less important than the way scenes are modeled . Proximities are more valuable for segmentation than similarity in colors while the benefit of 3D information is limited . It is also shown through practical experiments that with implementations on GPUs multi object segmentation and tracking using state of art MRF inference methods is feasible despite the computational costs typically associated with such methods . 


Summary: We present a framework for detection segmentation and tracking of multiple objects. The framework has minimal requirements on input for initialization. The choice of MRF inference method is less important than how scenes are modeled. Proximities are more important than colors as cues for segmentation. For real time application message passing is more feasible than graph cuts.
Recently new high level features have been proposed to describe the semantic content of images . These features that we call supervised are obtained by exploiting the information provided by an additional set of labeled images . Supervised features were successfully used in the context of image classification and retrieval where they showed excellent results . In this paper we will demonstrate that they can be effectively used also for unsupervised image categorization that is for grouping semantically similar images . We have experimented different state of the art clustering algorithms on various standard data sets commonly used for supervised image classification evaluations . We have compared the results obtained by using four supervised features against those obtained by using low level features . The results show that supervised features exhibit a remarkable expressiveness which allows to effectively group images into the categories defined by the data sets authors . 


Summary: We compared high and low level features for unsupervised image categorization. We verified that high level features significantly outperform low level features. We assessed how much the performance depends on the dimensionality of the feature vectors. We verified that a simple clustering on supervised features outperform strategies specifically designed for this task.
In this paper we present the reconstructed residual error which evaluates the quality of a given segmentation of a reconstructed image in tomography . This novel evaluation method which is independent of the methods that were used to reconstruct and segment the image is applicable to segmentations that are based on the density of the scanned object . It provides a spatial map of the errors in the segmented image based on the projection data . The reconstructed residual error is a reconstruction of the difference between the recorded data and the forward projection of that segmented image . The properties and applications of the algorithm are verified experimentally through simulations and experimental micro CT data . The experiments show that the reconstructed residual error is close to the true error that it can improve gray level estimates and that it can help discriminating between different segmentations . 


Summary: We introduce the reconstructed residual error a new segmentation evaluation measure. The method provides a spatial map of the errors in a segmented image in tomography. The original projection images are exploited in an unsupervised approach. Validation is performed through simulations and experimental micro CT data. The method can improve gray level estimates and discriminate between segmentations.
In this paper a framework is proposed to localize both Farsi Arabic and Latin scene texts with different sizes fonts and orientations . First candidate text regions are extracted via an MSER detector enhanced by weighted median filtering to adopt the low resolution texts . At the same time based on fuzzy inference system the input image is classified into images with a focused text content and incidental scene text images which the image does not focus on the text content . For the focused scene text images the non text candidates are filtered via an FIS . On the other hand for the incidental scene text images apart from the FIS an extra filtering algorithm based on low rank matrix recovery is proposed . Finally a new approach based on the clustering minimum area rectangle and radon transform techniques is proposed to create the single arbitrarily oriented text lines from the remaining text regions . To evaluate the proposed algorithm we created a collection of natural images containing both Farsi Arabic and Latin texts . Compared with the state of the art methods the proposed method achieves the best performance on our and Epshtein datasets and competitive performances on the ICDAR dataset . 


Summary: Focused and incidental scene text images are processed in a separate manner. Low rank matrix recovery is exploited to process the incidental scene text images. A text confidence map was designed via fuzzy inference system. The proposed algorithm handles both Latin and Farsi Arabic scripts. Farsi Arabic scene texts at arbitrary orientations are localized for the first time.
In this paper a new pipeline of structure from motion for ground view images is proposed that uses feature points on an aerial image as references for removing accumulative errors . The challenge here is to design a method for discriminating correct matches from unreliable matches between ground view images and an aerial image . If we depend on only local image features it is not possible in principle to remove all the incorrect matches because there frequently exist repetitive and or similar patterns such as road signs . In order to overcome this difficulty we employ geometric consistency verification of matches using the RANSAC scheme that comprises two stages sampling based local verification focusing on the orientation and scale information extracted by a feature descriptor and global verification using camera poses estimated by the bundle adjustment using sampled matches . 


Summary: A new SfM pipeline that uses aerial images as external references is proposed. Good matches between ground and aerial images are found by two stage verification. Consistency of orientation and scale from a feature descriptor is locally verified. Outliers are removed by global verification with sampling based bundle adjustment.
This paper proposes methods for people re identification across non overlapping cameras . We improve the robustness of re identification by using additional group features acquired from the groups of people detected by each camera . People are grouped by discriminatively classifying the spatio temporal features of their trajectories into those of grouped people and non grouped people . Thereafter three group features are obtained in each group and utilized with other general features of each person for people re identification . Our experimental results have demonstrated improvements in people grouping and people re identification when our proposed methods have been applied to a public dataset . 


Summary: People is grouped based on the spatio temporal features of their trajectories. From people groups new group features for people re identification are extracted. The group features can be employed with any kind of existing appearance features. Experiments demonstrated that the group features improve people re identification.
In the object recognition community much effort has been spent on devising expressive object representations and powerful learning strategies for designing effective classifiers capable of achieving high accuracy and generalization . In this scenario the focus on the training sets has been historically weak by and large training sets have been generated with a substantial human intervention requiring considerable time . In this paper we present a strategy for automatic training set generation . The strategy uses semantic knowledge coming from WordNet coupled with the statistical power provided by Google Ngram to select a set of meaningful text strings related to the text class label that are subsequently fed into the Google Images search engine producing sets of images with high training value . Focusing on the classes of different object recognition benchmarks our approach collects novel training images compared to the ones obtained by exploiting Google Images with the simple text class label . In particular we show that the gathered images are better able to capture the different visual facets of a concept thus encoding in a more successful manner the intra class variance . As a consequence training standard classifiers with this data produces performances not too distant from those obtained from the classical hand crafted training sets . In addition our datasets generalize well and are stable that is they provide similar performances on diverse test datasets . This process does not require manual intervention and is completed in a few hours . 


Summary: We present a strategy for automatic image training set generation. We use semantic and statistics to generate strings related to a target label. Our gathered images are good in capturing different visual facets of a concept.
In query by semantic example image retrieval images are ranked by similarity of semantic descriptors . These descriptors are obtained by classifying each image with respect to a pre defined vocabulary of semantic concepts . In this work we consider the problem of improving the accuracy of semantic descriptors through cross modal regularization based on auxiliary text . A cross modal regularizer composed of three steps is proposed . Training images and text are first mapped to a common semantic space . A regularization operator is then learned for each concept in the semantic vocabulary . This is an operator which maps the semantic descriptors of images labeled with that concept to the descriptors of the associated texts . A convex formulation of the learning problem is introduced enabling the efficient computation of concept specific regularization operators . The third step is the selection of the most suitable operator for the image to regularize . This is implemented through a quantization of the semantic space where a regularization operator is associated with each quantization cell . Overall the proposed regularizer is a non linear mapping implemented as a piecewise linear transformation of the semantic image descriptors to regularize . This transformation is a form of cross modal domain adaptation . It is shown to achieve better performance than recent proposals in the domain adaptation literature while requiring much simpler optimization . 


Summary: In depth literature review on related subjects. Two regularization strategies interpolation and classification. Regularization on images lacking auxiliar information. Extensive evaluation shows astonishing results on CBIR.
Recently head pose estimation in real world environments has been receiving attention in the computer vision community due to its applicability to a wide range of contexts . However this task still remains as an open problem because of the challenges presented by real world environments . The focus of most of the approaches to this problem has been on estimation from single images or video frames without leveraging the temporal information available in the entire video sequence . Other approaches frame the problem in terms of classification into a set of very coarse pose bins . In this paper we propose a hierarchical graphical model that probabilistically estimates continuous head pose angles from real world videos by leveraging the temporal pose information over frames . The proposed graphical model is a general framework which is able to use any type of feature and can be adapted to any facial classification task . Furthermore the framework outputs the entire pose distribution for a given video frame . This permits robust temporal probabilistic fusion of pose information over the video sequence and also probabilistically embedding the head pose information into other inference tasks . Experiments on large real world video sequences reveal that our approach significantly outperforms alternative state of the art pose estimation methods . The proposed framework is also evaluated on gender and facial hair estimation . By incorporating pose information into the proposed hierarchical temporal graphical mode superior results are achieved for attribute classification tasks . 


Summary: A hierarchical temporal model is used to estimate head pose in real world videos. Head pose classification in un constrained databases shows superior performance. Proposed model is used to classify facial traits in real world videos. Trait classification with and without using the estimated pose angle is performed. Facial trait classification using the proposed model show superior performance.
We present a method for efficiently generating dense relative depth estimates from video without requiring any knowledge of the imaging system either a priori or by estimating it during processing . Instead we only require that the epipolar constraint between any two frames is satisfied and that the fundamental matrix can be estimated . By tracking sparse features across many frames and aggregating the multiple depth estimates together we are able to improve the overall estimate for any given frame . Once the depth estimates are available we treat the generation of the depth maps as a label propagation problem . This allows us to combine the automatically generated depth maps with any user corrections and modifications . 


Summary: A method for generating sparse relative depth estimates from video. Depth estimates can be generated without access to past or future frames. Depth maps are created through efficient filter based propagation. Results are favourable when compared to more expensive SfM MVS techniques.
Tagging is nowadays the most prevalent and practical way to make images searchable . However in reality many manually assigned tags are irrelevant to image content and hence are not reliable for applications . A lot of recent efforts have been conducted to refine image tags . In this paper we propose to do tag refinement from the angle of topic modeling and present a novel graphical model regularized latent Dirichlet allocation . In the proposed approach tag similarity and tag relevance are jointly estimated in an iterative manner so that they can benefit from each other and the multi wise relationships among tags are explored . Moreover both the statistics of tags and visual affinities of images in the corpus are explored to help topic modeling . We also analyze the superiority of our approach from the deep structure perspective . The experiments on tag ranking and image retrieval demonstrate the advantages of the proposed method . 


Summary: A novel regularized latent Dirichlet allocation approach to tag refinement is proposed. The proposed approach can explore the multiple wise tag relations and visual relations. The tag relevance estimation using the proposed approach is interpreted in the form of the deep structure.
The interdigital palm region represents about 30 of the palm area and is inherently acquired during palmprint imaging nevertheless it has not yet attracted any noticeable attention in biometrics research . This paper investigates the ridge pattern characteristics of the interdigital palm region for its usage in biometric identification . An anatomical study of the interdigital area is initially carried out leading to the establishment of five categories according to the distribution of the singularities and three regions of interest for biometrics . With the identified regions our study analyzes the matching performance of the interdigital palm biometrics and its combination with the conventional palmprint matching approaches and presents comparative experimental results using four competing feature extraction methods . This study has been carried out with two publicly available databases . The first one consists of 2 080 images of 416 subjects acquired with a touchless low cost imaging device focused on acquiring the interdigital palm area . The second database is the publicly available BiosecurID hand database which consists of 3 200 images from 400 users . The experimental results presented in this paper suggest that features from the interdigital palm region can be used to achieve competitive performances as well as offer significant improvements for conventional palmprint recognition . 


Summary: First experimental study on the ridge pattern distribution in the interdigital palm region for biometrics. Novel classification methodology of palms according to five classes. Study of complementarity of the interdigital and traditional palm regions. Evaluation of personal recognition using interdigital region reaching EER 0.01 on database on 416 subjects . Interdigital palm region image database from 416 subjects in this paper is made publicly available.