Unsupervised steganalysis over social networks based on multi-reference sub-image sets

THINGS-data reflects three large-scale neuroimaging and behavioral datasets of object processing in humans, comprising densely sampled functional MRI and magnetoencephalographic recordings, as well as 4.70 million similarity judgments in response to thousands of photographic images for up to 1854 objects.

The ability to accurately predict natural backgrounds over a wide range of potential image acquisition conditions is key to wide-area change detection. The lack of robustness in pure image-based techniques has led to the development of site modeling and model-supported exploitation approaches. Instead of using multiple images to build a site model, we consider the possibility of using them directly to estimate the background of a new image for change detection. Our method uses a nonlinear mean-squared estimation technique to compute image backgrounds from multiple reference images. In comparing linear and nonlinear estimators, we find that although the performance of both improves as the number of reference images increases, the nonlinear estimator yields significantly better background estimates and is much less sensitive to registration errors between images than the linear estimator. We show that as the number of references increases, the pixels within a region become physically significant; i.e., they correspond to parts of the scene with similar physical properties. Synthetic aperture radar (SAR) background modeling and change detection results including a crosssensor change detection example in which SAR reference images are used to predict the background of an electro-optical (EO) image are presented. Significant performance gains over linear change detection (up to two orders of magnitude reduction in the false alarm rate) are demonstrated in each case over a 60-deg range of SAR sensor aspect angles.

In this paper we propose a hybrid representation of environment models by a point representation augmented to geometric representation of surface polygons reconstructed from multiple reference images, through which a real time walkthrough of a complex environment can be achieved. By the method, we start from classification of pixels of the source images into two categories, corresponding respectively to the planar and non-planar surfaces in 3D space. For the pixels corresponding to the planar surfaces, the plane coefficients are reconstructed and all their appearances in the reference images are merged to form uniformly sampled texture images by a comparison of sampling rate and resampling. For the pixels corresponding to the non-planar surfaces, a point representation is applied and the redundant pixels are removed again through sampling rate comparison. The remained pixels are organized by OBB-tree according to their space coordinates. At the same time, the holes that are unable to be captured by all the reference images are pre-filled in the preprocessing phase so that the probability of hole appearance in walkthrough is greatly reduced. Under this hybrid representation, texture mapping and point warping are employed to render the novel views, to take full advantages of the acceleration utility of graphics hardware.

Purpose: To clinically evaluate the accuracy of an automatic patient positioning system based on the image correlation of two edge images in radiotherapy.Method and Materials: Ninety‐six head & neck images from eight patients undergoing proton therapy were compared with a digitally reconstructedradiograph(DRR) of planning CT. Two edge images, a reference image and a test image, were extracted by applying a Canny edge detector algorithm to a DRR and a 2D x‐ray image, respectively, of each patient before positioning. The value for patient positioning was determined using an optimization algorithm based on an image correlation factor, which recognizes shifts between two edge images by translating and rotating the test image with respect to the reference image.Results: In a simulation using a humanoid phantom, performed to verify the effectiveness of the proposed method, no registration errors were observed for given ranges of rotation, pitch and translation in the x, y and z directions. For real patients, however, there were discrepancies between the automatic positioning method and manual positioning by physicians or technicians. Using edged head coronal‐ and sagittal‐view images, the average differences in registration between these two methods for the x, y and z directions were 0.10 cm, 0.09 cm and 0.11 cm, respectively, whereas the maximum discrepancies were 0.34 cm, 0.38 cm, and 0.50 cm, respectively. For rotation and pitch, the average registration errors were 0.96° and 1.25°, respectively, and the maximum errors were 3.6° and 2.3°, respectively. Conclusion: The proposed automatic patient positioning system based on edge image comparison was relatively accurate for head & neck patients. However, image deformation during treatment may render the automatic method less accurate, since the test image many differ significantly from the reference image.

Natural feeling color image fusion technology is an important direction in image processing.This paper first summarizes contributions from both domestic and foreign researchers.Then according to the theory of 2D color-lookup-table, it puts forwards the multiple reference images fusion algorithm, and gives the results of using two and three reference images.The comparing and analyzing of those results proves the validity of the algorithm.

Purpose:We develop a method to generate time varying volumetric images (3D fluoroscopic images) using patient‐specific motion models derived from four‐dimensional cone‐beam CT (4DCBCT).Methods:Motion models are derived by selecting one 4DCBCT phase as a reference image, and registering the remaining images to it. Principal component analysis (PCA) is performed on the resultant displacement vector fields (DVFs) to create a reduced set of PCA eigenvectors that capture the majority of respiratory motion. 3D fluoroscopic images are generated by optimizing the weights of the PCA eigenvectors iteratively through comparison of measured cone‐beam projections and simulated projections generated from the motion model. This method was applied to images from five lung‐cancer patients. The spatial accuracy of this method is evaluated by comparing landmark positions in the 3D fluoroscopic images to manually defined ground truth positions in the patient cone‐beam projections.Results:4DCBCT motion models were shown to accurately generate 3D fluoroscopic images when the patient cone‐beam projections contained clearly visible structures moving with respiration (e.g., the diaphragm). When no moving anatomical structure was clearly visible in the projections, the 3D fluoroscopic images generated did not capture breathing deformations, and reverted to the reference image. For the subset of 3D fluoroscopic images generated from projections with visibly moving anatomy, the average tumor localization error and the 95th percentile were 1.6 mm and 3.1 mm respectively.Conclusion:This study showed that 4DCBCT‐based 3D fluoroscopic images can accurately capture respiratory deformations in a patient dataset, so long as the cone‐beam projections used contain visible structures that move with respiration. For clinical implementation of 3D fluoroscopic imaging for treatment verification, an imaging field of view (FOV) that contains visible structures moving with respiration should be selected. If no other appropriate structures are visible, the images should include the diaphragm.This project was supported, in part, through a Master Research Agreement with Varian Medical Systems, Inc, Palo Alto, CA.

As the issue of robustness of face recognition based on depth image sets, we propose that multiple Kinect images is being as a set of images, and depth data captured is used to automatically estimate poses and crop face area. Firstly, divide image sets into c subsets, and divide the images in all the subsets into image blocks of 4×4. Then, simulate images in sets as a form of image blocks, dividing in accordance with posture. Each set is represented using covariance matrix. Finally, the simulation of images in subsets is on Riemannian manifold. In order to classify, separately learnt SVM models for each image subset on the Lie group of Riemannian manifold and introduce a fusion strategy to combine results from all image subsets. We have verified the effectiveness of the proposed method on the three largest public Kinect face data sets CurtinFaces, Biwi Kinect and UWA Kinect. Compared to other advanced methods, the recognition rate has improved greater, the standard deviation is kept low, with robust to the number of image sets, image sub-setting number and spatial resolution.

The results of development of formalized model of process of formation of decisive function of combined correlation-extreme navigation system of unmanned aerial vehicles are presented. The development of the model is carried out taking into account the state of the operational and tactical situation, as well as the effect of changing the geometry of the sighting. The peculiarity of the developed model consists in the formation of a decisive function using a set of reference images, which is necessary for the formation of a decisive function, the set of reference images is built taking into account changes in navigation parameters of an unmanned aerial vehicle and angular orientation parameters. The originality of the formalized model consists in the determined order of formation of the decisive function of the combined correlation-extreme navigation system, which, unlike the known ones, consists in step-by-step determination of coordinates of the reference object by clarifying the navigation parameters of unmanned aerial vehicles with subsequent refinement of the angular orientation parameters according to the following sequence: determining the height of formation of the current image, determining the angles of view and determining the angular orientation parameters. Each of the individual reference images corresponds to its own multidimensional matrix of luminance values of the corresponding pixels or radio-thermal temperature. This takes into account the area of objects located on the sighting surface, which leads to refinement of the coordinates of the reference object in the current image. Changes in the spatial position and orientation of unmanned aerial vehicles, geometric properties of the sighting surface, as well as taking into account variations in informative features of the sighting surface objects depending on external factors and the influence of the medium on the propagation of signals in the optical and radiometric ranges of waves and obstacles, which will reduce or completely eliminate the discrepancy of coordinates of the reference object to the current image determined in advance in the reference image.

A new security system is proposed using optical joint transform correlation technique which employs multiple phase-shifted reference images. In the proposed technique, the address code is used as the reference image and fed into four channels after performing phase shifting on them by different amount. The output signals from each channel are added with the input image to be encrypted for security purpose. Joint power spectra (JPS) signals can then be derived by applying Fourier transformation, and the resultant signals are phase-shifted and combined to form a modified JPS signal. Inverse Fourier transformation of the modified JPS signal yields the encrypted image which is now secure from any unauthorized access and/or loss of information. For decryption purpose, the received encrypted signal is first Fourier transformed and multiplied by the address code used in encryption, which is then inverse Fourier transformed to generate the output signal. The proposed technique does not involve any complex mathematical operation on the address code otherwise required in other security techniques. The proposed technique requires a simple architecture and operates fast, automatic and is invariant to noise and distortions. Performance of the proposed scheme is investigated using computer simulation using binary as well as gray images in both noise-free and noisy conditions.

Large sky surveys are increasingly relying on image subtraction pipelines for real-time (and archival) transient detection. In this process one has to contend with varying PSF, small brightness variations in many sources, as well as artifacts resulting from saturated stars, and, in general, matching errors. Very often the differencing is done with a reference image that is deeper than individual images and the attendant difference in noise characteristics can also lead to artifacts. We present here a deep-learning approach to transient detection that encapsulates all the steps of a traditional image subtraction pipeline -- image registration, background subtraction, noise removal, psf matching, and subtraction -- into a single real-time convolutional network. Once trained the method works lighteningly fast, and given that it does multiple steps at one go, the advantages for multi-CCD, fast surveys like ZTF and LSST are obvious.

This paper addresses a dense voxel-wise correspondence of cone-beam computed tomography (CBCT) images towards a non-rigid registration and treatments evaluation in clinical orthodontics. An unsupervised clustering randomized forest is employed to establish voxel-wise correspondence in a reduced subset of the original volume image. A geodesic coordinate is introduced to avoid the structural ambiguities. The geodesic coordinates updated with voxel-wise affinities yield a cascaded geodesic forest. Given a novel volume image, the appearance and cascaded geodesic forests produce a voxel-wise correspondence in the subsets. A regularization scheme is employed to propagate the subset correspondence to the whole images, which results in a dense displacement field of the non-rigid registration between the reference and target volume images. Our technique is based on the unsupervised clustering forests and does not need the predefined atlas for training. Quantitative assessment on practitioner-annotated ground truth demonstrates an improvement to the state-of-the-arts label propagation techniques of CBCT images.

To improve the spatial sampling of scanning hyperspectral cameras, it is often necessary to capture numerous overlapping image cubes and later mosaic them to form the overall image cube. For hyperspectral camera systems having broad-area coverage, whisk-broom scanning using an external mirror is often employed. Creating the final image cube mosaic requires sub-pixel correction of the scan-mirror distortion, as well as alignment of the individual image cubes. For systems lacking geo-positional information that relates sensor to scene, alignment of the image scans is nontrivial. Here we present a novel algorithm that removes scan distortion and aligns hyperspectral image cubes based on correlation of the cubes’ image content with a reference image. The algorithm is able to provide robust results by recognizing that the cubes’ image content will not always match identically with that of the reference image. For example, in cultural heritage applications, the reference color image of the finished painting need not match the under-painting seen in the SWIR. Our approach is to identify a corresponding set of points between the cubes and the reference image, using a subset of wavelet scales, and then filtering out matches that are inconsistent with a map of the distortion. The filtering is performed by removing points iteratively according to their proximity to a function fit to their disparity (distance between the matched points). Our method will be demonstrated and our results validated using hyperspectral image cubes (976-1680 nm) and visible reference images from the fields of remote sensing and cultural heritage preservation.

A technique is presented which uses an infrared camera and a computer system to determine the true temperature distribution on close up plane targets, and emittance and transmission distribution can be calculated pixcel by pixcel from multiple IR images in different condition. Four experiments were performed to verify the technique on opaque objects, and the results were in close agreement within 5% error of the infrared camera system. The conditions to achieve this result were: 1) the measurements were made in some rooms with uniform set temperature, 2) one reference image at a uniform temperature distribution, eg. at room temperature in thermal equilibrium condition, is required for each opaque sample, and 3) the references should be the same sample at same location exactly. For transparent material, though the principle is the same, the measurement is much more complicated than the opaque material. One more condition is needed for this measurement, that is, two reference image with uniform set temperature together with uniform and a controllable radiation source behind the sample are required to determine the transmission distribution, emittance distribution and true temperature distribution of transparent samples, in addition to the conditions for opaque objects.

Estimating the external calibration --- the pose --- of a camera with respect to its environment is a fundamental task in Computer Vision (CV). In this paper, we propose a novel method for estimating the unknown 6DOF pose of a camera with known intrinsic parameters from epipolar geometry only. For a set of geo-located reference images, we assume the camera position - but not the orientation - to be known. We estimate epipolar geometry between the image of the query camera and the individual reference images using image features. Epipolar geometry inherently contains information about the relative positioning of the query camera with respect to each of the reference cameras, giving rise to a set of relative pose estimates. Combining the set of pose estimates and the positions of the reference cameras in a robust manner allows us to estimate a full 6DOF pose for the query camera. We evaluate our algorithm on different datasets of real imagery in indoor and outdoor environments. Since our pose estimation method does not rely on an explicit reconstruction of the scene, our approach exposes several significant advantages over existing algorithms from the area of pose estimation.

Purpose: TX regulations and the ACR require that CT radiation output be measured within 30 days of major service. The most common major service is tube replacement. We hypothesized that historical QC data could be used instead to determine if output measurements are necessary, reducing the need for costly output measurements. Methods : We reviewed 66 records of tube replacements to determine with what frequency output falls outside specifications. We also conducted an experiment to verify that clinically significant output changes could be identified by comparing image noise in historical QC data with the same data after tube replacement. We used 30 days of historical QC data to establish a baseline noise level and 95% confidence interval (CI) for individual noise measurements. To simulate output changes, we acquired phantom images with our QC protocol while manually changing output (mA). We acquired 10 images using the baseline output and 10 images at each different “output”. We evaluated individual images and subsets of images at each “output” to determine if the system was within the manufacturer’s specifications. Results: None of the 66 tube replacements resulted in an output change that exceeded specifications. Analysis of 30 days of historic QC data for our experimental system indicated a mean noise of 5.4 HU with 95% CI of 5.1 ‒ 5.7 HU. When using the mean noise of 10 images acquired at each of the varying outputs, we were able to identify, with 100% accuracy, images acquired at outputs outside manufacturer’s specifications. Conclusion : The results of our review of historical tube replacement data indicated the likelihood of output falling outside manufacturer’s specifications is low. Considering this, it is likely that by using QC data from programs required by regulation and the ACR physicists can reliably verify radiation output stability remotely instead of making physical measurements. -------------------- Cite this article as: P Stauduhar, A Jones. Are output measurements always necessary after CT tube replacement? Int J Cancer Ther Oncol 2014; 2(2):020238. DOI: 10.14319/ijcto.0202.38