It is inspired by the fact that the human visual system HVS is highly sensitive to edges that are often encountered in SCIs; therefore, essential edge features are extracted and exploited for conducting IQA for the SCIs.
The key novelty of the proposed ESIM lies in the extraction and use of three salient edge features-i. The first two attributes are simultaneously generated from the input SCI based on a parametric edge model, while the last one is derived directly from the input SCI. The degree of similarity measured for each above-mentioned edge attribute is then computed independently, followed by combining them together using our proposed edge-width pooling strategy to generate the final ESIM score.
For each SCI, nine distortion types are investigated, and five degradation levels are produced for each distortion type. Reduced reference image quality assessment via sub- image Overlaps with device in metadevice state database for sexual offenders based redundancy measurement.
The reduced reference RR image quality assessment IQA has been attracting much attention from researchers for its loyalty to human perception and flexibility in practice. A promising RR metric should be able to predict the perceptual quality of an image accurately while using as few features as possible. In this paper, a novel RR metric is presented, whose novelty lies in two aspects. Firstly, it measures the image redundancy by calculating the so-called Sub- image Similarity SISand the image quality is measured by comparing the SIS between the reference image and the test image.
Experiments on two IQA databases i. LIVE and CSIQ databases show that "Overlaps with device in metadevice state database for sexual offenders" using only 6 features, the proposed metric can work very well with high correlations between the subjective and objective scores. In particular, it works consistently well across all the distortion types. During the past few years, there have been various kinds of content-aware image retargeting operators proposed for image resizing.
However, the lack of effective objective retargeting quality assessment metrics limits the further development of image retargeting techniques. Different from traditional Image Quality Assessment IQA metrics, the quality degradation during image retargeting is caused by artificial retargeting modifications, and the difficulty for Image Retargeting Quality Assessment IRQA lies in the alternation of the image resolution and content, which makes it impossible to directly evaluate the quality degradation like traditional IQA.
In this paper, we interpret the image retargeting in a unified framework of resampling grid generation and forward resampling. We show that the geometric change estimation is an efficient way to clarify the relationship between the images. The geometric change aims to provide the evidence about how the original image is resized into the target image.
Under the guidance of the geometric change, we develop a novel Aspect Ratio Similarity metric ARS to evaluate the visual quality of retargeted images by exploiting the local block changes with a visual importance pooling strategy. Gradient Magnitude Similarity Deviation: It is an important task to faithfully evaluate the perceptual quality of output images in many applications, such as image compression, image restoration, and multimedia streaming.
A good image quality assessment IQA model should not only deliver high quality prediction accuracy, but also be computationally efficient. The efficiency of IQA metrics is becoming particularly important due to the increasing proliferation of high-volume visual data in high-speed networks. The image gradients are sensitive to image distortions, while different local structures in a distorted image suffer different degrees of degradations. This motivates us to explore the use of global variation of gradient based local quality map for overall image quality prediction.
We find that the pixel-wise gradient magnitude similarity GMS between the reference and distorted images combined with a novel pooling strategy-the standard deviation of the GMS map-can predict accurately perceptual image quality. The resulting GMSD Overlaps with device in metadevice state database for sexual offenders is much faster than most state-of-the-art IQA methods, and delivers highly competitive prediction accuracy.
Image -guided radiotherapy quality control: Statistical process control using image similarity metrics. The purpose of this study was to demonstrate an objective quality control framework for the image review process. A total of cone-beam computed tomography CBCT registrations were retrospectively analyzed for 33 bilateral head and neck cancer patients who received definitive radiotherapy.
Two registration tracking volumes RTVs - cervical spine C-spine and mandible - were defined, within which a similarity metric was calculated and used as a registration quality tracking metric over the course of treatment. First, sensitivity to large misregistrations was analyzed for normalized cross-correlation NCC and mutual information MI in the context of statistical analysis.
Then, similarity metric control charts were created using a statistical process control SPC framework to objectively monitor the image registration and review process. Patient-specific control charts were created using NCC values from the first five fractions to set a patient-specific process capability limit. Population control charts were created using the average of the first five NCC values for all patients in the study.
For each patient, the similarity metrics were calculated as a function of unidirectional translation, referred to as the effective displacement. Patient-specific action limits corresponding to 5 mm effective displacements were defined. Furthermore, effective displacements of the ten registrations Overlaps with device in metadevice state database for sexual offenders the lowest similarity metrics were compared with a three dimensional 3DoF couch displacement required to align the anatomical landmarks.
Overlaps with device in metadevice state database for sexual offenders identified suboptimal registrations more effectively than MI within the framework of SPC. Deviations greater than 5 mm were detected at 2. No-reference image quality assessment based on natural scene statistics and gradient magnitude similarity.
However, the features used in the state-of-the-art "general purpose" NR-IQA algorithms are usually natural scene statistics NSS based or are perceptually relevant; therefore, the performance of these models is limited. The new method extracts features in both the spatial and gradient domains. In the spatial domain, we extract the point-wise statistics for single pixel values which are characterized by a generalized Gaussian distribution model to form the underlying features.
In the gradient domain, statistical features based on neighboring gradient magnitude similarity are extracted. Then a mapping is learned to predict quality scores using a support vector regression.
The experimental results on the benchmark image databases demonstrate that the proposed algorithm correlates highly with human judgments of quality and leads to significant performance improvements over state-of-the-art methods. Similarity analysis between quantum images. Similarity analyses between quantum images are so essential in quantum image processing that it provides fundamental research for the other fields, such as quantum image matching, quantum pattern recognition.
In this paper, a quantum scheme based on a novel quantum image representation and quantum amplitude amplification algorithm is proposed.
At the end of the paper, three examples and simulation experiments show that the measurement result must be 0 when two images are same, and the measurement result has high probability of being 1 when two images are different. Semantically enabled image similarity search. Georeferenced data of various modalities are increasingly available for intelligence and commercial use, however effectively exploiting these sources demands a unified data space capable of capturing the unique contribution of each input.
This work presents a suite of software tools for representing geospatial vector data and overhead imagery in a shared high-dimension vector or embedding" space that supports fused learning and similarity search across dissimilar modalities. While the approach is suitable for fusing arbitrary input types, including free text, the present work exploits the obvious but computationally difficult relationship between GIS and overhead imagery.
GIS is comprised of temporally-smoothed but information-limited content of a GIS, while overhead imagery provides an information-rich but temporally-limited perspective. This processing framework includes some important extensions of concepts in literature but, more critically, presents Overlaps with device in metadevice state database for sexual offenders means to accomplish them as a unified framework at scale on commodity cloud architectures.
Current subjective image quality assessments have been developed in the laboratory environments, under controlledconditions, and are dependent on the participation of limited numbers of observers. In this research, with the help of Web 2. A website with a simple user interface that enables Internet users from anywhere at any time to vote for a better quality version of a pair of the same image has been constructed.
Users' votes are recorded and used to rank the images according to their perceived visual qualities. We have developed three rank aggregation algorithms to process the recorded pair comparison data, the first uses a naive approach, the second employs a Condorcet method, and the third uses the Dykstra's extension of Bradley-Terry method. The website has been collecting data for about three months and has accumulated over 10, votes at the time of writing this paper. Results show that the Internet and its allied technologies such as crowdsourcing offer a promising new paradigm for image and video quality assessment where hundreds of "Overlaps with device in metadevice state database for sexual offenders" of Internet users can contribute to building more robust image quality metrics.
We have made Internet user generated social image quality SIQ data of a public image database available online http: The website continues to collect votes and will include more public image databases and will also be extended to include videos to collect social video quality SVQ data.
All data will be public available on the website in due course. A similar shot to the previous imagethis photograph, looking Toward image phylogeny forests: In the past few years, several near-duplicate detection methods appeared in the literature to identify the cohabiting versions of a given document online.
Following this trend, there are some initial attempts to go beyond the detection task, and look into the structure of evolution within a set of related images overtime. In this paper, we aim at automatically identify the structure of relationships underlying the imagescorrectly reconstruct their past history and ancestry information, and group them in distinct trees of processing history. We introduce a new algorithm that automatically handles sets of images comprising different related imagesand outputs the phylogeny trees also known as a forest associated with them.
Image phylogeny algorithms have many applications such as finding the first image within a set posted online useful for tracking copyright infringement perpetratorshint at child pornography content creators, and narrowing down a list of suspects for online harassment using photographs. In image classification, obtaining adequate data to learn a robust classifier has often proven to be difficult in several scenarios.
Classification of histological tissue images for health care analysis is a notable application in this context due to the necessity of surgery, biopsy or autopsy. To adequately exploit limited training data in classification, we propose a saliency guided dictionary learning method and subsequently an image similarity technique for histo-pathological image classification. Salient object detection from images aids in the identification of discriminative image features. We leverage the saliency values for the local image regions to learn a dictionary and respective sparse codes for an imagesuch that the more salient features are reconstructed with smaller error.
The dictionary learned from an image gives a compact representation of the image itself and is capable of representing images with similar content, with comparable sparse codes.