Relational graph location network for multi-view image localization

In multi-view image localization task,the features of the images captured from different views should be fused properly.This paper considers the classification-based image localization problem.We propose the relational graph location network(RGLN)to perform this task.In this network,we propose a heterogeneous graph construction approach for graph classification tasks,which aims to describe the location in a more appropriate way,thereby improving the expression ability of the location representation module.Experiments show that the expression ability of the proposed graph construction approach outperforms the compared methods by a large margin.In addition,the proposed localization method outperforms the compared localization methods by around 1.7%in terms of meter-level accuracy.

Value of MRI diffusion weighted imaging in localization of prostate cancer with whole-mount step section pathology

Objective To evaluate the value of MRI diffusion weighted imaging in localization of prostate cancer with whole-mount step section pathology. Methods We treated 36 patients using laparoscopic radical prostatectomy from Oct. 2009 to Jun. 2010. Patients who did not have an MRL /DWI examination or a surgical history of pros-

A Local Contrast Fusion Based 3D Otsu Algorithm for Multilevel Image Segmentation

To overcome the shortcomings of 1 D and 2 D Otsu’s thresholding techniques, the 3 D Otsu method has been developed.Among all Otsu’s methods, 3 D Otsu technique provides the best threshold values for the multi-level thresholding processes. In this paper, to improve the quality of segmented images, a simple and effective multilevel thresholding method is introduced. The proposed approach focuses on preserving edge detail by computing the 3 D Otsu along the fusion phenomena. The advantages of the presented scheme include higher quality outcomes, better preservation of tiny details and boundaries and reduced execution time with rising threshold levels. The fusion approach depends upon the differences between pixel intensity values within a small local space of an image;it aims to improve localized information after the thresholding process. The fusion of images based on local contrast can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions. Results show that the proposed method yields more promising segmentation results when compared to conventional1 D Otsu, 2 D Otsu and 3 D Otsu methods, as evident from the objective and subjective evaluations.

Splicing Image and Its Localization:A Survey

With the rapid development of information technology,digital images have become an important medium for information transmission.However,manipulating images is becoming a common task with the powerful image editing tools and software,and people can tamper the images content without leaving any visible traces of splicing in order to gain personal goal.Images are easily spliced and distributed,and the situation will be a great threat to social security.The survey covers splicing image and its localization.The present status of splicing image localization approaches is discussed along with a recommendation for future research.

The local quotient image method-an iuumination preproassing method for face recogaition

Differences in illumination of the same face can defeat simple face recognition systems,yet most methods that compensate are too difficult to implement. Local quotient image (LQI) is an efficient illumination preprocessing method for face recognition systems. An illumination model and a face model were developed,and their use in the new method was analyzed. Analysis of the method's computational complexity showed it to be efficient. Experimental results on Yale Face Database B showed that the method can effectively eliminate the effects of differences in illumination and provides considerable improvement in recognition rates.

An Image Localization System Based on Single Photon

As an essential part of artificial intelligence,many works focus on image processing which is the branch of computer vision.Nevertheless,image localization faces complex challenges in image processing with image data increases.At the same time,quantum computing has the unique advantages of improving computing power and reducing energy consumption.So,combining the advantage of quantum computing is necessary for studying the quantum image localization algorithms.At present,many quantum image localization algorithms have been proposed,and their efficiency is theoretically higher than the corresponding classical algorithms.But,in quantum computing experiments,quantum gates in quantum computing hardware need to work at very low temperatures,which brings great challenges to experiments.This paper proposes a single-photon-based quantum image localization algorithm based on the fundamental theory of single-photon image classification.This scheme realizes the operation of the mixed national institute of standards and technology database(MNIST)quantum image localization by a learned transformation for non-noise condition,noisy condition,and environmental attack condition,respectively.Compared with the regular use of entanglement between multi-qubits and low-temperature noise reduction conditions for image localization,the advantage of this method is that it does not deliberately require low temperature and entanglement resources,and it improves the lower bound of the localization success rate.This method paves a way to study quantum computer vision.

A Thorough Investigation on Image Forgery Detection

Image forging is the alteration of a digital image to conceal some of the necessary or helpful information.It cannot be easy to distinguish themodified region fromthe original image in somecircumstances.The demand for authenticity and the integrity of the image drive the detection of a fabricated image.There have been cases of ownership infringements or fraudulent actions by counterfeiting multimedia files,including re-sampling or copy-moving.This work presents a high-level view of the forensics of digital images and their possible detection approaches.This work presents a thorough analysis of digital image forgery detection techniques with their steps and effectiveness.These methods have identified forgery and its type and compared it with state of the art.This work will help us to find the best forgery detection technique based on the different environments.It also shows the current issues in other methods,which can help researchers find future scope for further research in this field.

Machine Learning Based Diagnosis for Diabetic Retinopathy for SKPD-PSC

The study aimed to apply to Machine Learning(ML)researchers working in image processing and biomedical analysis who play an extensive role in compre-hending and performing on complex medical data,eventually improving patient care.Developing a novel ML algorithm specific to Diabetic Retinopathy(DR)is a chal-lenge and need of the hour.Biomedical images include several challenges,including relevant feature selection,class variations,and robust classification.Although the cur-rent research in DR has yielded favourable results,several research issues need to be explored.There is a requirement to look at novel pre-processing methods to discard irrelevant features,balance the obtained relevant features,and obtain a robust classi-fication.This is performed using the Steerable Kernalized Partial Derivative and Platt Scale Classifier(SKPD-PSC)method.The novelty of this method relies on the appropriate non-linear classification of exclusive image processing models in har-mony with the Platt Scale Classifier(PSC)to improve the accuracy of DR detection.First,a Steerable Filter Kernel Pre-processing(SFKP)model is applied to the Retinal Images(RI)to remove irrelevant and redundant features and extract more meaningful pathological features through Directional Derivatives of Gaussians(DDG).Next,the Partial Derivative Image Localization(PDIL)model is applied to the extracted fea-tures to localize candidate features and suppress the background noise.Finally,a Platt Scale Classifier(PSC)is applied to the localized features for robust classification.For the experiments,we used the publicly available DR detection database provided by Standard Diabetic Retinopathy(SDR),called DIARETDB0.A database of 130 image samples has been collected to train and test the ML-based classifiers.Experimental results show that the proposed method that combines the image processing and ML models can attain good detection performance with a high DR detection accu-racy rate with minimum time and complexity compared to the state-of-the-art meth-ods.The accuracy and speed of DR detection for numerous types of images will be tested through experimental evaluation.Compared to state-of-the-art methods,the method increases DR detection accuracy by 24%and DR detection time by 37.

Meso-mechanical Interfacial Behavior of Elbow Steel Fiber Reinforced Concrete

The strain distributions near the interface when the elbow steel fiber is pulled out from the half-mould concrete matrix are directly measured using a combined method of single fiber pull-out test and digital image correlation. Meanwhile, the real-time processes of the bonding, debonding and sliding at the interface are observed. The micro-mechanism of the strain localization in the failure process of interface when debonding occurs and the strengthening mechanism at the imbedded fiber are discussed. The experimental results show that the meso-scale strain localization gives rise to the localization of shear damage near the fiber interface. This strain localization characterized by the debonding process near the interface occurs, develops and moves gradually at an apparently regular interval. At the elbow part of the imbedded fiber, the peak value of the shearing stress occurs. But the primary debonding does not occur at this place because the strength of the shear damage is increased at the local area of the elbow part in the concrete, displaying an apparent reinforced effect at the end of the fiber.

Modulating a Local Shape Descriptor through Biologically Inspired Color Feature

This paper presents a biologically inspired local image descriptor that combines color and shape features. Compared with previous descriptors, red-cyan cells associated with L, M, and S cones （L for long, M for medium, and S for short） are used to indicate one of the opponent color channels. Stepping forward from state-of-the-art color feature extraction, we exploit a new approach to compute the color orientation and magnitudes of three opponent color channels, namely, red-green, blue-yellow, and red-cyan, in two-dimensional space. Color orientation is calculated in histograms with magnitude weighting. We linearly concatenate the four-color-opponent-channel histogram and scale-invariant-feamre-transform histogram in the final step. We apply our biologically inspired descriptor to describe the local image feature. Quantitative comparisons with state-of-the-art descriptors demonstrate the significant advantages of maintaining invariance to photometric and geometric changes in image matching, particularly in cases, such as illumination variation and image blurring, where more color contrast information is observed.

Analyse and sound image localization experiment study on multi-channel planar surround sound system

In this paper the method of approximate expansion is used to analyse a perfect planar surround sound system, resulting in an order of new and upgrade systems. First reproductinn signals of the perfect system and the characteristics of different orders systems are analysed. The independent transmission signals and decoding (reproduction) equation of the systexns are given. The compatibility among different orders systems and the problem of simplifying output channels are discussed. The problem of signal picking up, recording,transmitting and the possibility of putting the systems into practical use are studied. A sound hoage localization experiment for the systems is carried out in order to study haage localization in relaion to the numbers of transmission signals and output channels. The experimental result is consistemt with the theoretical result. This work lay down a base for practical use.

A 1,8-naphthalimide-derived turn-on fluorescent probe for imaging lysosomal nitric oxide in living cells

Nitric oxide has played an important role in many physiological and pathological processes as a kind of important gas signal molecules. In this work, a new fluorescent probe LysoNO-Naph for detecting NO in lysosomes based on 1,8-naphthalimide was reported. LysoNO-Naph has sub-groups of o-phenylene- diamine as a NO reaction site and 4-（2-aminoethyl）-morpholine as a lysosome-targetable group. This probe exhibited good selectivity and high sensitivity （4.57 μmol/L） toward NO in a wide pH range from 4 to 12. Furthermore, LysoNO-Naph can be used for imaging NO in lysosomes in living cells.

Novel Model Using Kernel Function and Local Intensity Information for Noise Image Segmentation

It remains a challenging task to segment images that are distorted by noise and intensity inhomogeneity.To overcome these problems, in this paper, we present a novel region-based active contour model based on local intensity information and a kernel metric. By introducing intensity information about the local region, the proposed model can accurately segment images with intensity inhomogeneity. To enhance the model＇s robustness to noise and outliers, we introduce a kernel metric as its objective functional. To more accurately detect boundaries, we apply convex optimization to this new model, which uses a weighted total-variation norm given by an edge indicator function. Lastly, we use the split Bregman iteration method to obtain the numerical solution. We conducted an extensive series of experiments on both synthetic and real images to evaluate our proposed method, and the results demonstrate significant improvements in terms of efficiency and accuracy, compared with the performance of currently popular methods.

A leaf image localization based algorithm for different crops disease classification

Agricultural crop production is a major contributing element to any country’s economy.To maintain the economic growth of any country plants disease detection is a leading factor in agriculture.The contribution of the proposed algorithm is to optimize the extracted infor-mation from the available resources for the betterment of the result without any additional complexity.The proposed technique basically localizes the leaf region prior to the image classification into healthy and diseased.The novelty of this work is to fuse the information extracted from the available resources and optimize it to enhance the expected outcome.The leaf colors are analyzed using color transformation for the seed region identification.The mapping of a low-dimensional RGB color image into L*a*b color space provides an expansion of the spectral range.The neighboring pixels-based leaf region growing is applied on the initial seeds.In order to refine the leaf boundary and the disease-affected areas,we employed a random sample consensus(RANSAC)for suitable curve fitting.The feature sets using bag of visual words,Fisher vectors,and handcrafted features are extracted followed by classification using logistic regression,multilayer perceptron model,and support vector machine.The performance of the proposal is analyzed through PlantVillage datasets of apple,bell pepper,cherry,corn,grape,potato,and tomato.The simulation-based analysis of the proposed contextualization-based image categorization process outperforms as compared with the state of arts.The proposed approach provides average accuracy and area under the curve of 0.932 and 0.903,respectively.

Discriminatively learning for representing local image features with quadruplet model

Traditional hand-crafted features for representing local image patches are evolving into current data-driven and learning-based image feature, but learning a robust and discriminative descriptor which is capable of controlling various patch-level computer vision tasks is still an open problem. In this work, we propose a novel deep convolutional neural network(CNN) to learn local feature descriptors. We utilize the quadruplets with positive and negative training samples, together with a constraint to restrict the intra-class variance, to learn good discriminative CNN representations. Compared with previous works, our model reduces the overlap in feature space between corresponding and non-corresponding patch pairs, and mitigates margin varying problem caused by commonly used triplet loss. We demonstrate that our method achieves better embedding result than some latest works, like PN-Net and TN-TG, on benchmark dataset.

Positioning a fluorescent probe at the core of a glassy star polymer for detection of local dynamics

Accessing local dynamics within a single macromolecule is the key to understand the physical origin of the viscoelasticity and especially the glass transition. In order to extract specific information on the dynamics of the branch point of a star polymer around its glass transition temperature, four-arm star poly （n-butyl methacrylate） with a fluorescent core was synthesized using perylene diimide as initiator and polymerization conducted via atom transfer radical polymerization. The process is found to be effective in positioning the fluorophore at the branch point with the fluorophore intact, which allows the successful application of single molecule fluorescence defocus imaging in examining the local site- sensitive dynamics. The power spectra of rotation trajectories, the population of rotating fluorophores as well as the distribution of angular displacement were used to revel the difference in local dynamics between branch point and the arm＇s end. It is discovered that the local dynamics at the core of the star polymer is much less activated than that at the arm＇s end. The results demonstrate the strong effect dues to the topological constrain at the branch point and the more free space at the arm＇s end.

Interchannel phase difference and stereo sound image localization

By considering higher order approximation to the interaural phase difference, a more general localization equation for stereo sound image with interchannel phase difference is derived. At very low frequency or low interchannel phase difference, the equation can be simplified to Makita theory. In general, image position is obviously affected by frequency.It is shown that image position varying with freqllency is the main reason for image width broadening in stereo reproduction with interchannel phase difference. And an extra interaural sound level difference caused by interchannel phase difference is the main reason for image naturalness degrading. In practice, it is necessary to reduce the interchannel phase difference,at least, to less than 60°.

Background-free three-dimensional selective imaging of anisotropic plasmonic nanoparticles

There is an increasing demand for advanced optical imaging techniques that can detect and resolve nanosize objects at a spatial resolution below the optical diffraction limit, especially in three-dimensional （3D） cellular environments. In this study, using a polarization-activated localization scheme based on the orientation-dependent properties of anisotropic plasmonic metal nanoparticles （MNPs）, ＂photoswitchable＂ imaging of single gold nanorods （AuNRs） was accomplished not only in two dimensions but also in three dimensions. Moreover, the Rayleigh scattering background arising from the congested subcellular structures was efficiently suppressed. Thus, we obtained the 3D distributions of both the position and the orientation of the AuNRs inside the cells and investigated their intemalization kinetics. To our knowledge, this is the first demonstration of the confocal-like 3D imaging of non-fluorescence nanoparticles with a high resolution and almost zero background. This technique is easy to implement and should greatly facilitate MNP studies and applications in biomedicine and biology.

RB-SLAM:visual SLAM based on rotated BEBLID feature point description

The extraction and description of image features are very important for visual simultaneous localization and mapping(V-SLAM).A rotated boosted efficient binary local image descriptor(BEBLID)SLAM(RB-SLAM)algorithm based on improved oriented fast and rotated brief(ORB)feature description is proposed in this paper,which can solve the problems of low localization accuracy and time efficiency of the current ORB-SLAM3 algorithm.Firstly,it uses the BEBLID to replace the feature point description algorithm of the original ORB to enhance the expressiveness and description efficiency of the image.Secondly,it adds rotational invariance to the BEBLID using the orientation information of the feature points.It also selects the rotationally stable bits in the BEBLID to further enhance the rotational invariance of the BEBLID.Finally,it retrains the binary visual dictionary based on the BEBLID to reduce the cumulative error of V-SLAM and improve the loading speed of the visual dictionary.Experiments show that the dictionary loading efficiency is improved by more than 10 times.The RB-SLAM algorithm improves the trajectory accuracy by 24.75%on the TUM dataset and 26.25%on the EuRoC dataset compared to the ORB-SLAM3 algorithm.

Application of functional MRI in epilepsy

Objective To review the recent development of functional MRI application in epilepsy. Data sources Both Chinese and English language literatures were researched using MEDLINE/ CD ROM (1996-2005) and the Chinese Biomedical Literature Disk (1996-2005). Study selection Published articles about functional MRI application and epilepsy were selected.Data extraction Data were mainly extracted from 38 articles which are listed in the reference section of this review.Results fMRI can be used to localize seizure foci through detecting these cerebral hemodynamic changes produced by epileptiform discharges. EEG-triggered fMRI, which has higher spatial and temporal resolution, helps to detect the spatiotemporal pattern of spike origin and propagation, and define localization of the epileptogenic focus. fMRI is also useful in language and memory cognitive function assessment and presurgical assessment of refractory epilepsy. Atypically distributed cognitive function areas can be detected by fMRI, because of cortical language and memory areas reorganization during long-term epileptic activity in patients with epilepsy. Conclusions fMRI technique plays a very important role in cognitive function and presurgical assessment of patients with epilepsy. It is meaningful for understanding pathogenesis of epilepsy.