Information Of Different Images Research Articles

Joint Cross-Consistency Learning and Multi-Feature Fusion for Person Re-Identification

To solve the problem of inadequate feature extraction by the model due to factors such as occlusion and illumination in person re-identification tasks, this paper proposed a model with a joint cross-consistency learning and multi-feature fusion person re-identification. The attention mechanism and the mixed pooling module were first embedded in the residual network so that the model adaptively focuses on the more valid information in the person images. Secondly, the dataset was randomly divided into two categories according to the camera perspective, and a feature classifier was trained for the two types of datasets respectively. Then, two classifiers with specific knowledge were used to guide the model to extract features unrelated to the camera perspective for the two types of datasets so that the obtained image features were endowed with domain invariance by the model, and the differences in the perspective, attitude, background, and other related information of different images were alleviated. Then, the multi-level features were fused through the feature pyramid to concern the more critical information of the image. Finally, a combination of Cosine Softmax loss, triplet loss, and cluster center loss was proposed to train the model to address the differences of multiple losses in the optimization space. The first accuracy of the proposed model reached 95.9% and 89.7% on the datasets Market-1501 and DukeMTMC-reID, respectively. The results indicated that the proposed model has good feature extraction capability.

Focus-pixel estimation and optimization for multi-focus image fusion

To integrate the effective information and improve the quality of multi-source images, many spatial or transform domain-based image fusion methods have been proposed in the field of information fusion. The key purpose of multi-focus image fusion is to integrate the focused pixels and remove redundant information of each source image. Theoretically, if the focused pixels and complementary information of different images are detected completely, the fusion image with best quality can be obtained. For this goal, we propose a focus-pixel estimation and optimization based multi-focus image fusion framework in this paper. Because the focused pixels of an image are in the same depth of field (DOF), we propose a multi-scale focus-measure algorithm for the focused pixels matting to integrate the focused region firstly. Then, the boundaries of focused and defocused regions are obtained accurately by the proposed optimizing strategy. And the boundaries are also fused to reduce the influence of insufficient boundary precision. The experimental results demonstrate that the proposed method outperforms some previous typical methods in both objective evaluations and visual perception.

Perbandingan Informasi Citra Potongan Axial T2 Antara Turbo Spin Echo (TSE) Dengan Half-Fourier Aquisition Single-Shot Turbo Spin Echo (HASTE) Pada Pemeriksaan Magnetic Resonance Cholangiopancreatography (MRCP)

Backgroud: MRCP is a non-invasive imaging examination, which is used for the evaluation of biliary tract, pancreatic duct, and gallbladder. Pulse sequences which can be used to visualize organs in MRCP on T2 weighting is using a fast pulse sequences such as sequences TSE or Haste. The purpose of this research was to knowing the difference image information between T2 axial TSE with Haste and where better to use between the two sequences on axial T2 MRCP examination.Methods: This type of research is experimental observational approach, it has been carried out in the best MRI 1.5 Tesla at RSU Haji Surabaya. Sample used were 5 patients. Each patient performed two pieces of axial T2 sequences in which the TSE and Haste. Furthermore, the image submitted to the three doctors radiologist to fill out a questionnaire that has been provided to assess the image consisting of the liver, gallbladder, common bile duct (CBD), pancreas, intrahepatic duct and an assessment of the artifacts.Results: The results of analytical assessment Wilcoxon test, for the entire anatomy with ρ value of 0.002 which means that ρ 0.05, and the artifacts show the ρ value of 0.006, which means ρ 0.05. This proves that there are differences in image information between T2 axial TSE with T2 axial Haste the MRCP examination. At each of anatomy, liver has ρ value of 0,071 (ρ 0.05), gallbladder has ρ value of 0.317 (ρ 0.05), CBD has ρ value of 0.003 (ρ 0.05), pancreas has ρ value of 0.014 (ρ 0.05) and intrahepatic duct has ρ value of 0.004 (ρ 0.05). Based on the mean rank statistical test results show sequence Haste better in generating image information on the whole anatomy, but in each of anatomy based on the mean rank of gallbladder both sequences are equally good in showing gallbladder, whereas to display the liver, CBD, pancreas, and intrahepatic duct, the results showed T2 Haste mean rank better, it is because it has the characteristics of high Haste T2 signal intensity and better in reducing motion artifacts.Conclusion: Wilcoxon test analysis results expressed Ha accepted, meaning that there is a difference between the image information pieces axial T2 TSE with Haste the MRCP examination. The mean rank shows Haste superior to TSE, this is because the artifacts on TSE and therefore contributes to the respondent's assessment, other than that Haste has a high signal intensity so that it can show more clearly ducts.

HLFNet: High-low Frequency Network for Person Re-Identification

Person re-identification (re-ID) technology has attracted many scholars in the past few years. With the recent developments of deep learning technology, person re-ID has been greatly improved. However, the main chalenge of re-ID is to distinguish the detailed information in different images. Consequently, it is of significant importance to extract fine-grained features in the re-ID tasks. In the present study, a novel method, called the high-low frequency network (HLFNet), is proposed to effectively use the image information of different frequencies and focus on the detailed information between different individual images. In this regard, high frequency and low-frequency information are initially extracted from the original image, and then two backbones are applied to extract the features from the two information branches. Different frequencies of image information complement each other so that a better recognition effect can be achieved. Moreover, a local branch is utilized to extract the distinguishable local features for guiding the global feature branch in the training stage. Finally, only the extracted global feature from the trained network is required in the inference phase of re-ID. Performed experiments demonstrate that the proposed method can significantly enhance the feature representation accuracy and achieve the state-of-the-art performance on diverse benchmarks.

Pseudo Labels and Soft Multi-Part Corresponding Similarity for Unsupervised Deep Hashing

In recent years, unsupervised deep hashing methods have achieved great success in large-scale image retrieval. However, these approaches still suffer two major problems in real world applications. On the one hand, due to the lack of effective supervision information, hash codes of different categories are easily judged to be similar. On the other hand, binary semantic similarity matrices can not reflect ranking relationship and the internal structure information of different images. To solve these problems, we propose a novel unsupervised deep hashing method, named P seudo labels and S oft multi-part C orresponding similarity based H ashing (PSCH), to ensure the heterogeneity of the hash codes. Specifically, we propose a “pseudo labels” method that use ${k}$ -means clustering and a distance threshold to generate the pseudo labels. Further, in order to reflect the hash codes similarity between instances within the same class, we propose a novel soft multi-part corresponding similarity method to learn better hash codes. This method can divide deep feature maps into several groups and compute the attention map for multi-part similarity matrices. In addition, a novel loss function is proposed to support learning with pseudo labels and soft multi-part corresponding similarity for achieving better performance. Comprehensive experiments on CIFAR-10, NUSWIDE, and Flickr demonstrate that our method can generate high-quality hash codes and outperform state-of-the-art unsupervised hashing methods by a large margin.

THE OPTIMIZATION OF MSCT OF URINARY TRACT USING TRACKING WITH FILTERS VARIATION

Backgroud : Kidney disease is a clinical situation which is indicated by the decreasing of kidney functions and MSCT is one of the modalities to diagnose that function. Aim to identify the differences in image information with filter variation abdomen medium smooth and Mediastinum Standard. Methods : The method was the experimental research using research planning Post Test Only Group Design. The sampling was chosen consecutively. There were 3 Radiolog and 32 citra on the examination of MSCT abdomen without positive contrast media in Salatiga Public Hospital. Results : The intervention validity experiment after a tracking filter with variations, kruskal Wallis test results. There are meaningful differences shows vlue of p value 0,000 (p<0,05), so that continued analysis of the Mann Whitney. The results of the analysis of the Mann Whitney pointed out that there is a difference between real or significant group of filter Medium Smooth Abdomen with Abdominal filter Medium Sharp/Mediastinum Standard Sig<0.05 p value 0.000). Conclusion: a variation of the filter is able to identify the existence of a difference image information tracktus optimal unirarius MSCT abdomen after tracking by using the best filtr Medium Sharp Abdomen/Mediastinum Standard.

A fractional order derivative based active contour model for inhomogeneous image segmentation

• A new hybrid framework of adaptive-weighting active contour model is proposed. • The global term enhances the image contrast and accelerates the convergence rate. • The local term integrates fractional order differentiation and difference image information. • An adaptive weighting strategy and a termination criterion are employed. • Measures include the dice similarity coefficient and gray-leveled contrast. Segmenting intensity inhomogeneous images is a challenging task for both local and global methods. Some hybrid methods have great advantages over the traditional methods in inhomogeneous image segmentation. In this paper, a new hybrid method is presented, which incorporates image gradient, local environment and global information into a framework, called adaptive-weighting active contour model. The energy or level set functions in the framework mainly include two parts: a global term and local term. The global term aims to enhance the image contrast, and it can also accelerate the convergence rate when minimizing the energy function. The local term integrates fractional order differentiation, fractional order gradient magnitude, and difference image information into the well-known local Chan–Vese model, which has been shown to be effective and efficient in modeling the local information. The local term can also enhance low frequency information and improve the inhomogeneous image segmentation. An adaptive weighting strategy is proposed to balance the actions of the global and local terms automatically. When minimizing the level set functions, regularization can be imposed by applying Gaussian filtering to ensure smoothness in the evolution process. In addition, a corresponding stopping criterion is proposed to ensure the evolving curve automatically stops on true boundaries of objects. Dice similarity coefficient is employed as the comparative quantitative measures for the segmented results. Experiments on synthetic images as well as real images are performed to demonstrate the segmentation accuracy and computational efficiency of the presented hybrid method.

SAR IMAGE CHANGE DETECTION BASED ON FUZZY MARKOV RANDOM FIELD MODEL

Abstract. Most existing SAR image change detection algorithms only consider single pixel information of different images, and not consider the spatial dependencies of image pixels. So the change detection results are susceptible to image noise, and the detection effect is not ideal. Markov Random Field (MRF) can make full use of the spatial dependence of image pixels and improve detection accuracy. When segmenting the difference image, different categories of regions have a high degree of similarity at the junction of them. It is difficult to clearly distinguish the labels of the pixels near the boundaries of the judgment area. In the traditional MRF method, each pixel is given a hard label during iteration. So MRF is a hard decision in the process, and it will cause loss of information. This paper applies the combination of fuzzy theory and MRF to the change detection of SAR images. The experimental results show that the proposed method has better detection effect than the traditional MRF method.

Analisis Image Noise dan Nilai Dosis Radiasi Penggunaan Aplikasi Care Dose 4D dan Non Care Dose 4D pada Pesawat MSCT Siemens

Background : One effective technique is contained in a CT Scan to decrease the amount of radiation dose that is received in the use of automatic exposure control (AEC). AEC system of Siemens equipment is called Care Dose 4D. The research is based on unused of the application Care Dose 4D on CT Scan Imaging. The purpose of the research is to determine the differences of image noise and the differences of radiation doses (CTDI) using care dose 4D and Non Care Dose 4D in Siemens MSCT.Methods :The research was quantitative study with experimental approaches that is tested on four water phantom size type. Analysts data is done by statistical tests of Paired T-Test Samples to test the hypothesis and the level difference image information. In this statistical analysis is determined the level of confidence (level of Significance) with a value of α = 0.05.Results : On the using of Nasopharing protocol for children with 130 kV and 130 mAs parameters, using of Care Dose 4D has ability to decrease the image noise value is compared with non Care Dose 4D application. While the adult Nasopharing protocol with 130 kV and 220 mAs parameters, Care Dose 4D doesn’t provide enough impact in the reduction of image noise value if compared with conventional techniques or without using Care Dose 4D applications. Using of children nasopharing protocol application Care Dose 4D even increase radiation closes high enough value in CTDI vol that is 5,03 mGy, but using Nasopharing Care Dose 4D applications for adult can decrease radiation doses high enough value in CTDIvol, that is 2,64 mGy.Conclusion : On the use of children nasopharing protocol application Care Dose 4D even increase radiation closes high enough value in CTDI vol that is 5,03 mGy, while the adult Nasopharing using Care Dose 4D applications can decrease radiation doses high enough value in CTDIvol, that is 2,64 mGy.

Joint Dictionary Learning for Multispectral Change Detection.

Change detection is one of the most important applications of remote sensing technology. It is a challenging task due to the obvious variations in the radiometric value of spectral signature and the limited capability of utilizing spectral information. In this paper, an improved sparse coding method for change detection is proposed. The intuition of the proposed method is that unchanged pixels in different images can be well reconstructed by the joint dictionary, which corresponds to knowledge of unchanged pixels, while changed pixels cannot. First, a query image pair is projected onto the joint dictionary to constitute the knowledge of unchanged pixels. Then reconstruction error is obtained to discriminate between the changed and unchanged pixels in the different images. To select the proper thresholds for determining changed regions, an automatic threshold selection strategy is presented by minimizing the reconstruction errors of the changed pixels. Adequate experiments on multispectral data have been tested, and the experimental results compared with the state-of-the-art methods prove the superiority of the proposed method. Contributions of the proposed method can be summarized as follows: 1) joint dictionary learning is proposed to explore the intrinsic information of different images for change detection. In this case, change detection can be transformed as a sparse representation problem. To the authors' knowledge, few publications utilize joint learning dictionary in change detection; 2) an automatic threshold selection strategy is presented, which minimizes the reconstruction errors of the changed pixels without the prior assumption of the spectral signature. As a result, the threshold value provided by the proposed method can adapt to different data due to the characteristic of joint dictionary learning; and 3) the proposed method makes no prior assumption of the modeling and the handling of the spectral signature, which can be adapted to different data.

Combining Global Probability Density Difference and Local Gray Level Fitting for Ultrasound Image Segmentation

Because of low signal-noise ratio (SNR), low contrast and blurry boundaries, the segmentation of ultrasound image becomes a diffcult problem in the digital image processing field. In this paper, a novel active contour model combining global probability density difference and local gray level fitting is proposed for the segmentation of ultrasound image. In the proposed model, global information and local information are extracted in original ultrasound image and pre- processed image, respectively. In the original ultrasound image, by combining the background knowledge the distributions of gray levels of different regions are utilized for modeling the global information. For considering the local information, the ultrasound image is pre-processed, and in the pre-processed image, the local gray level fitting model is utilized for modeling the local information. By modeling the global and the local information in different images, the proposed method combines both approaches that utilize and remove speckle noise. With both simulated and clinical ultrasound images, the experimental results demonstrate that the proposed method is adaptive to the noise and robust to the initial conditions, and that it can segment ultrasound image accurately.

Convergence control of moving object using opto‐digital algorithm in the 3D robot vision system

In this paper, a new target extraction algorithm is proposed, in which the coordinates of target are obtained adaptively by using the difference image information and the optical BPEJTC(binary phase extraction joint transform correlator) with which the target object can be segmented from the input image and background noises are removed in the stereo vision system. First, the proposed algorithm extracts the target object by removing the background noises through the difference image information of the sequential left images and then contrails the pan/tilt and convergence angle of the stereo camera by using the coordinates of the target position obtained from the optical BPEJTC between the extracted target image and the input image. From some experimental results, it is found that the proposed algorithm can extract the target object from the input image with background noises and then, effectively track the target object in real time. Finally, a possibility of implementation of the adaptive stereo object tracking system by using the proposed algorithm is also suggested.

Quantitative chemical phase analysis of EFTEM elemental maps using scatter diagrams

Energy-filtered transmission electron microscope (EFTEM) images can yield elemental maps of very high lateral resolution (1–2 nm) in a short time (typically less than 3 min). Additionally, correlation techniques such as scatter diagrams yield information about the intensity relationships between the various elemental maps, thus leading to the calculation of chemical phase maps. The application of such techniques reduces the amount of data (i.e. number of images) by compressing information of different images into only a few. This makes interpretation a lot easier and clearer, since information in chemical phase maps is restricted to the chemical composition of the specimen. In this work we used a Gatan Imaging Filter (GIF) to acquire EFTEM images of a BaNd-titanate specimen. The scatter diagram technique together with quantification procedures was then applied to these images in order to show the distribution of chemical phases within the specimen. First, we quantified the elemental maps using atomic ratio images. Then we applied the scatter diagram technique on the atomic ratio images and calculated chemical phase maps.