2D Histogram Research Articles

A study on sleep posture analysis using fibre bragg grating arrays based mattress

Prolonged sleeping postures or unusual postures can lead to the development of various ailments such as subacromial impingement syndrome, sleep paralysis in the elderly, nocturnal gastroesophageal reflux, sore development, etc Fibre Bragg Gratings (a variety of optical sensors) have gained huge popularity due to their small size, higher sensitivity and responsivity, and encapsulation flexibilities. However, in the present study, FBG Arrays (two FBGs with 10 mm space between them) are employed as they are advantageous in terms of data collection, mitigating sensor location effects, and multiplexing features. In this work, Liquid silicone encapsulated FBG arrays are placed in the head (E), shoulder (C, D), and lower half body (A, B) region for analyzing the strain patterns generated by different sleeping postures namely, Supine (P1), Left Fetus (P2), Right Fetus (P3), and Over stomach (P4). These strain patterns were analyzed in two ways, combined (averaging the data from each FBG of the array) and Individual (data from each FBG was analyzed separately). Both analyses suggested that the FBGs in the arrays responded swiftly to the strain changes that occurred due to changes in sleeping postures. 3D histograms were utilized to track the strain changes and analyze different sleeping postures. A discussion regarding closely related postures and long hour monitoring has also been included. Arrays in the lower half (A, B) and shoulder (C, D) regions proved to be pivotal in discriminating body postures. The average standard deviation of strain for the different arrays was in the range of 0.1 to 0.19 suggesting the reliable and appreciable strain-handling capabilities of the Liquid silicone encapsulated arrays.

Quantifying the degree of scientific innovation breakthrough: Considering knowledge trajectory change and impact

Scientific breakthroughs have the potential to reshape the trajectory of knowledge flow and significantly impact later research. The aim of this study is to introduce the Degree of Innovation Breakthrough (DIB) metric to more accurately quantify the extent of scientific breakthroughs. The DIB metric takes into account changes in the trajectory of knowledge flow, as well as the deep and width of impact, and it modifies the traditional assumption of equal citation contributions by assigning weighted citation counts. The effectiveness of the DIB metric is assessed using ROC curves and AUC metrics, demonstrating its ability to differentiate between high and low scientific breakthroughs with high sensitivity and minimal false positives. Based on ROC curves, this study proposes a method to calculate the threshold for high scientific breakthrough, reducing subjectivity. The effectiveness of the proposed method is demonstrated through a dataset consisting of 1108 award-winning computer science papers and 9832 matched control papers, showing that the DIB metric surpasses single-dimensional metrics. The study also performs a granular analysis of the innovation breakthrough degree of non-award-winning papers, categorizing them into four types based on originality and impact through 2D histogram visualization, and suggests tailored management strategies. Through the adoption of this refined classification strategy, the management of innovation practices can be optimized, ultimately fostering the enhancement of innovative research outcomes. The quantitative tools introduced in this paper offer guidance for researchers in the fields of science intelligence mining and science trend prediction.

Unveiling Nanoscale Ordering in Amorphous Semiconducting Polymers Using Four-Dimensional Scanning Transmission Electron Microscopy.

We present four-dimensional (4D) scanning transmission electron microscopy (STEM) analysis to obtain a high level of detail regarding the nanoscale ordering within largely disordered organic semiconducting polymers. Understanding nanoscale molecular ordering in semiconducting polymers is crucial due to its connection to the materials' important properties. However, acquiring such information in a spatially localized manner has been limited by the lack of a nanoscale experimental probe, weak signal from ordering, and radiation damage to the sample. By collecting nanodiffraction patterns with a high dynamic range pixelated detector, we acquired statistically robust, high signal-to-noise ratio diffraction patterns from semiconducting organic materials, including poly(3-hexylthiophene-2,5-diyl) (P3HT), P3HT/[6,6]-phenyl C61 butyric acid methyl ester, and indacenodithiophene-co-benzothiadiazole (IDTBT), which largely have disordered structures. Real-space images of the ordered domains were reconstructed from the 4D-STEM data set for a variety of scattering vectors and in-plane angles to capture the different molecular stacking distances and their in-plane orientation. These were then analyzed to obtain the average size of the ordered domains within the sample. Such measurements were arranged in a two-dimensional (2D) histogram, which showed a direct relationship between the type and size of molecular ordering. Complementary analyses, such as intensity variance and angular correlation, were applied to obtain ordering and symmetry information. These analyses enabled us to directly characterize the alkyl and π-π stacking of P3HT, as well as the fullerene domains caused by donor segregation in the P3HT sample. Furthermore, the analysis also captured changes in the P3HT domains when the fullerenes are incorporated. Lastly, IDTBT showed a much lesser degree of ordering without much disinclination between the domains within the 2D histogram. The 4D-STEM analysis that we report here unveils new details of molecular ordering that can be used to optimize the properties of this important class of materials.

Reversible data hiding in JPEG images based on improved frequency selection and mapping strategy

JPEG images have become ubiquitous in our daily lives due to their ability to strike a balance between visual quality and file size. Consequently, reversible data hiding (RDH) schemes for JPEG images have emerged. However, existing methods by modifying discrete cosine transform (DCT) coefficients still have room for improvement in terms of distortion reduction and file size preservation. This paper proposes a novel two-dimensional (2D) histogram mapping strategy for RDH in JPEG images. Firstly, the quantized DCT coefficient blocks are sorted based on the count of zero alternating current (AC) coefficients in each block. This enables the estimation of distortion in non-zero AC coefficients at different locations within the block, facilitating the selection of frequency coefficients with minimal distortion for embedding. Additionally, leveraging statistical characteristics, six mapping types with different embedding efficiency are created and the improved 2D histogram mapping method is proposed. Furthermore, The unit distortion-increase ratio (UDIR) is employed as a comprehensive evaluation metric. Extensive experiments are conducted on four representative images and two widely-used image databases to validate the proposed scheme. The results consistently demonstrate superior visual quality, minimal file size increase (FSI), and higher UDIR in comparison to state-of-the-art RDH schemes for JPEG images.

Comparative Study of Machine Learning Methods for State of Health Estimation of Maritime Battery Systems

Abstract This paper tests two data-driven approaches for predicting the state of health (SOH) of lithium-ion-batteries (LIBs) for the purpose of monitoring maritime battery systems. First, non-sequential approaches are investigated and various models are tested: ridge, lasso, support vector regression, and gradient boosted trees. Binning is proposed for feature engineering for these types of models to capture the temporal structure in the data. Such binning creates histograms for the accumulated time the LIB has been within various voltage, temperature, and current ranges. Further binning to combine these histograms into 2D or 3D histograms is explored in order to capture relationships between voltage, temperature, and current. Second, a sequential approach is explored where different deep learning architectures are tried out: long short-term memory, transformer, and temporal convolutional network. Finally, the various models and the two approaches are compared in terms of their SOH prediction ability. Results indicate that the binning with ridge regression models performed best. The same publicly available sensor data from laboratory cycling tests are used for both approaches.

Exploring hidden signal: Fine-tuning ResNet-50 for dark matter detection

In pursuit of detecting dark matter signals, the Large Hadron Collider (LHC) at CERN has conducted proton-proton collisions to probe for these elusive particles, whose existence has been supported by astronomical observations. Despite extensive efforts by the CMS and ATLAS experiments, the direct detection of dark matter signals remains elusive. The current approaches employed for analyzing dark matter signatures utilize the cut-and-count method based on conventional techniques. This study introduces an alternative method for exploring dark matter signatures by utilizing fine-tuning of pre-trained models, such as ResNet-50, on 2D histograms generated from a combination of signal + background samples and background-only samples. By utilizing various signal-to-background ratios as benchmarks, an accuracy of about 90% for a signal-to-background ratio of 0.008 is achieved. This approach not only offers a more refined search for dark matter signals but also presents an efficient and effective means of analysis using machine learning techniques.

Medical image segmentation approach based on hybrid adaptive differential evolution and crayfish optimizer

Image segmentation plays a pivotal role in medical image analysis, particularly for accurately isolating tumors and lesions. Effective segmentation improves diagnostic precision and facilitates quantitative analysis, which is vital for medical professionals. However, traditional segmentation methods often struggle with multilevel thresholding due to the associated computational complexity. Therefore, determining the optimal threshold set is an NP-hard problem, highlighting the pressing need for efficient optimization strategies to overcome these challenges. This paper introduces a multi-threshold image segmentation (MTIS) method that integrates a hybrid approach combining Differential Evolution (DE) and the Crayfish Optimization Algorithm (COA), known as HADECO. Utilizing two-dimensional (2D) Kapur’s entropy and a 2D histogram, this method aims to enhance the efficiency and accuracy of subsequent image analysis and diagnosis. HADECO is a hybrid algorithm that combines DE and COA by exchanging information based on predefined rules, leveraging the strengths of both for superior optimization results. It employs Latin Hypercube Sampling (LHS) to generate a high-quality initial population. HADECO introduces an improved DE algorithm (IDE) with adaptive and dynamic adjustments to key DE parameters and new mutation strategies to enhance its search capability. In addition, it incorporates an adaptive COA (ACOA) with dynamic adjustments to the switching probability parameter, effectively balancing exploration and exploitation. To evaluate the effectiveness of HADECO, its performance is initially assessed using CEC’22 benchmark functions. HADECO is evaluated against several contemporary algorithms using the Wilcoxon signed rank test (WSRT) and the Friedman test (FT) to integrate the results. The findings highlight HADECO’s superior optimization abilities, demonstrated by its lowest average Friedman ranking of 1.08. Furthermore, the HADECO-based MTIS method is evaluated using MRI images for knee and CT scans for brain intracranial hemorrhage (ICH). Quantitative results in brain hemorrhage image segmentation show that the proposed method achieves a superior average peak signal-to-noise ratio (PSNR) and feature similarity index (FSIM) of 1.5 and 1.7 at the 6-level threshold. In knee image segmentation, it attains an average PSNR and FSIM of 1.3 and 1.2 at the 5-level threshold, demonstrating the method’s effectiveness in solving image segmentation problems.

Annotation and automated segmentation of single-molecule localisation microscopy data.

Single Molecule Localisation Microscopy (SMLM) is becoming a widely used technique in cell biology. After processing the images, the molecular localisations are typically stored in a table as xy (or xyz) coordinates, with additional information, such as number of photons, etc. This set of coordinates can be used to generate an image to visualise the molecular distribution, for example, a 2D or 3D histogram of localisations. Many different methods have been devised to analyse SMLM data, among which cluster analysis of the localisations is popular. However, it can be useful to first segment the data, to extract the localisations in a specific region of a cell or in individual cells, prior to downstream analysis. Here we describe a pipeline for annotating localisations in an SMLM dataset in which we compared membrane segmentation approaches, including Otsu thresholding and machine learning models, and subsequent cell segmentation. We used an SMLM dataset derived from dSTORM images of sectioned cell pellets, stained for the membrane proteins EGFR (epidermal growth factor receptor) and EREG (epiregulin) as a test dataset. We found that a Cellpose model retrained on our data performed the best in the membrane segmentation task, allowing us to perform downstream cluster analysis of membrane versus cell interior localisations. We anticipate this will be generally useful for SMLM analysis.

Multi-threshold image segmentation using new strategies enhanced whale optimization for lupus nephritis pathological images

Lupus Nephritis (LN) has been considered as the most prevalent form of systemic lupus erythematosus. Medical imaging plays an important role in diagnosing and treating LN, which can help doctors accurately assess the extent and extent of the lesion. However, relying solely on visual observation and judgment can introduce subjectivity and errors, especially for complex pathological images. Image segmentation techniques are used to differentiate various tissues and structures in medical images to assist doctors in diagnosis. Multi-threshold Image Segmentation (MIS) has gained widespread recognition for its direct and practical application. However, existing MIS methods still have some issues. Therefore, this study combines non-local means, 2D histogram, and 2D Renyi’s entropy to improve the performance of MIS methods. Additionally, this study introduces an improved variant of the Whale Optimization Algorithm (GTMWOA) to optimize the aforementioned MIS methods and reduce algorithm complexity. The GTMWOA fusions Gaussian Exploration (GE), Topology Mapping (TM), and Magnetic Liquid Climbing (MLC). The GE effectively amplifies the algorithm’s proficiency in local exploration and quickens the convergence rate. The TM facilitates the algorithm in escaping local optima, while the MLC mechanism emulates the physical phenomenon of MLC, refining the algorithm’s convergence precision. This study conducted an extensive series of tests using the IEEE CEC 2017 benchmark functions to demonstrate the superior performance of GTMWOA in addressing intricate optimization problems. Furthermore, this study executed an experiment using Berkeley images and LN images to verify the superiority of GTMWOA in MIS. The ultimate outcomes of the MIS experiments substantiate the algorithm’s advanced capabilities and robustness in handling complex optimization problems.

Image Segmentation Model for Vicinagearth Security Technology of Unmanned Aerial Vehicle Using Improved Pigeon-Inspired Optimization

The vicinagearth security technology system covers a wide range of fields such as low-altitude security, underwater security, and cross-domain security. Among them, unmanned aerial vehicle (UAV) security will become one of the evolving forms of its security technology, and how to improve the segmentation and recognition ability of UAV visual reconnaissance system for maritime targets through improvement will become the key to low-altitude security. Due to the fact that maritime target images are characterized by complex weather, strong interference, high speed requirement and large data volume, the traditional segmentation methods are not suitable for maritime small-target (MST) segmentation and recognition. Therefore, this paper proposes a threshold image segmentation (TIS) method based on an improved pigeon-inspired optimization (PIO) algorithm to provide a better method for segmentation and recognition of MST. First, this study proposes CCPIO based on the horizontal crossover search (HCS) and vertical crossover search (VCS) strategy, which effectively improves the search efficiency of PIO and the ability to jump out of local optimum. And the optimization performance of CCPIO is effectively verified by comparing it with 10 peer algorithms through benchmark function experiments. Further, in this paper, the proposed CCPIO-TIS segmentation model is proposed by combining CCPIO with non-local means, 2D histogram, and Kapur’s entropy. The proposed CCPIO-TIS model is also used for the segmentation and recognition of real MST images, and the results of the experimental comparison and evaluation analysis show that the proposed model has higher quality segmentation results than 12 models of the same type. In summary, this study can provide an efficient and accurate artificial intelligence model for segmentation and recognition of maritime small-target.

A new method for crop image segmentation based on 2D histogram using multi-strategy shuffled frog leaping algorithm

A new method for crop image segmentation based on 2D histogram using multi-strategy shuffled frog leaping algorithm

Brain magnetic resonance image (MRI) segmentation using multimodal optimization

AbstractOne of the highly focused areas in the medical science community is segmenting tumors from brain magnetic resonance imaging (MRI). The diagnosis of malignant tumors at an early stage is necessary to provide treatment for patients. The patient’s prognosis will improve if it is detected early. Medical experts use a manual method of segmentation when making a diagnosis of brain tumors. This study proposes a new approach to simplify and automate this process. In recent research, multi-level segmentation has been widely used in medical image analysis, and the effectiveness and precision of the segmentation method are directly tied to the number of segments used. However, choosing the appropriate number of segments is often left up to the user and is challenging for many segmentation algorithms. The proposed method is a modified version of the 3D Histogram-based segmentation method, which can automatically determine an appropriate number of segments. The general algorithm contains three main steps: The first step is to use a Gaussian filter to smooth the 3D RGB histogram of an image. This eliminates unreliable and non-dominating histogram peaks that are too close together. Next, a multimodal particle swarm optimization method identifies the histogram’s peaks. In the end, pixels are placed in the cluster that best fits their characteristics based on the non-Euclidean distance. The proposed algorithm has been applied to a Cancer Imaging Archive (TCIA) and brain MRI Images for brain Tumor detection dataset. The results of the proposed method are compared with those of three clustering methods: FCM, FCM_FWCW, and FCM_FW. In the comparative analysis of the three algorithms across various MRI slices. Our algorithm consistently demonstrates superior performance. It achieves the top mean rank in all three metrics, indicating its robustness and effectiveness in clustering. The proposed method is effective in experiments, proving its capacity to find the proper clusters.

Beyond local patches: Preserving global–local interactions by enhancing self-attention via 3D point cloud tokenization

Transformer-based architectures have recently shown impressive performance on various point cloud understanding tasks such as 3D object shape classification and semantic segmentation. Particularly, this can be attributed to their self-attention mechanism, which has the ability to capture long-range dependencies. However, current methods have constrained it to operate in local patches due to its quadratic memory constraints. This hinders their generalization ability and scaling capacity due to the loss of non-locality in early layers. To tackle this issue, we propose a window-based transformer architecture that captures long-range dependencies while aggregating information in the local patches. We do this by interacting each window with a set of global point cloud tokens — a representative subset of the entire scene — and augmenting the local geometry through a 3D Histogram of Oriented Gradients (HOG) descriptor. Through a series of experiments on segmentation and classification tasks, we show that our model exceeds the state-of-the-art on S3DIS semantic segmentation (+1.67% mIoU), ShapeNetPart part segmentation (+1.03% instance mIoU) and performs competitively on ScanObjectNN 3D object classification.11The code and trained models shall be made publicly available.

DRPSO:A multi-strategy fusion particle swarm optimization algorithm with a replacement mechanisms for colon cancer pathology image segmentation

Colon adenocarcinoma (COAD) is a type of colon cancers with a high mortality rate. Its early symptoms are not obvious, and its late stage is accompanied by various complications that seriously endanger patients' lives. To assist in the early diagnosis of COAD and improve the detection efficiency of COAD, this paper proposes a multi-level threshold image segmentation (MIS) method based on an enhanced particle swarm algorithm for segmenting COAD images. Firstly, this paper proposes a multi-strategy fusion particle swarm optimization algorithm (DRPSO) with a replacement mechanism. The non-linear inertia weight and sine-cosine learning factors in DRPSO help balance the exploration and exploitation phases of the algorithm. The population reorganization strategy incorporating MGO enhances population diversity and effectively prevents the algorithm from stagnating prematurely. The mutation-based final replacement mechanism enhances the algorithm's ability to escape local optima and helps the algorithm to obtain highly accurate solutions. In addition, comparison experiments on the CEC2020 and CEC2022 test sets show that DRPSO outperforms other state-of-the-art algorithms in terms of convergence accuracy and speed. Secondly, by combining the non-local mean 2D histogram and 2D Renyi entropy, this paper proposes a DRPSO algorithm based MIS method, which is successfully applied to the segments the COAD pathology image problem. The results of segmentation experiments show that the above method obtains relatively higher quality segmented images with superior performance metrics: PSNR = 23.556, SSIM = 0.825, and FSIM = 0.922. In conclusion, the MIS method based on the DRPSO algorithm shows great potential in assisting COAD diagnosis and in pathology image segmentation.

Fast Semi-Supervised t-SNE for Transfer Function Enhancement in Direct Volume Rendering-Based Medical Image Visualization

Magnetic resonance imaging and computed tomography produce three-dimensional volumetric medical images. While a scalar value represents each individual volume element, or voxel, volumetric data are characterized by features derived from groups of neighboring voxels and their inherent relationships, which may vary depending on the specific clinical application. Labeled samples are also required in most applications, which can be problematic for large datasets such as medical images. We propose a direct volume rendering (DVR) framework based on multi-scale dimensionality reduction neighbor embedding that generates two-dimensional transfer function (TF) domains. In this way, we present FSS.t-SNE, a fast semi-supervised version of the t-distributed stochastic neighbor embedding (t-SNE) method that works over hundreds of thousands of voxels without the problem of crowding and with better separation in a 2D histogram compared to traditional TF domains. Our FSS.t-SNE scatters voxels of the same sub-volume in a wider region through multi-scale neighbor embedding, better preserving both local and global data structures and allowing for its internal exploration based on the original features of the multi-dimensional space, taking advantage of the partially provided labels. Furthermore, FSS.t-SNE untangles sample paths among sub-volumes, allowing us to explore edges and transitions. In addition, our approach employs a Barnes–Hut approximation to reduce computational complexity from O(N2) (t-SNE) to O(NlogN). Although we require the additional step of generating the 2D TF domain from multiple features, our experiments show promising performance in volume segmentation and visual inspection.

Cascaded combination of total variation regularization and contrast limited adaptive histogram equalization based image dehazing

ABSTRACT Haze and fog can significantly reduce the visibility of distant objects by scattering light and creating blurry, washed-out images lacking in detail. Image dehazing is a technique used in computer vision to enhance the clarity of such obscured images. This study examines the effectiveness of various dehazing methods using real hazy images and synthetic images with artificially created haze. Performance metrics such as PSNR, SSIM, MSE, CII, and computation time are used to evaluate the proposed method. The evaluation is carried out on datasets like RESIDE, GMAN, O-Haze, I-Haze, NH-Haze, and Dense haze to compare the proposed method with existing models. The proposed method attains 27.46%, 20.63% and 21.09% higher PSNR and 12.36%, 23.95% and 36.12% lower Natural Image Quality Evaluator for RESIDE dataset when analyzed to the existing models, such as strategic method towards contrast enhancement by 2D histogram equalization under TV decomposition (CE-TDHE-TVD), multiple level framework basis contrast enhancement for uniform with non-uniform back ground imageries utilizing appropriate histogram equalization (CE-VHE), and Contrast enhancement with brightness preservation of low light pictures with the help of combined CLAHE and BPDHE histogram equalization (CE-CLAHE-BPDHE) respectively.

Reconstruction of the real 3D shape of the SARS-CoV-2 virus

The photographs of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus taken by electron transmission microscopy and cryoelectron microscopy provide only a 2D silhouette. The viruses appear to look like distorted circles. The present paper questions the real shape of the SARS-CoV-2 virus and makes an attempt to give an answer. Is this a general ellipsoid, a spheroid with rotational symmetry, a sphere, or something else? The answer requires the application of tools from three different disciplines: structural mechanics, microbiology, and statistics. A total of 590 virus photographs taken from 22 recently published papers were examined. From this experimental data pool, the histogram of diameter ratios was built from the 283 measurements where the virus images could be approximated as ellipses. The curve peaks at the diameter ratio of 1.22. The transformation equation for the spatial shape to the planar shade was derived for a fixed light source of the microscope. This equation involves an unknown orientation of the viruses with respect to the microscope. Two sets of models were developed, one with a uniform distribution of the virus orientation and the other with the orientation defined by the normalized beta distribution. In both sets of models, the unknown diameter ratio of the spheroidal virus was regarded as a random realization from translated gamma distributions. The parameters of the distribution of the kernel functions were determined by minimizing the mean square difference between the predicted and measured 2D histograms. The information included in the measured histograms was found to be insufficient to find an unknown distribution of the virus’s orientation. Simply too many unknown parameters render the solution physically unrealistic. The minimization procedure with a uniform probability of virus orientation predicted the peak of the aspect ratio of the 3D spheroid at 1.32. Based on this result, models of the virus will be developed in the continuation of this research for a full dynamic analysis.

Influential Metrics Estimation and Dynamic Frequency Selection Based on Two-Dimensional Mapping for JPEG-Reversible Data Hiding.

JPEG Reversible Data Hiding (RDH) is a method designed to extract hidden data from a marked image and perfectly restore the image to its original JPEG form. However, while existing RDH methods adaptively manage the visual distortion caused by embedded data, they often neglect the concurrent increase in file size. In rectifying this oversight, we have designed a new JPEG RDH scheme that addresses all influential metrics during the embedding phase and a dynamic frequency selection strategy with recoverable frequency order after data embedding. The process initiates with a pre-processing phase of blocks and the subsequent selection of frequencies. Utilizing a two-dimensional (2D) mapping strategy, we then compute the visual distortion and file size increment (FSI) for each image block by examining non-zero alternating current (AC) coefficient pairs (NZACPs) and their corresponding run lengths. Finally, we select appropriate block groups based on the influential metrics of each block group and proceed with data embedding by 2D histogram shifting (HS). Extensive experimentation demonstrates how our method's efficiently and consistently outperformed existing techniques with a superior peak signal-to-noise Ratio (PSNR) and optimized FSI.

Enhancing person re-identification on RGB-D data with noise free pose-regularized color and skeleton distance features

Noisy features may introduce irrelevant or incorrect features that can lead to incorrect classifications and lower accuracy. This can be especially problematic in tasks such as person re-identification (ReID), where subtle differences between individuals need to be accurately captured and distinguished. However, the existing ReID methods directly use noisy and limited multimodality features for similarity measures. It is crucial to use robust features and pre-processing techniques to reduce the effects of noise and ensure accurate classification. As a solution, we employ a Gaussian filter to eliminate the Gaussian noise from RGB-D data in the pre-processing stage. For similarity measure, the color descriptors are computed using the top eight peaks of the 2D histogram constructed from pose regularized partition grid cells, and eleven different skeleton distances are considered. The proposed method is evaluated on the BIWI RGBD-ID dataset, which comprises still (front view images) and walking set (images with varied pose and viewpoint) images. The obtained recognition rates of 99.15% and 94% on still and walking set images demonstrate the effectiveness of the proposed approach for the ReID task in the presence of pose and viewpoint variations. Further, the method is evaluated on and RGBD-ID and achieved improved performance over the existing techniques.

Brain tumor detection based on a novel and high-quality prediction of the tumor pixel distributions

The work presented in this paper is in the area of brain tumor detection. We propose a fast detection system with 3D MRI scans of Flair modality. It performs 2 functions, predicting the gray level distribution and location distribution of the pixels in the tumor regions and generating tumor masks with pixel-wise precision. To facilitate 3D data analysis and processing, we introduce a 2D histogram presentation encompassing the gray-level distribution and pixel-location distribution of a 3D object. In the proposed system, specific 2D histograms highlighting tumor-related features are established by exploiting the left-right asymmetry of a brain structure. A modulation function, generated from the input data of each patient case, is applied to the 2D histograms to transform them into coarsely or finely predicted distributions of tumor pixels. The prediction result helps to identify/remove tumor-free slices. The prediction and removal operations are performed to the axial, coronal and sagittal slice series of a brain image, transforming it into a 3D minimum bounding box of its tumor region. The bounding box is utilized to finalize the prediction and generate a 3D tumor mask. The proposed system has been tested extensively with the data of more than 1200 patient cases in BraTS2018∼2021 datasets. The test results demonstrate that the predicted 2D histograms resemble closely the true ones. The system delivers also very good tumor detection results, comparable to those of state-of-the-art CNN systems with mono-modality inputs. They are reproducible and obtained at an extremely low computation cost and without need for training.