Huge Image Research Articles

Плавучие гостиницы в структуре речных городов

Introduction. The river is the most important strategic city-forming resource, a historical factor of life support, economic development, trade, and tourism. The role of the backbone elements that form urban ensembles is always assigned to the areas along river banks. Hence, inefficiently used urban riverside territories are always conspicuous and disharmonious. Hotels on urban embankments is a stable trend that can increase the tourist attractiveness of cities, and therefore it requires a systematic and professional study. Materials and methods. The analysis and synthesis, based on foreign and domestic research and literary sources and design materials, prove that over the past decades, areas along river banks have been actively involved in the life of cities. In many cities complex, systemic programmes for the reconstruction of river bank areas are being developed. Results. The research results are presented in the form of an analysis of the world experience and relevant issues of the formation of public zones on embankments. The conditions for the construction of architectural facilities along river banks must be determined by the concept of transforming the embankment and the general principles of urban development. Trends in the arrangement of riverside areas and their general architectural principles are identified. The features and the international experience of the architectural design of floating hotels, the conditions for the interaction between water spaces and the structure of these hotels are also considered. Conclusions. The huge image potential of embankments is a significant factor for the tourist attractiveness of these areas. The insufficient legislative support of such facilities has been identified; the legislation, governing the operation of hotels, is to be revised to conform to the present-day reality.

Extended Distributed Framework for Feature Extraction in Remote Sensing Imagery with High Resolution

Remote Sensing imagery is used vastly in the areas of human activities investigation, environmental changes monitoring and geo-spatial data updation in a rapidly increasing way. Humans can easily and appropriately interpret the normally shot pictures but this is a difficult task for the computer to automatically interpret information from the given images. One of the prominent phases is in finding the way to extract the projected information from the given imagery and its conversion to wrath-ful data which can be used for further research. The motto is the generation of an algorithm which aims to be very efficient during of processing of huge images that include enhancement of efficiency in processing, correlation finding amongst given data and extraction of continuous features. In order to accomplish all these purposes as stated above, we hereby put forward an algorithm Extended Feature Extraction and Detection in High Resolution Remote Sensing (HRRS) Imagery to detect rivers. The proposed system is established with Hadoop Distributed Framework in order to enhance the efficiency of total system.

Dynamic optimization of hessian determinant image pyramid for memory‐efficient and high performance keypoint detection in SURF

With increasing demand for scale-invariant and fast object recognition, speeded up robust features (SURF) have emerged and become a widely used feature extraction algorithm in computer vision. Nevertheless, SURF still requires high memory usage and heavy computations caused by the keypoint detection procedure that produces huge image pyramid composed of many hessian determinant (HD) data for supporting the scale-invariance. Therefore, in this paper, dynamic optimization schemes of the HD image pyramid are proposed for completely removing the redundancies of the keypoint detection with keeping the original functionality of SURF without any loss. The proposed approach has shown to reduce memory usage by 45–51%, the execution time by 37–50% and the power consumption by 18–42% when compared with the keypoint detection in the original SURF algorithm.

An Efficient Road Surveillance Approach to Detect, Recognize & Tracking Vehicles Using Deep Learning Methods

In the current scenario on the increasing number of motor vehicles day by day, so traffic regulation faces many challenges on intelligent road surveillance and governance, this is one of the important research areas in the artificial intelligence or deep learning. Among various technologies, computer vision and machine learning algorithms have the most efficient, as a huge vehicles video or image data on road is available for study. In this paper, we proposed computer vision-based an efficient approach to vehicle detection, recognition and Tracking. We merge with one-stage (YOLOv4) and two-stage (R-FCN) detectors methods to improve vehicle detection accuracy and speed results. Two-stage object detection methods provide high localization and object recognition precision, even as one-stage detectors achieve high inference and test speed. Deep-SORT tracker method applied for detects bounding boxes to estimate trajectories. We analyze the performance of the Mask RCNN benchmark, YOLOv3 and Proposed YOLOv4 + R-FCN on the UA-DETRAC dataset and study with certain parameters like Mean Average Precisions (mAP), Precision recall.

Innovations in ex vivo Light Sheet Fluorescence Microscopy

Light Sheet Fluorescence Microscopy (LSFM) has revolutionized how optical imaging of biological specimens can be performed as this technique allows to produce 3D fluorescence images of entire samples with a high spatiotemporal resolution. In this manuscript, we aim to provide readers with an overview of the field of LSFM on ex vivo samples. Recent advances in LSFM architectures have made the technique widely accessible and have improved its acquisition speed and resolution, among other features. These developments are strongly supported by quantitative analysis of the huge image volumes produced thanks to the boost in computational capacities, the advent of Deep Learning techniques, and by the combination of LSFM with other imaging modalities. Namely, LSFM allows for the characterization of biological structures, disease manifestations and drug effectivity studies. This information can ultimately serve to develop novel diagnostic procedures, treatments and even to model the organs physiology in healthy and pathological conditions.

An Image Detection Method Based on Parameter Optimization of Support Vector Machine

With the progress and development of multimedia image processing technology, and the rapid growth of image data, how to efficiently extract the interesting and valuable information from the huge image data, and effectively filter out the redundant data, these have become an urgent problem in the field of image processing and computer vision. In recent years, as one of the important branches of computer vision, image detection can assist and improve a series of visual processing tasks. It has been widely used in many fields, such as scene classification, visual tracking, object redirection, semantic segmentation and so on. Intelligent algorithms have strong non-linear mapping capability, data processing capacity and generalization ability. Support vector machine (SVM) by using the structural risk minimization principle constructs the optimal classification hyper-plane in the attribute space to make the classifier get the global optimum and has the expected risk meet a certain upper bound at a certain probability in the entire sample space. This paper combines SVM and artificial fish swarm algorithm (AFSA) for parameter optimization, builds AFSA-SVM classification model to achieve the intelligent identification of image features, and provides reliable technological means to accelerate sensing technology. The experiment result proves that AFSA-SVM has better classification accuracy and indicates that the algorithm of this paper can effectively realize the intelligent identification of image features.

A Novel Deep Learning Pipeline Architecture based on CNN to Detect Covid-19 in Chest X-ray Images

Covid-19 is a severe public health problem worldwide. To date, it has spanned worldwide, with 24.6 million infected with 835,843 confirm the death. Covid-19 detection is indeed an important task and has to be done as quickly as possible so that treatment and monitoring can be carried out early. The current world standard RT-PCR screening for Covid-19 detection has to cope with the world population's great demand. There is a need to have an alternative way to cope with the demands. It has to be a quick and accurate detection procedure, such as using a chest x-ray for Covid-19 detection. This paper proposes a deep learning pipeline architecture called Gray Level Co-occurrence Matrix GLCM) with Convolutional Neural Network (CNN) for Covid-19 detection using chest X-ray image. The proposed method has two main diagnosis features, a quicker diagnosis, and a detailed diagnosis. The quicker diagnosis uses few GLCM features and a standard neural network (NN) algorithm to detect Covid-19 symptoms. It is a suitable method for rural areas where computing resources are minimal. The detailed diagnosis uses huge image pixel features and a deep convolutional neural network (CNN) algorithm to detect Covid-19 symptoms. It is a suitable method for places where computing resources are sufficient. The proposed work provides the highest classification performance, with 97.06% accuracy compared to other similar works.

A Symbolic Contour Representation of Shape for Recognition and Retrieval

Image retrieval technique is one of the most important and vital part of huge image storages. A precise retrieval engine is required to meet the needs of users. In this regard, content-based image retrieval (CBIR) methods based on image contents such as shape, color and texture have been introduced as an alternative of traditional methods to improve the retrieval precision. Of all features of an image, shape holds more valuable information than others. However, due to the dynamic nature of real worlds’ objects, a shape of an object is subject to occlusion, transformation and deformation. This is the main challenge of shape-based retrieval techniques and shape descriptors. In this paper, a new shape representation and description called Symbolic Contour-based Shape Retrieval (SCSR) method is introduced to overcome drawbacks of previous methods in terms of computation time and accuracy. The performance of the proposed method has been compared to other state-of-art methods on three popular datasets, Vehicles, Brown and Mpeg7.

Dual-path network with synergistic grouping loss and evidence driven risk stratification for whole slide cervical image analysis.

Cervical cancer has been one of the most lethal cancers threatening women's health. Nevertheless, the incidence of cervical cancer can be effectively minimized with preventive clinical management strategies, including vaccines and regular screening examinations. Screening cervical smears under microscope by cytologist is a widely used routine in regular examination, which consumes cytologists' large amount of time and labour. Computerized cytology analysis appropriately caters to such an imperative need, which alleviates cytologists' workload and reduce potential misdiagnosis rate. However, automatic analysis of cervical smear via digitalized whole slide images (WSIs) remains a challenging problem, due to the extreme huge image resolution, existence of tiny lesions, noisy dataset and intricate clinical definition of classes with fuzzy boundaries. In this paper, we design an efficient deep convolutional neural network (CNN) with dual-path (DP) encoder for lesion retrieval, which ensures the inference efficiency and the sensitivity on both tiny and large lesions. Incorporated with synergistic grouping loss (SGL), the network can be effectively trained on noisy dataset with fuzzy inter-class boundaries. Inspired by the clinical diagnostic criteria from the cytologists, a novel smear-level classifier, i.e., rule-based risk stratification (RRS), is proposed for accurate smear-level classification and risk stratification, which aligns reasonably with intricate cytological definition of the classes. Extensive experiments on the largest dataset including 19,303 WSIs from multiple medical centers validate the robustness of our method. With high sensitivity of 0.907 and specificity of 0.80 being achieved, our method manifests the potential to reduce the workload for cytologists in the routine practice.

A Survey Paper on Image Mining Techniques and Classification Brain Tumor

Image mining is a method of searching and discovering the valuable information and knowledge from a set of huge image data. Image mining essentially depends on the data mining, digital image processing, machine learning, image retrieval, and artificial intelligence. Image mining is a process which is conducted to extract the hidden information such as image data and the additional pattern that could not be observed from the image. The main problems could face the mining of the collected images can be summarized in two main points: first is the image must be suitable for the mining process and second is the image’s chosen objects and features in order to be treated to extract the most effective route to save the time, and to save the effort. This paper is a survey presents the steps of the image mining process and represented an intensive view on using the image mining to the classify the brain tumors. In addition, it’s proposed a general scheme to accomplish the processes and to analysis the latest techniques which have been used to classify the brain tumors with comparison to the training groups and the amount of accuracy that obtained from the analysis. In addition, the paper compares the relevant and most recent published literature. The high published accuracy claim to be 98% which was obtained using the Deep convolutional neural network (DCNN).

Hybrid Integration Method for Sunlight Atmospheric Scattering

In computer graphics, the efficient rendering of a clear sky may greatly enhance the realism of synthesised interactive virtual environments. However, light atmospheric scattering, lying behind a reliable sky synthesis, is a computationally demanding process. Obtaining the highest possible accuracy and quality of the synthesised image in an efficient manner is not trivial as even minor integration errors may result in huge image artefacts. In this paper we propose two new approaches, namely the first based on the splines and the second on the Taylor expansion, in order to obtain the efficient computing of the transmittance (optical depth) component of the physically based single scattering model. We used Bruneton’s framework to compare the proposed solution with state-of-the-art methods in a single, scattering-based clear sky synthesis, in terms of image quality and performance (time). We managed to improve the quality of the single scattering effect and time performance and we removed the necessity of pre-calculating values to look-up tables for the transmittance part of the scattering integral, reducing exploited memory usage.

Pre-trained CNNs Models for Content based Image Retrieval

Content based image retrieval (CBIR) systems is a ‎common recent method for image retrieval and is ‎based mainly ‎on two pillars extracted features and similarity measures. Low ‎level image presentations, ‎based on colour, texture and shape ‎properties are the most common feature extraction methods used ‎by ‎traditional CBIR systems. Since these traditional handcrafted ‎features require good prior domain ‎knowledge, inaccurate ‎features used for this type of CBIR systems may widen the ‎semantic gap and ‎could lead to very poor performance retrieval ‎results. Hence, features extraction methods, which ‎are ‎independent of domain knowledge and have automatic ‎learning capabilities from input image are ‎highly useful. Recently, ‎pre-trained deep convolution neural networks (CNN) with ‎transfer learning ‎facilities have ability to generate and extract ‎accurate and expressive features from image data. Unlike ‎other ‎types of deep CNN models which require huge amount of data ‎and massive processing time ‎for training purposes, the pre-‎trained CNN models have already trained for thousands of ‎classes of large-scale data, including huge ‎images and their ‎information could be easily used and transferred. ResNet18 ‎and ‎SqueezeNet are successful and effective examples of pre-‎trained CNN models used recently in many ‎machine learning ‎applications, such as classification, clustering and object ‎recognition. In this ‎study, we have developed CBIR systems ‎based on features extracted using ResNet18 and SqueezeNet ‎pre-‎trained CNN models. Here, we have utilized these pre-trained ‎CNN models to extract two groups of features ‎that are stored ‎separately and then later are used for online image searching and ‎retrieval. Experimental ‎results on two popular image datasets ‎Core-1K and GHIM-10K show that ResNet18 features ‎based on ‎the CBIR method have overall accuracy of 95.5% and 93.9% for ‎the two datasets, respectively, which ‎greatly outperformed the ‎traditional handcraft features based on the CBIR method.‎

Bidirectional Interaction Network for Person Re-Identification.

Person re-identification (ReID) task aims to retrieve the same person across multiple spatially disjoint camera views. Due to huge image changes caused by various factors such as posture variation and illumination transformation, images of different persons may share the more similar appearances than images of the same one. Learning discriminative representations to distinguish details of different persons is significant for person ReID. Many existing methods learn discriminative representations resorting to a human body part location branch which requires cumbersome expert human annotations or complex network designs. In this article, a novel bidirectional interaction network is proposed to explore discriminative representations for person ReID without any human body part detection. The proposed method regards multiple convolutional features as responses to various body part properties and exploits the inter-layer interaction to mine discriminative representations for person identities. Firstly, an inter-layer bilinear pooling strategy is proposed to feasibly exploit the pairwise feature relations between two convolution layers. Secondly, to explore interaction of multiple layers, an effective bidirectional integration strategy consisting of two different multi-layer interaction processes is designed to aggregate bilinear pooling interaction of multiple convolution layers. The interaction of multiple layers is implemented in a layer-by-layer nesting policy to ensure the two interaction processes are different and complementary. Extensive experiments validate the superiority of the proposed method on four popular person ReID datasets including Market-1501, DukeMTMC-ReID, CUHK03-NP and MSMT17. Specifically, the proposed method achieves a rank-1 accuracy of 95.1% and 88.2% on Market-1501 and DukeMTMC-ReID, respectively.

Unique Shape Descriptor Algorithm for Medicinal Plant Identification (SDAMPI) With Abridged Image Database

In image processing, leaf shape recognition requires a huge database of similar images. Normal leaf image database construction requires more time and space. On the other hand, mobile offline applications are not able to contain huge image database with high pixel ratio. To solve this problem, mainly in medicinal plant identification, small leaf descriptors are necessary to completely provide the required plant information. Presently, the Botanists use shape reference table to recognize the following shapes of the leaf: ovate, cordate, elliptical, oblong, lanceolate and linear. As the shape numbers are invariant under scale, rotation and translation, which is highly desirable property for object recognition and chain code techniques preserve data by allowing large data reduction. Hence, in the proposed Shape Descriptor Algorithm for Medicinal Plant Identification (SDAMPI), we developed a descriptor to resolve the pixel selection issue of Freeman chain code and generate a unique leaf shape number. This leaf shape digital descriptor will act as a reference table for medicinal plant leaf shape identification. The performance of the proposed descriptor is evaluated through Jaccard similarity index graph and Levenshtein distance (LD) graph. From the results, it is confirming that, SDAMPI descriptor can detect Medicinal plant leaf shapes more accurately than existing methods.

Deep image mining for convolution neural network

<p>Image mining is the method of searching and discovering valuable information and knowledge from a huge image dataset. Image mining is based on data mining, digital image processing, machine learning, image retrieval, and artificial intelligence. Image mining handled with the hidden information extraction, an association of image data and additional pattern which are not clearly visible in the image. Choosing the proper objects or the feature of the image to be suitable for image mining process is the main challenge would face the programmer. The process includes fine out the most efficient routes at a shorter time and saving the users effort. The main objective of this paper is to design and implement the image classification system with a higher performance, where a CIFAR-10 data set is used to train and testing classification models using CNN. A convolutional neural network is trustworthy, and it could lead to high-quality results. The high accuracy of 98% has been obtained using deep convolutional neural network (DCNN).</p>

Deep learning for scanning electron microscopy: Synthetic data for the nanoparticles detection

Deep learning algorithms are one of most rapid developing fields into the modern computation technologies. One of the bottlenecks into the implementation of such advaced algorithms is their requirement for a large amount of manually-labelled data for training. For the general-purpose tasks, such as general purpose image classification/detection the huge images datasets are already labelled and collected. For more subject specific tasks (such as electron microscopy images treatment), no labelled data available. Here I demonstrate that a deep learning network can be successfully trained for nanoparticles detection using semi-synthetic data. The real SEM images were used as a textures for rendered nanoparticles at the surface. Training of RetinaNet architecture using transfer learning can be helpful for the large-scale particle distribution analysis. Beyond such applications, the presented approach might be applicable to other tasks, such as image segmentation.

Coverless information hiding based on the generation of anime characters

To fundamentally resist the steganalysis, coverless information hiding has been proposed, and it has become a research hotspot in the field of covert communication. However, the current methods not only require a huge image database, but also have a very low hidden capacity, making it difficult to apply practically. In order to solve the above problems, we propose a coverless information hiding method based on the generation of anime characters, which first converts the secret information into an attribute label set of the anime characters, and then uses the label set as a driver to directly generate anime characters by using the generative adversarial networks (GANs). The experimental results show that compared with the current methods, the hidden capacity of the proposed method is improved by nearly 60 times, and it also has good performance in image quality and robustness.

Lensfree Diffraction Reconstruction Approach Enables Early Detection of Cancer In Vitro Based on Molecular Diagnosis.

Before there are any megascopic cancer clinical symptoms, molecular diagnosis is the main method for detecting cancer-associated gene and tumor marker. The existing detection facilities are all expensive, complicated to operate, and time-consuming, thereby making them difficult to popularize and benefit humans. In this study, we proposed a high-throughput and cost-effective approach, which enables accurate detection of extremely rare cancer cells based on molecular diagnosis of target tumor marker and a lensfree diffraction imaging platform. This approach achieves high-speed and high-quality reconstruction of huge images, which well solves the problem that precise recognition is almost impossible utilizing raw image because of significant pattern magnification and serious overlaps. Furthermore, the cells which are labeled with immune microbeads can be screened using the determined covered pixel sets, which are extracted in different focus reconstruction planes. The recognition strategy is implemented based on set intersection. With this method, the target cancer cells can be rapidly and accurately screened in a large number of benign cell samples. Besides, the detection equipment is cost-effective and easy to operate and popularize. It is expected to be widely used as a diagnostic tool for early detection of cancer.

Image Classification using Parallel CPU and GPU Computing

Image classification algorithms such as Convolutional Neural Network used for classifying huge image datasets takes a lot of time to perform convolution operations, thus increasing the computational demand of image processing. Compared to CPU, Graphics Processing Unit (GPU) is a good way to accelerate the processing of the images. Parallelizing multiple CPU cores is also another way to process the images faster. Increasing the system memory (RAM) can also decrease the computational time of image processing. Comparing the architecture of CPU and GPU, the former consists of a few cores optimized for sequential processing whereas the later has thousands of relatively simple cores clocked at approx. 1Ghz. The aim of this project is to compare the performance of parallelized CPUs and a GPU. Python’s Ray library is being used to parallelize multicore CPUs. The benchmark image classification algorithm used in this project is Convolutional Neural Network. The dataset used in this project is Plant Disease Image Dataset. Our results show that the GPU implementation achieves 80% speedup compared to the CPU implementation.

Selective Image Encryption Based on DCT, Hybrid Shift Coding and Randomly Generated Secret Key

Most of today’s techniques encrypt all of the image data, which consumes a tremendous amount of time and computational payload. This work introduces a selective image encryption technique that encrypts predetermined bulks of the original image data in order to reduce the encryption/decryption time and thecomputational complexity of processing the huge image data. This technique is applying a compression algorithm based on Discrete Cosine Transform (DCT). Two approaches are implemented based on color space conversion as a preprocessing for the compression phases YCbCr and RGB, where the resultant compressed sequence is selectively encrypted using randomly generated combined secret key.The results showed a significant reduction in image quality degradation when applying the system based on YCbCr over RGB, where the compression ratio was raised in some of the tested images to 50% for the same Peak Signal to Noise Ratio (PSNR). The usage of 1-D DCT reduced the transform time by 47:1 times comparedto the same transform using 2-D DCT. The values of the adaptive scalar quantization parameters were reduced to the half for the luminance (Y band) to preserve the visual quality, while the chrominance (Cb and Cr bands) were quantized by the predetermined quantization parameters. In the hybrid encoder horizontal zigzag,block scanning was applied to scan the image. The Detailed Coefficient (DC) coefficients are highly correlated in this arrangement- where DC are losslessly compressed by Differential Pulse Coding Modulation (DPCM) and theAccumulative Coefficients (AC) are compressed using Run Length Encoding (RLE). As a consequence, for the compression algorithm, the compression gain obtained was up to 95%. Three arrays are resulted from each band (DC coefficients, AC values, and AC runs), where the cipher is applied to some or all of those bulksselectively. This reduces the encryption decryption time significantly, where encrypting the DC coefficients provided the second best randomness and the least encryption/decryption time recorded (3 10-3 sec.) for the entire image. Although the compression algorithm consumes time but it is more efficient than the savedencryption time.