Image Acquisition Technology Research Articles

The Method of Pose Judgment of Potential Motion Damage in 3D Image Analysis.

Objective This work aimed to study the posture judgment method of 3D image analysis of potential motion damage. Methods The motion damage collection was implemented by the 3D image analysis method, and 3D image data were adopted to identify the motion damage data. Moreover, 3D image acquisition technology was adopted to analyze the model of potential motion damage and analyze the simulation judgment result of potential motion damage. Specifically, it included simulation parameters, motion damage posture collection effect, damage detection speed at the collection point, damage accuracy, and damage degree. Results (1) The analysis of the damage monitoring speed at multiple collection points of the athletes in the sports environment confirmed that the range of changes in different time periods was different, and the changes showed a fast to slow to fast trend. (2) The 3D image analysis had high accuracy in analyzing the posture of potential motion damage, which rationalized the evolution of injuries. (3) The degree of motion damage under a 3D image changed from rising to gradual, which was in line with the theoretical results (all p < 0.05). Conclusion 3D image analysis can collect a high degree of small-sample-size data, then perform specific analysis, judgment, and summary, and finally, obtain objective and reasonable data. It greatly reduced the risk of potential motion damage for athletes and also improved the efficiency of injury recognition. Moreover, it reduced the chances of blind prevention and error prevention by athletes, thereby avoiding waste of resources. The simulation test confirmed the advantages of 3D image data collection in the sports environment, and it was solved that the current athletes cannot accurately and timely judge the potential motion damage. It also met the instability needs of the movement personnel of the acquisition system in the changing sports environment and provided a reliable guarantee for the safety and health of the sport personnel.

A Low-Cost Digital Colorimetry Setup to Investigate the Relationship between Water Color and Its Chemical Composition.

Developments in digital image acquisition technologies and citizen science lead to more water color observations and broader public participation in environmental monitoring. However, the implications of the use of these simple water color indices for water quality assessment have not yet been fully evaluated. In this paper, we build a low-cost digital camera colorimetry setup to investigate quantitative relationships between water color indices and concentrations of optically active constituents (OACs). As proxies for colored dissolved organic matter (CDOM) and phytoplankton, humic acid and algae pigments were used to investigate the relationship between water chromaticity and concentration. We found that the concentration fits an ascending relationship with xy chromaticity values and a descending relationship with hue angle. Our investigations permitted us to increase the information content of simple water color observations, by relating them to chemical constituent concentrations in observed waters.

Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging

Medical imaging is a central part of clinical diagnosis and treatment guidance. Machine learning has increasingly gained relevance because it captures features of disease and treatment response that are relevant for therapeutic decision-making. In clinical practice, the continuous progress of image acquisition technology or diagnostic procedures, the diversity of scanners, and evolving imaging protocols hamper the utility of machine learning, as prediction accuracy on new data deteriorates, or models become outdated due to these domain shifts. We propose a continual learning approach to deal with such domain shifts occurring at unknown time points. We adapt models to emerging variations in a continuous data stream while counteracting catastrophic forgetting. A dynamic memory enables rehearsal on a subset of diverse training data to mitigate forgetting while enabling models to expand to new domains. The technique balances memory by detecting pseudo-domains, representing different style clusters within the data stream. Evaluation of two different tasks, cardiac segmentation in magnetic resonance imaging and lung nodule detection in computed tomography, demonstrate a consistent advantage of the method.

Monitoring and management of high-end tourism in protected areas based on 3D sensor image collection

The expansion of tourism demand and the rapid growth of tourism industry objectively promote the tourism development and utilization of nature reserves. At present, there are various difficulties in the practical application of tourism environmental capacity assessment technology in nature reserves. This article attempts to carry out tourism monitoring and management in protected areas by combining 3D sensor image acquisition and environmental capacity assessment technology. First of all, this paper proposes a dynamic monitoring plan for the ecological environment of the reserve using 3D sensor image acquisition technology, and realizes the monitoring of tourists in the reserve and the collection of natural environment data. Secondly, this article proposes a new natural reserve tourism environment capacity assessment and pricing plan, and takes the Changtang National Park high-end tourism project as an example, and discusses its application in the form of a case study. This program provides a new solution for balancing the economic and environmental benefits of the reserve. Through simulation tests and questionnaire surveys, we found that the program can effectively improve the nature protection and tourism inspection work in the reserve.

Compressive spectral image reconstruction using deep prior and low-rank tensor representation.

Compressive spectral imaging (CSI) has emerged as an alternative spectral image acquisition technology, which reduces the number of measurements at the cost of requiring a recovery process. In general, the reconstruction methods are based on handcrafted priors used as regularizers in optimization algorithms or recent deep neural networks employed as an image generator to learn a non-linear mapping from the low-dimensional compressed measurements to the image space. However, these deep learning methods need many spectral images to obtain good performance. In this work, a deep recovery framework for CSI without training data is presented. The proposed method is based on the fact that the structure of some deep neural networks and an appropriated low-dimensional structure are sufficient to impose a structure of the underlying spectral image from CSI. We analyzed the low-dimensional structure via the Tucker representation, modeled in the first net layer. The proposed scheme is obtained by minimizing the ${\ell _2}$-norm distance between the compressive measurements and the predicted measurements, and the desired recovered spectral image is formed just before the forward operator. Simulated and experimental results verify the effectiveness of the proposed method for the coded aperture snapshot spectral imaging.

A Defect Detection Method Based on Faster RCNN for Power Equipment

With the wide application of infrared image acquisition technology in power inspection, a large number of infrared images of power equipment have been obtained. The traditional machine learning method has low accuracy and poor generalization. Therefore, in this paper, the deep learning technology is applied to infrared image detection of power equipment, and a defect detection method based on Faster region convolution neural network (RCNN) is proposed. In this method, the deep residual network is used to extract image features, and the regional proposal network is optimized according to the shape characteristics of power equipment, and the network is trained with the help of shared convolution layer. The experimental results show that the proposed method has high detection accuracy, good robustness and generalization ability.

Fundamentals of automated human gesture recognition using 3D integral imaging: a tutorial

Automated human gesture recognition is receiving significant research interest, with applications ranging from novel acquisition techniques to algorithms, data processing, and classification methodologies. This tutorial presents an overview of the fundamental components and basics of the current 3D optical image acquisition technologies for gesture recognition, including the most promising algorithms. Experimental results illustrate some examples of 3D integral imaging, which are compared to conventional 2D optical imaging. Examples of classifying human gestures under normal and degraded conditions, such as low illumination and the presence of partial occlusions, are provided. This tutorial is aimed at an audience who may or may not be familiar with gesture recognition approaches, current 3D optical image acquisition techniques, and classification algorithms and methodologies applied to human gesture recognition.

Simultaneous Determination of Human Erythrocyte Deformability and Adhesion Energy: A Novel Approach Using a Microfluidic Chamber and the "Glass Effect".

The simultaneous determination of adhesion and deformability parameters of erythrocytes was carried out through a microfluidic device, which uses an inverted optical microscope with new image acquisition and analysis technologies. Also, an update of the models describing erythrocyte adhesion and deformation was proposed. Measurements were carried out with red blood cells suspended in saline solution with human serum albumin at different concentrations. Erythrocytes adhered to a glass surface were subjected to different low shear stress (from 0.04 to 0.25 Pa), causing cellular deformation and dissociation. The maximum value obtained of the erythrocyte deformability index was 0.3, and that of the adhesion energy per unit area was 1.1 × 10-6 Pa m, both according to previous works. The obtained images of RBCs adhered to glass reveal that the adhesion is stronger in a single point of the cell, suggesting a ligand migration that concentrates the adhesion in a "spike-like tip" in the cell. Moreover, adhesion energy results indicate that the energy required to separate erythrocytes in media with a lower albumin concentration is greater. Both results could be explained by the mobility of membrane receptors.

Multimodal System to Detect Driver Fatigue Using EEG, Gyroscope, and Image Processing

Sleepiness detection system that evaluates driver’s sleepiness level has always been the primary interest of researchers. There are a number of systems like electroencephalography-based sleepiness detection system (ESDS), vehicle based sleepiness estimator system, image acquisition technology and bio-mathematical models to detect drowsiness of drivers. However there has been less research on hybrid of these systems that detect sleepiness of drivers. In order to overcome the above limitation we propose a neural network based hybrid multimodal system that detects driver fatigue using electroencephalography(EEG) data, gyroscope data and image processing data. It was found that the proposed hybrid system performed well with a detection accuracy of 93.91% in identifying the drowsiness state of the driver.

Coffee Flower Identification Using Binarization Algorithm Based on Convolutional Neural Network for Digital Images.

Crop-type identification is one of the most significant applications of agricultural remote sensing, and it is important for yield estimation prediction and field management. At present, crop identification using datasets from unmanned aerial vehicle (UAV) and satellite platforms have achieved state-of-the-art performances. However, accurate monitoring of small plants, such as the coffee flower, cannot be achieved using datasets from these platforms. With the development of time-lapse image acquisition technology based on ground-based remote sensing, a large number of small-scale plantation datasets with high spatial-temporal resolution are being generated, which can provide great opportunities for small target monitoring of a specific region. The main contribution of this paper is to combine the binarization algorithm based on OTSU and the convolutional neural network (CNN) model to improve coffee flower identification accuracy using the time-lapse images (i.e., digital images). A certain number of positive and negative samples are selected from the original digital images for the network model training. Then, the pretrained network model is initialized using the VGGNet and trained using the constructed training datasets. Based on the well-trained CNN model, the coffee flower is initially extracted, and its boundary information can be further optimized by using the extracted coffee flower result of the binarization algorithm. Based on the digital images with different depression angles and illumination conditions, the performance of the proposed method is investigated by comparison of the performances of support vector machine (SVM) and CNN model. Hence, the experimental results show that the proposed method has the ability to improve coffee flower classification accuracy. The results of the image with a 52.5° angle of depression under soft lighting conditions are the highest, and the corresponding Dice (F1) and intersection over union (IoU) have reached 0.80 and 0.67, respectively.

Accuracy of Fuzzy Logic-based Contamination Grading System on Abaca Tissue Culture

This study aimed to support the coalition for abaca rehabilitation program of the different cooperating agencies of Southern Leyte with the use of intelligent systems in the grading of abaca tissue culture contamination. As the Southern Leyte State University Tissue Culture Laboratory has since used manual grading of contaminated specimen, the use of image acquisition technology can lessen the human effort involved in this activity. Fuzzy logic was used in the grading of the contamination. There were five inputs for the inference engine, namely red, green, blue, whitish and brownish. There were 62 rules identified covering the different combinations, and the triangular-shaped membership function was used for all the membership types of the inputs. Based on the result of the testing, it had an accuracy rate of 82.00% for the binary contamination in 50 sample specimens, and an over-all accuracy of 80.33% in multiclass contamination grading. The system could not match the precision of the human expert, but the model is comparable to that of the expected actual result, while minimizing human intervention in grading the contamination of tissue cultured abaca.

Video Image Acquisition Technology of Underwater Vehicle Based on 3D Sensor

ABSTRACT Guo, G., 2019. Video image acquisition technology of underwater vehicle based on 3D sensor. In: Hoang, A.T. and Aqeel Ashraf, M. (eds.), Research, Monitoring, and Engineering of Coastal, P...

Remotely sensed big data: evolution in model development for information extraction [point of view

Since the 1960s, remote sensing (as an innovative, comprehensive, and interdisciplinary academic area) has been adopted in a wide range of disciplines related to Earth observation, including hydrology, ecology, oceanography, glaciology, geology, military, intelligence, business, economy, and planning [1] – [3] . The constant development of the remote sensing image acquisition technology now allows for the collection of a wide variety of images with different characteristics and resolutions, obtained by remote sensing instruments mounted on spacecraft or aircraft platforms. These images record some type of signal or energy measured from the Earth’s surface, which depends on the type of sensor used.

Pavement crack image acquisition methods and crack extraction algorithms: A review

Abstract The extraction of pavement cracks is always a hard task in image processing. In airport and road construction, cracking is the main factor for pavement damage, which can decrease the quality of pavement and affect transportation seriously. Cracks also exist in other artificial or natural objects, such as buildings, bridges, tunnels, etc. Among all the object images, pavement crack images are the most complex, so the image processing and analysis for them is harder than other crack images. From the early image acquisition based on photography technology to the current 3D laser scanning technology, the pavement crack image acquisition technology is becoming more convenient and efficient, but there are still challenges in the automatic processing and recognition of cracks in images. From the early global thresholding to deep learning algorithms, the research for crack extraction has been developed for about 40 years. There are many methods and algorithms that are satisfactory in pavement crack applications, but there is no standard until today. Therefore, in order to know the developing history and the advanced research, we have collected a number of literature in this research topic for summarizing the research artwork status, and giving a review of the pavement crack image acquisition methods and 2D crack extraction algorithms. Also, for image acquisition methods and pavement crack image segmentation, more detailed comparison and discussions are made.

Aggregated Deep Fisher Feature for VHR Remote Sensing Scene Classification

With the development of very high resolution satellite image acquisition technology, remote sensing scene classification has become an important and challenging task. In this article, aiming at tackling this task, we propose a hybrid architecture, i.e., aggregated deep Fisher feature (ADFF), which can make full use of deep convolutional features’ rich semantic information and unsupervised encoding's high robustness. Unlike the previous methods, we first explore the optimal encoding layer in the pretraining CNN model, which naturally fuses the local and global image information in a novel way, making the ability of semantic acquisition further enhanced. ADFF can learn more suitable internal features from the remote sensing data, boosting the final performance. We evaluate our algorithm based on several public datasets, and the results show that our approach achieves superior performance compared with the state-of-the-art methods. The proposed ADFF obtains average classification accuracy of 98.81%, 95.21%, 86.01%, and 88.79%, respectively, on the UC Merced Land-Use, RSSCN7, NWPU-RESISC45 (10% for training), and NWPU-RESISC45 (20% for training) datasets.

A large field of view visible cameras image acquisition and processing technology in EAST device

A large field of view visible image acquisition and processing system was designed to address the discharge visual needs for estimating the state of plasma operation and the first wall conditions during the plasma discharge of the Experimental Advanced Superconducting Tokamak (EAST) device. The entire system, which consisted of software and hardware, was introduced in detail. Moreover, three sets of the visible camera diagnostic systems with the different field-of-view images acquired by three industrial personal computers (IPCs) were used for forming the large field of view through image stitching. Three IPCs, and data storage and image processing servers are connected by a reflective memory network, which can realize the collection and fast transmission of image data during discharge. In addition, an image stitching algorithm based on block matching scale-invariant feature transformation was adopted. To accelerate the processing of the algorithm, parallelization was achieved by using the Graphics Processing Unit (GPU). Through the mesa test and experimental results, the proposed system provides a feasible way of transmitting and stitching images quickly and fulfills the EAST experimental demands.

Image-based biomarkers for solid tumor quantification.

The last few decades have witnessed tremendous technological developments in image-based biomarkers for tumor quantification and characterization. Initially limited to manual one- and two-dimensional size measurements, image biomarkers have evolved to harness developments not only in image acquisition technology but also in image processing and analysis algorithms. At the same time, clinical validation remains a major challenge for the vast majority of these novel techniques, and there is still a major gap between the latest technological developments and image biomarkers used in everyday clinical practice. Currently, the imaging biomarker field is attracting increasing attention not only because of the tremendous interest in cutting-edge therapeutic developments and personalized medicine but also because of the recent progress in the application of artificial intelligence (AI) algorithms to large-scale datasets. Thus, the goal of the present article is to review the current state of the art for image biomarkers and their use for characterization and predictive quantification of solid tumors. Beginning with an overview of validated imaging biomarkers in current clinical practice, we proceed to a review of AI-based methods for tumor characterization, such as radiomics-based approaches and deep learning.Key Points• Recent years have seen tremendous technological developments in image-based biomarkers for tumor quantification and characterization.• Image-based biomarkers can be used on an ongoing basis, in a non-invasive (or mildly invasive) way, to monitor the development and progression of the disease or its response to therapy.• We review the current state of the art for image biomarkers, as well as the recent developments in artificial intelligence (AI) algorithms for image processing and analysis.

Design of Real Time Video Image Acquisition and Processing System Based on FPGA

Based on characteristics of instantaneously, large capacity and miniaturization of digital image acquisition and processing technology, a real-time video image acquisition and processing circuit system was designed, FPGA was used as the center of controlling the whole system and the center of processing image data. With DDR2 SDRAM the core component of high speed storage module, and CMOS 7670 the video image acquisition device. Through the Quratus II and Modelsim software for edge detection algorithm mand the control process, each module of the hardware design and simulation, the realization of the video images from the collection and storage to the treating and display, which shows smooth, clear and real-time.

Use of X-ray microtomography for analysis of whole plant regeneration in Passiflora organensis

P. organensis is a native species in Brazil and the first species of the genus Passiflora to have its genome completely sequenced. The present work intends to elaborate an efficient protocol for in vitro regeneration of whole plants of P. organensis through tissue culture and the implementation of an innovative technology of 3D image acquisition - the computerized X-ray microtomography (micro-CT).

Design and Realize a Snake-Like Robot in Complex Environment

Aiming at high performance requirements of snake-like robots under complex environment, we present a control system of our proposed design which utilizes a STM32 as the core processor and incorporates real-time image acquisition, multisensor fusion, and wireless communication technology. We use Solidworks to optimize the design of head, body, and tail joint structure of the snake-like robot. The system is a real-time system with a simple-circuit structure and multidegrees of freedom are attributed to the flawless design of control system and mechanical structure. We propose a control method based on our simplified CPG model. Meanwhile, we improve Serpenoid control function and then investigate how different parameters affect the motion gait in terms of ADAMS emulation. Finally, experimental results show that the snake-like robot can tackle challenging problems including multi-information acquisition and processing, multigait stability, and autonomous motion and further verify the reliability and accuracy of the system in our combinatory experiments.