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• A novel water transparency calculation algorithm is introduced, employing deep neural network and image processing technology. This algorithm obviates the need for a water gauge, allowing the calculation of water transparency solely through the analysisof Secchi disk videos. Notably, it demonstrates heightened detection efficiency and success rates. • An enhanced Siamese tracker, termed SiamDCFF (Siamese tracker based on dual correlation feature fusion), is proposed for Secchi disk recognition. Distinguishing features of SiamDCFF include a dual correlation module and a feature fusion module. On the Secchi disk dataset, SiamDCFF achieves the highest success rate, outperforming other methods detailed in this paper. • The simplified 3D-ResNet [41] is leveraged to assess the state of the Secchi disk, transforming the critical position determination problem into an optimization challenge. The objective function involves maximizing the number of visible and invisible Secchi disk images on both sides of the critical position. This method facilitates adaptive critical position determination. • A novel Gated Recurrent Unit based on auto-regression (ARGRU) is proposed, taking into account the motion characteristics of the Secchi disk. ARGRU achieves a higher success detection rate for water transparency compared to the water gauge recognition method. Notably, ARGRU outperforms RNN [42], GRU, and LSTM [43] on the dataset presented in this paper. Water transparency is traditionally assessed through visual inspection using a lowered Secchi disk (SD) into the water, with the disappearance depth of the SD recorded as the measure of transparency. However, manual and visually-dependent frequency measurements of the SD render this process labor-intensive and time-consuming. This paper presents a comprehensive machine-vision-based algorithm designed for the automatic computation of water transparency using Secchi disk videos. The algorithm leverages multiple deep neural networks, including an enhanced Siamese tracking model, a 3D-ResNet, and an improved GRU network. Trained models directly identify the SD in the video, calculating water transparency by processing pixel information. Experimental results demonstrate the algorithm's efficacy in estimating water transparency across diverse natural environments, achieving commendable accuracy (MAE = 3.6 cm MSE = 21.5 cm RMSE = 4.6 cm), rapid processing speed (average 6.87 s), and robust stability. In comparison with the former water-gauge-based algorithm, our proposed algorithm exhibits heightened efficiency and a superior detection success rate. [ABSTRACT FROM AUTHOR]

• Performance analysis of different video-based eye blinking detection. • Development of a low-cost imaging acquisition system. • Quality metrics analysis of proposed algorithms. • Performance comparison between RGB and Grayscale images. • The red channel of the RGB camera achieves in our experiments a 100 % eye blink detection rate for event detection. The eye is considered a rich source for gathering information on our daily lives. In addition, the necessity to classify the eye as open or closed and detect eye blinks is increasing in various fields. For instance, it has been proven that eye blinks can be related to different ophthalmological conditions. Nevertheless, accurate, and robust blink estimation poses significant challenges to the efficacy of real-time applications due to the variability of eye images and light conditions. In this paper eye blinking has been monitored using a chin rest and an iOS smartphone attached to a cartesian machinery. Five video-based eye blink algorithms to classify frames have been analyzed and compared in terms of performance. To evaluate the results, different widely used statistical metrics have been employed. The results show that the proposed techniques successfully detect eye blinks, whereas the frame classifier shows different scores depending on the algorithm used. [ABSTRACT FROM AUTHOR]

• This paper presents a future prediction technique for using in S.R. measurement. • The suggested method reduces the required data amount used in future prediction. • To obtain a quicker response, the time series analysis is combined with the ICA and MVE algorithms. • The maximum error of suggested method is equal or less than 8% for Ra ≥ 0.4 μm. The momentary control of manufacturing processes is one of the ways to increase the productivity of production lines. The online measurement of the surface roughness by non-contact methods can be utilized in order to predict the future of surface texture and modify the machining parameters. In the technique proposed at this paper, the surface texture is extracted by combining the 2D surface photography and wavelet approach. Then, by extracting the time delay parameters, the embedding dimension and the false nearest neighbor of the produced surface texture, the future surface roughness is predicted. The results show that this technique can be used in lapping, grinding, turning, and milling processes. Although the maximum roughness error occurred in the surface roughness prediction is 24%, the prediction error is almost constant after Ra = 0.4 μm in different machining processes (about 7%). This study is in line with the development of the proposed method by Pour (2018). [ABSTRACT FROM AUTHOR]

• The image Hash authentication algorithm for orthogonal moments of fractional order chaotic is proposed. • The image information is authenticated by the Hamming distance. • The robustness and tampering recognition accuracy of the proposed method is tested. In this paper, the image Hash authentication algorithm for orthogonal moments of fractional order chaotic scrambling coupling hyper-complex number Tchebichef is proposed. The improved PDE function and Ring segmentation mechanism are used to process the initial image and output the secondary image to improve the robustness of Hash's scaling and rotation; and by taking full account into the color information of the image, the image R, G and B position is regarded as its imaginary part. In addition, the invariant moment of the hyper-complex number Tchebichef is desgined, and the robust characteristics of the secondary image is fully extracted to greatly improve its ability to recognize the rotation tampering. At the same time, in order to improve the security of the Hash sequence, the fractional order Logistic mapping and chaotic scrambling technique is constructed to encrypt the eigenvector; and the image information is authenticated by the Hamming distance. Finally, the robustness and tampering recognition accuracy of the proposed Hash algorithm are tested. [ABSTRACT FROM AUTHOR]

• This paper proposes multiple sets of laser lines to assist belt defect detection. • The solution proposed in this paper can precisely detect the torn area of the belt. • This method supports users to customize the alarm level of tear detection. • This method is more accurate than the comparison method mentioned in the article. Conveyor is mainly used for ore collection and transportation, and it plays an important role in mining operations. Due to the harsh mining environment, the conveyor belt often has longitudinal tear. If the tears can not be found in time, a safety accident will occur and cause significant economic losses. In order to accurately and efficiently detect the longitudinal tearing of the conveyor belt, this paper proposes a computer vision detection algorithm based on multiple sets of lasers. This algorithm can accurately segment the laser stripe area through image processing and analysis, and accurately locate the torn area of the conveyor belt. And this method supports users to customize the alarm level of tear detection, which meets different industrial production scenarios. The experimental results show that the method proposed in this paper can accurately and quickly detect the longitudinal tearing defect of the conveyor belt. [ABSTRACT FROM AUTHOR]

With advantages of portability, low-cost and noninvasive detection, electrical impedance tomography (EIT) has great potential application value as a bedside monitor. Because image reconstruction in EIT involves addressing serious ill-posed inverse problems, realizing EIT online dynamic display still remains a challenging work. In this paper, an EIT imaging system with the function of online dynamic display was designed. Split augmented Lagrangian shrinkage algorithm (SALSA), which transforms an unconstrained optimization EIT inverse problem into an equivalent constrained optimization problem, was adopted in the proposed system. On the basis of EIT imaging simulation, a high-precise Compact PCI-based EIT system was implemented with FPGA. And experiments of EIT cylinder imaging and in-vivo monitoring were conducted to verify the feasibility of the system. The results indicated that the online dynamic display frame rate of the system, which was centered with a computer (Intel i7 2.2 GHz, 4 GB RAM), was up to 32 fps. [ABSTRACT FROM AUTHOR]

In this paper, a modified nonlocal means denoising (NL-means) algorithm is proposed for rolling element bearing fault diagnosis. Although, nonlocal means denoising is widely used in image processing, this algorithm is rarely used in 1-D signal processing. The present work deals with application of 1-D nonlocal means denoising method for enhancement of fault signature in rolling element bearings. The parameters for the NL-means method are obtained by maximizing kurtosis value of bearing vibration signal. The proposed method is compared with minimum entropy deconvolution (MED) technique and the results indicate that the proposed method performs better for bearing fault diagnosis. The method is shown to be robust against various noise levels. Further, envelope spectrum of bearing vibration signal is also used to obtain characteristic bearing defect frequencies. [ABSTRACT FROM AUTHOR]

Consider a set of images of a single object, or scenery, taken from different viewpoints and time. Panorama image creation is the process of stitching such images into a single coordinate system to generate a wider viewing panoramic image. Image stitching consists of two processes which are image registration and image blending. In image registration, parts of two overlapping or consecutive images are considered to find an appropriate merging position and transformation to combine the images. In image blending, the intensities of pixels along the stitching line are modified so that they flow naturally without any noticeable break. In this paper, we propose a novel method that utilizes the Dynamic Time Warping (DTW) algorithm to match pairs of images for image stitching. We also perform a dimension reduction scheme that significantly reduces the computational complexity of the standard DTW without affecting its performance. The effectiveness of the proposed method is demonstrated in stitching 50 pairs of medical X-ray images and its performance is compared to those of normalized cross correlation (NCC), Minimum Average Correlation Energy (MACE) filters, sum-of-square-differences (SSD) and sum-of-absolute-differences (SAD). For the database used, the dimensionally reduced DTW outperforms the NCC, MACE, SSD and SAD methods in accuracy and average execution time. The method also outperforms two widely used stitching programs available on the internet called Hugin and Autostitch. [ABSTRACT FROM AUTHOR]

Acoustic microscopy has found various applications from nondestructive testing (NDT) to medical imaging for quantitative information. Scanning acoustic microscope (SAM) used for imaging and sample preparation have significant bearing on the conclusions drawn. In most cases while scanning the sample, there appear lots of artifacts due to the presence of an inclination offset between the focal plane and the sample surface, which often leads to wrong conclusions. In this paper, we have demonstrated a novel algorithm which can correct this offset in post-processing, which contributes to more accurate quantitative information and qualitatively better images of the sample. The correction is performed for both the amplitudes as well as the time-of-flight. The proposed algorithm was tested in simulation as well as through experimentation for sensitivity and accuracy. The results are presented and they indicate significant correction of inclination with small error margins. The application of these algorithms can be found in real-time image processing a lot of industries as well as in research. • Correcting artifacts produced due to specimen's inclination. • Simulation has been performed for algorithm robustness. • Reliability of the algorithm verified on unknown biological sample. [ABSTRACT FROM AUTHOR]

• We propose a novel nano-scale absolute linear displacement measurement based on image recognition method. • We use a linear array image sensor to recognize the coding pattern on the grating and then realize the decoding. • We propose a high-resolution subdivision algorithm based on the location de-noising and fusion operation. • We set up an experimental system and test the performance of the proposed algorithm. There is urgent demand for new absolute Linear Displacement Measurement (LDM) technology with high resolution and high precision, during the development of high-end numerical control technology. In previous research, we found that displacement measurement based on the image processing method performs well, however, the volume increases when using an imaging lens, and the system is susceptible to factors such as virtual focus. In this paper, the lensless LDM image optical path is established based on grating projection imaging, then the absolute grating coding method is operated based on M −sequence pseudo-random coding. A linear displacement subdivision algorithm is then deployed based on the digital image recognition algorithm. An LDM device with a measuring range of 250 mm was designed to test the performance of the proposed method. The device shows a measurement resolution of 1 nm and measurement accuracy of 1.76 μ m in the range of 250 mm. This work lays a foundation for further research on high performance LDM technology. [ABSTRACT FROM AUTHOR]