Intelligent Error Correction of College English Spoken Grammar Based on the GA-MLP-NN Algorithm

With the development of neural networks in deep learning, artificial intelligence machine learning has become the main focus of researchers. In College English grammar detection, oral grammar is the most error rate content. So, this paper optimizes MLP based on GA in the deep learning neural network and then studies the intelligent image correction of College English spoken grammar. The main direction is to discuss and analyze GA-MLP-NN algorithm technology first and then predict the error correction model of spoken language grammar by combining the optimized algorithm. The results show that GA-MLP-NN provides excellent accuracy for the prediction of the whole syntax error correction model. Then, the paper studies the deep learning technology to build an intelligent image error correction model of College English spoken grammar. The results show that the effect of intelligent correction of spoken grammar is very fast and accurate.

STUDY OF THE TARGETS OF SCARY IMAGE CORRECTION IN PEOPLE WITH HIGH PERSONAL ANXIETY

Objectified fears and the corresponding frightening images are the most striking signs of the anxiety-phobic disorder formation. The high manifestation comorbidity of fear and anxiety necessitates a comprehensive and versatile corrective action, and the variety of existing psychotherapeutic approaches provides a wide range of intervention tools. This article presents a method of forming an eclectically oriented psychocorrection program based on the theoretical provisions of cognitive-behavioral, emotional-figurative and analytical approaches, as well as a method for assessing its effectiveness. In order to establish specific corrective targets of influence, the personal characteristics of the subjects were considered as predictors of the effectiveness of the developed program. The scheme of psychocorrectional influence developed within the framework of the study included the stages of actualization of the frightening image, desensitization and cognitive transformation. To actualize the image in the minds of the subjects, the techniques of imaginative (in the imagination) and virtual (using virtual reality equipment) exposure were used. The use of psychocorrectional influence on a sample of 20 volunteers who declared that they had objectified fear and demonstrated a high level of personal anxiety (according to the STAI scale) showed a statistically significant effectiveness. The following traits were established as predictors of changes in the state of the subjects according to the results of correction at different levels: high courage and low pedantry (for personal anxiety), practicality (for the level of expression of the worked out fear), exaltation and emotiveness (for well-being) and daydreaming (for activity and mood). Based on the data obtained, linear regression equations were drawn up, illustrating the comparative contribution of the listed personality traits to the effectiveness of the impact. Based on the results of the study, an eclectic model of corrective action on frightening images in people with high personal anxiety has been developed and their personality traits have been established, which are predictors of the effectiveness of exposure. The data will be useful both in practical work with manifestations of objectified fears, and in organizing further research in the field of psychotherapy.

Processing Algorithm of Irregular Table Image in Tobacco Package Based on Dual-coding Difference of Gaussians Method

Objectives: Currently, in a large number of print-out report documents from tobacco package, there exist irregular phenomena such as discontinuous vertical lines, misplaced frame lines and multi-page tables. Thus, the existing table recognition algorithm cannot be adopted to perform digital identification. In order to solve this problem, this paper proposes a table image processing algorithm based on the dual-coding difference of Gaussians iterative clustering. Firstly, the method of local regional sub-block is used to the skew correction threshold to conduct image correction. Secondly, the corrected images are coded by rows and columns, and 2D image features are transformed into 1D image features. Thirdly, the Gaussian differenced operation is adopted to obtain effective characteristic matrices that are stable and easily distinguishable. Then the iterative clustering analysis is performed to obtain the feature values of effective frame lines. Fourthly, after finishing the tasks, such as the table positioning, inner structure reconstruction, and text information identification, the dichotomy judgmentsof the integrity of multi-page tablesare realized according to the local pixel features. Finally, the text information inside the local regions and the reconstructed regions are merged, and the digital reproduction of the multi-page tables is realized. To validate the effectiveness of the proposed algorithm, an experiment in the sample set containing 12,840 table images with different resolutionsis carried out. The average detection accuracies of table positioning, table cell reconstructionand multi-page incompleteness are 98.95%, 99.80%, and 95.85%, respectively. The experimental results show that the proposed algorithm is simple and effective, and can accomplish the digital reproduction of irregular tables.

Impacts of acquisition and reconstruction parameters on the absolute technetium quantification of the cadmium–zinc–telluride-based SPECT/CT system: a phantom study

Background The digital cadmium–zinc–telluride (CZT)-based SPECT system has many advantages, including better spatial and energy resolution. However, the impacts of different acquisition and reconstruction parameters on CZT SPECT quantification might still need to be validated. This study aimed to evaluate the impacts of acquisition parameters (the main energy window and acquisition time per frame) and reconstruction parameters (the number of iterations, subsets in iterative reconstruction, post-filter, and image correction methods) on the technetium quantification of CZT SPECT/CT. Methods A phantom (PET NEMA/IEC image quality, USA) was filled with four target-to-background (T/B) ratios (32:1, 16:1, 8:1, and 4:1) of technetium. Mean uptake values (the calculated mean concentrations for spheres) were measured to evaluate the recovery coefficient (RC) changes under different acquisition and reconstruction parameters. The corresponding standard deviations of mean uptake values were also measured to evaluate the quantification error. Image quality was evaluated using the National Electrical Manufacturers Association (NEMA) NU 2–2012 standard. Results For all T/B ratios, significant correlations were found between iterations and RCs (r = 0.62–0.96 for 1–35 iterations, r = 0.94–0.99 for 35–90 iterations) as well as between the full width at half maximum (FWHM) of the Gaussian filter and RCs (r = − 0.86 to − 1.00, all P values < 0.05). The regression coefficients of 1–35 iterations were higher than those of 35–90 iterations (0.51–1.60 vs. 0.02–0.19). RCs calculated with AC (attenuation correction) + SC (scatter correction) + RR (resolution recovery correction) combination were more accurate (53.82–106.70%) than those calculated with other combinations (all P values < 0.05). No significant statistical differences (all P values > 0.05) were found between the 15% and 20% energy windows except for the 32:1 T/B ratio (P value = 0.023) or between the 10 s/frame and 120 s/frame acquisition times except for the 4:1 T/B ratio (P value = 0.015) in terms of RCs. Conclusions CZT-SPECT/CT of technetium resulted in good quantification accuracy. The favourable acquisition parameters might be a 15% energy window and 40 s/frame of acquisition time. The favourable reconstruction parameters might be 35 iterations, 20 subsets, the AC + SC + RR correction combination, and no filter.

Guided Facial Skin Color Correction

This paper proposes an automatic image correction method for portrait photographs, which promotes consistency of facial skin color by suppressing skin color changes due to background colors. In portrait photographs, skin color is often distorted due to the lighting environment (e.g., light reflected from a colored background wall and over-exposure by a camera strobe). This color distortion is emphasized when artificially synthesized with another background color, and the appearance becomes unnatural. In our framework, we, first, roughly extract the face region and rectify the skin color distribution in a color space. Then, we perform color and brightness correction around the face in the original image to achieve a proper color balance of the facial image, which is not affected by luminance and background colors. Our color correction process attains natural results by using a guide image, unlike conventional algorithms. In particular, our guided image filtering for the color correction does not require a perfectly-aligned guide image required in the original guide image filtering method proposed by He et al. Experimental results show that our method generates more natural results than conventional methods on not only headshot photographs but also natural scene photographs. We also show automatic yearbook style photo generation as another application.

Study on Image Correction and Optimization of Mounting Positions of Dual Cameras for Vehicle Test

Among surrounding information-gathering devices, cameras are the most accessible and widely used in autonomous vehicles. In particular, stereo cameras are employed in academic as well as practical applications. In this study, commonly used webcams are mounted on a vehicle in a dual-camera configuration and used to perform lane detection based on image correction. The height, baseline, and angle were considered as variables for optimizing the mounting positions of the cameras. Then, a theoretical equation was proposed for the measurement of the distance to the object, and it was validated via vehicle tests. The optimal height, baseline, and angle of the mounting position of the dual camera configuration were identified to be 40 cm, 30 cm, and 12°, respectively. These values were utilized to compare the performances of vehicles in stationary and driving states on straight and curved roads, as obtained by vehicle tests and theoretical calculations. The comparison revealed the maximum error rates in the stationary and driving states on a straight road to be 3.54% and 5.35%, respectively, and those on a curved road to be 9.13% and 9.40%, respectively. It was determined that the proposed method is reliable because the error rates were less than 10%.

A High-Precision Automatic Pointer Meter Reading System in Low-Light Environment

At present, pointer meters are still widely used because of their mechanical stability and electromagnetic immunity, and it is the main trend to use a computer vision-based automatic reading system to replace inefficient manual inspection. Many correction and recognition algorithms have been proposed for the problems of skew, distortion, and uneven illumination in the field-collected meter images. However, the current algorithms generally suffer from poor robustness, enormous training cost, inadequate compensation correction, and poor reading accuracy. This paper first designs a meter image skew-correction algorithm based on binary mask and improved Mask-RCNN for different types of pointer meters, which achieves high accuracy ellipse fitting and reduces the training cost by transfer learning. Furthermore, the low-light enhancement fusion algorithm based on improved Retinex and Fast Adaptive Bilateral Filtering (RBF) is proposed. Finally, the improved ResNet101 is proposed to extract needle features and perform directional regression to achieve fast and high-accuracy readings. The experimental results show that the proposed system in this paper has higher efficiency and better robustness in the image correction process in a complex environment and higher accuracy in the meter reading process.