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High throughput field phenotyping techniques employing multispectral cameras allow extracting a variety of variables and features to predict yield and yield related traits, but little is known about which types of multispectral features are optimal to forecast yield potential in the early growth phase. In this study, we aim to identify multispectral features that are able to accurately predict yield and aid in variety classification at different growth stages throughout the season. Furthermore, we hypothesize that texture features (TFs) are more suitable for variety classification than for yield prediction. Throughout 2021 and 2022, a trial involving 19 and 18 European wheat varieties, respectively, was conducted. Multispectral images, encompassing visible, Red-edge, and near-infrared (NIR) bands, were captured at 19 and 22 time points from tillering to harvest using an unmanned aerial vehicle (UAV) in the first and second year of trial. Subsequently, orthomosaic images were generated, and various features were extracted, including single-band reflectances, vegetation indices (VI), and TFs derived from a gray level correlation matrix (GLCM). The performance of these features in predicting yield and classifying varieties at different growth stages was assessed using random forest models. Measurements during the flowering stage demonstrated superior performance for most features. Specifically, Red reflectance achieved a root mean square error (RMSE) of 52.4 g m -2 in the first year and 64.4 g m -2 in the second year. The NDRE VI yielded the most accurate predictions with an RMSE of 49.1 g m -2 and 60.6 g m -2 , respectively. Moreover, TFs such as CONTRAST and DISSIMILARITY displayed the best performance in predicting yield, with RMSE values of 55.5 g m -2 and 66.3 g m -2 across the two years of trial. Combining data from different dates enhanced yield prediction and stabilized predictions across dates. TFs exhibited high accuracy in classifying low and high-yielding varieties. The CORRELATION feature achieved an accuracy of 88% in the first year, while the HOMOGENEITY feature reached 92% accuracy in the second year. This study confirms the hypothesis that TFs are more suitable for variety classification than for yield prediction. The results underscore the potential of TFs derived from multispectral images in early yield prediction and varietal classification, offering insights for HTP and precision agriculture alike.

Monitoring and estimating the density of microalgae in a closed cultivation system is a critical task in culturing algae since it allows growers to optimally control both nutrients and cultivating conditions. Among the estimation techniques proposed so far, image-based methods, which are less invasive, nondestructive, and more biosecure, are practically preferred. Nevertheless, the premise behind most of those approaches is simply averaging the pixel values of images as inputs of a regression model to predict density values, which may not provide rich information of the microalgae presenting in the images. In this work, we propose to exploit more advanced texture features extracted from captured images, including confidence intervals of means of pixel values, powers of spatial frequencies presenting in images, and entropies accounting for pixel distribution. These diverse features can provide more information of microalgae, which can lead to more accurate estimation results. More importantly, we propose to use the texture features as inputs of a data-driven model based on L1 regularization, called least absolute shrinkage and selection operator (LASSO), where their coefficients are optimized in a manner that prioritizes more informative features. The LASSO model was then employed to efficiently estimate the density of microalgae presenting in a new image. The proposed approach was validated in real-world experiments monitoring the Chlorella vulgaris microalgae strain, where the obtained results demonstrate its outperformance compared with other methods. More specifically, the average error in the estimation obtained by the proposed approach is 1.54, whereas those obtained by the Gaussian process and gray-scale-based methods are 2.16 and 3.68, respectively

Fu, Y., 2020. Texture feature extraction and recognition of underwater target image considering incomplete tree wavelet decomposition. In: Qiu, Y.; Zhu, H., and Fang, X. (eds.), Current Advancements in Marine and Coastal Research for Technological and Sociological Applications. Journal of Coastal Research, Special Issue No. 107, pp. 25-28. Coconut Creek (Florida), ISSN 0749-0208. Image texture feature extraction has become an important way of image retrieval, scene recognition and target location, which has become an important problem of image extraction and recognition. However, incomplete tree wavelet structure decomposition extraction will get better image clarity, which will be better for processing, such as preprocessing, image noise removal, contrast enhancement and so on. By analyzing the characteristics of image texture, we can decompose the incomplete tree wavelet structure of image, which will better describe the image texture in wavelet domain. In the naval battle, we must find out to launch weapon attack first, which needs the accuracy of automatic recognition of underwater target. Therefore, we need to improve the texture feature extraction and recognition of underwater target image. Firstly, this paper analyzes the image texture features and the algorithm of incomplete tree wavelet decomposition. Then, this paper demonstrates the texture feature extraction and recognition of underwater objects. Finally, some suggestions are put forward.

In this paper, we first studied the action recognition method based on image texture features, established a human body model, and realized the extraction of skeletal data and joint calibration. Based on the skeleton information, the aerobic movements are recognized, the difference between the aerobics movement sequence and the standard movement sequence is calculated, and the movements are scored. Then, the database for aerobic movements was established, and an AI-assistant aerobics teaching model was developed. Finally, the effect of the teaching method was analyzed through indicators of recognition effect, skill impact, and students’ learning interests. The results show that the accuracy of the system’s action recognition reaches 95%, and the overall response time is between 5.2-6.4s, with high real-time performance. And it has a significant effect on students’ movement participation, active interest, and independent learning behavior, with p-values of 0.013, 0.041, and 0.036, respectively, p<0.05. This study promotes the development and innovation of aerobics, which can scientifically adjust training countermeasures and enhance the skill level of aerobics athletes.

In this paper, the importance of modern packaging design and pattern design and the application of pattern design in modern packaging design are the first two aspects of achieving the development of information integration of packaging design and pattern design, and the visual design based on Markov random field is proposed for the image retrieval and classification problems in traditional model packaging design and pattern design. Wavelet transforms, multi-scale analysis of image texture features and region merging conditions in visual design using GBIS algorithm, and image retrieval in pattern design are examined and analyzed. The results show that the model trained with both loss functions has the best retrieval top-k accuracy on both datasets, reaching 54.3% on the DARN dataset and 3.9% accuracy on Deep Fashion. This paper demonstrates the model’s effectiveness for modifying and retrieving pattern design image attributes.

Intravascular optical coherence tomography (IVOCT) is becoming more and more popular in clinical diagnosis of coronary atherosclerotic. However, reading IVOCT images is of large amount of work. This article describes a method based on image feature extraction and support vector machine (SVM) to achieve semi-automatic segmentation of IVOCT images. The image features utilized in this work including light attenuation coefficients and image textures based on gray level co-occurrence matrix. Different sets of hyper-parameters and image features were tested. This method achieved an accuracy of 83% on the test images. Single class accuracy of 89% for fibrous, 79.3% for calcification and 86.5% lipid tissue. The results show that this method can be a considerable way for semi-automatic segmentation of atherosclerotic plaque components in clinical IVOCT images.

This work tackles a recent challenge in digital image processing: how to identify the steganographic images from a steganographer, who is unknown among multiple innocent actors. The method does not need a large number of samples to train classification model, and thus it is significantly different from the traditional steganalysis. The proposed scheme consists of textural features and clustering ensembles. Local ternary patterns (LTP) are employed to design low-dimensional textural features which are considered to be more sensitive to steganographic changes in texture regions of image. Furthermore, we use the extracted low-dimensional textural features to train a number of hierarchical clustering results, which are integrated as an ensemble based on the majority voting strategy. Finally, the ensemble is used to make optimal decision for suspected image. Extensive experiments show that the proposed scheme is effective and efficient and outperforms the state-of-the-art steganalysis methods with an average gain from 4 % to 6 % .

This study develops a classification algorithm designed to handle interval data and to apply effectively in image processing. The proposed algorithm utilizes an innovative measure called overlap distance to assess the similarity between two intervals within multidimensional space. In addition, it integrates an improved method for determining prior probabilities by employing a fuzzy clustering technique. Furthermore, the study introduces a classification rule based on the quasi-Bayes method specifically tailored for interval data. According to this rule, an interval is assigned to a particular group if it holds the highest prior probability and the minimum distance to that group. The proposed algorithm is systematically presented in a step-by-step manner, elucidated by a numerical example, and executed using a well-established Matlab procedure. Another significant contribution of this study is its application to images, wherein texture features are extracted and represented as two-dimensional intervals. The effectiveness and superiority of the proposed algorithm are demonstrated through its application to various sets of medical images.

Most patch-based texture synthesis algorithms using Markov Random Field for composite materials only considers color similarity between the corresponding pixels. The traditional algorithms are lack of adaptability, so the size of patches needs to be defined artificially in advance as the result of blurring of image texture features for composite materials. In order to improve above problems, a new patch-based sampling algorithm for synthesizing textures from an input sample image texture of composite materials is presented in this paper. By using patches of the sample texture as building blocks for image texture synthesis of composite materials, this algorithm makes high-quality texture synthesis for a wide variety of textures ranging regular to stochastic. The method is effective by our experimental results.

Image retrieval is one of the most applicable image processing techniques, which has been used extensively. Feature extraction is one of the most important procedures used for interpretation and indexing images in content-based image retrieval systems. Effective storage, indexing and managing a large number of image collections is a critical challenge in computer systems. There are many proposed methods to overcome these problems. However, the rate of accurate image retrieval and speed of retrieval is still an interesting field of research. In this study, the authors propose a new method based on combination of Hadamard matrix and discrete wavelet transform (HDWT) in hue-min-max-difference colour space. An average normalised rank and combination of precision and recall are considered as metrics to evaluate and compare the proposed method against different methods. The obtained results show that the use of HDWT provides better performance in comparison with Haar discrete wavelet transform, colour layout descriptor, dominant colour descriptor and scalable colour descriptor, Padua point and histogram intersection.