Computerized Image Analysis Research Articles

The early dodder gets the host: decoding the coiling patterns of Cuscuta campestris with automated image processing.

We developed an in-house Python-based image analysis pipeline to investigate the movement patterns of Cuscuta. Our analysis unveiled that the coiling and circumnutation movements of Cuscuta are regulated by its intrinsic circadian rhythm. Cuscuta spp., commonly known as dodders, are rootless and leafless stem parasitic plants. Upon germination, Cuscuta starts rotating immediately in a counterclockwise direction (circumnutation) to locate a host plant, creating a seamless vascular connection to steal water and nutrients from its host. In this study, our aim was to elucidate the dynamics of the coiling patterns of Cuscuta, which is an essential step for successful parasitism. Using time-lapse photography, we recorded the circumnutation and coiling movements of C. campestris at different inoculation times on non-living hosts. Subsequent image analyses were facilitated through an in-house Python-based image processing pipeline to detect coiling locations, angles, initiation and completion times, and duration of coiling stages in between. The study revealed that the coiling efficacy of C. campestris varied with the inoculation time of day, showing higher success and faster initiation in morning than in evening. These observations suggest that Cuscuta, despite lacking leaves and a developed chloroplast, can discern photoperiod changes, significantly determining its parasitic efficiency. The automated image analysis results confirmed the reliability of our Python pipeline by aligning closely with manual annotations. This study provides significant insights into the parasitic strategies of C. campestris and demonstrates the potential of integrating computational image analysis in plant biology for exploring complex plant behaviors. Furthermore, this method provides an efficient tool for investigating plant movement dynamics, laying the foundation for future studies on mitigating the economic impacts of parasitic plants.

Quantifying the recovery process of skeletal muscle on hematoxylin and eosin stained images via learning from label proportion

Visual observing muscle tissue regeneration is used to measure experimental effect size in biological research to discover the mechanism of muscle strength decline due to illness or aging. Quantitative computer imaging analysis for support evaluating the recovery phase has not been established because of the localized nature of recovery and the difficulty in selecting image features for cells in regeneration. We constructed MyoRegenTrack for segmenting cells and classifying their regeneration phase in hematoxylin–eosin (HE) stained images. A straightforward approach to classification is supervised learning. However, obtaining detailed annotations for each fiber in a whole slide image is impractical in terms of cost and accuracy. Thus, we propose to learn individual recovery phase classification utilizing the proportions of cell class depending on the days after muscle injection to induce regeneration. We extract implicit multidimensional features from the HE-stained tissue images and train a classifier using weakly supervised learning, guided by their class proportion for elapsed time on recovery. We confirmed the effectiveness of MyoRegenTrack by comparing its results with expert annotations. A comparative study of the recovery relation between two different muscle injections shows that the analysis result using MyoRegenTrack is consistent with findings from previous studies.

Rapid detection of microbial antibiotic susceptibility via deep learning supported analysis of angle-resolved scattered-light images of picoliter droplet cultivations

The progressive increase in microbial resistance to antibiotics is a global health threat that requires solutions for rapid and reliable determination of antibiotic susceptibility in order to select appropriate antibiotics and dosages prior to treatment. We have established a screening platform that enables the detection of cell growth after just a few cell divisions. Our methodological approach for a robust phenotypic antibiotic susceptibility testing is based on the innovative combination of three cutting-edge technologies: (i) a high-throughput microfluidic platform where individual bacterial cells are encapsulated in picoliter-sized droplets, (ii) a 2D angle-resolved light scattering sensor to perform label-free hourly screening of the droplets, and (iii) a computational image analysis approach based on convolutional neural networks to evaluate the dynamics of microbial growth in droplets. For the gram-positive Staphylococcus aureus and gram-negative Escherichia coli, we demonstrate that microbial growth in droplets can be successfully detected within one to two hours. Furthermore, the potential of this platform for rapid phenotypic antibiotic susceptibility testing is demonstrated as a proof-of-concept with the clinically relevant bacterium S. aureus under various concentrations of the antibiotic tetracycline. Notably, we reach a robust phenotypic decision regarding the sensitivity to this antibiotic within two hours.

Multistage selective diagenesis increasing poro-perm properties of Anisian sponge-microbial-coral patch reefs and adjacent facies in the Muschelkalk of Upper Silesia, Southern Poland

Carbonate rocks, particularly reefs and circum-reef facies, are significant reservoir rocks whose reservoir properties depend on their primary composition and diagenetic history. This paper reconstructs the influence of multistage selective diagenesis on the pore system evolution and porosity-permeability properties of the Anisian tropical reef system (Karchowice and Diplopora Beds) in the Muschelkalk of Upper Silesia, southern Poland, using field and slab observations, standard and cathodoluminescence petrography, bulk and SIMS carbon and oxygen stable isotope analyses, porosity-permeability plug study, and computer image analysis of macroporosity (>5 mm across). The reef system comprises two distinct levels of sponge-microbial(-coral) reef mounds surrounded by diverse bioclastic, peloidal, coated-grain, and micritic facies rich in Balanoglossites burrows. The strata experienced complex diagenetic alterations related to various fluids (marine, evaporitic, meteoric) and diagenetic environments (shallow substratal, burial, telogenetic), including (in chronological order): 1) early-marine precipitation of isopachous fibrous rims; 2) early dissolution during emersion; 3) brine-reflux dolomitization; 4) patchy silicification; 5) recrystallization; 6) burial calcite cementation by heated marine fluid; 7) deposition of vadose silt; 8) meteoric calcite cementation; 9) chemical compaction; 10) fracturing; and 11) final karstification, dedolomitization, FeO precipitation, and Liesegang ring formation by meteoric water during Cenozoic telogenesis. Many processes were selective and affected the same (more permeable) parts of the rocks. The strata exhibit porosities reaching 50% (combined plug porosity of 6–35% and macroporosity of up to 20%) and plug permeabilities of 0.07–70 mD, with the highest values occurring in the reefs and burrowed facies. The dominant late-stage telogenic dissolution pores originated from selective removal of dolomitized micrite within burrows and between sponge-microbial automicrite as well as dolomite crystals. The study illustrates the transition of the reef system from tight to porous due to multistage selective diagenesis and fluid flow, which enhanced subtle permeability differences between various types of micrite. This is the first case where four different fluids (dolomitizing, early dissolving, calcite-forming, and late dissolving) selectively migrated through the same rock parts at different times and modified them one after another.

The application of computer image analysis for assessment of surface porosity of architectural concrete

The application of computer image analysis for assessment of surface porosity of architectural concrete

Airways abnormalities in a prospective cohort of patients with rheumatoid arthritis

Rheumatoid arthritis (RA) affects roughly 1% of the population and commonly involves the lungs. Of lung involvement in RA, interstitial lung disease (ILD) is well-known; however, airways disease in RA is relatively understudied. What are the baseline airways abnormalities in a prospective cohort of patients with RA based on pulmonary function tests (PFT), high-resolution CT scans (HRCT) and computational imaging analysis and are there associations between these abnormalities and respiratory symptoms? In this single-center study, 188 patients with RA without a clinical diagnosis of ILD underwent HRCT and PFT. Radiologists assessed HRCTs for airway abnormalities. Computational imaging via VIDA Vision software and in-house quantitative CT (qCT) analysis was applied to 147 HRCTs to quantify airway abnormalities. Airways obstruction (FEV1/FVC ratio < 0.7) was present in 20.7% of patients, and associated with older age, male sex and higher smoking rate. Radiologists identified airway abnormalities in 61% of patients-55% had bronchial wall thickening, 12% bronchiectasis, and 5% mosaic attenuation; these airways findings were associated with older age, male sex, lower FEV1, FVC, FEV1/FVC ratios, and higher rates of rheumatoid factor positivity. Prespecified qCT metrics (wall thickening % and emphysema %) correlated with PFT obstruction and more severe respiratory symptoms including shortness of breath and cough. There were high rates of airways abnormalities in this prospective RA cohort based on three methods of detection. There were significant associations between qCT measures and respiratory symptoms. Airways disease may be an under-recognized extra-articular manifestation of RA and qCT may be a sensitive method to detect the clinical impact on respiratory symptoms.

Offsetting Dense Particle Sedimentation in Microfluidic Systems.

Sedimentation is an undesirable phenomenon that complicates the design of microsystems that exploit dense microparticles as delivery tools, especially in biotechnological applications. It often informs the integration of continuous mixing modules, consequently impacting the system footprint, cost, and complexity. The impact of sedimentation is significantly worse in systems designed with the intent of particle metering or binary encapsulation in droplets. Circumventing this problem involves the unsatisfactory adoption of gel microparticles as an alternative. This paper presents two solutions-a hydrodynamic solution that changes the particle sedimentation trajectory relative to a flow-rate dependent resultant force, and induced hindered settling (i-HS), which exploits Richardson-Zaki (RZ) corrections of Stokes' law. The hydrodynamic solution was validated using a multi-well fluidic multiplexing and particle metering manifold. Computational image analysis of multiplex metering efficiency using this method showed an average reduction in well-to-well variation in particle concentration from 45% (Q = 1 mL/min, n = 32 total wells) to 17% (Q = 10 mL/min, n = 48 total wells). By exploiting a physical property (cloud point) of surfactants in the bead suspension in vials, the i-HS achieved a 58% reduction in the sedimentation rate. This effect results from the surfactant phase change, which increases the turbidity (transient increase in particle concentration), thereby exploiting the RZ theories. Both methods can be used independently or synergistically to eliminate bead settling in microsystems or to minimize particle sedimentation.

Analysis of Tribological Properties of Hardfaced High-Chromium Layers Subjected to Wear in Abrasive Soil Mass.

This article presents the results of abrasion wear resistance tests of wear-resistant steel and surfacing under laboratory conditions and natural operation. Abrasion wear resistance determined on the basis of the study by determining geometrical characteristics of the alloying additives using computer image analysis methods, as well as examining the changes occurring on the surface of the workpieces and their wear intensity. Based on the results obtained from laboratory tests, it was noted that AR steel exhibited 14 times greater wear than the padding weld. This wear is affected by alloy additives, which, for the padding weld, are chromium additives. The microstructure image shows that soil mass had a destructive effect mainly on the matrix of the material, whereas in the areas with high concentrations of chromium precipitates, this effect was significantly weaker. The operational test results showed that within the area of the tine subjected to hardfacing, the material loss was lower than that for the same area of the tine in the as-delivered state. For the hardfaced tine, a 7% loss of volume was noted in relation to the operating part before testing and following the friction process. However, for the operating part in the as-delivered state, this difference amounted to 12%.

O-246 A cutting-edge artificial intelligence system tracking embryo development and pinpointing the optimal day for blastocyst utilization

Abstract Study question Can an AI model predict at which specific day (D) of development a blastocyst will reach expansion at a usable stage for biopsy/cryopreservation/transfer? Summary answer Our model achieved a high accuracy tracking embryos’ developmental times while forecasting the day when they would reach a usable blastocyst stage. What is known already Forecasting the optimal day for blastocyst utilization is crucial. It can facilitate informed decisions on transfer/biopsy/cryopreservation, while streamlining the laboratory workflow. Previous prediction models on this task relied solely on embryo morphological and morphokinetic data. However, emerging approaches, incorporating computer image analysis, offer an opportunity to enhance their accuracy and objectivity. To our knowledge, none has specifically predicted the precise day of embryo development when the blastocyst has expanded enough to become usable. This information is invaluable to plan workload distribution on D5/6/7 of development. Moreover, with the rise of PGT-A, optimizing the time allocated for biopsy becomes essential. Study design, size, duration Retrospective study conducted at a tertiary IVF centre, utilizing data from June 2015 to January 2023. A total of 14,704 time-lapse videos (TLV) from embryos of 1,805 patients, cultured across 2,475 cycles, were analyzed split into train/validation/test by patient (to avoid data leaking) with 60/20/20 proportion. Outcome parameters encompassed the times for all embryo developmental stages or morphokinetic parameters, blastocyst formation and utilization, as well as the specific day of blastocyst utilization. Participants/materials, setting, methods Embryos from patients with primary/secondary infertility were included. Laboratory culture conditions followed normal clinical routine. All embryos were monitored using embryoscope time-lapse systems and annotated by experienced embryologists. Blastocysts graded ≥BL3CC (Gardner) were biopsied/cryopreserved/transferred on D5/6/7. Only videos depicting embryos with normal fertilization were considered in the model. Predictions for the utilization day were made 48-hours and 24-hours ahead of the blastocyst usable stage. Main results and the role of chance We developed a customized variant of the Temporal Fusion Transformer model, modified for analyzing time-sequence-images from TLV. This model architecture allows the analysis of the sequence and clinical variables, identifying its most influential aspects from the model’s perspective. In the first two tasks of blastocyst prediction and forecasting blastocyst utilization day, data was split into training-set: 8,802 TLV (1,083 patients); validation-set: 2,925 TLV (361 patients); and test-set: 2,977 TLV (361 patients). Our trained model achieved a strong performance on ‘early warning’ 48-hour ahead forecasting of embryo reaching a usable blastocyst stage. Utilizing only unlabelled time-lapse image observations of the embryo, it reached 90 AUROC score on the test-set. For ‘late warning’, 24-hour ahead, we reached 97 AUROC score on the test-set, almost ideal prediction. Finally, 91 AUROC score was achieved on the binary blastocyst stage classification task, just considering the blastocyst formation at any expansion. In the embryo morphokinetic stage-labelling task, data was split into training-set: 8,420 TLV (1,078 patients); validation-set: 2,777 TLV (360 patients); and test-set: 2,780 TLV (360 patients). We achieved competitive results with 95.5 macro-averaged AUROC score for multi-class (17 classes) prediction for each time-lapse-image. Each individual parameter from tPB2 to tHB reached a AUROC higher than 90. Limitations, reasons for caution The model needs to undergo an extensive cross-validation, to evaluate its performance, followed by a prospective validation. Wider implications of the findings This model belongs to the realm of IVF lab automation, offering substantial assistance in daily operations. It aids embryologists not only in annotating developmental stages with high accuracy, but also predicts the blastocyst utilization day, thereby providing crucial support for efficient workload planning and streamlined operations. Trial registration number NA

A New Method for Evaluating the Homogeneity within and between Weave Repeats in Plain Fabric Structures Using Computer Image Analysis.

This article introduces a novel, rapid, and non-destructive method for assessing homogeneity within and between weave repeats in fabric structures, termed intra-repeat (IAR) and inter-repeat (IER) evaluation. The method focuses on structural parameters, including inter-thread pores (ITPs) and warp and weft pitches, using computer image analysis. Each parameter is assigned to a module in the repeat weave pattern, facilitating the sorting of modules in the IAR and IER fabric structure arrangement. The method was verified using artificial images and 30 real plain fabrics with varying degrees of warp grouping, employing the author's proprietary software, MagFABRIC version 2.1The general measurable coefficients of intra- and inter-homogeneity were defined and related to the airflow measurements of these fabrics. Multiple regression models of airflow revealed strong dependencies, particularly for F = 10, with the size, shape, and position of ITPs and warp and weft pitches showing significant correlation. These findings underscore the importance of the new homogeneity parameters in textile structure analysis, including both IAR and IER woven fabric structure homogeneity parameters. The research aims to model specialized fabrics (e.g., barrier, filtration, composite fabrics) to address local changes in fabric structure affecting properties such as filtration efficiency, air permeability, and mechanical properties, especially in applications like composites or medical implants.

Visual Question Answering Models for Zero-Shot Pedestrian Attribute Recognition: A Comparative Study

Pedestrian Attribute Recognition (PAR) poses a significant challenge in developing automatic systems that enhance visual surveillance and human interaction. In this study, we investigate using Visual Question Answering (VQA) models to address the zero-shot PAR problem. Inspired by the impressive results achieved by a zero-shot VQA strategy during the PAR Contest at the 20th International Conference on Computer Analysis of Images and Patterns in 2023, we conducted a comparative study across three state-of-the-art VQA models, two of them based on BLIP-2 and the third one based on the Plug-and-Play VQA framework. Our analysis focuses on performance, robustness, contextual question handling, processing time, and classification errors. Our findings demonstrate that both BLIP-2-based models are better suited for PAR, with nuances related to the adopted frozen Large Language Model. Specifically, the Open Pre-trained Transformers based model performs well in benchmark color estimation tasks, while FLANT5XL provides better results for the considered binary tasks. In summary, zero-shot PAR based on VQA models offers highly competitive results, with the advantage of avoiding training costs associated with multipurpose classifiers.

PhiHER2: phenotype-informed weakly supervised model for HER2 status prediction from pathological images.

Human epidermal growth factor receptor 2 (HER2) status identification enables physicians to assess the prognosis risk and determine the treatment schedule for patients. In clinical practice, pathological slides serve as the gold standard, offering morphological information on cellular structure and tumoral regions. Computational analysis of pathological images has the potential to discover morphological patterns associated with HER2 molecular targets and achieve precise status prediction. However, pathological images are typically equipped with high-resolution attributes, and HER2 expression in breast cancer (BC) images often manifests the intratumoral heterogeneity. We present a phenotype-informed weakly supervised multiple instance learning architecture (PhiHER2) for the prediction of the HER2 status from pathological images of BC. Specifically, a hierarchical prototype clustering module is designed to identify representative phenotypes across whole slide images. These phenotype embeddings are then integrated into a cross-attention module, enhancing feature interaction and aggregation on instances. This yields a phenotype-based feature space that leverages the intratumoral morphological heterogeneity for HER2 status prediction. Extensive results demonstrate that PhiHER2 captures a better WSI-level representation by the typical phenotype guidance and significantly outperforms existing methods on real-world datasets. Additionally, interpretability analyses of both phenotypes and WSIs provide explicit insights into the heterogeneity of morphological patterns associated with molecular HER2 status. Our model is available at https://github.com/lyotvincent/PhiHER2.

Evaluation of Parasight All-in-One system for the automated enumeration of helminth ova in canine and feline feces

Background Digital imaging combined with deep-learning-based computational image analysis is a growing area in medical diagnostics, including parasitology, where a number of automated analytical devices have been developed and are available for use in clinical practice.MethodsThe performance of Parasight All-in-One (AIO), a second-generation device, was evaluated by comparing it to a well-accepted research method (mini-FLOTAC) and to another commercially available test (Imagyst). Fifty-nine canine and feline infected fecal specimens were quantitatively analyzed by all three methods. Since some samples were positive for more than one parasite, the dataset consisted of 48 specimens positive for Ancylostoma spp., 13 for Toxocara spp. and 23 for Trichuris spp.ResultsThe magnitude of Parasight AIO counts correlated well with those of mini-FLOTAC but not with those of Imagyst. Parasight AIO counted approximately 3.5-fold more ova of Ancylostoma spp. and Trichuris spp. and 4.6-fold more ova of Toxocara spp. than the mini-FLOTAC, and counted 27.9-, 17.1- and 10.2-fold more of these same ova than Imagyst, respectively. These differences translated into differences between the test sensitivities at low egg count levels (< 50 eggs/g), with Parasight AIO > mini-FLOTAC > Imagyst. At higher egg counts Parasight AIO and mini-FLOTAC performed with comparable precision (which was significantly higher that than Imagyst), whereas at lower counts (> 30 eggs/g) Parasight was more precise than both mini-FLOTAC and Imagyst, while the latter two methods did not significantly differ from each other.ConclusionsIn general, Parasight AIO analyses were both more precise and sensitive than mini-FLOTAC and Imagyst and quantitatively correlated well with mini-FLOTAC. While Parasight AIO produced lower raw counts in eggs-per-gram than mini-FLOTAC, these could be corrected using the data generated from these correlations.Graphical

Computational image analysis of distortion, sharpness, and depth of field in a next-generation hybrid exoscopic and microsurgical operative platform.

The development of surgical microscope-associated cameras has given rise to a new operating style embodied by hybrid microsurgical and exoscopic operative systems. These platforms utilize specialized camera systems to visualize cranial neuroanatomy at various depths. Our study aims to understand how different camera settings in a novel hybrid exoscope system influence image quality in the context of neurosurgical procedures. We built an image database using captured cadaveric dissection images obtained with a prototype version of a hybrid (microsurgical/exoscopic) operative platform. We performed comprehensive 4K-resolution image capture using 76 camera settings across three magnification levels and two working distances. Computer algorithms such as structural similarity (SSIM) and mean squared error (MSE) were used to measure image distortion across different camera settings. We utilized a Laplacian filter to compute the overall sharpness of the acquired images. Additionally, a monocular depth estimation deep learning model was used to examine the image's capability to visualize the depth of deeper structures accurately. A total of 1,368 high-resolution pictures were captured. The SSIM index ranged from 0.63 to 0.85. The MSE was nearly zero for all image batches. It was determined that the exoscope could accurately detect both the sharpness and depth based on the Laplacian filter and depth maps, respectively. Our findings demonstrate that users can utilize the full range of camera settings available on the exoscope, including adjustments to aperture, color saturation, contrast, sharpness, and brilliance, without introducing significant image distortions relative to the standard mode. The evolution of the camera incorporated into a surgical microscope enables exoscopic visualization during cranial base surgery. Our result should encourage surgeons to take full advantage of the exoscope's extensive range of camera settings to match their personal preferences or specific clinical requirements of the surgical scenario. This places the exoscope as an invaluable asset in contemporary surgical practice, merging high-definition imaging with ergonomic design and adaptable operability.

TransLevelSet: Integrating vision transformers with level-sets for medical image segmentation

Recently, Vision Transformers (ViTs) have emerged as a breakthrough in computer vision and image analysis. Still, their exceptional performance depends on the availability of large amounts of annotated training data and the availability of considerable computational resources. This naturally raises the risk of overfitting and limited generalization capability in settings with limited training data. Aiming to cope with this issue, we propose the Level-Set Transformer (TransLevelSet), a hybrid methodology that encompasses an additional term in the loss function used in the ViT learning process, which has been originally defined in the context of Level-Set (LS) deformable models. This loss term avails the spatial information obtained by the level-set energy terms for image segmentation and mitigates the dependency of ViTs on the amount of available data. Moreover, this level-set loss promotes smooth and topologically consistent demarcation of structures, taking advantage of the capacity of ViTs to capture complex spatial relationships and contextual information. The main contributions of this work include: a) a pioneering approach to the integration of ViTs with level-sets; and, b) the application of the proposed methodology on two case studies related to cancer, namely the malignant melanoma, and colon cancer. We evaluate TransLevelSet on three different publicly available benchmark datasets for medical image segmentation, that include dermoscopic and histopathological images. The results of the experiments demonstrate consistent gains in terms of generalization capability introduced by the LS terms.

Quantifying spatial CXCL9 distribution with image analysis predicts improved prognosis of triple-negative breast cancer.

Background: The C-X-C motif chemokine ligand 9 (CXCL9) plays a pivotal role in tumor immunity by recruiting and activating immune cells. However, the relationship between CXCL9 expression and prognosis in triple-negative breast cancer (TNBC) is unclear. Methods: We investigated CXCL9 mRNA expression, clinicopathological features, and prognosis in TNBC patients. We also used computational image analysis to quantify and assess the distribution of CXCL9 protein in the tumor core (TC) and invasive margin (IM). Results: CXCL9 mRNA expression was significantly higher in TNBC tumors compared to normal tissue (p < 0.001) and was associated with smaller tumors (p = 0.022) and earlier stages (p = 0.033). High CXCL9 mRNA expression was correlated with improved overall survival (OS) in three independent cohorts (all p < 0.05). In a separate analysis, low CXCL9 protein expression was associated with increased lymph node metastasis (p = 0.018 and p = 0.036). High CXCL9 protein expression in the TC, IM, or both was associated with prolonged OS (all p < 0.001). Conclusion: High CXCL9 expression, at both the mRNA and protein levels, is associated with improved prognosis in TNBC patients. CXCL9 expression in the TC and/or IM may be an independent prognostic factor.

Image processing technology based on OMP reconstruction optimization algorithm

With the widespread application of digital images, image processing technology plays an important role in fields such as computer vision and image analysis. Based on the orthogonal matching pursuit algorithm, an image processing method is proposed. In the process, sparse representation and reconstruction algorithm are used for image compressed sensing to complete image sampling operation. Afterwards, the theory of overcomplete sparse representation is introduced to optimize sparse representation, and an overcomplete dictionary is used to remove Gaussian noise, achieving the goal of image processing. The experimental results indicate that the research method do not show significant deficiencies in signal reconstruction when testing reconstructed signals under sparsity of 8; When testing the calculation time, the calculation time of the research method is about 0.212 s when the sparsity is 5 in the Lenna; In the error test, the mean square difference of the research method in the Lenna is stable at about 14.6; When conducting application analysis, the variance eigenvalues of the research method remained below 9.4. This indicates that the research method has good performance and can effectively process images, providing new technical support for image processing.

Research on color and texture characteristics and visual perception of custom wardrobe finishes

In recent years, the custom wardrobe market has been steadily developing. While meeting the functional needs of users, it is gradually shifting towards aesthetic preferences. Rapidly grasping users’ preferences for the appearance of custom wardrobes is a key focus of current research. This study collected a large number of decorative surface images of custom wardrobes and objectively analyzed the design features based on color moments and Tamura texture feature data in computer image analysis methods. K-means cluster analysis was performed on the feature data. Collected images of the points closest to the cluster centers were further screened to select representative finish images, and finally a questionnaire survey was conducted at Nanjing Forestry University, with the help of semantic differential method and factor analysis. The characteristics of the samples were comprehensively summarized to infer design elements. The study found that warm-toned, medium-low saturation, and medium brightness surfaces were preferred by the panel. Different colors, contrasts, saturations, brightness, element features, and arrangements have significantly different effects on visual perception. These conclusions can provide a reference for subsequent custom wardrobe design.

Study on Joint Injury of Taijiquan Movement Based on Computer Image Analysis

As a traditional Chinese martial art, Taijiquan has a remarkable effect on the rehabilitation of joint injury with its unique movement. In this paper, the influence of Taijiquan on joint injury is analyzed by using the local depth feature representation method of image sampling. Then, the local feature coding algorithm is introduced, and the problems existing in the rehabilitation of joint injury are analyzed. An analysis algorithm of the influence of Taijiquan on joint injury based on CV model was proposed, and the effectiveness of the algorithm was verified. The results show that the proposed algorithm improves the MS-COCO dataset by 0.2%, 0.88%, 1.86% and 3.18%, respectively, compared with Hash Net. On the 15Scene dataset, CNN-VLAD’s classification results were 4.1% higher than those of the TNNCV model. On the Caltech 256 data set, the classification accuracy of SMVLADC algorithm is 7.7% higher than CNN-VLAD algorithm. This shows that the proposed algorithm is effective, and the local depth features extracted by CNN are more effective than the traditional artificial features. At the same time, the superiority of CV model based on improved significant regional features is further verified. This study provides a new theoretical basis and practical method for the rehabilitation treatment of joint injury by Taijiquan.

Compatibilities of Cyprinus carpio with Varied Colors of Robotic Fish

Visual selection plays a fundamental role in various aspects of animal behavior, such as colony formation, maintenance, defense, and courtship. This study investigated the effect of bionic robot fish color on carp behavior based on physiological characteristics that were observed during behavioral experiments. Through computer image processing and analysis of light attenuation, we observed changes in the number and positioning of carp with bionic robotic fish of different colors (white, red, blue, green, and yellow). The results indicated that (1) the attenuation coefficient of visible light in freshwater was red &gt; yellow &gt; green &gt; blue; (2) the order of the average change in the number of carp responding to different colors of robotic fish was white &gt; red &gt; green &gt; yellow &gt; blue, and carp were more sensitive and responsive to white and red robotic fish; and (3) the order of the distances between different colors of robotic fish and carp was white &lt; yellow &lt; blue &lt; green &lt; red, and white and yellow robotic fish were more attractive to carp. Therefore, the use of white or yellow robotic fish for relevant operations can reduce disturbance to fish schools.