Hierarchical Map Research Articles

Identifying gratification behind the consumption of the OTT platforms in an emerging market: a means-end theory approach

PurposeMillions of dollars are being spent by over-the-top platform (OTT) providers to produce content for the Indian market. These circumstances highlight the necessity for a solution that attracts and maintains customers, enabling OTT providers to generate revenues and profits. This study explores the underlying gratifications (e.g. informative; connectivity) obtained from over-the-top platform (OTT) attributes (e.g. global content; regional and cultural content) through consequences (e.g. create awareness; related to reality).Design/methodology/approachMeans-end chain theory uncovered gratifications consumers fulfill through the use of OTT platforms. The laddering technique explores the linkage among attributes, consequences and gratifications that influence OTT platforms' consumers' consumption patterns. In total, 27 interviews were conducted in India, and participants responded to questions regarding attributes of OTT platforms, consequences and gratifications in a one-on-one interview procedure. Hierarchical value maps were built to better understand customer selection of OTT platforms based on the replies.FindingsThe results suggested that six attributes of OTT platforms (for ex-global content; regional and rural content; rating before watching) were associated with the four consequences (for ex-create awareness; related to reality; saves time), which were associated with four gratifications, which are informativeness, connectivity, social enhancement and productivity.Originality/valueThe prospective function of OTT services in the entertainment and media business has grabbed consumer attention. However, limited literature focuses on identifying gratifications consumers gain from OTT attributes. Through these findings, managers and practitioners can gain insights regarding strategies to increase OTT adoption and help develop a loyal consumer base.

Exploring value-based motivations for culture and heritage tourism using the means-end chain and laddering approach

ABSTRACT While motivations for cultural and heritage tourism remain complex and diverse, previous studies overlook the structure and link of personal values, expected experience consequences, and destination attributes. Meanwhile, value pursuit as an enduring belief influencing tourists’ motivations and behavior has received limited research attention. Therefore, this study explores diverse motivations using the means-end chain and laddering interview techniques. The Five Great Avenues in Tianjin, China, a historical landscape embodying the social characteristics of China and Western colonialism in the 18th and 19th centuries, was used as the study site. The results show that self-development and socialization are the most important value pursuits, followed by hedonism and differentiation. Other value-based motivations include identification, existentialism, escapism, and freedom. A hierarchical map between value pursuits, tourism consequences, and destination attributes is constructed to elucidate the interactive nature of tourism motivation. Findings are utilized to develop a value-based motivation classification framework for understanding diverse value-based motivations. This study contributes to a comprehensive framework for understanding the complexity of motivation in the culture and heritage tourism context, which provides practical insights for segmentation and destination positioning.

Real‐time face perception based encoding strategy optimization method for UHD videos

AbstractFace regions containing rich semantic information appear frequently in the videos. As the video resolution increase dramatically, the face regions will inevitably attract more attentions. This paper proposes a face perception based coding scheme to improve the visual quality of the face regions in UHD videos. A specially tailored face perception model is first utilized to precisely and quickly locate the face regions. Then, a face perception map is generated based on a hierarchical mapping algorithm. Finally, the face perception map is employed as a guidance to optimize the encoding process, including mode decision, block partition and bit allocation. The proposed method is implemented on HEVC to demonstrate the effectiveness. Experimental results on a set of 4K test sequences show that the proposed method can obviously improve the objective and subjective quality of the face regions, while causing only slight quality decline over the rest of the frame. Additionally, the computation required for mode decision and block partition is reduced, thereby saving encoding time cost.

Growing Hierarchical Self-Organising Representation Map (GHSORM)

The Growing Hierarchical Self-Organising Representation Map (GHSORM) is a model fusing the denoising autoencoder, used to better represent a dataset, and the Growing Hierarchical Self-Organising Map, used for organizing and projecting the input data in clusters of varying detail. It is shown here that the GHSORM is instrumental in sub-grouping clusters that are not fully separable by a single SOM. This combined approach is first tested and illustrated on the problem of clustering handwritten digits where a modification of the Activation Maximisation method for use at the SOM output layer demonstrates the benefit of hierarchical growth in the GHSORM. In particular, the SOM Node Activation Maximisation method is used to visually represent the best approximation of each of the SOM nodes at the output layer. This demonstrates the improvement in representing difficult to separate digits in the hierarchical case. To test and measure the efficacy of the GHSORM hierarchical model in class and sub-class separation the method is applied to complex digital gene expression data. A cancer dataset, comprising of gene expression data that has samples of different classes and sub-classes, is used for this purpose. The GHSORM demonstrates robust capabilities to cluster and sub-cluster the different classes and subclasses of cancer, where the results are superior to both linear methods, currently in use, as well as the methods of its constituent algorithms.

1H NMR fecal metabolic phenotyping of periductal fibrosis- and cholangiocarcinoma-specific metabotypes defining perturbation in gut microbial-host co-metabolism.

The liver fluke Opisthorchis viverrini (OV), which subsequently inhabits the biliary system and results in periductal fibrosis (PDF), is one of the primarily causes of cholangiocarcinoma (CCA), a bile duct cancer with an exceptionally high incidence in the northeast of Thailand and other Greater Mekong Subregion (GMS) countries. Insights in fecal metabolic changes associated with PDF and CCA are required for further molecular research related to gut health and potential diagnostic biological marker development. In this study, nuclear magnetic resonance (NMR) metabolomics was applied for fecal metabolic phenotyping from 55 fecal water samples across different study groups including normal bile duct, PDF and CCA groups. By using NMR spectroscopy-based metabolomics, fecal metabolic profiles of patients with CCA or PDF and of individuals with normal bile duct have been established with a total of 40 identified metabolites. Further multivariate statistical analysis and hierarchical clustering heat map have demonstrated the PDF- and CCA-specific metabotypes through various altered metabolite groups including amino acids, alcohols, amines, anaerobic glycolytic metabolites, fatty acids, microbial metabolites, sugar, TCA cycle intermediates, tryptophan catabolism substrates, and pyrimidine metabolites. Compared to the normal bile duct group, PDF individuals showed the significantly elevated relative concentrations of fecal ethanol, glycine, tyrosine, and N-acetylglucosamine whereas CCA patients exhibited the remarkable fecal metabolic changes that can be evident through the increased relative concentrations of fecal uracil, succinate, and 5-aminopentanoate. The prominent fecal metabolic alterations between CCA and PDF were displayed by the reduction of relative concentration of methanol observed in CCA. The metabolic alterations associated with PDF and CCA progression have been proposed with the involvement of various metabolic pathways including TCA cycle, ethanol biogenesis, hexamine pathway, methanol biogenesis, pyrimidine metabolism, and lysine metabolism. Among them, ethanol, methanol, and lysine metabolism strongly reflect the association of gut-microbial host metabolic crosstalk in PDF and/or CCA patients. The PDF- and CCA-associated metabotypes have been investigated displaying their distinct fecal metabolic patterns compared to that of normal bile duct group. Our study also demonstrated that the perturbation in co-metabolism of host and gut bacteria has been involved from the early step since OV infection to CCA tumorigenesis.

Novel cartographer using an OAK-D smart camera for indoor robots location and navigation

In recent years, service robots have been widely used in people’s daily life, and with the development of more and more intelligence, people put forward higher requirements for autonomous positioning and navigation functions of robots. Like outdoor navigation, indoor navigation also needs the support of navigation data. Although the indoor positioning and navigation scheme based on cameras, lidars and other sensors is gradually developing, due to the complexity of the indoor structure, manual production of indoor navigation data is time-consuming and laborious, and the efficiency is relatively low. In order to solve the problem of low productivity and improve the accuracy of robot automatic navigation, we added a new type of intelligent camera, called OpenCV AI kit or OAK-D, and proposed a method to automatically build data files that can be used for indoor navigation and location services using indoor 3D point cloud data. This intelligent camera performs neural reasoning on chips that do not use GPUs. It can also use stereo drills for depth estimation, and use 4K color camera images as input to run the neural network model. Python API can be called to realize real-time detection of indoor doors, windows and other static objects. The target detection technology uses an artificial intelligence camera, and the robot can well identify and accurately mark on the indoor map. In this paper, a high-performance indoor robot navigation system is developed, and multisensor fusion technology is designed. Environmental information is collected through artificial intelligent camera (OAK-D), laser lidar, and data fusion is carried out. In the experiment part of this paper,The static fusion map module is created based on the laser sensor information and the sensor information of the depth camera, the hierarchical dynamic cost map module is created in the real-time navigation, and the global positioning of the robot is realized by combining the word bag model and the laser point cloud matching. Then a software system is realized by integrating each module. The experiment proves that the system is practical and effective, and has practical value.

Spatial-temporal variation of the carbon sequestration rate of afforestation in China: Implications for carbon trade and planning

Afforestation and reforestation (A&R) are nature-based and cost-effective solutions for enhancing terrestrial carbon sinks and facilitating faster carbon neutrality. However, the lack of hierarchical spatial-temporal maps for the carbon sequestration rate (CSR) from A&R at the national scale impedes the scientific implementation of forest management planning to a large extent. Here, we assessed the spatial-temporal CSR per area for A&R at the provincial, prefectural, and county levels in China using a forest carbon sequestration model under three climate scenarios. Results showed that the CSR of vegetation (CSRVeg), soil (CSRSoil), and the ecosystem (CSREco) significantly varied across space and time. In China, the CSRVeg, CSRSoil, and CSREco were primarily regulated by the spatial variations in temperature and precipitation. Additionally, CSRVeg was found to be positively influenced by precipitation and temperature, whereas temperature had a negative influence on CSRSoil. Therefore, the differences between the CSRVeg and CSRSoil should be emphasized in the future. These information on the spatiotemporal variation of CSR of A&R (vegetation, soil, and ecosystem) on unit area basis and at levels of province, prefecture, and county in China, can be used as a comparable protocol to estimate the carbon sinks of A&R at different scales. Overall, these hierarchical spatiotemporal maps for CSR on A&R may help to identify priority areas of forest management planning and carbon trade policy to achieve faster carbon neutrality for China in the future.

Mice identify subgoal locations through an action-driven mapping process

Mammals form mental maps of the environments by exploring their surroundings. Here, we investigate which elements of exploration are important for this process. We studied mouse escape behavior, in which mice are known to memorize subgoal locations-obstacle edges-to execute efficient escape routes to shelter. To test the role of exploratory actions, we developed closed-loop neural-stimulation protocols for interrupting various actions while mice explored. We found that blocking running movements directed at obstacle edges prevented subgoal learning; however, blocking several control movements had no effect. Reinforcement learning simulations and analysis of spatial data show that artificial agents can match these results if they have a region-level spatial representation and explore with object-directed movements. We conclude that mice employ an action-driven process for integrating subgoals into a hierarchical cognitive map. These findings broaden our understanding of the cognitive toolkit that mammals use to acquire spatial knowledge.

Semantic-agnostic progressive subtractive network for image manipulation detection and localization

In this paper, we propose a new detection and localization framework capable of detecting suspicious forgeries using the Semantic-Agnostic Progressive Subtractive Network (SAPS-Net). Our approach is based on the key observation that fluctuations in image content severely interfere with the capture of general manipulations by existing convolutional architecture. Distinct from the aggregation attention employed by traditional methods, we design the Semantic-Agnostic Manipulation Attention (SAMA) based on subtractive operation for mitigating the effect of rich image semantics on manipulation extraction. Initially, the Multi-Scale feature Iterative Fusion Block (MSIFB) and Multi-Kernel feature Fusion Residual Block (MKFRB) are designed to iteratively crawl potential semantic associations of different hierarchical feature mappings. Then, we further devise the subtractive operation to effectively remove the semantic associations as distractors and promote the network to adaptively learn general forgery. Notably, these semantic associations based on image content may be fundamentally different from the manipulation traces that alter the internal patterns of images. By progressively utilizing SAMAs, the network remains robust to image content manipulation with rich semantics. Extensive experiments on six challenge datasets show that our approach has more than 3.03% pixel-level AUC gains and 3.70% image-level AUC gains in cross-dataset scenarios compared to state-of-the-art methods, especially on the IMD20 dataset (pixel-level AUC: 0.859) and Wild dataset (pixel-level AUC: 0.821) with realistic scenarios.

Differences in soil physicochemical properties and rhizosphere microbial communities of flue-cured tobacco at different transplantation stages and locations.

Rhizosphere microbiota play an important role in regulating soil physical and chemical properties and improving crop production performance. This study analyzed the relationship between the diversity of rhizosphere microbiota and the yield and quality of flue-cured tobacco at different transplant times (D30 group, D60 group and D90 group) and in different regions [Linxiang Boshang (BS) and Linxiang ZhangDuo (ZD)] by high-throughput sequencing technology. The results showed that there were significant differences in the physicochemical properties and rhizosphere microbiota of flue-cured tobacco rhizosphere soil at different transplanting times, and that the relative abundance of Bacillus in the rhizosphere microbiota of the D60 group was significantly increased. RDA and Pearson correlation analysis showed that Bacillus, Streptomyces and Sphingomonas were significantly correlated with soil physical and chemical properties. PIGRUSt2 function prediction results showed that compared with the D30 group, the D60 group had significantly increased metabolic pathways such as the superpathway of pyrimidine deoxyribonucleoside salvage, allantoin degradation to glyoxylate III and pyrimidine deoxyribonucleotides de novo biosynthesis III metabolic pathways. The D90 group had significantly increased metabolic pathways such as ubiquitol-8 biosynthesis (prokaryotic), ubiquitol-7 biosynthesis (prokaryotic) and ubiquitol-10 biosynthesis (prokaryotic) compared with the D60 group. In addition, the yield and quality of flue-cured tobacco in the BS region were significantly higher than those in the ZD region, and the relative abundance of Firmicutes and Bacillus in the rhizosphere microbiota of flue-cured tobacco in the BS region at the D60 transplant stage was significantly higher than that in the ZD region. In addition, the results of the hierarchical sample metabolic pathway abundance map showed that the PWY-6572 metabolic pathway was mainly realized by Paenibacillus, and that the relative abundance of flue-cured tobacco rhizosphere microbiota (Paenibacillus) participating in PWY-6572 in the D60 transplant period in the BS region was significantly higher than that in the ZD region. In conclusion, different transplanting periods of flue-cured tobacco have important effects on soil physical and chemical properties and rhizosphere microbial communities. There were significant differences in the rhizosphere microbiota and function of flue-cured tobacco in different regions, which may affect the performance and quality of this type of tobacco.

Using clustering algorithms and GPM data to identify spatial precipitation patterns over southeastern Brazil

Southeastern Brazil comprises an important geoeconomic and populous region in South America. Consequently, it is essential to analyze and understand the precipitation profiles in this region. Among different data sources and techniques available to perform such study, the use of clustering algorithms and information from the Global Precipitation Measurement (GPM) project rises as a convenient yet few exploited alternative. Precisely, this study employs the K-Means, the Hierarchical Ward, and the Self-Organizing Maps methods to cluster the annual and seasonal precipitation data from GPM project recorded from 2001 to 2019. The adopted methods are compared in terms of quantitative measures and the number of clusters defined through a well-established rule. The results demonstrate that the annual and seasonal periods are organized according to different number of clusters. Moreover, the results allow: identify the presence of a spatially heterogeneous distribution in the study area; to conclude that the K-Means algorithm is a suitable clustering method in the context of this investigation when compared to Ward’s Hierarchical and Self-Organizing Maps methods in terms of the Calinski-Harabasz and Davies-Bouldin measures; and that the spatial precipitation distribution over Southeastern Brazil is represented by 10 clusters in annual and summer periods, 11 clusters in autumn and spring and 9 clusters in winter period.

Virtual tourist motivation: the differences between virtual tourism and on-site tourism

ObjetivoEl turismo virtual se ha popularizado en los últimos años. Sin embargo, aún existe un vacío en la investigación sobre la motivación del turista virtual. El estudio pretende identificar la motivación del turista virtual y explorar la relación entre el turismo virtual y el turismo in situ (real).Diseño/metodología/enfoqueEsta investigación identifica la motivación del turista virtual basándose en la teoría de la cadena medio-fin (MEC). Se realizaron entrevistas escalonadas a 32 encuestados para construir un mapa jerárquico de valores. Además, se elaboró un análisis de la motivación del turismo virtual y del turismo presencial basado en una revisión de la bibliografía pertinente.ResultadosEl estudio exploratorio reveló 12 atributos, 9 resultados y 4 valores que los turistas virtuales desean alcanzar e identificó 5 MEC en los que la autosatisfacción es la motivación basada en valores más importante. En comparación con las motivaciones del turismo in situ (real), el turismo virtual muestra posibilidades de sustituir, complementar y ampliar el turismo in situ (real) en determinadas circunstancias. Sin embargo, depende en gran medida de si los turistas se sienten atraídos por las características técnicas, la seguridad y las condiciones de experiencia del turismo virtual.Originalidad/valorEste estudio contribuye a comprender la motivación del turista virtual y ofrece perspectivas basadas en la motivación sobre la relación entre el turismo virtual y el turismo in situ (real). También se ofrecen implicaciones para la gestión sobre cómo atraer a los turistas potenciales en línea.

Anchor-free infrared pedestrian detection based on cross-scale feature fusion and hierarchical attention mechanism

An anchor-free infrared pedestrian detection algorithm is proposed to address the problems of multiscale, partial occlusion and environmental interference in infrared images, which make it difficult for traditional algorithms to perform accurate detection. First, a cross-scale feature fusion module is devised to improve the detection performance of multiscale and partially occluded objects. This module fuses the global features of different residual layers and multiscale local region features, thereby improving the multiscale feature fusion capability of the model while expanding the feature receptive field of the backbone network. Second, the object texture features of infrared images are weak owing to the imaging mechanism, which affects the accuracy of detection model. Thus, we construct a hierarchical attention mapping module to enhance the significance of pedestrian features in complex environments while suppressing the background information. Moreover, to improve the detection performance further, a novel dual-branch head detector is designed to decouple the input features from the channel dimension, which can be used for classification and location. Finally, by introducing the anchor-free concept into the network prediction mechanism, the prediction box can learn ground truth regression parameters by itself, thereby simplifying the network structure and improving the model generalization ability. The experimental results demonstrate that the proposed algorithm can accurately detect multiscale infrared pedestrian objects in complex environments and exhibits better objective evaluation indices compared to other pedestrian detection algorithms. The average precision and recall reached 98.78% and 98.67%, respectively.

FFR-SSD: feature fusion and reconstruction single shot detector for multi-scale object detection

Object detection is one of the most fundamental tasks toward image content understanding. Although numerous algorithms have been proposed, implementing effective and efficient object detection is still very challenging for now. To solve the challenges of small and multi-scale objects, we propose a hierarchical feature fusion and reconstruction method called feature fusion and reconstruction single shot detector (FFR-SSD). We first present a multi-scale visual attention model, which incorporates the channel-wise and space-wise information into the multi-branch feature enhancement module to improve the feature representation capacities. Second, a hierarchical feature map weighing mechanism is developed to fuse multi-layer feature maps, which contributes to describing the intact objects for the subsequent module. Third, we present an effective feature map reconstruction module to encourage the model to focus on pivotal information. Therefore, the overall contour information is preserved for the shallow enhanced response map and the semantic feature information is enriched for the deep enhanced feature map. Numerous experiments on two public benchmark datasets show that the proposed method achieves significant improvements over the state of the arts.

An automated robust algorithm for clustering multivariate data

Clustering analysis is widely used in various applications, such as marketing, biology, medical science, finance, data mining, image processing, data analysis and pattern recognition. For instance, clustering can be used to: characterize the customer groups based on their purchasing patterns by discovering the distinct groups in the customer base; derive plant and animal taxonomies, categorize genes with similar functionalities and gain insight into structures inherent to populations; identify the cancer cells; and detect credit card fraud. The k-means, Hierarchical and self-organizing (Kohonen) map are widely used clustering algorithms. The practice demonstrated that these clustering algorithms have some significant limitations and drawbacks. This manuscript gives an automated robust algorithm for clustering multivariate data without prior information about the number of clusters. Robust estimate of location and covariance matrix are used to define Mahalanobis distance and corresponding radius of clustering algorithm. The algorithm is designed in a way that it controls both masking and swamping effects. It automatically divides a given data set into a number of clusters. Some properties pertaining to the algorithm are demonstrated which helps in finding clusters that accommodates observations with large deviation. A method to avoid the use of a fixed cutoff for determining outlier is discussed. The performance of the proposed algorithm is compared with the existing clustering algorithms and robust multiple outlier detection methods.

Feature-Based Occupancy Map-Merging for Collaborative SLAM.

One of the most frequently used approaches to represent collaborative mapping are probabilistic occupancy grid maps. These maps can be exchanged and integrated among robots to reduce the overall exploration time, which is the main advantage of the collaborative systems. Such map fusion requires solving the unknown initial correspondence problem. This article presents an effective feature-based map fusion approach that includes processing the spatial occupancy probabilities and detecting features based on locally adaptive nonlinear diffusion filtering. We also present a procedure to verify and accept the correct transformation to avoid ambiguous map merging. Further, a global grid fusion strategy based on the Bayesian inference, which is independent of the order of merging, is also provided. It is shown that the presented method is suitable for identifying geometrically consistent features across various mapping conditions, such as low overlapping and different grid resolutions. We also present the results based on hierarchical map fusion to merge six individual maps at once in order to constrict a consistent global map for SLAM.

HexTile: A Hexagonal DGGS-Based Map Tile Algorithm for Visualizing Big Remote Sensing Data in Spark

The advent of the era of big remote sensing data has transformed traditional data management and analysis models, among which visualization analysis has gradually become an effective method, and map tiles for remote sensing data have always played an important role. However, in high-latitude regions, especially in polar regions, the deformation caused by map projection still exists, which lowers the accuracy of global or large-scale visual analysis, as well as the execution efficiency of big data. To solve the above problems, this paper proposes an algorithm called HexTile, which uses a hexagonal discrete global grid system (DGGS) model to effectively avoid problems caused by map projection and ensure global consistency. At the same time, the algorithm was implemented based on the Spark platform, which also has advantages in efficiency. Based on the DGGS model, hierarchical hexagon map tile construction and a visualization algorithm were designed, including hexagonal slicing, merging, and stitching. The above algorithms were parallelized in Spark to improve the big data execution efficiency. Experiments were carried out with Landsat-8, and the results show that the HexTile algorithm can not only guarantee the quality of global data, but also give full play to the advantages of the cluster in terms of efficiency. Additionally, the visualization was conducted with Cesium and OpenLayers to validate the integration and completeness of hexagon tiles. The scheme proposed in this paper could provide a reference for spatiotemporal big data visualization technology.

Removal of Redundant Information via Discrete Representation for Monocular Depth Estimation

Monocular depth estimation aims at inferring three-dimensional (3D) cues from a single RGB image. Although existing methods have achieved a certain degree of success, the impact of redundant information has rarely been studied. We propose to improve estimation accuracy by implicitly eliminating redundant information. To this end, we creatively apply discrete representation to monocular depth estimation. By mapping continuous variables into the corresponding learning-based discrete latent space, a hierarchical multi-scale latent map is acquired as the decoder input. Removing redundant information can enhance prediction performance by making the depth estimator balance the local and global. Furthermore, to fully take advantage of the discrete representation, a lightweight fusion mechanism is introduced to aggregate information in multi-scale feature maps.Experiments on NYU Depth V2 dataset demonstrate that our network is competitive with the state of the arts.

Hierarchical Topology Map with Explicit Corridor for global path planning of mobile robots

Hierarchical Topology Map with Explicit Corridor for global path planning of mobile robots

Cell morphology as a biological fingerprint of chondrocyte phenotype in control and inflammatory conditions.

Little is known how inflammatory processes quantitatively affect chondrocyte morphology and how single cell morphometric data could be used as a biological fingerprint of phenotype. We investigated whether trainable high-throughput quantitative single cell morphology profiling combined with population-based gene expression analysis can be used to identify biological fingerprints that are discriminatory of control vs. inflammatory phenotypes. The shape of a large number of chondrocytes isolated from bovine healthy and human osteoarthritic (OA) cartilages was quantified under control and inflammatory (IL-1β) conditions using a trainable image analysis technique measuring a panel of cell shape descriptors (area, length, width, circularity, aspect ratio, roundness, solidity). The expression profiles of phenotypically relevant markers were quantified by ddPCR. Statistical analysis, multivariate data exploration, and projection-based modelling were used for identifying specific morphological fingerprints indicative of phenotype. Cell morphology was sensitive to both cell density and IL-1β. In both cell types, all shape descriptors correlated with expression of extracellular matrix (ECM)- and inflammatory-regulating genes. A hierarchical clustered image map revealed that individual samples sometimes responded differently in control or IL-1β conditions than the overall population. Despite these variances, discriminative projection-based modeling revealed distinct morphological fingerprints that discriminated between control and inflammatory chondrocyte phenotypes: the most essential morphological characteristics attributable to non-treated control cells was a higher cell aspect ratio in healthy bovine chondrocytes and roundness in OA human chondrocytes. In contrast, a higher circularity and width in healthy bovine chondrocytes and length and area in OA human chondrocytes indicated an inflammatory (IL-1β) phenotype. When comparing the two species/health conditions, bovine healthy and human OA chondrocytes exhibited comparable IL-1β-induced morphologies in roundness, a widely recognized marker of chondrocyte phenotype, and aspect ratio. Overall, cell morphology can be used as a biological fingerprint for describing chondrocyte phenotype. Quantitative single cell morphometry in conjunction with advanced methods for multivariate data analysis allows identifying morphological fingerprints that can discriminate between control and inflammatory chondrocyte phenotypes. This approach could be used to assess how culture conditions, inflammatory mediators, and therapeutic modulators regulate cell phenotype and function.