Centroid Shape Research Articles

The Postural and Body Surface Temperature Response of Leisure Horses to Lunging with Selected Lunging Aids.

Incorporating lunging into a horse's daily routine aims to enhance fitness, physical condition, and specific skills or exercises when using lunging aids (LAs). To assess the effectiveness of lunging, non-contact technologies like geometric morphometrics and infrared thermography can be employed. This study seeks to evaluate lunging efficiency based on the horse's posture and surface temperature when lunging with different head and neck positions. The study aims to determine if changes in a horse's posture correspond to increased metabolic activity, as indicated by body surface temperature. Thirteen horses included in the study were lunged with chambon (CH), rubber band (RB), and triangle side reins (TRs) as well as with a freely moving head (FMH). Images were taken in visible light and infrared. Principal Component Analysis (PCA) was used to analyze horse posture changes and a Pixel-Counting Protocol (PCP) was used to quantify surface temperature patterns. The horses' posture exhibited contrasting changes, reflected by a changing centroid shape (p < 0.0001) but not size (p > 0.05) when lunged with RB and TRs, but not CH. Different (p < 0.0001) surface temperature patterns were observed during lunging. FMH lunging resulted in lower temperatures over a larger surface, CH induced moderate temperatures on a smaller area, RB caused moderate to high temperatures across a broader surface, and TRs led to higher temperatures over a smaller region. The studied lunging cases returned different (p < 0.0001) surface temperature patterns. Lunging with FMH returned lower temperatures over a larger surface, CH moderate temperatures on a smaller area, RB moderate to high temperatures across a broader surface, and TRs higher temperatures over a smaller region. The proposed methods can be applied to evaluate the efficiency of lunging in horses.

The Initial Horse’s Postural Response to the First Session of the Dorsal, Ventral, and Dorso–Ventral Massage Techniques

Since interest in equine manual therapy and rehabilitation is constantly growing, the need for quantification of the horse’s postural response to used alternative therapy has increased. This study implemented geometric morphometrics (GM) for a dorsal profile comparison between the horse groups. The dorsal profile was represented by the centroid size and the centroid shape. The horse groups were defined using four classifiers: horse’s age, height at the withers, time lap in the massage session, and technique of the massage (dorsal, ventral, and dorso–ventral). Out of a total of 900 photographs of 20 horses, 180 photos were analyzed using GM, including thirty landmarks. Variation of the principal components (PCs) representing consecutive dorsal profiles were reported for the first three PSs as 59.50% for PC1, 14.36% for PC2, and 9.01% for PC3. The dorsal profiles differed depending on the classifier ‘height’ in terms of centroid size (p &lt; 0.0001) as well as classifier ‘time’ (p &lt; 0.0001) and classifier ‘technique’ (p &lt; 0.0001) in terms of centroid shape, but not depending on the classifier ‘age’ (p &gt; 0.05). GM allows visualizing the differences in the horses’ posture resulting from the selected manual rehabilitation techniques. The quantification of the horse’s body posture in the studied protocol indicates horses’ body posture after being warmed-up and massaged using the dorso–ventral technique is the most desirable.

A geometric morphometric assessment of shape variation in adult pelvic morphology.

In humans, the pelvis is the most sexually dimorphic skeletal element and is often utilized in aging and sexing remains. The pelvis has become greatly relied upon in anthropological research (e.g., forensics, demographics, obstetrics, evolutionary history); however, pelvis morphology is highly variable, and very little is known about the nature, sources, patterning, and interpretation of this variation. This study aims to quantify pelvis shape variation, document sexual shape variation, and estimate the plasticity of morphology. This will ultimately give greater ability to interpret modern, archaeological, and evolutionary patterns to gain deeper insight into processes which shape human anatomy. Using a sample of 129 Medieval Danish skeletons, shape variation is documented in the greater sciatic notch (GSN), iliac crest (IC), arcuate line (AL), and sub-pubic angle (SPA) using 3D geometric morphometrics. The landmarking method applied here has the advantage of being applicable to fragmentary remains, rather than requiring whole bones. This allows it to be easily applied to archaeological samples and for the interpretation of separate bone features. Differences in shape were statistically analyzed by principle component analysis, linear discriminate analysis, and morphological disparity. Relationships between maximum femur length, body mass, and shape centroid size were also test by allometric regression. Results quantify the sexual dimorphism and shape variation present in these features. The GSN shape is the most variable, while the AL is the least. Similarly, the IC is the only feature which shows almost no dimorphism in shape, and instead best reflects lifestyle/activity patterns. Evidence of dimorphism in the IC is likely a result of cultural labor patterns rather than genetic and hormonal influence. Finally, the shapes of the GSN, AL, and SPA are more related to body mass than to femur length, such that individuals with increased mass exhibit more classically "male" shapes and those with less mass have more "female" shapes. The results have important implications for the evolution of pelvic anatomy, and sexual dimorphism, but also highlight the plasticity inherent in pelvic morphology. Analyzing pelvis features separately in a clearly defined, relatively genetically homogenous population gives insight into the determinants of bone morphology, which are not readily observable by other means. The relationship between body mass and shape suggests dimorphism in body size and composition may affect bone shape.

Parallel Evolution of Allometric Trajectories of Trophic Morphology between Sympatric Morphs of Mesoamerican Astyanax (Characidae)

Parallel evolution of the body shape and trophic-related traits has been detected between sympatric pairs of lake-dwelling characin fishes in Mesoamerica. Here, we evaluated the variation in and the ontogenetic allometric trajectories of trophic morphology between sympatric Astyanax morphs (elongate and deep-body) in two geographic systems, Lake Catemaco (Mexico) and San Juan River basin (Nicaragua and Costa Rica). Using geometric morphometrics, we determined the shape variation and disparity in the premaxillary bone, and the patterns of allometric trajectories between morphs in each system. We found a higher differentiation and disparity in the premaxilla shape between morphs from San Juan River basin than between the Lake Catemaco ones. We found shared (parallel evolution) patterns of divergence between systems, which included allometric trajectories showing a positive correlation between the premaxilla shape and log centroid size, as well as trajectories being extended in the elongated-body morph (truncated in the deep-body morph). Regarding the unique patterns of divergence, we recovered parallel allometric trajectories between morphs from Lake Catemaco, while the San Juan River basin morphs showed divergent trajectories. Our results are congruent with the hypothesis that divergence in trophic morphology can be considered a triggering factor in the divergence in the genus Astyanax from Mesoamerica.

Shape retrieval using angle-wise contour variance

Abstract In this study, we propose a geometric feature set for 2D shape retrieval. Conventional Hough feature gives the edge locations along with angle and creates Hough table if there are multiple intersections at borders. In this paper, a statistical way to represent the relation of repeating contours at each angle around the shape centroid is presented. The main contribution of this paper is to use the standard deviation of repeating contours. We calculate the angle between the shape centroid and each point on the contour. For each integer angle value, three features were extracted: the number of contour repetitions, the average distance of the points at that angle to the centroid, and the standard deviation of the points at the same angle. Thus, a 2D image was represented by a constant sized matrix, regardless of its size. In the case of similarity between two images, instead of merging features within a single expression, the algorithm picked the feature with the highest similarity rate for that comparison. We tested the proposed method on MPEG-7, Kimia99, ETH-80 datasets for a benchmark with the state-of-the-art. It outperformed most of the recent methods in terms of retrieval rate.

Limited-view binary tomography reconstruction assisted by shape centroid.

In this paper, the binary tomographic reconstruction problem for very limited projection data availability is considered. Being this inverse problem highly ill-posed, we propose a new reconstruction model that uses a shape centroid-based regularization term, i.e., we assume that the center of gravity of the object of interest is known, at least approximately, in advance. Motivation for this regularization is found in the close connection between the projection data and the object centroid, as we will show. Experimental evaluation underpins that reasonable results can be obtained from practically minimal amount of projection data, gathered from just one projection direction.

Extraction of compact boundary normalisation based geometric descriptors for affine invariant shape retrieval

Shape recognition and retrieval is a complex task on non-rigid objects and it can be effectively performed by using a set of compact shape descriptors. This paper presents a new technique for generating normalised contour points from shape silhouettes, which involves the identification of object contour from images and subsequently the object area normalisation (OAN) method is used to partition the object into equal part area segments with respect to shape centroid. Later, these contour points are used to derive six descriptors such as compact centroid distance (CCD), central angle (ANG), normalized points distance (NPD), centroid distance ratio (CDR), angular pattern descriptor (APD) and multi-triangle area representation (MTAR). These descriptors are a 1D shape feature vector which preserve contour and region information of the shapes. The performance of the proposed descriptors is evaluated on MPEG-7 Part-A, Part-B and multi-view curve dataset images. The present experiments are aimed to check proposed shape descriptor's robustness to affine invariance property and image retrieval performance. Comparative study has also been carried out for evaluating our approach with other state of the art approaches. The results show that image retrieval rate in OAN approach performs significantly better than that in other existing shape descriptors.

Selecting the shape of centroids of round and non-round gears

In the design of gears, the most important task is to obtain centroids. Features of selection of centroids of round and non-round gears obtained by means of functions of harmonious form are considered, process of its formation is shown. Analytical dependencies are presented describing the geometry of the centroid of a round and non-round gearwheel. An example of an implementation of a mechanism based on dependencies describing the construction of a centroid is given.

3D Morphometric Analysis Reveals Similar Ecomorphs for Early Kangaroos (Macropodidae) and Fanged Kangaroos (Balbaridae) from the Riversleigh World Heritage Area, Australia

Understanding feeding ecology of extinct kangaroos is fundamental to understanding the evolution of kangaroos and the Australia paleoenvironment during the Oligo-Miocene. Comparisons with extant species have suggested that the macropodiforms of the Oligo/Miocene (kangaroos and allies) from the Riversleigh World Heritage Area, northern Australia, were predominantly folivorous browsers or fungivores, unlike the majority of extant species. To further test this hypothesis, we investigate the relationship between variation in cranial and mandibular shape of extant and extinct macropodiforms and ecological factors such as diet, locomotion, and body mass using 3D geometric morphometric analysis of 42 living species and eight extinct species from two radiations (the extinct clade of Balbaridae and some early representatives of the extant Macropodidae. Dietary class (fungivore, browser, grazer, and mixed feeder) correlated strongly with variation in cranial shape (20–25% of variance explained). There was also significant association between cranial shape, and both locomotor mode and body mass. In a principal component analysis of shape variation for crania (including the shape of the molar row), Riversleigh macropodiforms cluster with extant folivorous browsers on principal components (PC) 1 and 3, providing support for previous interpretations of these species as browsing kangaroos. However, as a group and regardless of phylogenetic association, the shape centroid of extinct species differs significantly from that of extant species. Riversleigh macropodiforms cluster with regular hoppers or arboreal tree kangaroos, but this may be a result of the correlation between diet and locomotor mode in kangaroos. Their similarity to extant browsers supports previous interpretations of rainforest and woodland environments at Riversleigh during the early and middle Miocene, respectively. Procrustes ANOVA Analysis of the full shape dataset and diet also shows that diet accounts for a significant portion of variation; however, when phylogeny is taken into account these results become nonsignificant. In analyses of dentary shape, some balbarid species cluster with extant mixed feeders, although this may reflect phylogenetic differences rather than ecological signal.

Spherical Harmonic Energy Over Gaussian Sphere for Incomplete 3D Shape Retrieval

Spherical harmonic (SH) has shown excellent advantages in terms of accuracy and efficiency in the retrieval of complete three-dimensional shapes. However, since the spherical function directly takes the shape centroid as the global reference point, the SH features depend heavily on the central position. In this context, the features become no longer reliable in the query of incomplete shapes that may cause erratic centroid. In this work, we propose a novel shape descriptor, namely spherical harmonic energy over the Gaussian sphere (SHE-GS), especially for the incomplete shape retrieval. Firstly, all unit normal vectors on the shape surface are mapped to points on a Gaussian sphere, which has the constant center. Secondly, kernel density estimation is used to establish a Gaussian Sphere Model (GSM) to describe the density change of these mapping points. Finally, the shape descriptor is generated by applying an SH transformation on the model. According to the way of GSM being regarded as a surface model or a volume model, we have separately designed two specific algorithm implementations. Experimental results, on two engineering shape sets containing artificially defective shapes, indicate that the proposed method outperforms other traditional methods also defined in the sphere space for incomplete shape retrieval. The superiority is verified for both similar objects in the same category or the single specific object of query.

Brain tumor segmentation of normal and lesion tissues using hybrid clustering and hierarchical centroid shape descriptor

ABSTRACTRobust segmentation of the brain magnetic resonance (MR) images is extremely important for diagnosing the tissues quantitatively. It is crucial to detect the changes caused by the growth of edema and tumor in healthy tissues for better medical treatment planning. In order to increase the image quality, skull stripping or brain extraction is an essential pre-processing step in neuroimaging before the segmentation process. Hybrid algorithm made up of K-means clustering, and Fuzzy C-Means clustering (KFCM) algorithm offers advantages in the aspect of accuracy on soft tissues of brain MR images. KFCM algorithm clusters the images into the cerebrospinal fluid, white matter, gray matter and abnormal region. The segmented abnormal region has some false positive pixels which can not be removed by low order image processing techniques. In this study, we present the use of Hierarchical Centroid Shape Descriptor (HCSD) on the already segmented abnormal region by the above said method. The HCSD selects the region of interest only, i.e. abnormal region. Our algorithm offers considerable improvement in segmentation accuracy validated by the truth map. The quantitative evaluation and validation of experiments were carried out on 20 high-grade glioma suffering patient and 10 T1-weighted anatomical models of healthy brains.

Fall Detection Depth-Based Using Tilt Angle and Shape Deformation

The population of elderly people is in growth. Falls risk their life, to disabilities, and to fears. Automatic fall detection systems provide them secure living; helping them to be independent at home. Computer vision offers efficient systems over many developed systems. In this article, the authors propose a new vision-based fall detection using depth camera. It combines human shape analysis, centroid detection and motion where it exploits the 3D information provided by a Kinect to compute the tilt angle to discriminate falls. Experimental tests were done with SDUFall dataset that contains 20 subjects performing five daily activities and falls, demonstrate the efficiency of the proposed system, and show that our method is promising achieving satisfactory results up to 84.66%.

Spatiotemporal Interpolation Methods for Solar Event Trajectories

Abstract This paper introduces four spatiotemporal interpolation methods that enrich complex, evolving region trajectories that are reported from a variety of ground-based and space-based solar observatories every day. Our interpolation module takes an existing solar event trajectory as its input and generates an enriched trajectory with any number of additional time–geometry pairs created by the most appropriate method. To this end, we designed four different interpolation techniques: MBR-Interpolation (Minimum Bounding Rectangle Interpolation), CP-Interpolation (Complex Polygon Interpolation), FI-Interpolation (Filament Polygon Interpolation), and Areal-Interpolation, which are presented here in detail. These techniques leverage k-means clustering, centroid shape signature representation, dynamic time warping, linear interpolation, and shape buffering to generate the additional polygons of an enriched trajectory. Using ground-truth objects, interpolation effectiveness is evaluated through a variety of measures based on several important characteristics that include spatial distance, area overlap, and shape (boundary) similarity. To our knowledge, this is the first research effort of this kind that attempts to address the broad problem of spatiotemporal interpolation of solar event trajectories. We conclude with a brief outline of future research directions and opportunities for related work in this area.

Chorionic vascular “fit” in the human placenta: Relationship to fetoplacental outcomes

BackgroundNovel measures of the chorionic plate and vessels are used to test the hypothesis that variation in placental structure is correlated with reduced birth weight (BW) independent of placental weight (PW), suggesting functionally compromised placentas. Methods916 mothers recruited to the Pregnancy, Infection and Nutrition Study delivering singleton live born infants at >30 gestational weeks had placentas collected, digitally photographed and weighed prior to formalin fixation. The fetal-placental weight ratio (FPR) was calculated as birthweight/placental weight. Beta (beta) was calculated as ln(PW)/ln(BW). Chorionic disk perimeter was traced and chorionic surface shape (CS) area was calculated. “Fit” was defined as the ratio of the area of the vascular to the full chorionic surface area. The sites at which chorionic vessels dived beneath the chorionic surface were marked to calculate the chorionic surface vessel (CV) area. The centroids of shapes, the distance between centroids and other measures of shape irregularities were calculated. Principal components analysis (PCA) created three independent factors. Factors were used in regression analyses to explore relations to birth weight, trimmed placental weight, FPR, and beta. Specific measures of shape irregularity were also examined in regression analyses for interrelationships and to predict birth weight, placental weight, FPR, and beta. ResultsVariables related to disk size (CS area, perimeter) were correlated with BW, GA, trimmed PW and beta. “Fit” (the ratio of CV area to CS area), measures of shape irregularities, and the distance between the cord insertion and the centroids of surface and vascular areas were also correlated with one or more of the clinical outcome variables. PCA yielded three factors that had independent effects on birth weight, placental weight, the fetal-placental weight ratio, and beta (each p < 0.0001). Addition of GA did not alter the factors’ associations with outcomes. Chorionic “fit” (ratio of areas), also included within the factor analysis, was a positive predictor of birth weight (p = 0.005) and FPR (p = 0.002) and a negative predictor of beta (p = 0.01). Fit was statistically significantly associated with greater distances between the umbilical cord insertion site and the CS (p < 0.001) and CV centroids (p < 0.001), and to lesser displacement between CS and CV centroids (p < 0.001). ConclusionsMeasures of CS and CV account for variation in placental efficiency defined by beta, independent of GA. Macroscopic placenta measurements can identify suboptimal placental development.

BIOSIMILAR ARTIFICIAL KNEE FOR TRANSFEMORAL PROSTHESES AND EXOSKELETONS

Artificial knees play an important role in transfemoral prostheses, lower extremity exoskeletons and walking robots. Their designs must provide natural kinematics, high strength and stiffness required in the stance phase of gait. Additionally, modern artificial knee is the principal module by means of which the prosthesis control is performed. This paper presents a prototype of an artificial polycentric knee, designed on the basis of the hinge mechanism with cross links. In order to increase strength and stiffness, the elements of the joint have curved supporting surfaces formed in the shape of centroids in relative motion of links of the hinge mechanism. Such construction is a mechanical system with redundant links but it allows for providing desirable characteristics of the artificial knee. Synthesis of the hinge mechanism is made by a method of systematic study of the parameter space, uniformly distributed in a finite dimensional cube. Stiffness of bearing surfaces elements of knee was determined by solving the contact problem with slippage of surfaces relative to each other.

Localized active contour model with background intensity compensation applied on automatic MR brain tumor segmentation

This paper presents a Localized Active Contour Model (LACM) integrating an additional step of background intensity compensation. The region-based active contour models that use statistical intensity information are more sensitive to the high mean intensity distance between consecutive regions. In Magnetic Resonance Imaging (MRI) this distance is great between the foreground and the background, hence it leads to an incorrect delineation of the target. In order to resolve this problem, an automatic process is introduced in our model for balancing the mean intensity distance between an image foreground and its background. The aim is to minimize the attraction effect of the active contour model to the undesired borderlines defined by these two mentioned image regions. By using this approach not only the obtained accuracy outperforms the traditional localized mean separation active contour model, but also it reduces the computation time of the segmentation task. In addition, this method was efficiently applied on automatic brain tumor segmentation in multimodal MRI data. The Hierarchical Centroid Shape Descriptor (HCSD) was used for detecting the region of interest i.e. abnormal tissue so as to automatically initialize the active contour. The validation of experiments was carried out on synthetic images and the quantitative evaluation was performed on the BRATS2012 database. Finally, the accuracy achieved by the proposed method was compared to the localized mean separation intensity, the localized Chan-Vese, the local Gaussian distribution fitting and the local binary fitting models by using the Dice coefficient, Sensitivity, Specificity and the Hausdorff distance. The computation time of the methods was also measured for comparison purposes. The obtained results show that the proposed model outperforms the accuracy of the selected state of the art methods. Moreover, it is also faster than the comparative methods in the medical image segmentation task.

Shape Interpretation of Second-Order Moment Invariants

This paper deals with the following problem: What can be said about the shape of an object if a certain invariant of it is known? Such a, herein called, shape invariant interpretation problem has not been studied/solved for the most of invariants, but also it is not known to which extent the shape interpretation of certain invariants does exist. In this paper, we consider a well-known second-order affine moment invariant. This invariant has been expressed recently Xu and Li (2008) as the average square area of triangles whose one vertex is the shape centroid while the remaining two vertices vary through the shape considered. The main results of the paper are (i) the ellipses are shapes which minimize such an average square triangle area, i.e., which minimize the affine invariant considered; (ii) this minimum is $$1/(16\pi ^2)$$1/(16?2) and is reached by the ellipses only. As by-products, we obtain several results including the expression of the second Hu moment invariant in terms of one shape compactness measure and one shape ellipticity measure. This expression further leads to the shape interpretation of the second Hu moment invariant, which is also given in the paper.

Cloth invariant gait recognition using pooled segmented statistical features

A natural and normal gait can be used as a biometric cue in finding a solution to the human identification problem. An individual׳s appearance is likely to change with the variation in different clothes which further compounds the problem of gait identification. The clothing differences between gallery and probe datasets capture the possible changes in their silhouette׳s shape which increases the inability to discriminate between individuals. In this paper, an attempt has been made to provide a novel statistical shape analysis method based on Gait Energy Image (GEI) which is decomposed into three independent shape segmentations such as horizontal, vertical and grid resolution. The pooled segmented statistical features describe the shape of the GEI edge contour. The higher order moments about the shape centroid are likely to be invariant to small changes in silhouette shape. They implicitly describe the underlying distribution of the shape and can be used in conjunction with a set of other area based features to increase the efficacy of the classification results. The features reliability test has been performed with three classical statistical methods such as intra cloths variance (F-Statistics), inter subject distance (t-Statistics) and Intra-Class Correlation (ICC) on each set of segment of features. This analysis illustrates that combination of features holds less discrimination in comparison to grid based shape segmentation for different clothes. The similarity measurement comprises of different classification techniques (k-Nearest Neighbor, Naïve Bayes׳, Decision Tree (C4.5) and Random Forest) to produce acceptable recognition results on OU-ISIR dataset. The degree of discriminability of these classifiers has been measured by statistical metrics such as F1-Score, Precision, Recall, and ROC curve.

Circle Views Signature: A Novel Shape Representation for Shape Recognition and Retrieval

An important problem in computer vision is object recognition, which has received considerable attention in the literature. The performance of any object recognition system depends on the shape representation used and on the matching algorithm applied. In this paper, we propose a novel circle views (CVs) shape signature for recognizing 2-D object silhouettes. Many views from one circular orbit (or more) centered at the shape centroid are defined based on the distances from each viewing point on the circular orbit to a fixed number of sampled shape contour points. One compact and robust shape descriptor is obtained by applying the Fourier transform to the proposed signature. The obtained descriptor is translation, rotation, and scale invariant. Two popular shape benchmarks have been used for testing: 1) MPEG-7 and 2) Kimia’s-99 databases. The proposed CVs signature provides a promising retrieval rate (83.71% on MPEG-7 database). A further increase in the retrieval rate (90.35%) has been achieved by applying a shape context learning technique. A slight modification to the learning technique has been proposed that reduces its computational cost significantly. An attractive feature of the proposed CVs signature is its simplicity and computational efficiency, which makes the CVs signature more practical for different application areas.

Fast 2D Shape Matching with Centroid Shape Context in Polar Configuration

Fast 2D Shape Matching with Centroid Shape Context in Polar Configuration