Optical flow estimation is essential for video processing tasks, such as restoration and action recognition. The quality of videos is constantly increasing, with current standards reaching 8K resolution. However, optical flow methods are usually designed for low resolution and do not generalize to large inputs due to their rigid architectures. They adopt downscaling or input tiling to reduce the input size, causing a loss of details and global information. There is also a lack of optical flow benchmarks to judge the actual performance of existing methods on high-resolution samples. Previous works only conducted qualitative high-resolution evaluations on hand-picked samples. This paper fills this gap in optical flow estimation in two ways. We propose DPFlow, an adaptive optical flow architecture capable of generalizing up to 8K resolution inputs while trained with only low-resolution samples. We also introduce Kubric-NK, a new benchmark for evaluating optical flow methods with input resolutions ranging from 1K to 8K. Our high-resolution evaluation pushes the boundaries of existing methods and reveals new insights about their generalization capabilities. Extensive experimental results show that DPFlow achieves state-of-the-art results on the MPI-Sintel, KITTI 2015, Spring, and other high-resolution benchmarks. The code and dataset are available at https://github.com/hmorimitsu/ptlflow/tree/main/ptlflow/models/dpflow.

Extracting motion information from videos with optical flow estimation is vital in multiple practical robot applications. Current optical flow approaches show remarkable accuracy, but top-performing methods have high computational costs and are unsuitable for embedded devices. Although some previous works have focused on developing low-cost optical flow strategies, their estimation quality has a noticeable gap with more robust methods. In this paper, we develop a novel method to efficiently estimate high-quality optical flow in embedded devices. Our proposed RAPIDFlow model combines efficient NeXt1D convolution blocks with a fully recurrent structure based on feature pyramids to decrease computational costs without significantly impacting estimation accuracy. The adaptable recurrent encoder produces multi-scale features with a single shared block, which allows us to adjust the pyramid length at inference time and make it more robust to changes in input size. Also, it enables our model to offer multiple tradeoffs between accuracy and speed to suit different applications. Experiments using a Jetson Orin NX embedded system on the MPI-Sintel and KITTI public benchmarks show that RAPIDFlow outperforms previous approaches by significant margins at faster speeds. Our code is available at https://github.com/hmorimitsu/ptlflow/tree/main/ptlflow/models/rapidflow.

We present a comprehensive study and evaluation of existing single image deraining algorithms, using a new large-scale benchmark consisting of both synthetic and real-world rainy images.This dataset highlights diverse data sources and image contents, and is divided into three subsets (rain streak, rain drop, rain and mist), each serving different training or evaluation purposes. We further provide a rich variety of criteria for dehazing algorithm evaluation, ranging from full-reference metrics, to no-reference metrics, to subjective evaluation and the novel task-driven evaluation. Experiments on the dataset shed light on the comparisons and limitations of state-of-the-art deraining algorithms, and suggest promising future directions.

We introduce a framework for dynamic evaluation of the fingers movements: flexion, extension, abduction and adduction. This framework estimates angle measurements from joints computed by a hand pose estimation algorithm using a depth sensor (Realsense SR300). Given depth maps as input, our framework uses Pose-REN [1], which is a state-of-art hand pose estimation method that estimates 3D hand joint positions using a deep convolutional neural network. The pose estimation algorithm runs in real-time, allowing users to visualise 3D skeleton tracking results at the same time as the depth images are acquired. Once 3D joint poses are obtained, our framework estimates a plane containing the wrist and MCP joints and measures flexion/extension and abduction/adduction angles by applying computational geometry operations with respect to this plane. We analysed flexion and abduction movement patterns using real data, extracting the movement trajectories. Our preliminary results show this method allows an automatic discrimination of hands with Rheumatoid Arthritis (RA) and healthy patients. The angle between joints can be used as an indicative of current movement capabilities and function. Although the measurements can be noisy and less accurate than those obtained statically through goniometry, the acquisition is much easier, non-invasive and patient-friendly, which shows the potential of our approach. The system can be used with and without orthosis. Our framework allows the acquisition of measurements with minimal intervention and significantly reduces the evaluation time.

The shape of a neuron can reveal many interesting properties about its function. Therefore, organizing neuronal cells into appropriate classes according to their respective shape is a fundamental endeavor in neuroscience. Available online datasets allow new data-oriented approaches to solve such neuroscience problems. Here we analyze the feasibility of classifying neurons according not to their respective wholes, but to its constituent parts. Such a study may reveal interesting insights, including whether parts of the neuronal dendritic arborization preserve proper information about the morphology of the whole neuron. Experimental results using open datasets are reported, thus corroborating our approach.

Online communities provide a fertile ground for analyzing people's behavior and improving our understanding of social processes. Because both people and communities change over time, we argue that analyses of these communities that take time into account will lead to deeper and more accurate results. Using Reddit as an example, we study the evolution of users based on comment and submission data from 2007 to 2014. Even using one of the simplest temporal differences between users---yearly cohorts---we find wide differences in people's behavior, including comment activity, effort, and survival. Further, not accounting for time can lead us to misinterpret important phenomena. For instance, we observe that average comment length decreases over any fixed period of time, but comment length in each cohort of users steadily increases during the same period after an abrupt initial drop, an example of Simpson's Paradox. Dividing cohorts into sub-cohorts based on the survival time in the community provides further insights; in particular, longer-lived users start at a higher activity level and make more and shorter comments than those who leave earlier. These findings both give more insight into user evolution in Reddit in particular, and raise a number of interesting questions around studying online behavior going forward.

The accurate determination of the sex of human skeletal remains is a critical challenge in forensic pathology and skeletal anthropology. The pelvis and the skull are the most commonly used skeletal sites for determining the sex of skeletons. In the skull, the supraorbital region, which includes the supraorbital margin, is considered a specific sexually dimorphic trait. In the traditional approach, sex is determined through visual and tactile assessment. This paper introduces a methodology for the objective quantification of sexually dimorphic features using wavelet transform, which is a multiscale mathematical tool that allows for the measurement of shape variations that are hidden at different scales of resolution. The method was successfully applied for the sex determination of a pilot sample of 3D meshes-digital records of supraorbital morphology. This information can be used by experts to improve the accuracy of biologic profile assessment of a human skeleton, and to describe the geographic and temporal variations within and among populations.

In modeling social interaction online, it is important to understand when people are reacting to each other. Many systems have explicit indicators of replies, such as threading in discussion forums or replies and retweets in Twitter. However, it is likely these explicit indicators capture only part of people's reactions to each other, thus, computational social science approaches that use them to infer relationships or influence are likely to miss the mark. This paper explores the problem of detecting non-explicit responses, presenting a new approach that uses tf-idf similarity between a user's own tweets and recent tweets by people they follow. Based on a month's worth of posting data from 449 ego networks in Twitter, this method demonstrates that it is likely that at least 11% of reactions are not captured by the explicit reply and retweet mechanisms. Further, these uncaptured reactions are not evenly distributed between users: some users, who create replies and retweets without using the official interface mechanisms, are much more responsive to followees than they appear. This suggests that detecting non-explicit responses is an important consideration in mitigating biases and building more accurate models when using these markers to study social interaction and information diffusion.

BACKGROUND: In this study, clustering was performed using a bitmap representation of HIV reverse transcriptase and protease sequences, to produce an unsupervised classification of HIV sequences. The classification will aid our understanding of the interactions between mutations and drug resistance. 10,229 HIV genomic sequences from the protease and reverse transcriptase regions of the pol gene and antiretroviral resistant related mutations represented in an 82-dimensional binary vector space were analyzed. RESULTS: A new cluster representation was proposed using an image inspired by microarray data, such that the rows in the image represented the protein sequences from the genotype data and the columns represented presence or absence of mutations in each protein position.The visualization of the clusters showed that some mutations frequently occur together and are probably related to an epistatic phenomenon. CONCLUSION: We described a methodology based on the application of a pattern recognition algorithm using binary data to suggest clusters of mutations that can easily be discriminated by cluster viewing schemes.

The Brazilian Lattes Platform is an important academic/résumé data set that registers all academic activities of researchers associated with different major knowledge areas. The academic information collected in this data set is used to evaluate, analyze, and document the scientific production of research groups. Information about the interactions between Brazilian researchers in the form of coauthorships, however, has not been analyzed. In this article, we identified and characterized Brazilian academic coauthorship networks of researchers registered in the Lattes Platform using topological properties of graphs. For this purpose, we explored (a) strategies to develop a large Lattes curricula vitae data set, (b) an algorithm for identifying automatic coauthorships based on bibliographic information, and (c) topological metrics to investigate interactions among researchers. This study characterized coauthorship networks to gain an in‐depth understanding of the network structures and dynamics (social behavior) among researchers in all available major Brazilian knowledge areas. In this study, we evaluated information from a total of 1,131,912 researchers associated with the eight major Brazilian knowledge areas: agricultural sciences; biological sciences; exact and earth sciences; humanities; applied social sciences; health sciences; engineering; and linguistics, letters, and arts.

The influence of a particular individual in a scientific collaboration network could be measured in several ways. Estimating influence commonly requires calculating computationally costly global measures, which may be impractical on networks with hundreds of thousands of vertices. In this paper, we introduce new local measures to estimate the collaborative influence of individual researchers in a collaboration network. Our approach is based on the link prediction technique, and its underlying rationale is to assess how the presence/absence of a researcher affects the link prediction outcome in the network as a whole. It is natural to assume that the absence of a researcher with strong influence in the network will cause negative impact in the correct link prediction. Scientists are represented as vertices in the collaboration graph, and a vertex removal and corresponding link prediction process are performed iteratively for all vertices, each vertex being handled independently. The SVM supervised learning model has been adopted as link predictor. The proposed approach has been tested on real collaboration networks relative to multiple time periods, processing the networks in order to assign more relevance to recent than to older collaborations. The experimental tests suggest that our measure of impact on link prediction has high negative correlation with standard vertex importance measures such as between ness and closeness centrality.

The present work describes a framework for morphological characterization of galaxies based on the Spectral Graph Wavelet Transform. A galaxy image is sampled with a number of points randomly chosen, whose Delaunay triangulation results in an arbitrary graph. The average intensity value in a 5 × 5 vicinity of a pixel related to a graph vertex is assigned to the corresponding graph vertex. A weight inversely proportional to the photometric distance between each pair of vertices is assigned to the respective graph edge. The Spectral Graph Wavelet Transform is computed from this weighted graph with real-valued vertices yielding a high-dimensional feature vector, which is reduced to a two dimensional vector through Principal Component Analysis. The proposed framework has been assessed through two case studies, namely, the case study of analyzing (i) 2D binary images from shapes and preliminary results of (ii) 2D gray tone images from galaxies. The obtained results imply the suitability of this framework for the characterization of galaxies images.

Plankton microorganisms constitute the base of the marine food web and play a great role in global atmospheric carbon dioxide draw down. Moreover, being very sensitive to any environmental changes they allow noticing (and potentially counteracting) them faster than with any other means. As such they not only influence the fishery industry but are also frequently used to analyze changes in exploited coastal areas and the influence of these interferences on local environment and climate. As a consequence, there is a strong need for highly efficient systems allowing long time and large volume observation of plankton communities. The adopted sensors typically provide huge amounts of data that must be processed efficiently. This would provide us with better understanding of their role on global climate as well as help maintain the fragile environmental equilibrium. In this paper a new system for large volume plankton monitoring system is presented. It is based on visual analysis of small particles immersed in a water flux. The image sequences are analyzed with Visual Rhythm-based method which greatly accelerates the processing time and allows higher volume throughput. To assure maximal performance the algorithm was implemented using CUDA for GPGPU. The method was then tested on a large data set and compared with alternative frame-by-frame approach. The results prove that the method can be successfully applied for the large volume plankton monitoring problem, as well as in any other application where targets are to be detected and counted while moving in a unidirectional flux.

This paper introduces a new computational method to automatically estimate the International Publication Ratio (IPR) based on the analysis of bibliographical productions of Brazilian research groups, a task that would be too difficult (in many cases, impossible) to be performed manually. The proposed method explores the DOI number to identify the countries of every co-author who participated in each publication. Considering the bibliometric data from the Brazilian Lattes platform we show that is possible to make a good estimation of the IPR for research groups. Calculating the IPR is important in order to make a quantitative evaluation of the science progress and to establish a comparison between the academic institutions or knowledge areas. The experiments considering research groups, belonging to the 100 more collaborative researchers of five Brazilian major knowledge areas, confirm that the our proposal leads to an effective way to infer the IPR.

Mutations in HIV patients' reverse transcriptase and protease may be related to drug resistance. There are many issues that make difficult the complete elucidation of the relationship between these mutations and drug resistance, such as cross resistance and the limitations to detect the relevance of resistance. Look up tables and rule-based systems are an attempt to classify sequences and predict treatment failure. However, they depend on the scientific literature and their quality and reliability. Data-intensive analysis of HIV mutation databases may help to corroborate or to improve such knowledge spread in the literature. Pattern recognition algorithms classify data extracting information from different data domain. Clustering and biclustering classification algorithms have been explored to group scientific and business data based on measures of similarities. K-means is a popular algorithm for clustering and Bimax is used with binary data. Considering this scenario, the main contribution of this work is to develop a new methodology based on K-means and Bimax using a binary data representation of reverse transcriptase and protease sequences, in an attempt to get an unsupervised classification of the sequences that may be related to drug resistance. In our work, 14,393 sequences with selected positions of the proteins, known to be related to drug resistance, represented in an 82-dimensional vector space are analyzed by pattern recognition algorithms. The sequences are represented as binary vectors. Suitable visualization of such vectors is produced for medical interpretation and indicates some correspondence to the prediction of drug resistance given by the brazilian look up table, used by brazilian physicians, but that depends on the literature on HIV and it's quality to be created. As a consequence, in this work we describe a methodology based on the application of pattern recognition algorithms using binary data in order to suggest clusters of mutations and their relations with drug resistance using a different cluster visualization scheme.

Bioresource utilization represents an important interdisciplinary research that integrates academic and industrial expertise across diverse scientific domains, including physics, chemistry, biology, and engineering. The present paper describes a cyber-infrastructure being created at the Brazilian Bioethanol Science and Technology Laboratory (CTBE) to assist scientists working on the field. One key element of the infrastructure is the LignoCel Platform, a tailor-made database for upload, curation, and sharing of lignocellulose data. Particularly, LignoCel allows querying the data and exporting subsets that are analyzed for knowledge extraction. In the present paper, a case-study is described, in which scientists want to investigate the dimensions that relate cellulose structure and water uptake. Data analysis and dimensionality reduction using Principal Component Analysis (PCA) is employed. Different PCA-based measurements are extracted and visualized through automatically-generated HTML pages available for the domain scientists. In this case study, the workflow successfully provided dimensionality reduction from a data matrix originated from a heterogeneous set of materials. PCA scores and loadings are explored for data analysis and visualization. PCA reduced the 11 measured features (obtained from three different experimental techniques, 55 possible combinations of size 2) into a two-dimensional PC1PC2 loadings plot representing 89% of data variance. Examples of the output produced by the system are available at http://data.bioetanol.org. br/~liu.ling/pca-lignocel/.

The ballast water transported by commercial ships is one of the major un-natural means of species dispersal in aquatic ecosystems. An important technological challenge for monitoring ballast water standards concerns the establishment of analytical techniques that generate reliable results on organisms concentration, size and viability quickly and without the need for intensive manual work of specialists. Moreover, an automatic monitoring system must be able to store and analyze possibly large databases (hours of videos acquired with high frame rate and high resolution digital cameras). In the present paper, a new image analysis method for ballast water monitoring is presented. The main objective was to detect and count planktonic microorganisms using the Visual Rhythm of the video records during ballast water discharges. The results prove the efficacy of the proposed approach in the analysis of long movie sequences.

This work presents a new approach for the 3D human facial expressions analysis. Our methodology is based on 2D and 3D wavelet transforms, which are used to estimate multi-scale features from real a face acquired by a 3D scanner. The proposed methodology starts by considering a dataset composed by faces displaying seven different facial expressions. An automatic pre-processing method, adopting an ellipsoidal cropping, is applied to the dataset. Thereafter, the 2D and 3D descriptors are extracted from different scales of wavelet transforms for the purpose of obtaining the facial expression features. The multi-scale features are represented in a multi-variate feature space, which is analysed by the Sequential Forward Floating Selection algorithm using an entropy criterion function to select the subset of features that best represents each facial expression model. The obtained results corroborate the potential of multi-scale feature extraction for analysis of 3D facial expression.

In this paper, we propose a hierarchical Markovian framework to extract the road network with optical and synthetic aperture radar (SAR) data. We propose a generalization of a previous method based on a low-level step (features extraction) and a high-level step (use of contextual information). The main novelties of the proposed approach are the use of more general elements to represent road candidates, which simplifies and generalizes the method, the fusion of different sensors during both lower and higher levels and the introduction of a second MRF in a hierarchical way. The approach is tested and evaluated using TerraSAR-X and Quickbird data.

Microaneurysms (MAs) are the earliest sign of diabetic retinopathy and manifest as small reddish spots on the retina. Generally, algorithm design for MAs detection starts by separating the vascular system from the background for a posterior analysis of candidate MAs presence. Following this approach, this paper assesses three different methods for vessel segmentation and how they affect posterior MAs detection. The robustness in developing automatic screening systems for MAs detection is discussed and a methodology to detect candidate MAs in retinal images is introduced. The algorithm combines different vessel segmentation methods with region growing to evaluate which is the best to provide candidate MAs detection.

Abstract

BACKGROUND: Feature selection is a pattern recognition approach to choose important variables according to some criteria in order to distinguish or explain certain phenomena (i.e., for dimensionality reduction). There are many genomic and proteomic applications that rely on feature selection to answer questions such as selecting signature genes which are informative about some biological state, e.g., normal tissues and several types of cancer; or inferring a prediction network among elements such as genes, proteins and external stimuli. In these applications, a recurrent problem is the lack of samples to perform an adequate estimate of the joint probabilities between element states. A myriad of feature selection algorithms and criterion functions have been proposed, although it is difficult to point the best solution for each application. RESULTS: The intent of this work is to provide an open-source multiplatform graphical environment for bioinformatics problems, which supports many feature selection algorithms, criterion functions and graphic visualization tools such as scatterplots, parallel coordinates and graphs. A feature selection approach for growing genetic networks from seed genes (targets or predictors) is also implemented in the system. CONCLUSION: The proposed feature selection environment allows data analysis using several algorithms, criterion functions and graphic visualization tools. Our experiments have shown the software effectiveness in two distinct types of biological problems. Besides, the environment can be used in different pattern recognition applications, although the main concern regards bioinformatics tasks.

An important topic in genomic sequence analysis is the identification of protein coding regions. In this context, several coding DNA model-independent methods, based on the occurrence of specific patterns of nucleotides at coding regions, have been proposed. Nonetheless, these methods have not been completely suitable due to their dependence on an empirically pre-defined window length required for a local analysis of a DNA region. We introduce a method, based on a modified Gabor-wavelet transform (MGWT), for the identification of protein coding regions. This novel transform is tuned to analyze periodic signal components and presents the advantage of being independent of the window length. We compared the performance of the MGWT with other methods using eukaryote datasets. The results show that the MGWT outperforms all assessed model-independent methods with respect to identification accuracy. These results indicate that the source of at least part of the identification errors produced by the previous methods is the fixed working scale. The new method not only avoids this source of errors, but also makes available a tool for detailed exploration of the nucleotide occurrence.

An image segmentation method based on structural pattern recognition is presented. Two graphs are generated from the image to be segmented. A model graph is generated from an oversegmentation of the image and from traces provided by the user. An input graph is generated from the oversegmented image. Image segmentation is then obtained by matching the input graph to the model graph. An objective function is defined and optimized using a new approach to find the most suitable clique of the corresponding association graph. The structural information encoded in the graphs leads to a robust segmentation performance even in the case of non-homogeneous textured regions. Successful experimental results obtained from real images are provided.

BACKGROUND: One goal of gene expression profiling is to identify signature genes that robustly distinguish different types or grades of tumors. Several tumor classifiers based on expression profiling have been proposed using microarray technique. Due to important differences in the probabilistic models of microarray and SAGE technologies, it is important to develop suitable techniques to select specific genes from SAGE measurements. RESULTS: A new framework to select specific genes that distinguish different biological states based on the analysis of SAGE data is proposed. The new framework applies the bolstered error for the identification of strong genes that separate the biological states in a feature space defined by the gene expression of a training set. Credibility intervals defined from a probabilistic model of SAGE measurements are used to identify the genes that distinguish the different states with more reliability among all gene groups selected by the strong genes method. A score taking into account the credibility and the bolstered error values in order to rank the groups of considered genes is proposed. Results obtained using SAGE data from gliomas are presented, thus corroborating the introduced methodology. CONCLUSION: The model representing counting data, such as SAGE, provides additional statistical information that allows a more robust analysis. The additional statistical information provided by the probabilistic model is incorporated in the methodology described in the paper. The introduced method is suitable to identify signature genes that lead to a good separation of the biological states using SAGE and may be adapted for other counting methods such as Massive Parallel Signature Sequencing (MPSS) or the recent Sequencing-By-Synthesis (SBS) technique. Some of such genes identified by the proposed method may be useful to generate classifiers.

A novel biologically motivated face-recognition algorithm based on polar frequency is presented. Polar frequency descriptors are extracted from face images by Fourier--Bessel transform (FBT). Next, the Euclidean distance between all images is computed and each image is now represented by its dissimilarity to the other images. A pseudo-Fisher linear discriminant was built on this dissimilarity space. The performance of discrete Fourier transform (DFT) descriptors and a combination of both feature types was also evaluated. The algorithms were tested on a 40- and 1196-subjects face database (ORL and FERET, respectively). With five images per subject in the training and test datasets, error rate on the ORL database was 3.8, 1.25, and 0.2% for the FBT, DFT, and the combined classifier, respectively, as compared to 2.6% achieved by the best previous algorithm. The most informative polar frequency features were concentrated at low-to-medium angular frequencies coupled to low radial frequencies. On the FERET database, where an affine normalization preprocessing was applied, the FBT algorithm outperformed only the PCA in a rank recognition test. However, it achieved performance comparable to state-of-the-art methods when evaluated by verification tests. These results indicate the high informative value of the polar frequency content of face images in relation to recognition and verification tasks and that the Cartesian frequency content can complement information about the subjects' identity, but possibly only when the images are not prenormalized. Possible implications for human face recognition are discussed.

We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and two-dimensional Gabor wavelet transform responses taken at multiple scales. The Gabor wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE (Staal et al., 2004) and STARE (Hoover et al., 2000) databases of manually labeled images. On the DRIVE database, it achieves an area under the receiver operating characteristic curve of 0.9614, being slightly superior than that presented by state-of-the-art approaches. We are making our implementation available as open source MATLAB scripts for researchers interested in implementation details, evaluation, or development of methods.

The spatial relation "between" is a notion which is intrinsically both fuzzy and contextual, and depends, in particular, on the shape of the objects. The literature is quite poor on this and the few existing definitions do not take into account these aspects. In particular, an object B that is in a concavity of an object A1 not visible from an object A2 is considered between A1 and A2 for most definitions, which is counter intuitive. Also, none of the definitions deal with cases where one object is much more elongated than the other. Here, we propose definitions which are based on convexity, morphological operators, and separation tools, and a fuzzy notion of visibility. They correspond to the main intuitive exceptions of the relation. We distinguish between cases where objects have similar spatial extensions and cases where one object is much more extended than the other. Extensions to cases where objects, themselves, are fuzzy and to three-dimensional space are proposed as well. The original work proposed in this paper covers the main classes of situations and overcomes the limits of existing approaches, particularly concerning nonvisible concavities and extended objects. Moreover, the definitions capture the intrinsic imprecision attached to this relation. The main proposed definitions are illustrated on real data from medical images.

We present a new approach to shape representation and analysis, which consists in treating the contour of a 2D shape as an 1D signal and analyzing it with the 1D continuous wavelet transform. Application is made to the detection of corners, natural scales and fractal behavior in 2D shapes.

This work presents a new approach to shape characterization and analysis based on multiscale bending energy. Shapes are represented by their outlines and their respective multiscale curvatures, i.e. the curvegram, are estimated by using a scheme based on Fourier descriptors. The shrinkage problem is circumvented through perimeter normalization, which is crucial for allowing the quantitative analysis of the curvegram. Local and global multiscale bending energies are calculated from the curvegram, and the latter (i.e. global energy) is used in order to define the natural scales of the shape under analysis. Several issues concerning multiscale curvature quantitative analysis are discussed. The results regarding the application of the proposed technique to neuromorphometry are presented and discussed.