Abstract Human pose estimation (HPE) is a computer vision application that estimates human body joints from images. It gives machines the capability to better understand the interaction between humans and the environment. For this accomplishment, many HPE methods have been deployed in robots, vehicles, and unmanned aerial vehicles (UAVs). This effort raised the challenge of balance between algorithm performance and efficiency, especially in UAVs, where computational resources are limited for saving battery power. Despite the considerable progress in the HPE problem, there are very few methods that are proposed to face this challenge. To highlight the severity of this fact, the proposed paper presents a brief review and an HPE benchmark from the aspect of algorithms performance and efficiency under UAV operation. More specifically, the contribution of HPE methods in the last 22 years is covered, along with the variety of methods that exist. The benchmark consists of 36 pose estimation models in 3 known datasets with metrics that fulfill the paper aspect. From the results, MobileNet-based models achieved competitive performance and the lowest computational cost, in comparison with ResNet-based models. Finally, benchmark results are projected in edge devices hardware specifications to analyze the appropriateness of these algorithms for UAV deployment.

This paper presents a variation of fuzzy c-means (FCM) algorithm that provides image clustering. The proposed algorithm incorporates the local spatial information and gray level information in a novel fuzzy way. The new algorithm is called fuzzy local information C-Means (FLICM). FLICM can overcome the disadvantages of the known fuzzy c-means algorithms and at the same time enhances the clustering performance. The major characteristic of FLICM is the use of a fuzzy local (both spatial and gray level) similarity measure, aiming to guarantee noise insensitiveness and image detail preservation. Furthermore, the proposed algorithm is fully free of the empirically adjusted parameters (a, ¿(g), ¿(s), etc.) incorporated into all other fuzzy c-means algorithms proposed in the literature. Experiments performed on synthetic and real-world images show that FLICM algorithm is effective and efficient, providing robustness to noisy images.

This paper presents a novel approach for object skeleton family extraction. The introduced technique utilizes a 2-D physics-based deformable model that parameterizes the objects shape. Deformation equations are solved exploiting modal analysis, and proportional to model physical characteristics, a different skeleton is produced every time, generating, in this way, a family of skeletons. The theoretical properties and the experiments presented demonstrate that obtained skeletons match to hand-labeled skeletons provided by human subjects, even in the presence of significant noise and shape variations, cuts and tears, and have the same topology as the original skeletons. In particular, the proposed approach produces no spurious branches without the need of any known skeleton pruning method.

This paper presents a novel fast model for active contours to detect objects in an image, based on techniques of curve evolution. The proposed model can detect objects whose boundaries are not necessarily defined by gradient, based on the minimization of a fuzzy energy, which can be seen as a particular case of a minimal partition problem. This fuzzy energy is used as the model motivation power evolving the active contour, which will stop on the desired object boundary. However, the stopping term does not depend on the gradient of the image, as most of the classical active contours, but instead is related to the image color and spatial segments. The fuzziness of the energy provides a balanced technique with a strong ability to reject "weak" local minima. Moreover, this approach converges to the desired object boundary very fast, since it does not solve the Euler-Lagrange equations of the underlying problem, but, instead, calculates the fuzzy energy alterations directly. The theoretical properties and various experiments presented demonstrate that the proposed fuzzy energy-based active contour is better and more robust than classical snake methods based on the gradient or other kind of energies.

This paper addresses the issue of accurate, effective, computationally efficient, fast, and fully automated 2-D object orientation and scaling factor estimation. The object orientation is calculated using object principal axes estimation. The approach relies on the object's frequency-based features. The frequency-based features used by the proposed technique are extracted by a 2-D physics-based deformable model that parameterizes the objects shape. The method was evaluated on synthetic and real images. The experimental results demonstrate the accuracy of the method, both in orientation and the scaling estimations.

A major disadvantage of image retrieval systems is their lack of objects orientation and scaling matching. This paper addresses the issue of accurate, effective, computationally efficient, fast and fully-automated 2D object orientation and scaling determination. The approach relies on the objects frequency-based features. The frequency-based features used by the proposed technique, are extracted by a 2D physics-based deformable model that parameterizes the objects shape. The method was evaluated on synthetic and real images. The experimental results demonstrate the accuracy of the method, both in orientation and the scaling estimations

In this paper, the generalized fuzzy mathematical morphology (GFMM) is proposed, based on a novel definition of the fuzzy inclusion indicator (FII). FII is a fuzzy set used as a measure of the inclusion of a fuzzy set into another, that is proposed to be a fuzzy set. It is proven that the FII obeys a set of axioms, which are proposed to be extensions of the known axioms that any inclusion indicator should obey, and which correspond to the desirable properties of any mathematical morphology operation. The GFMM provides a very powerful and flexible tool for morphological operations. The binary and grayscale mathematical morphologies can be considered as special cases of the proposed GFMM. An application for robust skeletonization and shape decomposition of two-dimensional (2-D) and three-dimensional (3-D) objects is presented. Simulation examples show that the object reconstruction from their skeletal subsets that can be achieved by using the GFMM is better than by using the binary mathematical morphology in most cases. Furthermore, the use of the GFMM for skeletonization and shape decomposition preserves the shape and the location of the skeletal subsets and spines.

In this paper, the morphological skeleton interpolation (MSI) algorithm is presented. It is an efficient, shape-based interpolation method used for interpolating slices in a three-dimensional (3-D) binary object. It is based on morphological skeletonization, which is used for two-dimensional (2-D) slice representation. The proposed morphological skeleton matching process provides translation, rotation, and scaling information at the same time. The interpolated slices preserve the shape of the original object slices, when the slices have similar shapes. It can also modify the shape of an object when the successive slices do not have similar shapes. Applications on artificial and real data are also presented.

In this paper, the fuzzy scalar median (FSM) is proposed, defined by using ordering of fuzzy numbers based on fuzzy minimum and maximum operations defined by using the extension principle. Alternatively, the FSM is defined from the minimization of a fuzzy distance measure, and the equivalence of the two definitions is proven. Then, the fuzzy vector median (FVM) is proposed as an extension of vector median, based on a novel distance definition of fuzzy vectors, which satisfy the property of angle decomposition. By defining properly the fuzziness of a value, the combination of the basic properties of the classical scalar and vector median (VM) filter with other desirable characteristics can be succeeded.

The use of clustering algorithms for decision-level data fusion is proposed. Person authentication results coming from several modalities (e.g., still image, speech), are combined by using fuzzy k-means (FKM) and fuzzy vector quantization (FVQ) algorithms, and a median radial basis function (MRBF) network. The quality measure of the modalities data is used for fuzzification. Two modifications of the FKM and FVQ algorithms, based on a fuzzy vector distance definition, are proposed to handle the fuzzy data and utilize the quality measure. Simulations show that fuzzy clustering algorithms have better performance compared to the classical clustering algorithms and other known fusion algorithms. MRBF has better performance especially when two modalities are combined. Moreover, the use of the quality via the proposed modified algorithms increases the performance of the fusion system.

In this paper a new variation of Hough transform is proposed. The parameter space of Hough transform is recursively roughly split into fuzzy cells which are defined as fuzzy numbers. This fuzzy partition of the parameter space provides the advantage to use the uncertainty of the contour points location, which is increased when noisy images have to be used. It can be used to detect contours in an image, with better accuracy, especially in noisy images. Moreover, the regions of the parameter space having no contours are rejected during the iterations. As a result the computation time is significantly decreased.