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Human activity recognition (HAR) remains an essential field of research with increasing real-world applications ranging from healthcare to industrial environments. As the volume of publications in this domain continues to grow, staying abreast of the most pertinent and innovative methodologies can be challenging. This survey provides a comprehensive overview of the state-of-the-art methods employed in HAR, embracing both classical machine learning techniques and their recent advancements. We investigate a plethora of approaches that leverage diverse input modalities including, but not limited to, accelerometer data, video sequences, and audio signals. Recognizing the challenge of navigating the vast and ever-growing HAR literature, we introduce a novel methodology that employs large language models to efficiently filter and pinpoint relevant academic papers. This not only reduces manual effort but also ensures the inclusion of the most influential works. We also provide a taxonomy of the examined literature to enable scholars to have rapid and organized access when studying HAR approaches. Through this survey, we aim to inform researchers and practitioners with a holistic understanding of the current HAR landscape, its evolution, and the promising avenues for future exploration. [ABSTRACT FROM AUTHOR]

This work presents a complete pipeline for folding a pile of clothes using a dual-armed robot. This is a challenging task both from the viewpoint of machine vision and robotic manipulation. The presented pipeline is comprised of the following parts: isolating and picking up a single garment from a pile of crumpled garments, recognizing its category, unfolding the garment using a series of manipulations performed in the air, placing the garment roughly flat on a work table, spreading it, and, finally, folding it in several steps. The pile is segmented into separate garments using color and texture information, and the ideal grasping point is selected based on the features computed from a depth map. The recognition and unfolding of the hanging garment are performed in an active manner, utilizing the framework of active random forests to detect grasp points, while optimizing the robot actions. The spreading procedure is based on the detection of deformations of the garment's contour. The perception for folding employs fitting of polygonal models to the contour of the observed garment, both spread and already partially folded. We have conducted several experiments on the full pipeline producing very promising results. To our knowledge, this is the first work addressing the complete unfolding and folding pipeline on a variety of garments, including T-shirts, towels, and shorts. [ABSTRACT FROM AUTHOR]

This work presents a novel method performing shape matching between folded garments and corresponding unfolded templates. It incorporates both partial and global shape analysis techniques, estimating point correspondences between contours of folded garments and generic templates of unfolded garments. The established correspondences are used in the estimation of polygonal models of the folded garments. The initial matching results are also used for modifying the original templates to better fit the folded garments. The method is applied in an iterative fashion, using the fitted templates as a new input, resulting to more accurate matching as indicated by the experimental results. The produced polygon models can be used for planning the unfolding strategy during autonomous robotic manipulation. [ABSTRACT FROM AUTHOR]

Advances in image sensors and evolution of digital computation is a strong stimulus for development and implementation of sophisticated methods for capturing, processing and analysis of 3-D data from dynamic scenes. Research on perspective time-varying 3-D scene capture technologies is important for the upcoming 3DTV displays. Methods such as shape-from-texture, shape-from-shading, shape-from-focus, and shape-from-motion extraction can restore 3-D shape information from a single camera data. The existing techniques for 3-D extraction from single-camera video sequences are especially useful for conversion of the already available vast mono-view content to the 3DTV systems. Scene- oriented single-camera methods such as human face reconstruction and facial motion analysis, body modeling and body motion tracking, and motion recognition solve efficiently a variety of tasks. 3-D multicamera dynamic acquisition and reconstruction, their hardware specifics including calibration and synchronization and software demands form another area of intensive research. Different classes of multiview stereo algorithms such as those based on cost function computing and optimization, fusing of multiple views, and feature-point reconstruction are possible candidates for dynamic 3-D reconstruction. High-resolution digital holography and pattern projection techniques such as coded light or fringe projection for real-time extraction of 3-D object positions and color information could manifest themselves as an alternative to traditional camera-based methods. Apart from all of these approaches, there also are some active imaging devices capableof 3-D extraction such as the 3-D time-of-flight camera, which provides 3-D image data of its environment by means of a modulated infrared light source. [ABSTRACT FROM AUTHOR]

In this paper, a novel local surface descriptor is proposed and applied to the problem of aligning partial views of a 3D object. The descriptor is based on taking ‘snapshots’ of the surface over each point using a virtual camera oriented perpendicularly to the surface. This representation has the advantage of imposing minimal loss of information be robust to self-occlusions and also be very efficient to compute. Then, we describe an efficient search technique to deal with the rotation ambiguity of our representation and experimentally demonstrate the benefits of our approaches which are pronounced especially when we align views with small overlap. [ABSTRACT FROM AUTHOR]

This paper presents a complete face authentication system integrating both two-dimensional (color or intensity) and three-dimensional (3-D) range data, based on a low-cost 3-D sensor, capable of real-time acquisition of 3-D and color images. Novel algorithms are proposed that exploit depth information to achieve robust face detection and localization under conditions of back- ground clutter, occlusion, face pose alteration, and harsh illumination. The well-known embedded hidden Markov model technique for face authentication is applied to depth maps and color images. To cope with pose and illumination variations, the enrichment of face databases with synthetically generated views is proposed. The performance of the proposed authentication scheme is tested thoroughly on two distinct face databases of significant size. Experimental results demonstrate significant gains resulting from the combined use of depth and color or intensity information. [ABSTRACT FROM AUTHOR]

This paper presents a machine vision system for the high-accuracy 3D measurement of holes on the surface of industrial components. This is a very important application for inline quality inspection in assembly plants. A CAD-based stereo vision approach is adopted. The introduction of several novel techniques enables the system to achieve high robustness in versatile industrial environments, rapid response, and accuracy below 0.1 mm. These are demonstrated by extensive experiments with synthetic and real data. [ABSTRACT FROM AUTHOR]

Presents information on a study which determined the optimal mechanisms for the hierarchical decomposition of wire-frame surfaces generated by box-splines. Use of box-splines for the optimal hierarchical representation of wire-mesh surfaces; Specific wire mesh hierarchical decompositions; Conclusions.

Presents a study which applied a temporal learning-filtering procedure to refine the left ventricle boundary in echocardiographic images. Initial boundary estimation; Contour tracking; Experimental results; Conclusions and suggestions for further work.

Presents a motion based algorithm based on spatiotemporal warping of an image sequence. Two dimensional warping techniques; Description of fast algorithm for the estimation of the motion parameters and motion compensation; Comparison of the proposed algorithm to a two-dimensional (2-D) warping algorithm.

Presents an algorithm for the estimation of the motion of textured objects undergoing nonrigid deformations over a sequence of images. Finite elements; Image warping; Construction of the mesh by using an adaptive triangulation procedure that places more triangles over high detail areas; Model update and tracking.

Presents an object-based coding of stereo image sequences by the use of three dimensional models. Discussion on the use of motion parameters to construct predicted images; Information on the transmission relayed on multiview receiver systems; Discussion on the use of preanalyzed scenes in video productions.