TY - GEN A1 - Jäger, Matthias A1 - Eberhardt, Jörg A1 - Cunningham, Douglas W. T1 - 3D reconstruction of partial foot scans using different state of the art neural network approaches T2 - Footwear Science N2 - As online shopping continues to grow in popularity, shoes are increasingly being purchased without being physically tried on. This has resulted in a significant surge in returns, causing both financial and environmental consequences. To tackle this issue, several systems are available to measure foot dimensions accurately either in-store or at home. By obtaining precise foot measurements, individuals can determine their ideal shoe size and prevent unnecessary returns. In order to make such a system as simple as possible for the user, only a single image should be sufficient to measure the foot. To make this possible, point clouds from one side of the foot, which are generated by taking a depth image, are to be used. Since these point clouds represent only one side of the foot, the other side has to be generated. For this purpose, different existing state of the art networks were tested and compared to determine which architecture is best suited for this task. After implementing, re-training on our own dataset and testing the different architectures, it can be concluded that the point/transormer-based network SnowflakeNet is the most efficient to be used for our task. KW - Foot measurement KW - Shape completion KW - Shape reconstruction KW - GAN KW - Neural network KW - Machine learning KW - CNN Y1 - 2024 U6 - https://doi.org/10.1080/19424280.2024.2319796 VL - Volume 16 IS - 2 SP - 105 EP - 114 PB - Taylor&Francis ER - TY - GEN A1 - Fotsing, Cedrique A1 - Tchuitcheu, Willy Carlos A1 - Besong, Lemopi Isidore A1 - Cunningham, Douglas William A1 - Bobda, Christophe ED - Mei, Guofeng T1 - A specialized pipeline for efficient and reliable 3D semantic model reconstruction of buildings from indoor point clouds T2 - Journal of Imaging N2 - Recent advances in laser scanning systems have enabled the acquisition of 3D point cloud representations of scenes, revolutionizing the fields of Architecture, Engineering, and Construction (AEC). This paper presents a novel pipeline for the automatic generation of 3D semantic models of multi-level buildings from indoor point clouds. The architectural components are extracted hierarchically. After segmenting the point clouds into potential building floors, a wall detection process is performed on each floor segment. Then, room, ground, and ceiling extraction are conducted using the walls 2D constellation obtained from the projection of the walls onto the ground plan. The identification of the openings in the walls is performed using a deep learning-based classifier that separates doors and windows from non-consistent holes. Based on the geometric and semantic information from previously detected elements, the final model is generated in IFC format. The effectiveness and reliability of the proposed pipeline are demonstrated through extensive experiments and visual inspections. The results reveal high precision and recall values in the extraction of architectural elements, ensuring the fidelity of the generated models. In addition, the pipeline’s efficiency and accuracy offer valuable contributions to future advancements in point cloud processing. KW - point cloud KW - segmentation KW - building information modeling KW - 3D semantic model KW - IFC Y1 - 2024 U6 - https://doi.org/10.3390/jimaging10100261 SN - 2313-433X VL - 10 IS - 10 PB - MDPI ER - TY - GEN A1 - Schorradt, Martin T. A1 - Cunningham, Douglas William ED - Tumuluri, Raj ED - Sebe, Nicu ED - Pingali, Gopal ED - Jayagopi, Dinesh Babu ED - Dhall, Abhinav ED - Singh, Richa ED - Anthony, Lisa ED - Salah, Albert Ali T1 - Age Regression for Human Voices T2 - ICMI '22: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION Bengaluru India November 7 - 11, 2022 N2 - The human voice is one of our most important tools for communicating with other people. Besides pure semantic meaning it also conveys syntactical information such as emphasis as well as personal information such as emotional state, gender, and age. While the physical changes that occur to a person’s voice are well studied, there is surprisingly little work on the perception of those changes. To hold the range of subtleties present in a given utterance constant and thus focus on the changes caused by age, this paper takes adult recordings (three males, and three females) and artificially resynthesizes them (using values from measurements of real children’s voices) to create a childlike versions of the utterance at different target ages. In particular, we focus on a systematic, factorial combination pitch shifting and formant shifting. To get an insight about the influence of these factors on the estimated age, we performed a perceptual experiment. Since the resynthesis method we used can produce a wide range of voices, not all of which are physically consistent, we also asked the participants to rate how natural the voices sounded. Furthermore, since former studies suggest that people are not able to distinguish between males and females of young ages, participants were also asked to rate how male or female the voices sounded. Overall, we found that although the synthesis method produced physically plausible signals (compared average values for real children), the degree of signal manipulation was correlated with perceived unnaturalness. We also found that pitch shift had only a small affect on perceived age, that formant shift had a strong affect on perceived age, and that these effects depended on the original gender of the recording. As expected, people had difficulty guessing the gender of younger sounding voices. KW - Age Synthesis KW - Age Regression KW - Speech Synthesis KW - Speech Processing Y1 - 2022 SN - 978-1-4503-9390-4 U6 - https://doi.org/10.1145/3536221.3556599 SP - 570 EP - 578 PB - Association for Computing Machinery New York NY United States ER - TY - GEN A1 - Sterzik, Anna A1 - Lichtenberg, Nils A1 - Krone, Michael A1 - Cunningham, Douglas William A1 - Lawonn, Kai ED - Raidou, Renata G. ED - Sommer, Björn ED - Kuhlen, Torsten W. ED - Krone, Michael ED - Schultz, Thomas ED - Wu, Hsiang-Yun T1 - Perceptual Evaluation of Common Line Variables for Displaying Uncertainty on Molecular Surfaces T2 - Eurographics Workshop on Visual Computing for Biology and Medicine N2 - Data are often subject to some degree of uncertainty, whether aleatory or epistemic. This applies both to experimental data acquired with sensors as well as to simulation data. Displaying these data and their uncertainty faithfully is crucial for gaining knowledge. Specifically, the effective communication of the uncertainty can influence the interpretation of the data and the users' trust in the visualization. However, uncertainty-aware visualization has gotten little attention in molecular visualization. When using the established molecular representations, the physicochemical attributes of the molecular data usually already occupy the common visual channels like shape, size, and color. Consequently, to encode uncertainty information, we need to open up another channel by using feature lines. Even though various line variables have been proposed for uncertainty visualizations, they have so far been primarily used for two-dimensional data and there has been little perceptual evaluation. Therefore, we conducted a perceptual study to determine the suitability of the line variables sketchiness, dashing, grayscale, and width for distinguishing several uncertainty values on molecular surfaces. KW - Human centered computing KW - Empirical studies in visualization KW - Scientific visualization KW - Computing methodologies KW - Non-photorealistic rendering KW - Applied computing KW - Imaging KW - CCS Concepts Y1 - 2022 UR - https://diglib.eg.org/handle/10.2312/vcbm20221186 SN - 978-3-03868-177-9 U6 - https://doi.org/10.2312/vcbm.20221186 SN - 2070-5786 SP - 41 EP - 51 PB - The Eurographics Association ER - TY - GEN A1 - Schorradt, Martin T. A1 - Cunningham, Douglas William ED - Lau, Manfred ED - Robb, Andrew ED - Barnett-Cowan, Michael ED - Serrano, Ana ED - Malpica, Sandra T1 - Erosion as a novel Approach for removing Semantics and Comparison of different State-of-Art-Methods T2 - SAP '22: ACM Symposium on Applied Perception 2022 N2 - Through language, people convey not only pure semantics, but also information about themselves, such as age, gender, state of mind or health. The supralingual features that carry this information have been a subject of research for a long time. Various procedures have been proposed to remove unneeded semantics from speech recordings, in order to study supralingual information in natural speech. In this paper, we propose a new method for removing sematics, based on erosion, a morphological operator. We compare its effectiveness to different state-of-the-art methods. As established methods we consider two low pass filters with cut off frequencies of 450Hz and 1150Hz and Brownian noise. As a newer method we investigate a filter for spectro-temporal frequencies. To evaluate each method, appropriately processed recordings were presented to a group of participants in a perceptual experiment. The intelligibility was measured by means of the Levenshtein distance. Our results show that erosion itself performs similarly to the established methods, while a combination of erosion and low-pass filter outperforms all other methods. Y1 - 2022 SN - 978-1-4503-9455-0 U6 - https://doi.org/10.1145/3548814.3551458 SP - 1 EP - 6 PB - Association for Computing Machinery New York NY United States ER - TY - GEN A1 - Jäger, Matthias C. A1 - Eberhardt, Jörg A1 - Cunningham, Douglas William T1 - Experimental Analysis of Commercial Optical Methods for Foot Measurement T2 - Sensors N2 - Due to the increasing trend of online shopping, shoes are more and more often bought without being tried on. This leads to a strong increase in returns, which results in a high financial as well as ecological burden. To prevent this, feet can be measured either in the store or at home by various systems to determine the exact dimensions of the foot and derive an optimal shoe size. In this paper, we want to present an overview of the methods currently available on the market for the measurement of feet. The most important commercial systems are classified according to the underlying basic technology. Subsequently, the most promising methods were implemented and tested. The results of the different methods were finally compared to find out the strengths and weaknesses of each technology. After determining the measurement accuracy of the length and width for each measurement method and also comparing the general shape of the 3D reconstruction with the GT, it can be said that the measurement using a ToF sensor is currently the most robust, the easiest and, among other methods, the most accurate method. KW - foot measurement KW - time of flight KW - structured light KW - image processing KW - shape completion Y1 - 2022 U6 - https://doi.org/10.3390/s22145438 SN - 1424-8220 VL - 22 IS - 14 ER - TY - GEN A1 - Fotsing, Cedrique A1 - Hahn, Philipp A1 - Cunningham, Douglas William A1 - Bobda, Christophe T1 - Volumetric wall detection in unorganized indoor point clouds using continuous segments in 2D grids T2 - Automation in Construction N2 - The quality of 3D models of existing buildings reconstructed from point clouds is strongly related to the segmentation process used to detect structural elements. A new wall detection method in the indoor point clouds of buildings is presented in this study. The point clouds are segmented into horizontal layers, and a concept of continuous segments in a 2D grid representation is used to extract the footprints of the wall structures, and 2D blocks are projected into 3D space to obtain the wall segments in the initial 3D point cloud. The results obtained from the execution of the proposed method demonstrate that wall blocks in indoor point clouds are detected independently of their shape. Executing the proposed method on a set of 9 in-door point clouds revealed better performance in terms of result quality and execution time compared to RANSAC. The robustness of the method can be improved by adding a classification step to eliminate non-consistent blocks. KW - Point cloud KW - Segmentation KW - Wall detection Y1 - 2022 U6 - https://doi.org/10.1016/j.autcon.2022.104462 SN - 0926-5805 IS - 141 ER - TY - GEN A1 - Fotsing, Cedrique A1 - Cunningham, Douglas William ED - Andres, Bjoern ED - Campen, Marcel ED - Sedlmair, Michael T1 - Context Aware Exemplar-based Image Inpainting using Irregular Patches T2 - Vision, Modeling, and Visualization N2 - We propose a new exemplar-based image inpainting method in this paper. Our method is based on the Criminisi pipeline. We focused on three main stages of the pipeline; calculation of priorities, construction of patches, and the search for the best match. To assign a high priority to patches constructed from the edge pixels, we use the ability of segmentation algorithms to divide an image into different texture blocks. The patches built from pixels located at the border between several texture blocks receive a high priority. Unlike most patch-based image inpainting methods which use regular patches (rectangle, square), the shape and size of our patches depend on the textural composition around the original pixel. The patches are built using a region growing principle in the different texture blocs around the original pixel. The search for the best match is done contextually. We search for the best match of the patch with the highest priority in a similar environment to its neighborhood around the target zone. The method is simple and easy to implement. The experiments show that our method obtains more plausible results than the basic method of Criminisi and its improved version Amoeba in most cases. KW - Capturing KW - Rendering Y1 - 2021 UR - https://diglib.eg.org/handle/10.2312/vmv20211373 SN - 978-3-03868-161-8 U6 - https://doi.org/10.2312/vmv.20211373 SP - 71 EP - 81 PB - The Eurographics Association ET - 1. Auflage ER - TY - GEN A1 - Fotsing, Cedrique A1 - Menadjou, Nareph A1 - Bobda, Christophe T1 - Iterative closest point for accurate plane detection in unorganized point clouds T2 - Automation in Construction N2 - Plane detection is an important step in the reconstruction of 3D models of buildings from point clouds. The results of plane detection methods based on the region growing approach mainly depend on the choice of seed points. In this study, we introduce a novel region growing-based method for plane detection in unorganized point clouds. Our method uses the Iterative Closest Point (ICP) algorithm to extract reliable seeds. To enhance the performance and the quality of the results, we used voxel grids representation of the point clouds in the growing process. The classification of the candidate planes is improved by using the number of voxel cells covering accumulated segments. The method is deterministic, runs in O(nlog(n)), and does not take into account the orientation of the point clouds. The results of plane detection using the proposed method on a benchmark consisting of 9 point clouds of buildings show a better precision of extracted planes compared to those obtained with 3-D KHT and PCL-RANSAC. Although slower than 3-D KHT, our method requires an execution time (3 x times) shorter than PCL-RANSAC. KW - Plane detection KW - Unorganized point clouds KW - Region growing Y1 - 2021 UR - https://www.sciencedirect.com/science/article/pii/S0926580521000613 U6 - https://doi.org/10.1016/j.autcon.2021.103610 SN - 0926-5805 VL - Vol. 125 ER - TY - GEN A1 - Fotsing, Cedrique A1 - Nziengam, Nafissetou A1 - Bobda, Christophe T1 - Large Common Plansets-4-Points Congruent Sets for Point Cloud Registration T2 - ISPRS International Journal of Geo-Information N2 - Point cloud registration combines multiple point cloud data sets collected from different positions using the same or different devices to form a single point cloud within a single coordinate system. Point cloud registration is usually achieved through spatial transformations that align and merge multiple point clouds into a single globally consistent model. In this paper, we present a new segmentation-based approach for point cloud registration. Our method consists of extracting plane structures from point clouds and then, using the 4-Point Congruent Sets (4PCS) technique, we estimate transformations that align the plane structures. Instead of a global alignment using all the points in the dataset, our method aligns 2-point clouds using their local plane structures. This considerably reduces the data size, computational workload, and execution time. Unlike conventional methods that seek to align the largest number of common points between entities, the new method aims to align the largest number of planes. Using partial point clouds of multiple real-world scenes, we demonstrate the superiority of our method compared to raw 4PCS in terms of quality of result (QoS) and execution time. Our method requires about half the execution time of 4PCS in all the tested datasets and produces better alignment of the point clouds. KW - point clouds KW - registration KW - segmentation Y1 - 2020 UR - https://www.mdpi.com/2220-9964/9/11/647 U6 - https://doi.org/10.3390/ijgi9110647 SN - 2220-9964 VL - 9 IS - 11 ER -