Binary morphological shape-based interpolation applied to 3-D tooth reconstruction

In this paper we propose an interpolation algorithm using a mathematical morphology morphing approach. The aim of this algorithm is to reconstruct the $n$-dimensional object from a group of (n-1)-dimensional sets representing sections of that object. The morphing transformation modifies pairs of consecutive sets such that they approach in shape and size. The interpolated set is achieved when the two consecutive sets are made idempotent by the morphing transformation. We prove the convergence of the morphological morphing. The entire object is modeled by successively interpolating a certain number of intermediary sets between each two consecutive given sets. We apply the interpolation algorithm for 3-D tooth reconstruction

Quantifying the knowledge in Deep Neural Networks: an overview

Deep Neural Networks (DNNs) have proven to be extremely effective at learning a wide range of tasks. Due to their complexity and frequently inexplicable internal state, DNNs are difficult to analyze: their black-box nature makes it challenging for humans to comprehend their internal behavior. Several attempts to interpret their operation have been made during the last decade, but analyzing deep neural models from the perspective of the knowledge encoded in their layers is a very promising research direction, which has barely been touched upon. Such a research approach could provide a more accurate insight into a DNN model, its internal state, learning progress, and knowledge storage capabilities. The purpose of this survey is two-fold: a) to review the concept of DNN knowledge quantification and highlight it as an important near-future challenge, as well as b) to provide a brief account of the scant existing methods attempting to actually quantify DNN knowledge. Although a few such algorithms have been proposed, this is an emerging topic still under investigation

Facial expression recognition using shape and texture information

A novel method based on shape and texture information is proposed in this paper for facial expression recognition from video sequences. The Discriminant Non-negative Matrix Factorization (DNMF) algorithm is applied at the image corresponding to the greatest intensity of the facial expression (last frame of the video sequence), extracting that way the texture information. A Support Vector Machines (SVMs) system is used for the classi cation of the shape information derived from tracking the Candide grid over the video sequence. The shape information consists of the di erences of the node coordinates between the rst (neutral) and last (fully expressed facial expression) video frame. Subsequently, fusion of texture and shape information obtained is performed using Radial Basis Function (RBF) Neural Networks (NNs). The accuracy achieved is equal to 98,2% when recognizing the six basic facial expressionsIFIP International Conference on Artificial Intelligence in Theory and Practice - Machine VisionRed de Universidades con Carreras en Informática (RedUNCI

Political Tweet Sentiment Analysis For Public Opinion Polling

Public opinion measurement through polling is a classical political analysis task, e.g. for predicting national and local election results. However, polls are expensive to run and their results may be biased primarily due to improper population sampling. In this paper, we propose two innovative methods for employing tweet sentiment analysis’ results for public opinion polling. Our first method utilizes merely the tweet sentiment analysis’ results outperforming a plethora of well-recognised methods. In addition, we introduce a novel hybrid way to estimate electorally results from both public opinion polls and tweets. This method enables more accurate, frequent and inexpensive public opinion estimation and used for estimating the result of the 2023 Greek national election. Our method managed to achieve lower deviation than the conventional public opinion polls from the actual election’s results, introducing new possibilities for public opinion estimation using social media platforms

Multimodal Stereoscopic Movie Summarization Conforming to Narrative Characteristics

Video summarization is a timely and rapidly developing research field with broad commercial interest, due to the increasing availability of massive video data. Relevant algorithms face the challenge of needing to achieve a careful balance between summary compactness, enjoyability, and content coverage. The specific case of stereoscopic 3D theatrical films has become more important over the past years, but not received corresponding research attention. In this paper, a multi-stage, multimodal summarization process for such stereoscopic movies is proposed, that is able to extract a short, representative video skim conforming to narrative characteristics from a 3D film. At the initial stage, a novel, low-level video frame description method is introduced (frame moments descriptor) that compactly captures informative image statistics from luminance, color, optical flow, and stereoscopic disparity video data, both in a global and in a local scale. Thus, scene texture, illumination, motion, and geometry properties may succinctly be contained within a single frame feature descriptor, which can subsequently be employed as a building block in any key-frame extraction scheme, e.g., for intra-shot frame clustering. The computed key-frames are then used to construct a movie summary in the form of a video skim, which is post-processed in a manner that also considers the audio modality. The next stage of the proposed summarization pipeline essentially performs shot pruning, controlled by a user-provided shot retention parameter, that removes segments from the skim based on the narrative prominence of movie characters in both the visual and the audio modalities. This novel process (multimodal shot pruning) is algebraically modeled as a multimodal matrix column subset selection problem, which is solved using an evolutionary computing approach. Subsequently, disorienting editing effects induced by summarization are dealt with, through manipulation of the video skim. At the last step, the skim is suitably post-processed in order to reduce stereoscopic video defects that may cause visual fatigue