Extracting accurate information from video data in the surveillance system plays a key role in understanding the field situation and solving the problem. In this paper, a new method for estimating the height of a tilted object from a single image is proposed. The existing single view metrology method has the limitation that it can be applied only when the object is perpendicular to the ground. On the other hand, the proposed method is designed to be able to estimate the height even if the object is not perpendicular. The tilted angle of the object was estimated through gravity direction detection and the ground point was estimated by using the estimated angle and geometric characteristics. Using the estimated tilt and ground point, the actual tilted angle and vertical height of the distorted object in the image can be estimated, and finally the height of the tilted object can be accurately measured. As a result of the experiment, the proposed method achieved the average error 1.09 cm and the average error ratio 1.21% to the object, proving the accurate estimation performance close to the actual object height.

The exponential growth of video content has made it increasingly difficult to locate specific information within large-scale video repositories. To address this challenge, we present a multimodal video indexing framework that integrates audio transcription and visual scene analysis into a unified, searchable structure. The system uses speech-to-text models to transcribe spoken content and applies vision-language models to generate semantic descriptions from video frames. To evaluate the effectiveness of the proposed framework, we conducted experiments on 174 hours of broadcast television content. The results, which include both retrieval accuracy and query latency, confirm the ability of the system to locate relevant sections efficiently and support scalable search in data-intensive environments.

Video anomaly detection involves automatically identifying unusual or abnormal events in videos, such as crimes and accidents. This paper proposes a novel framework for video anomaly detection named Video Anomaly Transformer for Monitoring Accidents and Nefariousness (VATMAN). Existing approaches often implicitly train the model to generalize the normal data into a single distribution, then detect data that are less generalized as anomalies during the evaluation. However, such methods often struggle with complex data. To address this, our framework leverages the self-attention mechanisms of Transformers, combined with pre-trained 3D Convolutional Neural Networks (3DCNNs). The proposed anomaly score based on the self-attention mechanism can achieve anomaly detection that is less sensitive to data complexity. Experiments on the Abnormal Behavior CCTV Video Dataset demonstrate that VATMAN outperforms existing anomaly detection methods and shows many favorable properties.

In data analysis, unsupervised anomaly detection holds an important position for identifying statistical outliers that signify atypical behavior, erroneous readings, or interesting patterns within data. The Transformer model, known for its ability to capture dependencies within sequences, has revolutionized areas such as text and image data analysis. However, its potential for tabular data, where sequence dependencies are not inherently present, remains underexplored. This paper introduces Transformer for Point Anomaly Detection (TransPAD), a novel Transformer-based AutoEncoder framework specifically designed for point anomaly detection. Our method captures interdependencies across entire datasets, addressing the challenges posed with non-sequential, tabular data. It incorporates unique random and criteria sampling strategies for effective training and anomaly identification, and avoids the common pitfall of trivial generalization that affects many conventional methods. By leveraging an attention weight-based anomaly scoring system, TransPAD offers a more precise approach to detect anomalies. Extensive testing on a range of benchmark tabular datasets shows that TransPAD consistently outperforms existing methods. Our source code is available at https://github.com/nth221/TransPAD.

Summary In this work, we conducted a literature review about deep learning (DNN, RNN, CNN, and so on) for analyzing EEG data for decoding the activity of human's brain and diagnosing disease and explained details about various architectures for understanding the details of CNN and RNN. It has analyzed a word, which presented a model based on CNN and LSTM methods, and how these methods can be used to both optimize and set up the hyper parameters of deep learning architecture. Later, it is studied how semi‐supervised learning on EEG data analytics can be applied. We review some studies about different methods of semi‐supervised learning on EEG data analytics and discussing the importance of semi‐supervised learning for analyzing EEG data. In this paper, we also discuss the most common applications for human EEG research and review some papers about the application of EEG data analytics such as Neuromarketing, human factors, social interaction, and BCI. Finally, some future trends of development and research in this area, according to the theoretical background on deep learning, are given.

In this paper, a novel method of active contours based on the formulation of partial differential equation (PDE) is proposed for image segmentation. The evolution equation incorporates a force term that pushes the contour towards object boundary, a regularisation term which takes into account the smoothness of the level set function and an edge term which helps to stop the contour at required boundaries. The proposed method integrates an image convolved by a variable kernel into an energy formulation, where the width of the kernel varies in each iteration. Therefore, it takes local region information when the width of the kernel is small while for the larger width of the kernel, the proposed method considers global region information across the regions. Due to the use of both local and global image information, the method easily detects objects in the complex background and also segments the objects where intensity changes within the object. Moreover, the proposed method totally eliminates the need of the contour initialisation by using constant initialisation scheme. Experimental results on real and medical images prove the robustness of the proposed method. Finally, the authors validate their method on PH2 database for skin lesion segmentation.

In this paper, we introduce geometry-dependent lighting that allows lighting parameters to be defined independently and possibly discrepantly over an object or scene based on the local geometry. We present and discuss Light Collages, a lighting design system with geometry-dependent lights for effective feature-enhanced visualization. Our algorithm segments the objects into local surface patches and places lights that are locally consistent but globally discrepant to enhance the perception of shape. We use spherical harmonics for efficiently storing and computing light placement and assignment. We also outline a method to find the minimal number of light sources sufficient to illuminate an object well with our globally discrepant lighting approach.

Research over the last decade has built a solid mathematical foundation for representation and analysis of 3D meshes in graphics and geometric modeling. Much of this work however does not explicitly incorporate models of low-level human visual attention. In this paper we introduce the idea of mesh saliency as a measure of regional importance for graphics meshes. Our notion of saliency is inspired by low-level human visual system cues. We define mesh saliency in a scale-dependent manner using a center-surround operator on Gaussian-weighted mean curvatures. We observe that such a definition of mesh saliency is able to capture what most would classify as visually interesting regions on a mesh. The human-perception-inspired importance measure computed by our mesh saliency operator results in more visually pleasing results in processing and viewing of 3D meshes. compared to using a purely geometric measure of shape. such as curvature. We discuss how mesh saliency can be incorporated in graphics applications such as mesh simplification and viewpoint selection and present examples that show visually appealing results from using mesh saliency.

We introduce Light Collages - a lighting design system for effective visualization based on principles of human perception. Artists and illustrators enhance perception of features with lighting that is locally consistent and globally inconsistent. Inspired by these techniques, we design the placement of light sources to convey a greater sense of realism and better perception of shape with globally inconsistent lighting. Our algorithm segments the objects into local surface patches and uses a number of perceptual heuristics, such as highlights, shadows, and silhouettes, to enhance the perception of shape. We show our results on scientific and sculptured datasets.