Video Anomaly Detection (VAD) remains a fundamental yet formidable task in the video understanding community, with promising applications in areas such as information forensics and public safety protection. Due to the rarity and diversity of anomalies, existing methods only use easily collected regular events to model the inherent normality of normal spatial-temporal patterns in an unsupervised manner. Although such methods have made significant progress benefiting from the development of deep learning, they attempt to model the statistical dependency between observable videos and semantic labels, which is a crude description of normality and lacks a systematic exploration of its underlying causal relationships. Previous studies have shown that existing unsupervised VAD models are incapable of label-independent data offsets (e.g., scene changes) in real-world scenarios and may fail to respond to light anomalies due to the overgeneralization of deep neural networks. Inspired by causality learning, we argue that there exist causal factors that can adequately generalize the prototypical patterns of regular events and present significant deviations when anomalous instances occur. In this regard, we propose Causal Representation Consistency Learning (CRCL) to implicitly mine potential scene-robust causal variable in unsupervised video normality learning. Specifically, building on the structural causal models, we propose scene-debiasing learning and causality-inspired normality learning to strip away entangled scene bias in deep representations and learn causal video normality, respectively. Extensive experiments on benchmarks validate the superiority of our method over conventional deep representation learning. Moreover, ablation studies and extension validation show that the CRCL can cope with label-independent biases in multi-scene settings and maintain stable performance with only limited training data available.

Acquiring large-scale, well-annotated datasets is essential for training robust scene text detectors, yet the process is often resource-intensive and time-consuming. While some efforts have been made to explore the synthesis of scene text images, a notable gap remains between syn-thetic and authentic data. In this paper, we introduce a novel method that utilizes Neural Radiance Fields (NeRF) to model real-world scenes and emulate the data collection process by rendering images from diverse camera per-spectives, enriching the variability and realism of the synthesized data. A semi-supervised learning framework is proposed to categorize semantic regions within 3D scenes, ensuring consistent labeling of text regions across various viewpoints. Our method also models the pose, and view-dependent appearance of text regions, thereby offering precise control over camera poses and significantly improving the realism of text insertion and editing within scenes. Employing our technique on real-world scenes has led to the creation of a novel scene text image dataset (https://github.com/cuijl-ai/TextNeRF). Compared to other existing benchmarks, the proposed dataset is distinctive in providing not only standard annotations such as bounding boxes and transcriptions but also the information of 3D pose attributes for text regions, enabling a more detailed evaluation of the robustness of text detection algorithms. Through extensive experiments, we demonstrate the effectiveness of our proposed method in enhancing the performance of scene text detectors.

This paper presents a new approach called the empirical mode decomposition — window fractal (EMDWF) algorithm in classification of fingerprint of medicinal herbs. In this way, we consider a glycyrrhiza fingerprint of medicinal herb as a signal sequence, and apply empirical mode decomposition (EMD) and Hiaguchis fractal dimension to construct a feature vector. By using EMD, the glycyrrhiza fingerprint of medicinal herb can be decomposed into some intrinsic mode functions (IMFs). As window fractal dimension (WFD) is applied to each IMF and original signal, the features of the glycyrrhiza fingerprint of medicinal herb can be obtained. Thereafter, SVM is applied as a classifier. The results of the experiments state clearly that the feature extracted by EMDWF is better than that of the existing methods including the pure EMD. With the increase of the number of training samples and the increase of the number of layers in EMD, the classification result achieves more stability.

Handwriting-based personal identification, which is also called handwriting-based writer identification, is an active research topic in pattern recognition. Despite continuous effort, offline handwriting-based writer identification still remains as a challenging problem because writing features can only be extracted from the handwriting image. As a result, plenty of dynamic writing information, which is very valuable for writer identification, is unavailable for offline writer identification. In this paper, we present a novel wavelet-based Generalized Gaussian Density (GGD) method for offline writer identification. Compared with the 2-D Gabor model, which is currently widely acknowledged as a good method for offline handwriting identification, GGD method not only achieves a better identification accuracy but also greatly reduces the elapsed time on calculation in our experiments.

Handwriting-based writer identification is a hot research topic in the pattern recognition field. Nowadays, online handwriting-based writer identification is steadily growing toward its maturity. On the contrary, offline handwriting-based writer identification still remains as a challenging problem because writing features only can be extracted from the handwriting image in this situation. As a result, plenty of dynamic writing information, which is very valuable for writer identification, is lost. At present, 2D Gabor filter method is widely acknowledged as a good method for offline handwriting identification, however it still suffers from some inherent disadvantages, such as the high computational cost. In this paper, we present a novel wavelet-based GGD method to replace the traditional 2D Gabor filters. Shown in our experiments, this novel method not only achieves better experiment results but also greatly reduces the elapsed time on calculation.