The visual quality of point clouds is critical for perception-centric immersive media. Point Cloud Quality Assessment (PCQA) is crucial for reducing costs associated with human evaluation, optimizing compression pipeline and enhancing human visual perception. However, real-valued PCQA methods often struggle to capture the coupled geometric and perceptual cues that govern quality. Com-PCQA, a novel no-reference PCQA framework leveraging complex-valued feature learning, is proposed. First, a Hilbert dual-stream module transforms multi-modal inputs of point clouds and images into analytic signals in the complex domain, enabling joint modeling of global structure and local texture with efficient tensor operations. Second, a complex amplitude-phase attention (CAPA) module explicitly decomposes and fuses amplitude features that describe geometric structure and phase features that capture fine-grained details, and it can be seamlessly integrated into other PCQA frameworks to enhance performance. Third, an adversarial joint scoring module integrates adversarial training with collaborative learning to calibrate multi-modal, multi-scale representations and enhance robustness. Extensive experiments on three public databases show that Com-PCQA achieves state-of-the-art correlations with subjective scores and consistently outperforms recent PCQA methods, demonstrating its effectiveness and robustness. The code will be available at https://openi.pcl.ac.cn/OpenPointCloud and https://github.com/LareinaSu/Com-PCQA.

The increasing demand for large-scale, high-quality datasets in dynamic point cloud compression (PCC) and human visual perception research underscores the limitations of existing datasets, which are often constrained by limited scale and insufficient dynamism, hindering algorithm validation and perceptual analysis in complex scenarios. To address this gap, we present DPCSet, a comprehensive dynamic point cloud dataset designed to support advanced research in PCC, human perception, and related domains. Comprising 100 dynamic object point clouds-the largest collection of its kind-DPCSet includes 200-frame sequences with geometry and attribute information, capturing diverse object types across real and virtual environments. Organized into seven superclasses, the dataset ensures broad scenario coverage. By rigorous selection, format conversion, quantization, DPCSet delivers standardized, high-precision point cloud data. Evaluation of multiple compression algorithms on a curated subset demonstrates DPCSet's efficacy in assessing trade-offs between compression efficiency and quality loss, positioning it as a potential benchmark for PCC. Furthermore, just noticeable distortion (JND) experiments on a compression-distorted subset reveal distinct perceptual characteristics of dynamic point clouds, offering valuable insights for perception-driven compression algorithms. The dataset is released at https://openi.pcl.ac.cn/gaowx/DPCSet.

Fine-grained plant pathology classification is an important task for precision agriculture, but at the same time, it is challenging due to the subtle difference in plant categories. Variances in the lighting conditions, position, and stages of disease symptoms usually lead to degradation of classification accuracy. Knowledge distillation is a popular method to improve the model performance to deal with the indistinguishable image classification problem. It aims to have a well-optimised small student network guided by a large teacher network. Existing knowledge distillation methods mainly consider training a teacher network that needs a high storage space and considerable computing resources. Self-knowledge distillation methods have been proposed to distil knowledge from the same network. Although self-knowledge distillation saves time and space compared with knowledge distillation, it only learns label knowledge. In this paper, we propose a novel self-distillation method to recognize the fine-grained plant category, which considers holistic knowledge based on the Squeeze and Excitation Network. We label this new method as holistic self-distillation because it captures knowledge through spatial features and labels. The performance validation of the proposed approach is performed on two public fine-grained plant datasets: Plant Pathology 2021 and Plant Pathology 2020 with the accuracy of 98.22% and 90.72% respectively. We also present experiments on the state-of-the-art algorithm (ResNet-50). The classification results demonstrate the effectiveness of the proposed approach with respect to accuracy.

Computer vision and deep learning have made substantial progress in the areas of agriculture and smart farming, particularly for enhancing crop production using image segmentation techniques for crop yield prediction. Further improvements to crop yield prediction results can be achieved by developing accurate and efficient methods. In response to such demands, this paper proposes a novel convolutional neural network architecture, called densely connected SegNet (D-SegNet) and demonstrates its advantages on plant segmentation using a new morning glory plant dataset, and also on a complimentary publicly available dataset to promote research in this direction. The D-SegNet is evaluated using 10-fold cross validation. It achieves performance better than the state-of-the-art SegNet algorithm. The evaluated precision, recall and F1-score values are 98.20%, 90.64% and 94.26%, respectively, for the morning glory plant dataset. The intersection over union (IoU) value in the image segmentation tasks is 90.56%. A series of experiments on the morning glory plant dataset as well as on the publicly available dataset were conducted. The results show that the proposed method achieves accurate segmentation results and can be useful for assessing the plant weight during harvesting. In summary, this new plant segmentation network, D-SegNet, could form an important component of future cloud-based machine learning systems to predict crop yield from noisy smartphone images taken in the field.