In this letter, we present a design that enables multispectral Time Delay and Integration (TDI) imaging to be performed with a single coded measurement. A pixel-level spectral coded aperture, which resembles a spectral Bayer pattern, is integrated onto a TDI-CCD sensor, and thus, the spectral cube is coded and aliased during the TDI scanning. Subsequently, reconstruction can be performed within the framework of snapshot compressive imaging from the resulting aliased measurement. This method bypasses the conventional power scaling issue, a critical limitation of current TDI-CCD sensors, ensuring that an increase in spectral bands does not lead to a corresponding increase in readout power dissipation. Numerical experiments performed on ten distinct scenes demonstrate the method’s capacity for robust reconstruction, achieving average scores of Q14 = 0.903, SAM = 1.729, ERGAS = 1.774, SSIM = 0.96, and PSNR = 34.7dB.

Medical image segmentation remains a challenging task due to the intricate nature of anatomical structures and the wide range of target sizes. In this paper, we propose a novel U-shaped segmentation network that integrates CNN and Transformer architectures to address these challenges. Specifically, our network architecture consists of three main components. In the encoder, we integrate an attention-guided multi-scale feature extraction module with a dual-path downsampling block to learn hierarchical features. The decoder employs an advanced feature aggregation and fusion module that effectively models inter-dependencies across different hierarchical levels. For the bottleneck, we explore multi-scale feature activation and multi-layer context Transformer modules to facilitate high-level semantic feature learning and global context modeling. Additionally, we implement a multi-resolution input-output strategy throughout the network to enrich feature representations and ensure fine-grained segmentation outputs across different scales. The experimental results on diverse multi-modal medical image datasets (ultrasound, gastrointestinal polyp, MR, and CT images) demonstrate that our approach can achieve superior performance over state-of-the-art methods in both quantitative measurements and qualitative assessments.

With the rapid development of social media, visual sentiment analysis from image or video has become a hot spot in visual understanding researches. In this work, we propose an effective approach using visual and textual fusion for sentiment analysis of short GIF videos with textual descriptions. We extract both sequence-level and frame-level visual features for each given GIF video. Next, we build a visual sentiment classifier by using the extracted features. We also define a mapping function, which converts the sentiment probability from the classifier to a sentiment score used in our fusion function. At the same time, for the accompanied textual annotations, we employ the Synset forest to extract the sets of the meaningful sentiment words and utilize the SentiWordNet3.0 model to obtain the textual sentiment score. Then, we design a joint visual-textual sentiment score function weighted with visual sentiment component and textual sentiment one. To make the function more robust, we introduce a noticeable difference threshold to further process the fused sentiment score. Finally, we adopt a grid search technique to obtain relevant model hyper-parameters by optimizing a sentiment aware score function. Experimental results and analysis extensively demonstrate the effectiveness of the proposed sentiment recognition scheme on three benchmark datasets including T-GIF dataset, GSO-2016 dataset and Adjusted-GIFGIF dataset.

The linear-chain CRFs is one of the most popular discriminative models for human action recognition, as it can achieve good prediction performance in temporal sequential labeling by capturing the one-or few-timestep interactions of the target states. However, existing CRFs formulations have limited capabilities to capture deeper intermediate representations within the target states and higher order dependence between the given states, which are potentially useful and significant in the modeling of complex action recognition scenarios. To address these issues, we formulate a deep recursive and hierarchical conditional random fields (DR-HCRFs) model in an infinite-order dependencies framework. The DR-HCRFs model is able to capture richer contextual information in the target states, and infinite-order temporal-dependencies between the given states. Moreover, we derive a mean-field-like approximation of the model marginal likelihood to efficiently facilitate the model inference. The parameters of the predefined model are learnt with the block-coordinate primal-dual Frank-Wolfe algorithm in a structured support vector machine framework. Experimental results on the CAD-120 benchmark dataset demonstrate that the proposed approach can achieve high scalability and perform better than other state-of-the-art methods in terms of the evaluation criteria.

In this paper, we proposed a set of translation and rotation invariants extracted from Tchebichef moments. A set of Tchebichef moment invariants is derived from the relationship between Tchebichef moments of the original image and those of the transformed image. These invariants are then used for symmetric image recognition. Contrarily to the methods based on the complex moments in symmetric image analysis, our method does not need the pre-selection of moment values. Experimental results show that the proposed method achieves better performance compared to the existing methods.

In this paper, we introduce a new algorithm to register images with rotation and image blurring. The characteristic of this approach is that a set of Legendre moment invariants are used to establish the correspondence of corner points between the reference image and the distorted image, after these points are extracted by Harris corner detector. Transformation parameters can be estimated from those matched points.