Understanding how people perceive visualizations is crucial for designing effective visual data representations; however, many heuristic design guidelines are derived from specific tasks or visualization types, without considering the constraints or conditions under which those guidelines hold. In this work, we aimed to assess existing design heuristics for categorical visualization using well-established psychological knowledge. Specifically, we examine the impact of the subitizing phenomenon in cognitive psychology-people's ability to automatically recognize a small set of objects instantly without counting-in data visualizations. We conducted three experiments with multi-class scatterplots-between 2 and 15 classes with varying design choices-across three different tasks-class estimation, correlation comparison, and clustering judgments-to understand how performance changes as the number of classes (and therefore set size) increases. Our results indicate if the category number is smaller than six, people tend to perform well at all tasks, providing empirical evidence of subitizing in visualization. When category numbers increased, performance fell, with the magnitude of the performance change depending on task and encoding. Our study bridges the gap between heuristic guidelines and empirical evidence by applying well-established psychological theories, suggesting future opportunities for using psychological theories and constructs to characterize visualization perception.

Audio-Visual Segmentation (AVS) aims to identify and delineate sounding objects within a video stream guided by auditory cues. Existing research focuses on audio-visual interactions in the spatial domain while overlooking the intrinsic frequency-domain information, leading to insufficient multimodal feature integration and alignment. In response, we introduce FDAVS, which incorporates frequency-driven designs to achieve synergistic utilization of spatial and frequency information of multimodal features. To begin with, we propose Frequency-Oriented Audio Integration (FOAI), which decouples and optimizes the high- and low-frequency components of visual features within the image encoder while integrating audio signals. Subsequently, we employ Frequency-Based Cross-modal Fusion (FBCF) between the pixel decoder and mask decoder to enhance the guidance of multimodal frequency information on the visual modality, increasing the consistency of audiovisual features. FDAVS achieves state-of-the-art segmentation performance across three AVS benchmarks, highlighting the effectiveness of frequency-driven modality enhancement.

As the core step of multibaseline synthetic aperture radar interferometry (InSAR), multibaseline phase unwrapping (MBPU) always faces noise challenges. Therefore, a new robust InSAR U-shaped MBPU algorithm with extended Margolus automata (U-EMA) is proposed. This algorithm is divided into two stages: U-shaped model solving and extended Margolus automaton (EMA) noise robustness processing. First, an N-dimensional standard discrete optimization model has been established for ambiguity numbers, and the N-dimensional discrete model, which has been maximally reduced in dimensionality, is solved in a U-shaped manner. Then, EMA was introduced in phase unwrapping (PU) for the first time to process discrete solutions, thereby improving the noise robustness to spatially varying noise, and finally, PU was achieved. Two sets of PU experiments were organized under the conditions of interferogram signal-to-noise ratio (SNR) of 3 and 4 dB compared with minimum cost flow (MCF), clustering analysis (CAs), and two-stage programming approach (TSPA). The results show the effectiveness and noise robustness of the U-EMA method, and the root-mean-square error (RMSE) of this algorithm is reduced by 63.7% and 14.6%, and 63.2% and 31.3%, respectively, compared with the mainstream algorithms MCF for single-baseline PU (SBPU) and TSPA for MBPU.

Saliency mechanism has been considered crucial in the human visual system and helpful to object detection and recognition. This paper addresses a novel feature-based model for visual saliency detection. It consists of two steps: first, using the learned overcomplete sparse bases to represent image patches; and then, estimating saliency information via low-rank and sparsity matrix decomposition. We compare our model with the previous methods on natural images. Experimental results on both natural images and psychological patterns show that our model performs competitively for visual saliency detection task, and suggest the potential application of matrix decomposition and convex optimization for image analysis.