Yunliang Cai

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The efficient and robust extraction of invariant patterns from an image is a long-standing problem in computer vision. Invariant structures are often related to repetitive or near-repetitive patterns. The perception of repetitive patterns in an image is strongly linked to the visual interpretation and composition of textures. Repetitive patterns are(More)
Detection of repetitive patterns in texture images is a longstanding problem in texture analysis. In the textile industry, this is particularly useful in isolating repeats in woven fabric designs. Based on repetitive patterns, textile designers can identify and classify complex textures. In this paper, we propose a new method for detecting, locating, and(More)
Visual patterns generated by color patches, texture regions, and repetitive textons in an image can be organized into higher-level structural forms such as geometric shapes, arrays, and partition groups. Understanding the information content formed by these visual pattern compositions is important both from a theoretical point of view as well as in the(More)
Translation symmetry is one of the most important pattern characteristics in natural and man-made environments. Detecting translation symmetry is a grand challenge in computer vision. This has a large spectrum of real-world applications from industrial settings to design, arts, entertainment and eduction. This paper describes the algorithm we have submitted(More)
Reliable prediction and diagnosis of concussion is important for its effective clinical management. Previous model-based studies largely employ peak responses from a single element in a pre-selected anatomical region of interest (ROI) and utilize a single training dataset for injury prediction. A more systematic and rigorous approach is necessary to(More)
Computer-aided diagnosis of spine problems relies on the automatic identification of spine structures in images. The task of automatic vertebra recognition is to identify the global spine and local vertebra structural information such as spine shape, vertebra location and pose. Vertebra recognition is challenging due to the large appearance variations in(More)
Monodisperse silver nanocages (AgNCs) with specific interiors were successfully synthesized by an azeotropic distillation (AD) assisted method and exhibited excellent catalytic activities for reduction of 4-nitrophenol (4-NP) into 4-aminophenol (4-AP) due to the unique hollow morphology and small thickness of the silver shell.
Developing an accurate and reliable injury prediction method is central to the biomechanics studies of traumatic brain injury. Previous efforts have relied on empirical metrics based on kinematics or model-estimated tissue responses explicitly pre-defined in a specific region of interest. A single “training” dataset has also been used to evaluate(More)