The recent release of the Apple Vision Pro has reignited interest in the metaverse, showcasing the intensified efforts of technology giants in developing platforms and devices to facilitate its growth. As the metaverse continues to proliferate, it is foreseeable that everyday environments will become increasingly saturated with its presence. Consequently, uncovering links to these metaverse items will be a crucial first step to interacting with this new augmented world. In this paper, we address the problem of establishing connections with virtual worlds within everyday environments, especially those that are not readily discernible through direct visual inspection. We introduce a vision-based approach leveraging Artcode visual markers to uncover hidden metaverse links embedded in our ambient surroundings. This approach progressively localises the access points to the metaverse, transitioning from coarse to fine localisation, thus facilitating an exploratory interaction process. Detailed experiments are conducted to study the performance of the proposed approach, demonstrating its effectiveness in Artcode localisation and enabling new interaction opportunities.

Saliency ranking detection (SRD) has emerged as a challenging task in computer vision, aiming not only to identify salient objects within images but also to rank them based on their degree of saliency. Existing SRD datasets have been created primarily using mouse-trajectory data, which inadequately captures the intricacies of human visual perception. Addressing this gap, this paper introduces the first large-scale SRD dataset, SIFR, constructed using genuine human fixation data, thereby aligning more closely with real visual perceptual processes. To establish a baseline for this dataset, we propose QAGNet, a novel model that leverages salient instance query features from a transformer detector within a tri-tiered nested graph. Through extensive experiments, we demonstrate that our approach outperforms existing state-of-the-art methods across two widely used SRD datasets and our newly proposed dataset. Code and dataset are available at https://github.com/EricDengbowen/QAGNet.

Salient object detection plays an important role in many downstream tasks. However, complex real-world scenes with varying scales and numbers of salient objects still pose a challenge. In this paper, we directly address the problem of detecting multiple salient objects across complex scenes. We propose a network architecture incorporating non-local feature information in both the spatial and channel spaces, capturing the long-range dependencies between separate objects. Traditional bottom-up and non-local features are combined with edge features within a feature fusion gate that progressively refines the salient object prediction in the decoder. We show that our approach accurately locates multiple salient regions even in complex scenarios. To demonstrate the efficacy of our approach to the multiple salient objects problem, we curate a new dataset containing only multiple salient objects. Our experiments demonstrate the proposed method presents state-of-the-art results on five widely used datasets without any pre-processing and post-processing. We obtain a further performance improvement against competing techniques on our multi-objects dataset. The dataset and source code are available at: https://github.com/EricDengbowen/DSLRDNet.

The recent Facebook rebranding to Meta has drawn renewed attention to the metaverse. Technology giants, amongst others, are increasingly embracing the vision and opportunities of a hybrid social experience that mixes physical and virtual interactions. As the metaverse gains in traction, it is expected that everyday objects may soon connect more closely with virtual elements. However, discovering this “hidden” virtual world will be a crucial first step to interacting with it in this new augmented world. In this paper, we address the problem of connecting phys-ical objects with their virtual counterparts, especially through connections built upon visual markers. We propose a unified recognition framework that guides approaches to the metaverse access points. We illustrate the use of the framework through experimental studies under different conditions, in which an interactive and visually attractive decoration pattern, an Artcode, is used as the approach to enable the connection. This paper will be of interest to, amongst others, researchers working in Interaction Design or Augmented Reality who are seeking techniques or guidelines for augmenting physical objects in an unobtrusive, complementary manner.

Abstract Over recent years, many approaches have been proposed for the denoising or semantic segmentation of X-ray computed tomography (CT) scans. In most cases, high-quality CT reconstructions are used; however, such reconstructions are not always available. When the X-ray exposure time has to be limited, undersampled tomograms (in terms of their component projections) are attained. This low number of projections offers low-quality reconstructions that are difficult to segment. Here, we consider CT time-series (i.e. 4D data), where the limited time for capturing fast-occurring temporal events results in the time-series tomograms being necessarily undersampled. Fortunately, in these collections, it is common practice to obtain representative highly sampled tomograms before or after the time-critical portion of the experiment. In this paper, we propose an end-to-end network that can learn to denoise and segment the time-series’ undersampled CTs, by training with the earlier highly sampled representative CTs. Our single network can offer two desired outputs while only training once, with the denoised output improving the accuracy of the final segmentation. Our method is able to outperform state-of-the-art methods in the task of semantic segmentation and offer comparable results in regard to denoising. Additionally, we propose a knowledge transfer scheme using synthetic tomograms. This not only allows accurate segmentation and denoising using less real-world data, but also increases segmentation accuracy. Finally, we make our datasets, as well as the code, publicly available.

There is an increase in consumption of agricultural produce as a result of the rapidly growing human population, particularly in developing nations. This has triggered high-quality plant phenotyping research to help with the breeding of high-yielding plants that can adapt to our continuously changing climate. Novel, low-cost, fully automated plant phenotyping systems, capable of infield deployment, are required to help identify quantitative plant phenotypes. The identification of quantitative plant phenotypes is a key challenge which relies heavily on the precise segmentation of plant images. Recently, the plant phenotyping community has started to use very deep convolutional neural networks (CNNs) to help tackle this fundamental problem. However, these very deep CNNs rely on some millions of model parameters and generate very large weight matrices, thus making them difficult to deploy infield on low-cost, resource-limited devices. We explore how to compress existing very deep CNNs for plant image segmentation, thus making them easily deployable infield and on mobile devices. In particular, we focus on applying these models to the pixel-wise segmentation of plants into multiple classes including background, a challenging problem in the plant phenotyping community. We combined two approaches (separable convolutions and SVD) to reduce model parameter numbers and weight matrices of these very deep CNN-based models. Using our combined method (separable convolution and SVD) reduced the weight matrix by up to 95% without affecting pixel-wise accuracy. These methods have been evaluated on two public plant datasets and one non-plant dataset to illustrate generality. We have successfully tested our models on a mobile device.

Plant phenotyping is the quantitative description of a plant's physiological, biochemical, and anatomical status which can be used in trait selection and helps to provide mechanisms to link underlying genetics with yield. Here, an active vision- based pipeline is presented which aims to contribute to reducing the bottleneck associated with phenotyping of architectural traits. The pipeline provides a fully automated response to photometric data acquisition and the recovery of three-dimensional (3D) models of plants without the dependency of botanical expertise, whilst ensuring a non-intrusive and non-destructive approach. Access to complete and accurate 3D models of plants supports computation of a wide variety of structural measurements. An Active Vision Cell (AVC) consisting of a camera-mounted robot arm plus combined software interface and a novel surface reconstruction algorithm is proposed. This pipeline provides a robust, flexible, and accurate method for automating the 3D reconstruction of plants. The reconstruction algorithm can reduce noise and provides a promising and extendable framework for high throughput phenotyping, improving current state-of-the-art methods. Furthermore, the pipeline can be applied to any plant species or form due to the application of an active vision framework combined with the automatic selection of key parameters for surface reconstruction.

The emerging discipline of plant phenomics aims to measure key plant characteristics, or traits, though as yet the set of plant traits that should be measured by automated systems is not well defined. Methods capable of recovering generic representations of the 3D structure of plant shoots from images would provide a key technology underpinning quantification of a wide range of current and future physiological and morphological traits. We present a fully automatic approach to image-based 3D plant reconstruction which represents plants as series of small planar sections that together model the complex architecture of leaf surfaces. The initial boundary of each leaf patch is refined using a level set method, optimising the model based on image information, curvature constraints and the position of neighbouring surfaces. The reconstruction process makes few assumptions about the nature of the plant material being reconstructed. As such it is applicable to a wide variety of plant species and topologies, and can be extended to canopy-scale imaging. We demonstrate the effectiveness of our approach on real images of wheat and rice plants, an artificial plant with challenging architecture, as well as a novel virtual dataset that allows us to compute distance measures of reconstruction accuracy. We also illustrate the method’s potential to support the identification of individual leaves, and so the phenotyping of plant shoots, using a spectral clustering approach.

SUMMARY: The original RootTrace tool has proved successful in measuring primary root lengths across time series image data. Biologists have shown interest in using the tool to address further problems, namely counting lateral roots to use as parameters in screening studies, and measuring highly curved roots. To address this, the software has been extended to count emerged lateral roots, and the tracking model extended so that strongly curved and agravitropic roots can be now be recovered. Here, we describe the novel image analysis algorithms and user interface implemented within the RootTrace framework to handle such situations and evaluate the results. AVAILABILITY: The software is open source and available from http://sourceforge.net/projects/roottrace.

This paper presents a new methodology for improving the tracking of multiple targets in complex scenes. The new method,Motion Parameter Sharing, incorporates social motion information into tracking predictions. This is achieved by allowing a tracker to share motion estimates within groups of targets which have previously been moving in a coordinated fashion. The method is intuitive and, as well as aiding the prediction estimates, allows the implicit formation of 'social groups' of targets as a side effect of the process. The underlying reasoning and method are presented, as well as a description of how the method fits into the framework of a typical Bayesian tracking system. This is followed by some preliminary results which suggest the method is more accurate and robust than algorithms which do not incorporate the social information available in multiple target scenarios.