Hou Rongo is a multi-sensory, interactive exhibition revitalising Moriori culture and evoking the feeling of being on Rekohu (Chatham Island). Many different exhibits were created for this event, in close collaboration and co-design with Ta Imi Moriori (community). In this work we discuss two of the exhibits that use visual computing technologies to allow visitors to interact with the forests and birds of Rekohu. The first, AR Rakau Momori, allows visitors to closely examine culturally significant rakau momori in augmented reality. The second, Manu Moriori, creates a forest filled with colourful birds drawn by visitors and reflects the importance of birds in Moriori life and culture. Although just a small part of the broader exhibition, these examples illustrate how careful co-design can lead to interactive experiences that reflect culture and place.
Zero-shot image denoising, the process of removing noise from images without ground truth, is becoming increasingly important across various fields. Current denoising methods often downsample noisy images and employ residual and consistency loss functions to learn and subtract noise from base image. However, these methods struggle to discern the superiority between different downsampled images. To address this limitation, we propose an alpha-conditional loss function, combined with a 3x3 window downsampler, and a light-weight convolutional neural network, which effectively handles various noise types and levels. Notably, our method, named ZS-ACL, is computationally efficient by consisting of just 6K model parameters, thus distinguishing itself from others in the field. The experimental results on established real-world datasets demonstrate that ZS-ACL either outperforms or matches existing approaches in various scenarios, even with significantly fewer parameters. It learns from only a single image, thus presenting an efficient dataset-free denoising solution. Moreover, it showcases versatility and robustness by achieving better results for varying noise levels. The code is made available on Github at https://github.com/kshahmir49/ZS-ACL.
Event cameras, which capture pixel-level intensity changes asynchronously, offer minimal motion blur, high temporal resolution, and low power usage compared to traditional cameras. However, with limited availability and the high cost of real event data, synthetic event data has become a crucial alternative for applications such as retrospective gaze analysis in medical research, user behavior studies, and neurocognitive research. It allows simulation of experimental conditions, optimization of gaze estimation algorithms, and detailed scenario testing, including rare events, without costly real-world data collection. In this paper, we demonstrate that synthetic event data can match the performance of real event data in saccadic gaze prediction. To support this, we introduce a synthetic event-based gaze dataset focused on saccadic eye movements and propose a novel Dual ResUNet architecture designed to process consecutive frames with synthetic encoded events for gaze estimation. Our network achieves feature-level domain adaptation and temporally consecutive centroid prediction, delivering 99.20% accuracy within a 100-pixel radius. Our research underscores the potential of synthetic temporally encoded images for precise gaze vector prediction while effectively minimizing the domain gap with real event data.
Atmospheric turbulence generates wavefront distortions in light rays from distant sources, thus reducing the resolution of astronomical images. Adaptive Optics (AO) systems enhance telescopes' imaging capabilities by compensating for these aberrations in real time. This paper presents the design and laboratory prototyping of a high-order AO system intended for the University of Canterbury's 0.61 m Boller & Chivens (B&C) Telescope. The AO system incorporates a 12 x 12 actuator Micro-Electro-Mechanical Systems Deformable Mirror to simulate atmospheric conditions and static aberration screens, and a Thorlabs DM and Shack-Hartmann Wavefront Sensor for closed loop correction. The system was tested in the laboratory under simulated atmospheric conditions, with a focus on correcting higher-order aberrations, such as defocus and astigmatism. Experimental results demonstrate significant improvements in the Full Width at Half Maximum of the Point Spread Function, indicating the system's potential for improving image quality. The prototype's design allows for simple integration with the B&C Telescope, paving the way for its future implementation at the University of Canterbury's Mount John Observatory.
This research focuses on the development of a deep learning based method to enable a drone equipped with a stereo vision camera to accurately detect and measure the spatial positions of tree branches. YOLO is employed for branch segmentation, while two depth estimation approaches, monocular and stereo, are investigated. In comparison to Semi-Global Block Matching(SGBM), deep learning techniques produce more refined and accurate depth maps. In the absence of ground-truth data, a fine-tuning process with deep neural networks is applied to generate the depth map that most closely approximates the ground-truth. This methodology achieves accurate branch detection and precise distance measurement, addressing key challenges in automating pruning operations. The results indicate substantial improvements in accuracy, though further optimization is required to enhance processing speed, demonstrating the potential of deep learning to advance automation in agricultural systems.
