Quantum Vision Clustering

Unsupervised visual clustering has recently received considerable attention. It aims to explain distributions of unlabeled visual images by clustering them via a parameterized appearance model. From a different perspective, the clustering algorithms can be treated as assignment problems, often NP-hard. They can be solved precisely for small instances on current hardware. Adiabatic quantum computing (AQC) offers a solution, as it can soon provide a considerable speedup on a range of NP-hard optimization problems. However, current clustering formulations are unsuitable for quantum computing due to their scaling properties. Consequently, in this work, we propose the first clustering formulation designed to be solved with AQC. We employ an Ising model representing the quantum mechanical system implemented on the AQC. Our approach is competitive compared to state-of-the-art optimization-based approaches, even using of-the-shelf integer programming solvers. Finally, we demonstrate that our clustering problem is already solvable on the current generation of real quantum computers for small examples and analyze the properties of the measured solutions.

High precision control and deep learning-based corn stand counting algorithms for agricultural robot

This paper presents high precision control and deep learning-based corn stand counting algorithms for a low-cost, ultra-compact 3D printed and autonomous field robot for agricultural operations. Currently, plant traits, such as emergence rate, biomass, vigor, and stand counting, are measured manually. This is highly labor-intensive and prone to errors. The robot, termed TerraSentia, is designed to automate the measurement of plant traits for efficient phenotyping as an alternative to manual measurements. In this paper, we formulate a Nonlinear Moving Horizon Estimator (NMHE) that identifies key terrain parameters using onboard robot sensors and a learning-based Nonlinear Model Predictive Control (NMPC) that ensures high precision path tracking in the presence of unknown wheel-terrain interaction. Moreover, we develop a machine vision algorithm designed to enable an ultra-compact ground robot to count corn stands by driving through the fields autonomously. The algorithm leverages a deep network to detect corn plants in images, and a visual tracking model to re-identify detected objects at different time steps. We collected data from 53 corn plots in various fields for corn plants around 14 days after emergence (stage V3 - V4). The robot predictions have agreed well with the ground truth with $C_{robot}=1.02 \times C_{human}-0.86$ and a correlation coefficient $R=0.96$. The mean relative error given by the algorithm is $-3.78\%$, and the standard deviation is $6.76\%$. These results indicate a first and significant step towards autonomous robot-based real-time phenotyping using low-cost, ultra-compact ground robots for corn and potentially other crops.