OysterNet: Enhanced Oyster Detection Using Simulation

Oysters play a pivotal role in the bay living ecosystem and are considered the living filters for the ocean. In recent years, oyster reefs have undergone major devastation caused by commercial over-harvesting, requiring preservation to maintain ecological balance. The foundation of this preservation is to estimate the oyster density which requires accurate oyster detection. However, systems for accurate oyster detection require large datasets obtaining which is an expensive and labor-intensive task in underwater environments. To this end, we present a novel method to mathematically model oysters and render images of oysters in simulation to boost the detection performance with minimal real data. Utilizing our synthetic data along with real data for oyster detection, we obtain up to 35.1% boost in performance as compared to using only real data with our OysterNet network. We also improve the state-of-the-art by 12.7%. This shows that using underlying geometrical properties of objects can help to enhance recognition task accuracy on limited datasets successfully and we hope more researchers adopt such a strategy for hard-to-obtain datasets

Detecting Olives with Synthetic or Real Data? Olive the Above

Modern robotics has enabled the advancement in yield estimation for precision agriculture. However, when applied to the olive industry, the high variation of olive colors and their similarity to the background leaf canopy presents a challenge. Labeling several thousands of very dense olive grove images for segmentation is a labor-intensive task. This paper presents a novel approach to detecting olives without the need to manually label data. In this work, we present the world's first olive detection dataset comprised of synthetic and real olive tree images. This is accomplished by generating an auto-labeled photorealistic 3D model of an olive tree. Its geometry is then simplified for lightweight rendering purposes. In addition, experiments are conducted with a mix of synthetically generated and real images, yielding an improvement of up to 66% compared to when only using a small sample of real data. When access to real, human-labeled data is limited, a combination of mostly synthetic data and a small amount of real data can enhance olive detection

You only look as much as you have to : using the free energy principle for active vision

Active vision considers the problem of choosing the optimal next viewpoint from which an autonomous agent can observe its environment. In this paper, we propose to use the active inference paradigm as a natural solution to this problem, and evaluate this on a realistic scenario with a robot manipulator. We tackle this problem using a generative model that was learned unsupervised purely from pixel-based observations. We show that our agent exhibits information-seeking behavior, choosing viewpoints of regions it has not yet observed. We also show that goal-seeking behavior emerges when the agent has to reach a target goal, and it does so more efficiently than a systematic grid search

Dynamic, Task-Related and Demand-Driven Scene Representation

Humans selectively process and store details about the vicinity based on their knowledge about the scene, the world and their current task. In doing so, only those pieces of information are extracted from the visual scene that is required for solving a given task. In this paper, we present a flexible system architecture along with a control mechanism that allows for a task-dependent representation of a visual scene. Contrary to existing approaches, our system is able to acquire information selectively according to the demands of the given task and based on the system’s knowledge. The proposed control mechanism decides which properties need to be extracted and how the independent processing modules should be combined, based on the knowledge stored in the system’s long-term memory. Additionally, it ensures that algorithmic dependencies between processing modules are resolved automatically, utilizing procedural knowledge which is also stored in the long-term memory. By evaluating a proof-of-concept implementation on a real-world table scene, we show that, while solving the given task, the amount of data processed and stored by the system is considerably lower compared to processing regimes used in state-of-the-art systems. Furthermore, our system only acquires and stores the minimal set of information that is relevant for solving the given task

Leadership in Orchestra Emerges from the Causal Relationships of Movement Kinematics

Non-verbal communication enables efficient transfer of information among people. In this context, classic orchestras are a remarkable instance of interaction and communication aimed at a common aesthetic goal: musicians train for years in order to acquire and share a non-linguistic framework for sensorimotor communication. To this end, we recorded violinists' and conductors' movement kinematics during execution of Mozart pieces, searching for causal relationships among musicians by using the Granger Causality method (GC). We show that the increase of conductor-to-musicians influence, together with the reduction of musician-to-musician coordination (an index of successful leadership) goes in parallel with quality of execution, as assessed by musical experts' judgments. Rigorous quantification of sensorimotor communication efficacy has always been complicated and affected by rather vague qualitative methodologies. Here we propose that the analysis of motor behavior provides a potentially interesting tool to approach the rather intangible concept of aesthetic quality of music and visual communication efficacy

Disambiguating Multi–Modal Scene Representations Using Perceptual Grouping Constraints

In its early stages, the visual system suffers from a lot of ambiguity and noise that severely limits the performance of early vision algorithms. This article presents feedback mechanisms between early visual processes, such as perceptual grouping, stereopsis and depth reconstruction, that allow the system to reduce this ambiguity and improve early representation of visual information. In the first part, the article proposes a local perceptual grouping algorithm that — in addition to commonly used geometric information — makes use of a novel multi–modal measure between local edge/line features. The grouping information is then used to: 1) disambiguate stereopsis by enforcing that stereo matches preserve groups; and 2) correct the reconstruction error due to the image pixel sampling using a linear interpolation over the groups. The integration of mutual feedback between early vision processes is shown to reduce considerably ambiguity and noise without the need for global constraints