Semi-Supervised Domain Adaptation for Holistic Counting under Label Gap

Semi-Supervised Domain Adaptation for Holistic Counting under Label Gap

This paper proposes a novel approach for semi-supervised domain adaptation for holistic regression tasks, where a DNN predicts a continuous value y∈R given an input image x. The current literature generally lacks specific domain adaptation approaches for this task, as most of them mostly focus on classification. I...
Plant phenotyping on-demand: an integrative web-based framework using drones and participatory sensing in greenhouses

Plant phenotyping on-demand: an integrative web-based framework using drones and participatory sensing in greenhouses

In this paper, we present an empirical evaluation of 30 features used in touch-based continuous authentication. It is essential to identify the most significant features for each user, as behaviour is different amongst humans. Thus, a fixed feature set cannot be applied to all models. We highlight this...
CAPE: Context-Aware Private Embeddings for Private Language Learning

CAPE: Context-Aware Private Embeddings for Private Language Learning

Deep learning-based language models have achieved state-of-the-art results in a number of applications including sentiment analysis, topic labelling, intent classification and others. Obtaining text representations or embeddings using these models presents the possibility of encoding personally identifiable...
Towards Continuous User Authentication Using Personalised Touch-Based Behaviour

Towards Continuous User Authentication Using Personalised Touch-Based Behaviour

In this paper, we present an empirical evaluation of 30 features used in touch-based continuous authentication. It is essential to identify the most significant features for each user, as behaviour is different amongst humans. Thus, a fixed feature set cannot be applied to all models. We highlight this...
Affordable and robust phenotyping framework to analyse root system architecture of soil-grown plants

Affordable and robust phenotyping framework to analyse root system architecture of soil-grown plants

The phenotypic analysis of root system growth is important to inform efforts to enhance plant resource acquisition from soils. However, root phenotyping still remains challenging due to soil opacity, requiring systems that facilitate root system visibility and image acquisition. Previously reported systems...
Doing More With Less: A Multitask Deep Learning Approach in Plant Phenotyping

Doing More With Less: A Multitask Deep Learning Approach in Plant Phenotyping

Image-based plant phenotyping has been steadily growing and this has steeply increased the need for more efficient image analysis techniques capable of evaluating multiple plant traits. Deep learning has shown its potential in a multitude of visual tasks in plant phenotyping, such as segmentation and...
Unsupervised Rotation Factorization in Restricted Boltzmann Machines

Unsupervised Rotation Factorization in Restricted Boltzmann Machines

Finding suitable image representations for the task at hand is critical in computer vision. Different approaches extending the original Restricted Boltzmann Machine (RBM) model have recently been proposed to offer rotation-invariant feature learning. In this paper, we present an extended novel RBM that...
Leaf Counting Without Annotations Using Adversarial Unsupervised Domain Adaptation

Leaf Counting Without Annotations Using Adversarial Unsupervised Domain Adaptation

Deep learning is making strides in plant phenotyping and agriculture. But pretrained models require significant adaptation to work on new target datasets originating from a different experiment even on the same species. The current solution is to retrain the model on the new target data implying the...
Adversarial Large-scale Root Gap Inpainting

Adversarial Large-scale Root Gap Inpainting

Root imaging of a growing plant in a non-invasive, affordable, and effective way remains challenging. One approach is to image roots by growing them in a rhizobox, a soil-filled transparent container, imaging them with digital cameras, and segmenting root from soil background. However, due to soil occlusion...
Understanding deep neural networks for regression in leaf counting

Understanding deep neural networks for regression in leaf counting

Deep learning methods are constantly increasing in popularity and success across a wide range of computer vision applications. However, they are perceived as `black boxes', due to the lack of an intuitive interpretation of their decision processes. We present a study aimed at understanding how Deep Neural...
Learning to Count Leaves of Plants

Learning to Count Leaves of Plants

Plant phenotyping refers to the measurement of plant visual traits. In the past, the collection of such traits has been done manually by plant scientists, which is a tedious, error-prone, and time-consuming task. For this reason, image-based plant phenotyping is used to facilitate the measurement of...
Pheno-Deep Counter: a unified and versatile deep learning architecture for leaf counting

Pheno-Deep Counter: a unified and versatile deep learning architecture for leaf counting

Direct observation of morphological plant traits is tedious and a bottleneck for high-throughput phenotyping. Hence, interest in image-based analysis is increasing, requiring software that can reliably extract plant traits, such as leaf count, preferably across a variety of species and growth conditions....
Root Gap Correction with a Deep Inpainting Model

Root Gap Correction with a Deep Inpainting Model

Imaging roots of growing plants in a non-invasive and affordable fashion has been a long-standing problem in image-assisted plant breeding and phenotyping. One of the most affordable and diffuse approaches is the use of mesocosms, where plants are grown in soil against a glass surface that permits the...
Citizen crowds and experts: observer variability in image-based plant phenotyping

Citizen crowds and experts: observer variability in image-based plant phenotyping

Background Image-based plant phenotyping has become a powerful tool in unravelling genotype–environment interactions. The utilization of image analysis and machine learning have become paramount in extracting data stemming from phenotyping experiments. Yet we rely on observer (a human expert) input t...
ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network

ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network

In recent years, there has been an increasing interest in image-based plant phenotyping, applying state-of-the-art machine learning approaches to tackle challenging problems, such as leaf segmentation (a multi-instance problem) and counting. Most of these algorithms need labelled data to learn a model...
Leveraging multiple datasets for deep leaf counting

Leveraging multiple datasets for deep leaf counting

The number of leaves a plant has is one of the key traits (phenotypes) describing its development and growth. Here, we propose an automated, deep learning based approach for counting leaves in model rosette plants. While state-of-the-art results on leaf counting with deep learning methods have recently...
Phenotiki: An Open Software And Hardware Platform For Affordable And Easy Image-based Phenotyping Of Rosette-shaped Plants

Phenotiki: An Open Software And Hardware Platform For Affordable And Easy Image-based Phenotyping Of Rosette-shaped Plants

Phenotyping is important to understand plant biology but current solutions are either costly, not versatile or difficult to deploy. To solve this problem, we present Phenotiki, an affordable system for plant phenotyping which, relying on off-the-shelf parts, provides an easy to install and maintain platform,...
Multimodal MR Synthesis Via Modality-Invariant Latent Representation

Multimodal MR Synthesis Via Modality-Invariant Latent Representation

We propose a multi-input multi-output fully convolutional neural network model for MRI synthesis. The model is robust to missing data, as it benefits from, but does not require, additional input modalities. The model is trained end-to-end, and learns to embed all input modalities into a shared modalityinvariant...
Theta-RBM: Unfactored Gated Restricted Boltzmann Machine for Rotation-Invariant Representations

Theta-RBM: Unfactored Gated Restricted Boltzmann Machine for Rotation-Invariant Representations

Learning invariant representations is a critical task in computer vision. In this paper, we propose the Theta-Restricted Boltzmann Machine (θ-RBM in short), which builds upon the original RBM formulation and injects the notion of rotation-invariance during the learning procedure. In contrast to previous ...
Rotation-Invariant Restricted Boltzmann Machine using Shared Gradient Filters

Rotation-Invariant Restricted Boltzmann Machine using Shared Gradient Filters

Finding suitable features has been an essential problem in computer vision. We focus on Restricted Boltzmann Machines (RBMs), which, despite their versatility, cannot accommodate transformations that may occur in the scene. As a result, several approaches have been proposed that consider a set of transformations,...
Whole Image Synthesis Using a Deep Encoder-Decoder Network

Whole Image Synthesis Using a Deep Encoder-Decoder Network

The synthesis of medical images is an intensity transformation of a given modality in a way that represents an acquisition with a different modality (in the context of MRI this represents the synthesis of images originating from different MR sequences). Most methods follow a patch-based approach, which...
Learning to Count Leaves in Rosette Plants

Learning to Count Leaves in Rosette Plants

Counting the number of leaves in plants is important for plant phenotyping, since it can be used to assess plant growth stages. We propose a learning-based approach for counting leaves in rosette (model) plants. We relate image-based descriptors learned in an unsupervised fashion to leaf counts using...
An interactive tool for semi-automated leaf annotation

An interactive tool for semi-automated leaf annotation

High throughput plant phenotyping is emerging as a necessary step towards meeting agricultural demands of the future. Central to its success is the development of robust computer vision algorithms that analyze images and extract phenotyping information to be associated with genotypes and environmental...
On Blind Source Camera Identification Algorithms

On Blind Source Camera Identification Algorithms

An interesting and challenging problem in digital image forensics is the identification of the device used to acquire an image. Although the source imaging device can be retrieved exploiting the file header (e.g., EXIF), this information can be easily tampered. This lead to the necessity of blind techniques...
Exploiting time-multiplexing structured light with picoprojectors

Exploiting time-multiplexing structured light with picoprojectors

When a picture is shot all the information about the distance between the object and the camera gets lost. Depth estimation from a single image is a notable issue in computer vision. In this work we present a hardware and software framework to accomplish the task of 3D measurement through structured...