714,435 research outputs found
TasselNet: Counting maize tassels in the wild via local counts regression network
Accurately counting maize tassels is important for monitoring the growth
status of maize plants. This tedious task, however, is still mainly done by
manual efforts. In the context of modern plant phenotyping, automating this
task is required to meet the need of large-scale analysis of genotype and
phenotype. In recent years, computer vision technologies have experienced a
significant breakthrough due to the emergence of large-scale datasets and
increased computational resources. Naturally image-based approaches have also
received much attention in plant-related studies. Yet a fact is that most
image-based systems for plant phenotyping are deployed under controlled
laboratory environment. When transferring the application scenario to
unconstrained in-field conditions, intrinsic and extrinsic variations in the
wild pose great challenges for accurate counting of maize tassels, which goes
beyond the ability of conventional image processing techniques. This calls for
further robust computer vision approaches to address in-field variations. This
paper studies the in-field counting problem of maize tassels. To our knowledge,
this is the first time that a plant-related counting problem is considered
using computer vision technologies under unconstrained field-based environment.Comment: 14 page
The More You Know: Using Knowledge Graphs for Image Classification
One characteristic that sets humans apart from modern learning-based computer
vision algorithms is the ability to acquire knowledge about the world and use
that knowledge to reason about the visual world. Humans can learn about the
characteristics of objects and the relationships that occur between them to
learn a large variety of visual concepts, often with few examples. This paper
investigates the use of structured prior knowledge in the form of knowledge
graphs and shows that using this knowledge improves performance on image
classification. We build on recent work on end-to-end learning on graphs,
introducing the Graph Search Neural Network as a way of efficiently
incorporating large knowledge graphs into a vision classification pipeline. We
show in a number of experiments that our method outperforms standard neural
network baselines for multi-label classification.Comment: CVPR 201
Knowledge-based machine vision systems for space station automation
Computer vision techniques which have the potential for use on the space station and related applications are assessed. A knowledge-based vision system (expert vision system) and the development of a demonstration system for it are described. This system implements some of the capabilities that would be necessary in a machine vision system for the robot arm of the laboratory module in the space station. A Perceptics 9200e image processor, on a host VAXstation, was used to develop the demonstration system. In order to use realistic test images, photographs of actual space shuttle simulator panels were used. The system's capabilities of scene identification and scene matching are discussed
Reasoning with visual knowledge in an object recognition system
The impact of artificial intelligence on computer vision has provided various perspectives and approaches to solving problems of the human visual system. Some of the symbolic processing and knowledge-based techniques implemented in vision systems represent a meaningful extension to the low-level, algorithmic processing which has been emphasized since the advent of the computer vision field. The higher-level processes attempt to capture the essence of visual cognition, specifically by encompassing a model of the visual world and the reasoning processes that manipulate this stored visual knowledge and environmental cues. This thesis includes a discussion of existing computer vision systems surveyed from a high-level perspective. The goal of this thesis is to develop a high-level inference system that implements reasoning processes and utilizes a visual memory model to achieve object recognition in a specific domain. The focus is on symbolically representing and reasoning with high-level knowledge using a frame-based approach. The organization and structuring of domain knowledge, reasoning processes and control and search strategies are emphasized. The implementation utilizes a frame package written in Prolog
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