2,516 research outputs found
A probabilistic neural network computer vision system for corn kernel damage evaluation
An investigation was conducted to determine whether image processing and machine vision technology could be used for identification of the damage factor in corn kernels. Prominent types of corn kernel damage were found to be germ damage and blue-eye mold damage. A sample set containing 720 kernels with approximately equal numbers of blue-eye mold-damaged, germ-damaged, and sound kernels was obtained and evaluated by human inspectors and the computer vision system. While the computer vision system developed was slightly less consistent in classification than trained human inspectors, it did prove to be a promising step toward inspection automation;Two probabilistic neural network architectures were implemented. The first network, based on a universal smoothing factor algorithm, was used to segment the collected images into blue-eye mold-damaged, germ-damaged, sound germ, shadow in sound germ, hard starch, and soft starch areas. Morphological features from each of the segmented areas were then input to a second probabilistic neural network which used genetic algorithms to optimize a unique smoothing factor for each network input. Output of the second layer network was overall kernel classification of blue-eye mold-damaged, germ-damaged, and sound. Overall accuracy of classification on unseen images was 78%, 94%, and 93% for blue-eye mold-damaged, germ-damaged, and sound categories, respectively. Correct classification for sound and damaged categories on unseen images was 92% and 93%, respectively
Computer vision in target pursuit using a UAV
Research in target pursuit using Unmanned Aerial Vehicle (UAV) has gained attention in recent years, this is primarily due to decrease in cost and increase in demand of small UAVs in many sectors. In computer vision, target pursuit is a complex problem as it involves the solving of many sub-problems which are typically concerned with the detection, tracking and following of the object of interest. At present, the majority of related existing methods are developed using computer simulation with the assumption of ideal environmental factors, while the remaining few practical methods are mainly developed to track and follow simple objects that contain monochromatic colours with very little texture variances. Current research in this topic is lacking of practical vision based approaches. Thus the aim of this research is to fill the gap by developing a real-time algorithm capable of following a person continuously given only a photo input.
As this research considers the whole procedure as an autonomous system, therefore the drone is activated automatically upon receiving a photo of a person through Wi-Fi. This means that the whole system can be triggered by simply emailing a single photo from any device anywhere. This is done by first implementing image fetching to automatically connect to WIFI, download the image and decode it. Then, human detection is performed to extract the template from the upper body of the person, the intended target is acquired using both human detection and template matching. Finally, target pursuit is achieved by tracking the template continuously while sending the motion commands to the drone.
In the target pursuit system, the detection is mainly accomplished using a proposed human detection method that is capable of detecting, extracting and segmenting the human body figure robustly from the background without prior training. This involves detecting face, head and shoulder separately, mainly using gradient maps. While the tracking is mainly accomplished using a proposed generic and non-learning template matching method, this involves combining intensity template matching with colour histogram model and employing a three-tier system for template management. A flight controller is also developed, it supports three types of controls: keyboard, mouse and text messages. Furthermore, the drone is programmed with three different modes: standby, sentry and search.
To improve the detection and tracking of colour objects, this research has also proposed several colour related methods. One of them is a colour model for colour detection which consists of three colour components: hue, purity and brightness. Hue represents the colour angle, purity represents the colourfulness and brightness represents intensity. It can be represented in three different geometric shapes: sphere, hemisphere and cylinder, each of these shapes also contains two variations.
Experimental results have shown that the target pursuit algorithm is capable of identifying and following the target person robustly given only a photo input. This can be evidenced by the live tracking and mapping of the intended targets with different clothing in both indoor and outdoor environments. Additionally, the various methods developed in this research could enhance the performance of practical vision based applications especially in detecting and tracking of objects
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
Advances in deep learning methods for pavement surface crack detection and identification with visible light visual images
Compared to NDT and health monitoring method for cracks in engineering
structures, surface crack detection or identification based on visible light
images is non-contact, with the advantages of fast speed, low cost and high
precision. Firstly, typical pavement (concrete also) crack public data sets
were collected, and the characteristics of sample images as well as the random
variable factors, including environmental, noise and interference etc., were
summarized. Subsequently, the advantages and disadvantages of three main crack
identification methods (i.e., hand-crafted feature engineering, machine
learning, deep learning) were compared. Finally, from the aspects of model
architecture, testing performance and predicting effectiveness, the development
and progress of typical deep learning models, including self-built CNN,
transfer learning(TL) and encoder-decoder(ED), which can be easily deployed on
embedded platform, were reviewed. The benchmark test shows that: 1) It has been
able to realize real-time pixel-level crack identification on embedded
platform: the entire crack detection average time cost of an image sample is
less than 100ms, either using the ED method (i.e., FPCNet) or the TL method
based on InceptionV3. It can be reduced to less than 10ms with TL method based
on MobileNet (a lightweight backbone base network). 2) In terms of accuracy, it
can reach over 99.8% on CCIC which is easily identified by human eyes. On
SDNET2018, some samples of which are difficult to be identified, FPCNet can
reach 97.5%, while TL method is close to 96.1%.
To the best of our knowledge, this paper for the first time comprehensively
summarizes the pavement crack public data sets, and the performance and
effectiveness of surface crack detection and identification deep learning
methods for embedded platform, are reviewed and evaluated.Comment: 15 pages, 14 figures, 11 table
License Plate Recognition using Convolutional Neural Networks Trained on Synthetic Images
In this thesis, we propose a license plate recognition system and study the feasibility
of using synthetic training samples to train convolutional neural networks for a
practical application.
First we develop a modular framework for synthetic license plate generation; to
generate different license plate types (or other objects) only the first module needs
to be adapted. The other modules apply variations to the training samples such as
background, occlusions, camera perspective projection, object noise and camera
acquisition noise, with the aim to achieve enough variation of the object that the
trained networks will also recognize real objects of the same class.
Then we design two convolutional neural networks of low-complexity for license
plate detection and character recognition. Both are designed for simultaneous
classification and localization by branching the networks into a classification and a
regression branch and are trained end-to-end simultaneously over both branches, on
only our synthetic training samples.
To recognize real license plates, we design a pipeline for scale invariant license
plate detection with a scale pyramid and a fully convolutional application of the
license plate detection network in order to detect any number of license plates and
of any scale in an image. Before character classification is applied, potential plate
regions are un-skewed based on the detected plate location in order to achieve an as
optimal representation of the characters as possible. The character classification is
also performed with a fully convolutional sweep to simultaneously find all characters
at once.
Both the plate and the character stages apply a refinement classification where
initial classifications are first centered and rescaled. We show that this simple, yet
effective trick greatly improves the accuracy of our classifications, and at a small
increase of complexity. To our knowledge, this trick has not been exploited before.
To show the effectiveness of our system we first apply it on a dataset of photos
of Italian license plates to evaluate the different stages of our system and which
effect the classification thresholds have on the accuracy. We also find robust training
parameters and thresholds that are reliable for classification without any need for
calibration on a validation set of real annotated samples (which may not always be
available) and achieve a balanced precision and recall on the set of Italian license
plates, both in excess of 98%.
Finally, to show that our system generalizes to new plate types, we compare our
system to two reference system on a dataset of Taiwanese license plates. For this, we
only modify the first module of the synthetic plate generation algorithm to produce
Taiwanese license plates and adjust parameters regarding plate dimensions, then we
train our networks and apply the classification pipeline, using the robust parameters,
on the Taiwanese reference dataset. We achieve state-of-the-art performance on plate
detection (99.86% precision and 99.1% recall), single character detection (99.6%)
and full license reading (98.7%)
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