1,550 research outputs found
Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining
Single image rain streaks removal has recently witnessed substantial progress
due to the development of deep convolutional neural networks. However, existing
deep learning based methods either focus on the entrance and exit of the
network by decomposing the input image into high and low frequency information
and employing residual learning to reduce the mapping range, or focus on the
introduction of cascaded learning scheme to decompose the task of rain streaks
removal into multi-stages. These methods treat the convolutional neural network
as an encapsulated end-to-end mapping module without deepening into the
rationality and superiority of neural network design. In this paper, we delve
into an effective end-to-end neural network structure for stronger feature
expression and spatial correlation learning. Specifically, we propose a
non-locally enhanced encoder-decoder network framework, which consists of a
pooling indices embedded encoder-decoder network to efficiently learn
increasingly abstract feature representation for more accurate rain streaks
modeling while perfectly preserving the image detail. The proposed
encoder-decoder framework is composed of a series of non-locally enhanced dense
blocks that are designed to not only fully exploit hierarchical features from
all the convolutional layers but also well capture the long-distance
dependencies and structural information. Extensive experiments on synthetic and
real datasets demonstrate that the proposed method can effectively remove
rain-streaks on rainy image of various densities while well preserving the
image details, which achieves significant improvements over the recent
state-of-the-art methods.Comment: Accepted to ACM Multimedia 201
Logarithmic Morphological Neural Nets robust to lighting variations
Morphological neural networks allow to learn the weights of a structuring
function knowing the desired output image. However, those networks are not
intrinsically robust to lighting variations in images with an optical cause,
such as a change of light intensity. In this paper, we introduce a
morphological neural network which possesses such a robustness to lighting
variations. It is based on the recent framework of Logarithmic Mathematical
Morphology (LMM), i.e. Mathematical Morphology defined with the Logarithmic
Image Processing (LIP) model. This model has a LIP additive law which simulates
in images a variation of the light intensity. We especially learn the
structuring function of a LMM operator robust to those variations, namely : the
map of LIP-additive Asplund distances. Results in images show that our neural
network verifies the required property.Comment: Submitted to DGMM 2022 - Second International Conference on Discrete
Geometry and Mathematical Morpholog
Rain Removal in Traffic Surveillance: Does it Matter?
Varying weather conditions, including rainfall and snowfall, are generally
regarded as a challenge for computer vision algorithms. One proposed solution
to the challenges induced by rain and snowfall is to artificially remove the
rain from images or video using rain removal algorithms. It is the promise of
these algorithms that the rain-removed image frames will improve the
performance of subsequent segmentation and tracking algorithms. However, rain
removal algorithms are typically evaluated on their ability to remove synthetic
rain on a small subset of images. Currently, their behavior is unknown on
real-world videos when integrated with a typical computer vision pipeline. In
this paper, we review the existing rain removal algorithms and propose a new
dataset that consists of 22 traffic surveillance sequences under a broad
variety of weather conditions that all include either rain or snowfall. We
propose a new evaluation protocol that evaluates the rain removal algorithms on
their ability to improve the performance of subsequent segmentation, instance
segmentation, and feature tracking algorithms under rain and snow. If
successful, the de-rained frames of a rain removal algorithm should improve
segmentation performance and increase the number of accurately tracked
features. The results show that a recent single-frame-based rain removal
algorithm increases the segmentation performance by 19.7% on our proposed
dataset, but it eventually decreases the feature tracking performance and
showed mixed results with recent instance segmentation methods. However, the
best video-based rain removal algorithm improves the feature tracking accuracy
by 7.72%.Comment: Published in IEEE Transactions on Intelligent Transportation System
Intelligent Assessment of Sun Flower Seeds Using Machine Learning Approaches
Pakistan is an agricultural country. Sun flower is the major crop of Pakistan which is being sowing in many areas of country. It fulfills the requirement of edible oil. In this paper we are trying to identify the best quality from different sun flowers seeds verities by using machine learning approaches. We take the images of four kinds of sunflower seeds which names Top sun(A), High Sun(B),US666(C) and Seji(D) for classification. We get eight different images of each kind of sunflower. In this paper sun flowers seeds varieties were categorized by using Computer vision image processing tool (CVIP). The experience and knowledge of inspectors are required to perfectly perform this assessment process. We use the RST-Invariant Features, Histogram Features, Texture Features, and Pattern Classification and also use the nearest neighbor and k-nearest neighbor algorithms for final classification. We achieved the final results of four kinds of sunflower using nearest neighbor on distance one and two 89% and 72% average and on k-nearest neighbor 89% and 73% average percentage. These are the best percentage results using these algorithms for classification. In this way we can easily classify the sunflower seeds and also these methods provide opportunity to farmer and other people for identify and select the different better and healthy sunflower seeds for better benefits. Keywords: RST-Invariant Features, Histogram Features, Texture Features, Classification Algorithm
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