Review on convolutional neural network (CNN) applied to plant leaf disease classification
J Lu, L Tan, H Jiang - Agriculture, 2021 - mdpi.com
Crop production can be greatly reduced due to various diseases, which seriously endangers
food security. Thus, detecting plant diseases accurately is necessary and urgent. Traditional …
food security. Thus, detecting plant diseases accurately is necessary and urgent. Traditional …
A brief survey on RGB-D semantic segmentation using deep learning
Semantic segmentation is referred to as a process of linking each pixel in an image to a
class label. With this pragmatic technique, it is possible to recognize different objects in an …
class label. With this pragmatic technique, it is possible to recognize different objects in an …
LMFFNet: A well-balanced lightweight network for fast and accurate semantic segmentation
M Shi, J Shen, Q Yi, J Weng, Z Huang… - … on Neural Networks …, 2022 - ieeexplore.ieee.org
Real-time semantic segmentation is widely used in autonomous driving and robotics. Most
previous networks achieved great accuracy based on a complicated model involving mass …
previous networks achieved great accuracy based on a complicated model involving mass …
Deep learning based 3D segmentation: A survey
3D segmentation is a fundamental and challenging problem in computer vision with
applications in autonomous driving and robotics. It has received significant attention from the …
applications in autonomous driving and robotics. It has received significant attention from the …
Dpsnet: Multitask learning using geometry reasoning for scene depth and semantics
J Zhang, Q Su, B Tang, C Wang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Multitask joint learning technology continues gaining more attention as a paradigm shift and
has shown promising performance in many applications. Depth estimation and semantic …
has shown promising performance in many applications. Depth estimation and semantic …
Efficient active contour model for medical image segmentation and correction based on edge and region information
Y Yang, X Hou, H Ren - Expert Systems with Applications, 2022 - Elsevier
The inhomogeneity of images is always a challenge in the field of image segmentation.
Aiming at the problem of segmentation and correction of inhomogeneous images, this paper …
Aiming at the problem of segmentation and correction of inhomogeneous images, this paper …
Multilevel edge features guided network for image denoising
Image denoising is a challenging inverse problem due to complex scenes and information
loss. Recently, various methods have been considered to solve this problem by building a …
loss. Recently, various methods have been considered to solve this problem by building a …
A local–global dual-stream network for building extraction from very-high-resolution remote sensing images
Buildings constitute one of the most important landscapes in remote sensing (RS) images
and have been broadly analyzed in a wide range of applications from urban planning to …
and have been broadly analyzed in a wide range of applications from urban planning to …
Self-supervised monocular depth estimation with self-perceptual anomaly handling
It is attractive to extract plausible 3-D information from a single 2-D image, and self-
supervised learning has shown impressive potential in this field. However, when only …
supervised learning has shown impressive potential in this field. However, when only …
RESLS: region and edge synergetic level set framework for image segmentation
The active contour models with level set evolution have been visited with a vast number of
methods for image segmentation. They can be mainly classified into region-based and edge …
methods for image segmentation. They can be mainly classified into region-based and edge …