2 research outputs found
A Review of methods for Textureless Object Recognition
Textureless object recognition has become a significant task in Computer
Vision with the advent of Robotics and its applications in manufacturing
sector. It has been very challenging to get good performance because of its
lack of discriminative features and reflectance properties. Hence, the
approaches used for textured objects cannot be applied for textureless objects.
A lot of work has been done in the last 20 years, especially in the recent 5
years after the TLess and other textureless dataset were introduced. In our
research, we plan to combine image processing techniques (for feature
enhancement) along with deep learning techniques (for object recognition). Here
we present an overview of the various existing work in the field of textureless
object recognition, which can be broadly classified into View-based,
Feature-based and Shape-based. We have also added a review of few of the
research papers submitted at the International Conference on Smart Multimedia,
2018. Index terms: Computer Vision, Textureless object detection, Textureless
object recognition, Feature-based, Edge detection, Deep LearningComment: 25 page
Deep Learning on Image Denoising: An overview
Deep learning techniques have received much attention in the area of image
denoising. However, there are substantial differences in the various types of
deep learning methods dealing with image denoising. Specifically,
discriminative learning based on deep learning can ably address the issue of
Gaussian noise. Optimization models based on deep learning are effective in
estimating the real noise. However, there has thus far been little related
research to summarize the different deep learning techniques for image
denoising. In this paper, we offer a comparative study of deep techniques in
image denoising. We first classify the deep convolutional neural networks
(CNNs) for additive white noisy images; the deep CNNs for real noisy images;
the deep CNNs for blind denoising and the deep CNNs for hybrid noisy images,
which represents the combination of noisy, blurred and low-resolution images.
Then, we analyze the motivations and principles of the different types of deep
learning methods. Next, we compare the state-of-the-art methods on public
denoising datasets in terms of quantitative and qualitative analysis. Finally,
we point out some potential challenges and directions of future research