Mathematical Problems in Engineering

Optimization for Detection and Recognition in Images and Videos


Publishing date
18 Nov 2016
Status
Published
Submission deadline
01 Jul 2016

Lead Editor

1Samsung Advanced Institute of Technology, Gyeonggi-do, Republic of Korea

2Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea

3University of Milan-Bicocca, Milan, Italy


Optimization for Detection and Recognition in Images and Videos

Description

With increasing demand of intelligent systems on mobile and robot applications, automatic identification and extraction of attributes related to a higher semantic level in a given image and video have become critical in these days. The optimization techniques have a great ability to provide huge opportunities for efficiently and accurately finding a solution for a given task. Therefore, such mathematical skills have been popularly employed to resolve traditional problems in the field of image and video processing, for example, camera calibration, denoising, and segmentation, and now start to be applied for more advanced applications such as object detection, classification, and recognition. Toward further improvement for mobile-based and robot-based scenarios under the limited-computing power environment, a variety of optimization methods need to be applied in a more efficient way. To this end, many researchers have devoted considerable efforts to constructing simple yet powerful optimization frameworks.

We kindly invite investigators to contribute review as well as original papers describing recent findings and breakthrough developments which are expected to revolutionize the field of image and video processing by optimization techniques.

Potential topics include, but are not limited to:

  • Convex optimization and its conceptual study for image and video processing
  • Active contour models based on the variational optimization technique
  • Total variations and their applications including illumination normalization, segmentation, denoising, and recoloring
  • Sparse representation and low-rank-based image and video processing approaches
  • Optimization for decomposition of the image (e.g., a technique discriminating textures from the structural information in a given image)
  • Combinatorial optimization for visual recognition
  • Optimization for learning the deep neural network (including the convolutional neural network (CNN) for visual recognition)
  • Bayesian optimization under uncertainty and Gaussian processes
  • Optimization for learning convolutional deep belief networks

Articles

  • Special Issue
  • - Volume 2017
  • - Article ID 5190490
  • - Editorial

Optimization for Detection and Recognition in Images and Videos

Wonjun Kim | Chanho Jung | Simone Bianco
  • Special Issue
  • - Volume 2017
  • - Article ID 1376726
  • - Research Article

Feature Extraction and Fusion Using Deep Convolutional Neural Networks for Face Detection

Xiaojun Lu | Xu Duan | ... | Xiangde Zhang
  • Special Issue
  • - Volume 2016
  • - Article ID 6795352
  • - Research Article

Adaptive Deep Supervised Autoencoder Based Image Reconstruction for Face Recognition

Rongbing Huang | Chang Liu | ... | Jiliu Zhou
  • Special Issue
  • - Volume 2016
  • - Article ID 5376087
  • - Research Article

Customized Dictionary Learning for Subdatasets with Fine Granularity

Lei Ye | Can Wang | ... | Hui Qian
  • Special Issue
  • - Volume 2016
  • - Article ID 3180357
  • - Research Article

Objectness Supervised Merging Algorithm for Color Image Segmentation

Haifeng Sima | Aizhong Mi | ... | Youfeng Zou
  • Special Issue
  • - Volume 2016
  • - Article ID 7638985
  • - Research Article

Visibility Video Detection with Dark Channel Prior on Highway

Jiandong Zhao | Mingmin Han | ... | Xin Xin
  • Special Issue
  • - Volume 2016
  • - Article ID 8740593
  • - Research Article

Graph-Based Salient Region Detection through Linear Neighborhoods

Lijuan Xu | Fan Wang | ... | Yuanyuan Sun
  • Special Issue
  • - Volume 2016
  • - Article ID 3460281
  • - Research Article

Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification

Yidong Tang | Shucai Huang | Aijun Xue
Mathematical Problems in Engineering
 Journal metrics
Acceptance rate28%
Submission to final decision70 days
Acceptance to publication38 days
CiteScore1.130
Impact Factor1.179
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