Automatic Vehicle Detection Techniques in Image Processing Using Satellite Imaginary

2015 ◽  
Vol 3 (3) ◽  
pp. 1178-1181 ◽  
Author(s):  
Ekta Saxena ◽  
Keyword(s):  
Image Processing ◽  
Vehicle Detection ◽  
Detection Techniques

A Review of Vehicle Detection Techniques for Intelligent Vehicles

2022 ◽  
pp. 1-21
Author(s):  
Zhangu Wang ◽  
Jun Zhan ◽  
Chunguang Duan ◽  
Xin Guan ◽  
Pingping Lu ◽  
...  
Keyword(s):  
Vehicle Detection ◽  
Intelligent Vehicles ◽  
Detection Techniques

Facial Image Processing in Computer Vision

2013 ◽  
pp. 1111-1123
Author(s):  
Moi Hoon Yap ◽  
Hassan Ugail
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Computer Animation ◽  
Feature Detection ◽  
Research Field ◽  
Future Research ◽  
Expression Recognition ◽  
Facial Image ◽  
Detection Techniques ◽  
Future Challenges

The application of computer vision in face processing remains an important research field. The aim of this chapter is to provide an up-to-date review of research efforts of computer vision scientist in facial image processing, especially in the areas of entertainment industry, surveillance, and other human computer interaction applications. To be more specific, this chapter reviews and demonstrates the techniques of visible facial analysis, regardless of specific application areas. First, the chapter makes a thorough survey and comparison of face detection techniques. It provides some demonstrations on the effect of computer vision algorithms and colour segmentation on face images. Then, it reviews the facial expression recognition from the psychological aspect (Facial Action Coding System, FACS) and from the computer animation aspect (MPEG-4 Standard). The chapter also discusses two popular existing facial feature detection techniques: Gabor feature based boosted classifiers and Active Appearance Models, and demonstrate the performance on our in-house dataset. Finally, the chapter concludes with the future challenges and future research direction of facial image processing.

A Study on Different Edge Detection Techniques in Digital Image Processing

2017 ◽  
pp. 100-122 ◽  
Author(s):  
Shouvik Chakraborty ◽  
Mousomi Roy ◽  
Sirshendu Hore
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Basic Feature ◽  
Detection Methods ◽  
Natural Image ◽  
Detection Techniques ◽  
Image Edges

Image segmentation is one of the fundamental problems in image processing. In digital image processing, there are many image segmentation techniques. One of the most important techniques is Edge detection techniques for natural image segmentation. Edge is a one of the basic feature of an image. Edge detection can be used as a fundamental tool for image segmentation. Edge detection methods transform original images into edge images benefits from the changes of grey tones in the image. The image edges include a good number of rich information that is very significant for obtaining the image characteristic by object recognition and analyzing the image. In a gray scale image, the edge is a local feature that, within a neighborhood, separates two regions, in each of which the gray level is more or less uniform with different values on the two sides of the edge. In this paper, the main objective is to study the theory of edge detection for image segmentation using various computing approaches.

A Study on Glaucoma Disease Detection with Image Processing Methods

2020 ◽  
Vol 37 ◽  
pp. 25-35
Author(s):  
Shashilata Rawat ◽  
Uma Shankar Kurmi
Keyword(s):  
Image Processing ◽  
Optic Nerve ◽  
Imaging Techniques ◽  
Advanced Imaging ◽  
Detection Techniques ◽  
Glaucoma Detection ◽  
Mode Decomposition ◽  
Processing Techniques ◽  
The Brain ◽  
Aided Diagnosis

The glaucoma is a developing slow eye that effects optic nerve damage in its most common form. Once the optic nerve has been impaired, visual data is not passed to the brain and permanently visual impairment is caused. Glaucoma computer-aided diagnosis (CAD) is a rising area in which medical imaging is analyzed. The CAD is a more precise approach for glaucoma detection, inspired by recent advanced imaging techniques and high-velocity computers. Laser ophthalmoscope scanning, tomography with optical coherence, and retina tomography of Heidelberg have widely used imaging techniques for detecting glaucoma. In this paper, we provide a study of glaucoma disease with its types and detection techniques. Moreover, this paper tells about image processing techniques to detect glaucoma. Variational mode decomposition has also discussed here.

Comparison and Evaluation of Edge Detection Segmentation Techniques

2014 ◽  
Vol 889-890 ◽  
pp. 1069-1072
Author(s):  
Yu Bing Dong ◽  
Ming Jing Li ◽  
Hai Yan Wang
Keyword(s):  
Image Processing ◽  
Root Mean Square Error ◽  
Edge Detection ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Basic Problem ◽  
Mean Square ◽  
Detection Techniques ◽  
Image Edge Detection ◽  
Image Edge

Edge detection is the basic problem in the field of image processing. Various image edge detection techniques are introduced. Using various edge detection techniques different images are analyzed and compared by MATLAB7.0. In order to evaluate the effect of edge segmentation, the root mean square error is used. The experimental results show that no an edge detection technique works well for all types of images.

Detection of Plant Leaf Diseases by Applying Image Processing Schemes

2019 ◽  
Vol 16 (9) ◽  
pp. 3728-3734
Author(s):  
Navneet Kaur ◽  
V. Devendran ◽  
Sahil Verma
Keyword(s):  
Image Processing ◽  
Yield Loss ◽  
Early Stage ◽  
Crop Protection ◽  
Recognition System ◽  
Assessment System ◽  
Plant Leaf ◽  
Detection Techniques ◽  
Leaf Disease ◽  
Key Factor

Timely diagnosis of the disease is the key factor in agricultural productivity. If timely detection of the disease is not taken into account, it may lead to crop yield loss. Hence, agriculturists and agronomists face troubles to detect diseases successfully at an early stage or later stage. To support these personnels to diagnose disease syndromes in infected plants, deep learning plays an important role. The machine based recognition system based on image processing not only saves time but also is more robust and efficient in comparison to manual assessment system. It helps the growers to take timely steps involved in the judicious treatment of the concerned leaf diseases for crop protection. Maximizing the production or minimizing the production loss is the primary goal of automatic plant leaf disease recognition system. Following review presents some leaf disease detection techniques.

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