Performance Evaluation of Pest Detection Techniques via Image Processing in Agriculture

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.

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A Study on Different Edge Detection Techniques in Digital Image Processing

Advances in Multimedia and Interactive Technologies - Feature Detectors and Motion Detection in Video Processing ◽

10.4018/978-1-5225-1025-3.ch005 ◽

2017 ◽

pp. 100-122 ◽

Cited By ~ 8

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.

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A Study on Glaucoma Disease Detection with Image Processing Methods

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.37.25 ◽

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.

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