Instagram Image Filtration with Computer Vision

Proceedings of Intelligent Computing and Technologies Conference ◽

10.21467/proceedings.115.22 ◽

2021 ◽

Author(s):

Tanjimul Ahad Asif ◽

Baidya Nath Saha

Keyword(s):

Image Processing ◽

Human Face ◽

Image Processing Techniques ◽

Search Results ◽

Search Result ◽

Face Images ◽

Image Filtration ◽

The Face ◽

Processing Techniques ◽

Human Faces

Instagram is one of the famous and fast-growing media sharing platforms. Instagram allows users to share photos and videos with followers. There are plenty of ways to search for images on Instagram, but one of the most familiar ways is ’hashtag.’ Hashtag search enables the users to find the precise search result on Instagram. However, there are no rules for using the hashtag; that is why it often does not match the uploaded image, and for this reason, Users are unable to find the relevant search results. This research aims to filter any human face images on search results based on hashtags on Instagram. Our study extends the author’s [2] work by implementing image processing techniques that detect human faces and separate the identified images on search results based on hashtags using the face detection technique.

Download Full-text

HUMAN FACE IMAGE PROCESSING TECHNIQUES

International Journal of Image and Graphics ◽

10.1142/s021946780100013x ◽

2001 ◽

Vol 01 (02) ◽

pp. 197-215 ◽

Cited By ~ 5

Author(s):

HONG YAN

Keyword(s):

Image Processing ◽

Research Work ◽

Facial Feature ◽

Face Image ◽

Computer Algorithms ◽

Human Face ◽

Feature Location ◽

Image Processing Techniques ◽

Face Image Processing ◽

Processing Techniques

Human face image processing techniques have many applications, such as in security operations, entertainment, medical imaging and telecommunications. In this paper, we provide an overview of existing computer algorithms for face detection and facial feature location, face recognition, image compression and animation. We also discuss limitations of current methods and research work needed in the future.

Download Full-text

Extraction the Facial Features of the Human Face

10.20850/9781534203655 ◽

2018 ◽

Author(s):

Farah Al-khalidi

Keyword(s):

Image Processing ◽

Skin Color ◽

Facial Features ◽

Horizontal Line ◽

Human Face ◽

Image Processing Techniques ◽

Face Area ◽

Important Region ◽

Elliptical Area ◽

Processing Techniques

This project presents a new approach for automatically tracking the human face as well as facial features (nose, mouth, eyes)in a clear way. This technique became required in various future visual communication applications, such as teleconferencing, Facial recognition systems, Biometrics and Human computer interface etc. The principle behind detecting the face feature is used to measure the respiration rate in the future as the nose represents the important region in the human face for breathing. Human face detection as elliptical area was investigated then image processing techniques were used to extract human face as elliptical area from the rest of image. Several techniques were applied to detect the nose inside the elliptical area as rectangle region and then the mouth and eyes regions were extracted inside the elliptical face area. A skin-color segmentation with image processing techniques played an important role in detecting the human face as elliptic area and then several techniques were used such as enhancement, thresholding, Morphological, edge detections as well as binarization techniques to achieve the aims of the suggested methods. Nose detection as a rectangle region was also investigated by looking for the longest vertical line in the elliptical area. The nose was detected and extracted as rectangle region. Detecting the mouth was achieved by looking for the longest horizontal line under the tip of the nose then thresholding this region to detect the lips of the mouth; by extracting the points of the lips corners we extracted the mouth as elliptical region. Finally, the eye regions were tracked in the upper part of ellipse above the tip of the nose and detected as rectangular regions. Further work is in progress to enhance these techniques to take place in real time images as well as apply them in the medical field.

Download Full-text

Three-Dimensional Structure of Rotavirus-Fab Complex

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100179944 ◽

1990 ◽

Vol 48 (1) ◽

pp. 238-239

Author(s):

B.V.V. Prasad ◽

E. Marietta ◽

J.W. Burns ◽

M.K. Estes ◽

W. Chiu

Keyword(s):

Image Processing ◽

Smooth Surface ◽

Three Dimensional ◽

Outer Shell ◽

Dimensional Structure ◽

Structural Studies ◽

Cell Penetration ◽

Electron Cryomicroscopy ◽

Image Processing Techniques ◽

Processing Techniques

Rotaviruses are spherical, double-shelled particles. They have been identified as a major cause of infantile gastroenteritis worldwide. In our earlier studies we determined the three-dimensional structures of double-and single-shelled simian rotavirus embedded in vitreous ice using electron cryomicroscopy and image processing techniques to a resolution of 40Å. A distinctive feature of the rotavirus structure is the presence of 132 large channels spanning across both the shells at all 5- and 6-coordinated positions of a T=13ℓ icosahedral lattice. The outer shell has 60 spikes emanating from its relatively smooth surface. The inner shell, in contrast, exhibits a bristly surface made of 260 morphological units at all local and strict 3-fold axes (Fig.l).The outer shell of rotavirus is made up of two proteins, VP4 and VP7. VP7, a glycoprotein and a neutralization antigen, is the major component. VP4 has been implicated in several important functions such as cell penetration, hemagglutination, neutralization and virulence. From our earlier studies we had proposed that the spikes correspond to VP4 and the rest of the surface is composed of VP7. Our recent structural studies, using the same techniques, with monoclonal antibodies specific to VP4 have established that surface spikes are made up of VP4.

Download Full-text

A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit2063211 ◽

2020 ◽

pp. 967-973

Author(s):

V. Deepika ◽

T. Rajasenbagam

Keyword(s):

Image Processing ◽

Brain Tumor ◽

Soft Tissues ◽

Tumor Detection ◽

Tumor Classification ◽

Normal Brain ◽

Image Processing Techniques ◽

Normal Brain Function ◽

Processing Techniques ◽

The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

Download Full-text