scholarly journals A new method for vehicles detection and tracking using information and image processing

2021 ◽  
Vol 11 (6) ◽  
pp. 4942
Author(s):  
Mazouzi Amine ◽  
Kerfa Djoudi ◽  
Ismail Rakip Karas
Keyword(s):  
Image Processing ◽  
Mathematical Morphology ◽  
New Method ◽  
Video Sequences ◽  
Detection Level ◽  
Low Level ◽  
Detection And Tracking ◽  
High Level

<span lang="EN-US">In this article, a new method of vehicles detecting and tracking is presented: A thresholding followed by a mathematical morphology treatment are used. The tracking phase uses the information about a vehicle. An original labeling is proposed in this article. It helps to reduce some artefacts that occur at the detection level. The main contribution of this article lies in the possibility of merging information of low level (detection) and high level (tracking). In other words, it is shown that many artefacts resulting from image processing (low level) can be detected, and eliminated thanks to the information contained in the labeling (high level). The proposed method has been tested on many video sequences and examples are given illustrating the merits of our approach.</span>

scholarly journals A Study on Image Segmentation Techniques

2019 ◽  
Vol 8 (S2) ◽  
pp. 75-78
Author(s):  
S. Abdul Saleem ◽  
G. Vinitha
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Color Image ◽  
Video Frame ◽  
Challenging Problem ◽  
Digital Form ◽  
Low Level ◽  
Grey Scale ◽  
High Level

Image processing is a technique to transform an image into digital form and implement some operations on it; in order to acquire an improved image or to abstract some useful information from it. It is a kind of signal exemption in which input is image, like video frame or photograph and output may be image or characteristics related with that image. Segmentation partitions an image into separate regions comprising each pixel with similar attributes. To be significant and useful for image analysis and clarification, the regions should powerfully relate to depicted objects or features of interest. Meaningful segmentation is the first step from low-level image processing converting a grey scale or color image into one or more other images to high-level image depiction in terms of objects, features, and scenes. The achievement of image analysis depends on reliability of segmentation, but an exact partitioning of an image is mostly a very challenging problem.

scholarly journals New Edge Detection Method Using Elisabeth Method: Case Study Javanese Batiks

2010 ◽  
Vol 1 (1) ◽  
Author(s):  
Elisabeth Denis Setiani ◽  
Suyoto Suyoto
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Detection Method ◽  
New Method ◽  
Unique Pattern ◽  
Low Level ◽  
Edge Detection Method

Abstract. This article will introduce a new edge detection method called Elisabeth method to analyze image. The case study here is Javanese Batik’s motif. Edges are basic low level primitives for image processing. It helps to identify pictures. Methods used are the combination between Sobel and Prewitt. This method is completely new to analyze Javanese Batik’s motif. Every batik motif has unique pattern. The purpose of this research is to improving edge detection method that already known now. The result is a new method in edge detection problems. Batik is one of the Indonesian Heritage that avowed as a Heritage World Cultures. With this research it hoped can help our country to classify and identify Batik’s motif items in Indonesia. Keywords: Prewitt, Sobel, Elisabeth, Javanese Batik, Parang, Kawung Abstrak. Metode Baru Deteksi Tepi Menggunakan Metode Elisabeth: Studi Kasus Batik Jawa. Artikel ini akan memperkenalkan sebuah metode baru deteksi tepi yang disebut dengan metode Elisabeth untuk menganalisis citra. Studi kasus yang digunakan disini adalah motif Batik Jawa. Tepi adalah primitif level dasar untuk pemrosesan citra. Ini membantu mengidentifikasi gambar. Metode yang digunakan adalah kombinasi antara Sobel dan Prewitt. Metode ini benar-benar baru untuk menganalisis motif Batik Jawa. Setiap motif batik memiliki pola yang unik. Tujuan dari penelitian ini adalah untuk meningkatkan metode deteksi tepi yang sudah dikenal sekarang. Hasilnya adalah metode baru dalam masalah deteksi tepi. Batik adalah salah satu Warisan Indonesia yang diakui sebagai Warisan Budaya Dunia. Dengan penelitian ini diharapkan dapat membantu negara kita untuk mengklasifikasikan dan mengidentifikasi motif Batik di Indonesia. Kata Kunci: Prewitt, Sobel, Elisabeth, Batik Jawa, Parang, Kawung

Applications of Image Processing in Laparoscopic Surgeries

2018 ◽  
pp. 1518-1544
Author(s):  
Toktam Khatibi ◽  
Mohammad Mehdi Sepehri ◽  
Pejman Shadpour ◽  
Seyed Hessameddin Zegordi
Keyword(s):  
Image Processing ◽  
Soft Tissue ◽  
Video Processing ◽  
Position Estimation ◽  
Surgical Instrument ◽  
Tissue Segmentation ◽  
Detection And Tracking ◽  
Video Frames ◽  
High Level ◽  
Laparoscopic Videos

Laparoscopy is a minimally-invasive surgery using a few small incisions on the patient's body to insert the tools and telescope and conduct the surgical operation. Laparoscopic video processing can be used to extract valuable knowledge and help the surgeons. We discuss the present and possible future role of processing laparoscopic videos. The various applications are categorized for image processing algorithms in laparoscopic surgeries including preprocessing video frames by laparoscopic image enhancement, telescope related applications (telescope position estimation, telescope motion estimation and compensation), surgical instrument related applications (surgical instrument detection and tracking), soft tissue related applications (soft tissue segmentation and deformation tracking) and high level applications such as safe actions in laparoscopic videos, summarization of laparoscopic videos, surgical task recognition and extracting knowledge using fusion techniques. Some different methods have been proposed previously for each of the mentioned applications using image processing.

Applications of Image Processing in Laparoscopic Surgeries

2017 ◽  
pp. 317-343
Author(s):  
Toktam Khatibi ◽  
Mohammad Mehdi Sepehri ◽  
Pejman Shadpour ◽  
Seyed Hessameddin Zegordi
Keyword(s):  
Image Processing ◽  
Soft Tissue ◽  
Video Processing ◽  
Position Estimation ◽  
Surgical Instrument ◽  
Tissue Segmentation ◽  
Detection And Tracking ◽  
Video Frames ◽  
High Level ◽  
Laparoscopic Videos

Laparoscopy is a minimally-invasive surgery using a few small incisions on the patient's body to insert the tools and telescope and conduct the surgical operation. Laparoscopic video processing can be used to extract valuable knowledge and help the surgeons. We discuss the present and possible future role of processing laparoscopic videos. The various applications are categorized for image processing algorithms in laparoscopic surgeries including preprocessing video frames by laparoscopic image enhancement, telescope related applications (telescope position estimation, telescope motion estimation and compensation), surgical instrument related applications (surgical instrument detection and tracking), soft tissue related applications (soft tissue segmentation and deformation tracking) and high level applications such as safe actions in laparoscopic videos, summarization of laparoscopic videos, surgical task recognition and extracting knowledge using fusion techniques. Some different methods have been proposed previously for each of the mentioned applications using image processing.

Binocular Rivalry Disrupts Visual Priming

1998 ◽  
Vol 9 (4) ◽  
pp. 299-302 ◽  
Author(s):  
Carolyn Backer Cave ◽  
Randolph Blake ◽  
Timothy P. McNamara
Keyword(s):  
Binocular Rivalry ◽  
Repetition Priming ◽  
Sensory Attributes ◽  
Perceptual Processing ◽  
New Method ◽  
Processing Stage ◽  
Low Level ◽  
Visual Priming ◽  
High Level

Many results implicate perceptual processing in repetition priming, but little is known of potential mechanisms for priming. A new method was used to help determine the processing stage at which priming occurs. Priming pictures were presented under dominance or suppression generated by binocular rivalry. Although low-level, sensory attributes can be processed under rivalry suppression, there is no evidence that repetition priming can be supported by such low-level processing. Priming was found only for stimuli that were processed sufficiently to be identified in the priming stage. The results demonstrate that repetition priming requires processing of stimulus attributes into relatively high-level representations.

Extraction of Image with Complex Noise Based on Wavelet Analysis and Morphology

2014 ◽  
Vol 556-562 ◽  
pp. 5081-5084
Author(s):  
Xing Yan Hua
Keyword(s):  
Image Processing ◽  
Wavelet Analysis ◽  
Mathematical Morphology ◽  
Operation Time ◽  
Spatial Filter ◽  
New Method ◽  
Wavelet Filter ◽  
Noise Resistance ◽  
Image Edge ◽  
Edge Based

The spatial filter and wavelet filter were used to denoise the image with much complex noise. The mathematical morphology and threshold segmentation were integrated to detect the image edge. Based on comparison between the method given in this paper and the traditional methods, the new method can result in satisfying image processing result. The detecting precision is high. Also, the noise resistance is very good. The detected edge outlines are continuous, smooth and integrated. Moreover, the operation time is less.

Discussion on Image Contour Extracting Method of Homogeneous Objects

2008 ◽  
Vol 392-394 ◽  
pp. 935-940
Author(s):  
Q.G. Zeng ◽  
S.J. Song ◽  
C.F. Qiao ◽  
J.F. Zhang
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Mathematical Morphology ◽  
Traditional Method ◽  
Light Variation ◽  
Low Level ◽  
Image Contour

Contour extracting plays an important role in image processing and computer vision, and it’s one of the most important components in low level processing. Based on many tests, this paper applies hue and texture as feature combined with mathematical morphology to have a rough segmentation of homogeneous objects, then restores the obtained contour based on h component. Tests prove that acceptable contour can be obtained. Compared with traditional method of contour extracting, the algorithm mentioned here includes self-differing, contour restoring based on hue and so on, so it has better robustness to light variation.

scholarly journals Connecting Low-Level Image Processing and High-Level Vision via Deep Learning

2018 ◽  
Author(s):  
Ding Liu
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
Learning Mechanisms ◽  
Daily Lives ◽  
Low Level ◽  
Feature Representations ◽  
Technological Advances ◽  
High Level ◽  
Tremendous Impact

The latest development of computer vision has made exciting progress and tremendous impact in our daily lives. In this exciting era of technological advances, deep learning has gained huge popularity as a powerful tool for solving a lot of computer vision problems, and has added a great boost to this already rapidly developing field. Conventionally, the connection between different vision tasks is fragile. For example, low-level image processing and high-level vision tasks are usually coped with separately. However, the inherent relation of feature representations among various tasks should be effectively utilized rather than omitted. My research focuses on connecting low-level image processing and high-level vision via deep learning. Specifically, my goal is to design deep learning mechanisms that can efficiently and effectively learn features from low-level image processing and use them to improve the performance of high-level vision tasks.

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