scholarly journals A Color Image Authentication Method Using Partitioned Palette and Morphological Operations

2008 ◽  
Vol E91-D (1) ◽  
pp. 54-61 ◽  
Author(s):  
C.-C. CHANG ◽  
P.-Y. LIN
Keyword(s):  
Color Image ◽  
Image Authentication ◽  
Morphological Operations

scholarly journals Extraction of urban buildings using color image processing and morphological operations.

2021 ◽  
Vol 11 (1) ◽  
pp. 45-66
Author(s):  
Mete Durlu ◽  
Ozan Eski ◽  
Emre Sumer
Keyword(s):  
Image Processing ◽  
Satellite Images ◽  
Color Image ◽  
Color Image Segmentation ◽  
Morphological Operations ◽  
Morphological Image Processing ◽  
Urban Buildings ◽  
Colour Image ◽  
High Resolution Satellite Images

In many geospatial applications, automated detection of buildings has become a key concern in recent years. Determination of building locations provides great benefits for numerous geospatial applications such as urban planning, disaster management, infrastructure planning, environmental monitoring. The study  aims to present a practical technique for extracting the buildings from high-resolution satellite images using color image segmentation and binary morphological image processing. The proposed method is implemented on satellite images of 4 different selected study areas of the city of Batikent, Ankara.  According to experiments conducted on the study areas, overall accuracy, sensitivity, and F1 values were computed to be on average, respectively. After applying morphological operations, the same metrics are calculated . The results show that the determination of urban buildings can be done more successfully with the suitable combination of morphological operations using rectangular structuring element. Keywords: Building Extraction; Colour Image Processing;Colour space conversion; Image Morphology; Remote Sensing        

The analysis of lane detection algorithms using histogram shapes and Hough transform

2015 ◽  
Vol 8 (3) ◽  
pp. 262-278 ◽  
Author(s):  
Mahasak Ketcham ◽  
Thittaporn Ganokratanaa
Keyword(s):  
Hough Transform ◽  
Color Image ◽  
Lane Detection ◽  
Morphological Operations ◽  
Grayscale Image ◽  
Video File ◽  
Content Type ◽  
Detection Analysis ◽  
Area Of Interest ◽  
Similarities And Differences

Purpose – The purpose of this paper is to develop a lane detection analysis algorithm by Hough transform and histogram shapes, which can effectively detect the lane markers in various lane road conditions, in driving system for drivers. Design/methodology/approach – Step 1: receiving image: the developed system is able to acquire images from video files. Step 2: splitting image: the system analyzes the splitting process of video file. Step 3: cropping image: specifying the area of interest using crop tool. Step 4: image enhancement: the system conducts the frame to convert RGB color image into grayscale image. Step 5: converting grayscale image to binary image. Step 6: segmenting and removing objects: using the opening morphological operations. Step 7: defining the analyzed area within the image using the Hough transform. Step 8: computing Houghline transform: the system operates the defined segment to analyze the Houghline transform. Findings – This paper presents the useful solution for lane detection by analyzing histogram shapes and Hough transform algorithms through digital image processing. The method has tested on video sequences filmed by using a webcam camera to record the road as a video file in a form of avi. The experimental results show the combination of two algorithms to compare the similarities and differences between histogram and Hough transform algorithm for better lane detection results. The performance of the Hough transform is better than the histogram shapes. Originality/value – This paper proposed two algorithms by comparing the similarities and differences between histogram shapes and Hough transform algorithm. The concept of this paper is to analyze between algorithms, provide a process of lane detection and search for the algorithm that has the better lane detection results.

scholarly journals Detection and Classification of Ring, Rust and Yellow Sugarcane Leaf Diseases

2019 ◽  
Vol 8 (6) ◽  
pp. 3310-3315
Keyword(s):  
Social Life ◽  
Color Image ◽  
Region Of Interest ◽  
Support Vector ◽  
Svm Classifier ◽  
Morphological Operations ◽  
Color Processing ◽  
Grey Scale ◽  
Average Accuracy ◽  
Crop Disease

Agriculture is an important sector in Economic and Social life. Crop disease detection is an emerging field in India. We can minimize the diseases infection on sugarcane leaf by detecting and grading the leaf disease in early stages. In this paper, we are detecting and recognize Sugar cane leaf diseases by using grey scale and color image processing and analyze the efficacy by comparing both. In grey scale processing, we presented Gradient Magnitude, Otsu method, Morphological Operations and Normalization to extract the Region of interest (ROI) i.e., disease part. In color processing initially converted RGB to L*a*b format, later K-means clustering and edge detection operations are applied on L*a*b image format. The features of Grey scale & color processed image are extracted and feed to Support Vector Machine (SVM) classifier which classifies ring, rust & yellow spot sugarcane leaf diseases. The Sugarcane leaf diseases are classified successfully with an average accuracy of 84% & 92% for grey scale & color features respectively.

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