Using computer vision to recognize composition of construction waste mixtures: A semantic segmentation approach

2022 ◽  
Vol 178 ◽  
pp. 106022
Author(s):  
Weisheng Lu ◽  
Junjie Chen ◽  
Fan Xue
Keyword(s):  
Computer Vision ◽  
Semantic Segmentation ◽  
Construction Waste ◽  
Segmentation Approach

Data normalization for training and analysis of the MaskRCNN model using the k-means method for computer vision of smart refrigerator

2021 ◽  
Vol 4 ◽  
pp. 74-80
Author(s):  
M. G. Dorrer ◽  
◽  
A.E. Alekhina ◽  
Keyword(s):  
Computer Vision ◽  
Semantic Segmentation ◽  
Correct Choice ◽  
Training Sample ◽  
Artificial Vision ◽  
K Value ◽  
High K ◽  
Real World Datasets ◽  
Segmentation Problem

This paper proposes using the k-means method for the controlled adjustment of the training sample for semantic image segmentation in the artificial vision of a smart refrigerator. To solve this problem, a new two-stage architecture for computer vision is proposed. In the proposed architecture, various sets of settings for optimizing the contrast of images are used to classify pixels according to their belonging to fragments of the studied image. Extensive experimental evaluation shows that the proposed method has critical advantages over existing work. Firstly, the obtained pixel classes can be directly clustered into semantic groups using k-means. Secondly, the method can be used for additional training of artificial intelligence in solving the semantic segmentation problem. The developers propose an approach to the correct choice of the number k of centroids to obtain good quality clusters, which is difficult to determine at a high k value. To overcome the problem of initializing the k-means method, an incremental k-means clustering method is proposed, which improves the quality of clusters to reduce the sum of squared errors. Comprehensive experiments have been carried out compared to the traditional k-means algorithm and its new versions to evaluate the performance of the proposed method on synthetically generated datasets and some real-world datasets.

scholarly journals Spinal cord segmentation and classification of degenerative disease

2019 ◽  
Vol 10 (3) ◽  
pp. 2426-2432 ◽  
Author(s):  
Arjun ◽  
Kanchana V
Keyword(s):  
Image Processing ◽  
Spinal Cord ◽  
Human Life ◽  
Semantic Segmentation ◽  
Processing Technique ◽  
Degenerative Disease ◽  
Image Processing Technique ◽  
Segmentation Approach ◽  
Work First ◽  
Mri Image

spinal cord plays an important role in human life. In our work, we are using digital image processing technique, the interior part of the human body can be analyzed using MRI, CT and X-RAY etc. Medical image processing technique is extensively used in medical field. In here we are using MRI image to perform our work In our proposed work, we are finding degenerative disease from spinal cord image. In our work first, we are preprocessing the MRI image and locate the degenerative part of the spinal cord, finding the degenerative part using various segmentation approach after that classifying degenerative disease or normal spinal cord using various classification algorithm. For segmentation, we are using an efficient semantic segmentation approach

Hybrid Image Segmentation Within a Computer Vision Hierarchy

1993 ◽  
Vol 30 (1) ◽  
pp. 51-64
Author(s):  
Ray Thomas ◽  
Fariborz Zahedi
Keyword(s):  
Computer Vision ◽  
Image Segmentation ◽  
Vision System ◽  
Critical Role ◽  
Generic Model ◽  
Test Results ◽  
Segmentation Approach ◽  
Edge Based

Hybrid image segmentation within a computer vision hierarchy A generic model of a computer vision system is presented which highlights the critical role of image segmentation. A hybrid segmentation approach, utilising both edge-based and region-based techniques, is proposed for improved quality of segmentation. An image segmentation architecture is outlined and test results are presented and discussed.

Semantic segmentation approach for tunnel roads’ analysis

2019 ◽  
Author(s):  
Arcadi Llanza ◽  
Assan Sanogo ◽  
Marouan Khata ◽  
Alami Khalil ◽  
Nadiya Shvai ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Segmentation Approach

scholarly journals FaceParser – A new face segmentation approach and labeleddatabase

2018 ◽  
Vol 7 (2.5) ◽  
pp. 1
Author(s):  
Khalil Khan ◽  
Nasir Ahmad ◽  
Irfan Uddin ◽  
Muhammad Ehsan Mazhar ◽  
Rehan Ullah Khan
Keyword(s):  
Semantic Segmentation ◽  
New Method ◽  
Facial Image ◽  
Data Set ◽  
Semantic Classes ◽  
Discriminative Model ◽  
Face Segmentation ◽  
Segmentation Approach ◽  
Class Labels ◽  
Pixel Labeling

Background and objective: A novel face parsing method is proposed in this paper which partition facial image into six semantic classes. Unlike previous approaches which segmented a facial image into three or four classes, we extended the class labels to six. Materials and Methods: A data-set of 464 images taken from FEI, MIT-CBCL, Pointing’04 and SiblingsDB databases was annotated. A discriminative model was trained by extracting features from squared patches. The built model was tested on two different semantic segmentation approaches – pixel-based and super-pixel-based semantic segmentation (PB_SS and SPB_SS).Results: A pixel labeling accuracy (PLA) of 94.68% and 90.35% was obtained with PB_SS and SPB_SS methods respectively on frontal images. Conclusions: A new method for face parts parsing was proposed which efficiently segmented a facial image into its constitute parts.

scholarly journals A SEMANTIC 3D POINT CLOUD SEGMENTATION APPROACH BASED ON OPTIMAL VIEW SELECTION FOR 2D IMAGE FEATURE EXTRACTION

2019 ◽  
Vol XLII-2/W17 ◽  
pp. 9-14
Author(s):  
A. Adam ◽  
L. Grammatikopoulos ◽  
G. Karras ◽  
E. Protopapadakis ◽  
K. Karantzalos
Keyword(s):  
Feature Extraction ◽  
Point Cloud ◽  
Structural Information ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Image Feature ◽  
Hybrid Features ◽  
Semantic Class ◽  
Segmentation Approach ◽  
Traditional Approaches

Abstract. 3D semantic segmentation is the joint task of partitioning a point cloud into semantically consistent 3D regions and assigning them to a semantic class/label. While the traditional approaches for 3D semantic segmentation typically rely only on structural information of the objects (i.e. object geometry and shape), the last years many techniques combining both visual and geometric features have emerged, taking advantage of the progress in SfM/MVS algorithms that reconstruct point clouds from multiple overlapping images. Our work describes a hybrid methodology for 3D semantic segmentation, relying both on 2D and 3D space and aiming at exploring whether image selection is critical as regards the accuracy of 3D semantic segmentation of point clouds. Experimental results are demonstrated on a free online dataset depicting city blocks around Paris. The experimental procedure not only validates that hybrid features (geometric and visual) can achieve a more accurate semantic segmentation, but also demonstrates the importance of the most appropriate view for the 2D feature extraction.

scholarly journals DETECTION OF LANE LINES FOR SELFDRIVING CARS BY USING EFFECTIVE COMPUTER VISION TECHNIQUES WITH OPENCV IN PYTHON

2021 ◽  
pp. 69-72
Author(s):  
Aryan Verma
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Autonomous Vehicles ◽  
Semantic Segmentation ◽  
Visual Features ◽  
Traffic Lights ◽  
Classification Of Images ◽  
Pedestrian Crossing ◽  
Source Of Information

Presently computer vision is amongst the hottest topics in Artificial Intelligence and is being extensively used in Robotics, Detecting Objects, Classification of Images, Autonomous Vehicles & tracking, Semantic Segmentation along with photo correction in various apps. In Self driven cars/ vehicles, vision remains the main source of information for detecting lanes, traffic lights, pedestrian crossing and other visual features. [2]

scholarly journals SEMANTIC SEGMENTATION OF BUILDING ELEMENTS USING POINT CLOUD HASHING

2018 ◽  
Vol XLII-2 ◽  
pp. 241-250 ◽  
Author(s):  
M. Chizhova ◽  
A. Gurianov ◽  
M. Hess ◽  
T. Luhmann ◽  
A. Brunn ◽  
...  
Keyword(s):  
Point Cloud ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Probabilistic Networks ◽  
Component Design ◽  
Semantic Modelling ◽  
Segmentation Approach ◽  
Novel Method ◽  
Human Interpretation ◽  
Definition Of

For the interpretation of point clouds, the semantic definition of extracted segments from point clouds or images is a common problem. Usually, the semantic of geometrical pre-segmented point cloud elements are determined using probabilistic networks and scene databases. The proposed semantic segmentation method is based on the psychological human interpretation of geometric objects, especially on fundamental rules of primary comprehension. Starting from these rules the buildings could be quite well and simply classified by a human operator (e.g. architect) into different building types and structural elements (dome, nave, transept etc.), including particular building parts which are visually detected. The key part of the procedure is a novel method based on hashing where point cloud projections are transformed into binary pixel representations. A segmentation approach released on the example of classical Orthodox churches is suitable for other buildings and objects characterized through a particular typology in its construction (e.g. industrial objects in standardized enviroments with strict component design allowing clear semantic modelling).

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