A Computer Vision Framework for Automated Shape Retrieval

2020 ◽  
Vol 1 (1) ◽  
pp. 1-15
Author(s):  
Sourav Saha ◽  
Sahibjot Kaur ◽  
Jayanta Basak ◽  
Priya Ranjan Sinha Mahapatra
Keyword(s):  
Computer Vision ◽  
Similarity Score ◽  
Vital Role ◽  
Shape Retrieval ◽  
Retrieval Task ◽  
Tree Generation ◽  
String Edit Distance ◽  
Frame Work ◽  
Motivational Focus ◽  
Structured Representation

With the increasing number of images generated every day, textual annotation of images for image mining becomes impractical and inefficient. Thus, computer vision based image retrieval has received considerable interest in recent years. One of the fundamental characteristics of any image representation of an object is its shape which plays a vital role to recognize the object at primitive level. Keeping this view as the primary motivational focus, we propose a shape descriptive frame work using a multilevel tree structured representation called Hierarchical Convex Polygonal Decomposition (HCPD). Such a frame work explores different degrees of convexity of an object’s contour-segments in the course of its construction. The convex and non-convex segments of an object’s contour are discovered at every level of the HCPD-tree generation by repetitive convex-polygonal approximation of contour segments. We have also presented a novel shape-string-encoding scheme for representing the HCPD-tree which allows us touse the popular concept of string-edit distance to compute shape similarity score between two objects. The proposed framework when deployed for similar shape retrieval task demonstrates reasonably good performance in comparison with other popular shape-retrieval algorithms.

scholarly journals Lungs Cancer Detection Using Digital Image Processing Techniques: A Review

2019 ◽  
Vol 38 (2) ◽  
pp. 351-360 ◽  
Author(s):  
Mati ullah ◽  
Mehwish Bari ◽  
Adeel Ahmed ◽  
Sajid Naveed
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Computer Vision ◽  
Cancer Detection ◽  
Medical Image ◽  
Vital Role ◽  
Classification Methods ◽  
The People ◽  
Medical Diagnostic ◽  
Processing Techniques

From last decade, lung cancer become sign of fear among the people all over the world. As a result, many countries generate funds and give invitation to many scholars to overcome on this disease. Many researchers proposed many solutions and challenges of different phases of computer aided system to detect the lung cancer in early stages and give the facts about the lung cancer. CV (Computer Vision) play vital role to prevent lung cancer. Since image processing is necessary for computer vision, further in medical image processing there are many technical steps which are necessary to improve the performance of medical diagnostic machines. Without such steps programmer is unable to achieve accuracy given by another author using specific algorithm or technique. In this paper we highlight such steps which are used by many author in pre-processing, segmentation and classification methods of lung cancer area detection. If pre-processing and segmentation process have some ambiguity than ultimately it effects on classification process. We discuss such factors briefly so that new researchers can easily understand the situation to work further in which direction.

scholarly journals An Agri vigilance System based on Computer Vision and Deep Learning

2021 ◽  
Vol 2 (2) ◽  
pp. 1-8
Author(s):  
Karan Owalekar ◽  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
National Income ◽  
Efficient Solutions ◽  
Vital Role ◽  
Disease Detection ◽  
Detailed Report ◽  
Crop Monitoring ◽  
Crop Disease ◽  
Area Of Concern

In an agricultural-based country like India, farming and farming activities play a vital role in the growth of the economy as it is the main source of GNI (Gross National Income). This dependence of GNI on agriculture makes it important to address the issues faced by the farmers. The main area of concern for farmers revolves around crops and livestock. Precise farming techniques like cattle counting and crop disease detection are the need of the hour. The introduction of computer vision and deep learning has enabled us to make improvements in farming techniques. To accomplish this, a computer vision-based system is proposed which will be implemented using ResNet and YOLOv3-tiny. The proposed system will take images and videos as input and run them on the inference. The output will be updated in the database and the farmer will be notified in case of any inconsistency. The detailed report can be accessed by government agencies. The system will increase efficiency in farming processes like crop monitoring, livestock tracking, crop disease detection by providing fast and efficient solutions for the problems faced by the farmers.

Analysis of Plant Disease Detection and Classification Models: A Computer Vision Perspective

2020 ◽  
Vol 17 (12) ◽  
pp. 5422-5428
Author(s):  
K. Jayaprakash ◽  
S. P. Balamurugan
Keyword(s):  
Computer Vision ◽  
Feature Extraction ◽  
Plant Disease ◽  
Weather Conditions ◽  
Vital Role ◽  
Plant Diseases ◽  
Disease Detection ◽  
Classification Models ◽  
Extraction Techniques ◽  
Identification Process

Presently, rapid and precise disease identification process plays a vital role to increase agricultural productivity in a sustainable manner. Conventionally, human experts identify the existence of anomaly in plants occurred due to disease, pest, nutrient deficient, weather conditions. Since manual diagnosis process is a tedious and time consuming task, computer vision approaches have begun to automatically detect and classify the plant diseases. The general image processing tasks involved in plant disease detection are preprocessing, segmentation, feature extraction and classification. This paper performs a review of computer vision based plant disease detection and classification techniques. The existing plant disease detection approaches including segmentation and feature extraction techniques have been reviewed. Additionally, a brief survey of machine learning (ML) and deep learning (DL) models to identify plant diseases also takes place. Furthermore, a set of recently developed DL based tomato plant leaf disease detection and classification models are surveyed under diverse aspects. To further understand the reviewed methodologies, a detailed comparative study also takes place to recognize the unique characteristics of the reviewed models.

scholarly journals Improving Shape Retrieval by Integrating AIR and Modified MutualkNN Graph

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Nouman Qadeer ◽  
Dongting Hu ◽  
Xiabi Liu ◽  
Shahzad Anwar ◽  
Malik Saad Sultan
Keyword(s):  
Image Retrieval ◽  
Shape Matching ◽  
Contextual Information ◽  
Similarity Score ◽  
Feature Representation ◽  
Shape Retrieval ◽  
Computational Time ◽  
Integration Scheme ◽  
Invariant Representation ◽  
Face Datasets

In computer vision, image retrieval remained a significant problem and recent resurgent of image retrieval also relies on other postprocessing methods to improve the accuracy instead of solely relying on good feature representation. Our method addressed the shape retrieval of binary images. This paper proposes a new integration scheme to best utilize feature representation along with contextual information. For feature representation we used articulation invariant representation; dynamic programming is then utilized for better shape matching followed by manifold learning based postprocessing modified mutualkNN graph to further improve the similarity score. We conducted extensive experiments on widely used MPEG-7 database of shape images by so-called bulls-eye score with and without normalization of modified mutualkNN graph which clearly indicates the importance of normalization. Finally, our method demonstrated better results compared to other methods. We also computed the computational time with another graph transduction method which clearly shows that our method is computationally very fast. Furthermore, to show consistency of postprocessing method, we also performed experiments on challenging ORL and YALE face datasets and improved baseline results.

scholarly journals Image Representation Using Stacked Colour Histogram

Algorithms ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 228
Author(s):  
Ezekiel Mensah Martey ◽  
Hang Lei ◽  
Xiaoyu Li ◽  
Obed Appiah
Keyword(s):  
Image Retrieval ◽  
Image Representation ◽  
Vital Role ◽  
Image Texture ◽  
Visual Features ◽  
Retrieval Task ◽  
Classification Rate ◽  
Memory Space ◽  
Image Blurring ◽  
Neighbourhood Information

Image representation plays a vital role in the realisation of Content-Based Image Retrieval (CBIR) system. The representation is performed because pixel-by-pixel matching for image retrieval is impracticable as a result of the rigid nature of such an approach. In CBIR therefore, colour, shape and texture and other visual features are used to represent images for effective retrieval task. Among these visual features, the colour and texture are pretty remarkable in defining the content of the image. However, combining these features does not necessarily guarantee better retrieval accuracy due to image transformations such rotation, scaling, and translation that an image would have gone through. More so, concerns about feature vector representation taking ample memory space affect the running time of the retrieval task. To address these problems, we propose a new colour scheme called Stack Colour Histogram (SCH) which inherently extracts colour and neighbourhood information into a descriptor for indexing images. SCH performs recurrent mean filtering of the image to be indexed. The recurrent blurring in this proposed method works by repeatedly filtering (transforming) the image. The output of a transformation serves as the input for the next transformation, and in each case a histogram is generated. The histograms are summed up bin-by-bin and the resulted vector used to index the image. The image blurring process uses pixel’s neighbourhood information, making the proposed SCH exhibit the inherent textural information of the image that has been indexed. The SCH was extensively tested on the Coil100, Outext, Batik and Corel10K datasets. The Coil100, Outext, and Batik datasets are generally used to assess image texture descriptors, while Corel10K is used for heterogeneous descriptors. The experimental results show that our proposed descriptor significantly improves retrieval and classification rate when compared with (CMTH, MTH, TCM, CTM and NRFUCTM) which are the start-of-the-art descriptors for images with textural features.

scholarly journals Human Face Recognition using LBPH

2020 ◽  
Vol 8 (6) ◽  
pp. 3208-3212
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Face Detection ◽  
Local Binary Pattern ◽  
Human Face Recognition ◽  
Smart Systems ◽  
Finger Print ◽  
The Face ◽  
Face Detection And Recognition ◽  
Frame Work

During the beginning of seventieth centuries, human facial recognition has become one among the researched areas in the area of finger print scanning and computer vision. Identifying a person with an image has been popularized through the mass media. The recent technologies are totally focusing on developing the smart systems that will recognize the faces for biometric purposes. In this context automatic face recognition is applied for security purposes to find the criminal, attendance system, scientific laboratories etc. This research paper presents the frame work for real time face detection. However, it is less robust to finger print or retina scanning. This paper describes about the face detection and recognition. These technologies are available in the Open-Computer-Vision (OpenCV) library and methodology to implement them using Python in image processing and machine learning. For face detection, Haar-Cascades algorithms were used and for face recognition the algorithm like Eigen faces, and Local binary pattern histograms were used.

scholarly journals Hierarchical Instance Feature Alignment for 2D Image-Based 3D Shape Retrieval

2020 ◽  
Author(s):  
Heyu Zhou ◽  
Weizhi Nie ◽  
Wenhui Li ◽  
Dan Song ◽  
An-An Liu
Keyword(s):  
Feature Learning ◽  
Industrial Applications ◽  
Shape Retrieval ◽  
3D Shape Retrieval ◽  
Retrieval Task ◽  
3D Shape ◽  
Domain Variations ◽  
Visual Characteristics ◽  
High Level ◽  
Feature Alignment

2D image-based 3D shape retrieval has become a hot research topic since its wide industrial applications and academic significance. However, existing view-based 3D shape retrieval methods are restricted by two settings, 1) learn the common-class features while neglecting the instance visual characteristics, 2) narrow the global domain variations while ignoring the local semantic variations in each category. To overcome these problems, we propose a novel hierarchical instance feature alignment (HIFA) method for this task. HIFA consists of two modules, cross-modal instance feature learning and hierarchical instance feature alignment. Specifically, we first use CNN to extract both 2D image and multi-view features. Then, we maximize the mutual information between the input data and the high-level feature to preserve as much as visual characteristics of an individual instance. To mix up the features in two domains, we enforce feature alignment considering both global domain and local semantic levels. By narrowing the global domain variations we impose the identical large norm restriction on both 2D and 3D feature-norm expectations to facilitate more transferable possibility. By narrowing the local variations we propose to minimize the distance between two centroids of the same class from different domains to obtain semantic consistency. Extensive experiments on two popular and novel datasets, MI3DOR and MI3DOR-2, validate the superiority of HIFA for 2D image-based 3D shape retrieval task.

scholarly journals Charge Transfer Interactions of p-Azoxyanisole Complexes for Electrooptical Activity

2020 ◽  
Vol 32 (7) ◽  
pp. 1603-1608
Author(s):  
Ch. Ravi Shankar Kumar ◽  
S. Deepthi ◽  
Anjali Jha
Keyword(s):  
Charge Transfer ◽  
Metal Organic Framework ◽  
Spectroscopic Studies ◽  
Vital Role ◽  
Supramolecular Structures ◽  
Molecular Electronic ◽  
Metal Organic ◽  
Frame Work ◽  
Electrooptical Properties ◽  
Inorganic Molecules

In particular interactions due to organic-inorganic molecules results self assembled structures organize supramolecular structures for molecular, electronic and electrooptical properties. Supramolecular structures originated are complexes with organic (p-azoxyanisole) molecule, synthesized with metal nanoparticles (iron, copper and aluminium.) Spectroscopic studies interpret infrared spectra with wavenumbers of characteristic bands in assigned regions; wavenumbers with reduced intensity in Raman spectra attribute metal-organic framework with charge transfer. Designed frame work with dense participation of carriers interpret electron correlation and exchange interaction specifying molecular, electronic and electrooptical properties with Gaussian package using electron density method. Deterministic procedure attribute vital role of charge transfer interactions responsible in formation of complex with improvement in properties responsible for electrooptical activity.

scholarly journals A fast region-based active contour for non-rigid object tracking and its shape retrieval

2021 ◽  
Vol 7 ◽  
pp. e373
Author(s):  
Hiren Mewada ◽  
Jawad F. Al-Asad ◽  
Amit Patel ◽  
Jitendra Chaudhari ◽  
Keyur Mahant ◽  
...  
Keyword(s):  
Object Tracking ◽  
Video Processing ◽  
Active Contour ◽  
Active Contour Model ◽  
Mean Shift ◽  
Computation Time ◽  
Vital Role ◽  
Shape Retrieval ◽  
Rigid Object ◽  
Contour Model

Conventional tracking approaches track objects using a rectangle bounding box. Gait, gesture and many medical analyses require non-rigid shape extraction. A non-rigid object tracking is more difficult because it needs more accurate object shape and background separation in contrast to rigid bounding boxes. Active contour plays a vital role in the retrieval of image shape. However, the large computation time involved in contour tracing makes its use challenging in video processing. This paper proposes a new formation of the region-based active contour model (ACM) using a mean-shift tracker for video object tracking and its shape retrieval. The removal of re-initialization and fast deformation of the contour is proposed to retrieve the shape of the desired object. A contour model is further modified using a mean-shift tracker to track and retrieve shape simultaneously. The experimental results and their comparative analysis concludes that the proposed contour-based tracking succeed to track and retrieve the shape of the object with 71.86% accuracy. The contour-based mean-shift tracker resolves the scale-orientation selection problem in non-rigid object tracking, and resolves the weakness of the erroneous localization of the object in the frame by the tracker.

Sign in / Sign up

Export Citation Format

Share Document