scholarly journals Automated Failure Detection in Computer Vision Systems

2016 ◽  
Vol 2 (1) ◽  
Author(s):  
H. Yan ◽  
A. Achkar ◽  
Akshaya Mishra ◽  
K. Naik
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Vehicle Detection ◽  
Failure Detection ◽  
Convolution Neural Network ◽  
Vision Systems ◽  
Testing Data ◽  
Computer Vision Systems ◽  
Human Validation

Human validation of computer vision systems increase their operatingcosts and limits their scale. Automated failure detection canmitigate these constraints and is thus of great importance to thecomputer vision industry. Here, we apply a deep neural networkto detect computer vision failures on vehicle detection tasks. Theproposed model is a convolution neural network that estimates theoutput quality of a vehicle detector. We train the network to learnto estimate a pixel-level F1 score between the vehicle detector andhuman annotated data. The model generalizes well to testing data,providing a mechanism for identifying detection failures.

scholarly journals APPROACH TO IMAGE ANALYSIS FOR COMPUTER VISION SYSTEMS

Doklady BGUIR ◽  
2020 ◽  
Vol 18 (2) ◽  
pp. 62-70
Author(s):  
N. A. Iskra
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Image Analysis ◽  
Object Detection ◽  
Image Annotation ◽  
Spatial Relations ◽  
Image Captioning ◽  
Vision Systems ◽  
Genome Database ◽  
Computer Vision Systems

This paper suggests an approach to the semantic image analysis for application in computer vision systems. The aim of the work is to develop a method for automatically construction of a semantic model, that formalizes the spatial relationships between objects in the image and research thereof. A distinctive feature of this model is the detection of salient objects, due to which the construction algorithm analyzes significantly less relations between objects, which can greatly reduce the image processing time and the amount of resources spent for processing. Attention is paid to the selection of a neural network algorithm for object detection in an image, as a preliminary stage of model construction. Experiments were conducted on test datasets provided by Visual Genome database, developed by researchers from Stanford University to evaluate object detection algorithms, image captioning models, and other relevant image analysis tasks. When assessing the performance of the model, the accuracy of spatial relations recognition was evaluated. Further, the experiments on resulting model interpretation were conducted, namely image annotation, i.e. generating a textual description of the image content. The experimental results were compared with similar results obtained by means of the algorithm based on neural networks algorithm on the same dataset by other researchers, as well as by the author of this paper earlier. Up to 60 % improvement in image captioning quality (according to the METEOR metric) compared with neural network methods has been shown. In addition, the use of this model allows partial cleansing and normalization of data for training neural network architectures, which are widely used in image analysis among others. The prospects of using this technique in situational monitoring are considered. The disadvantages of this approach are some simplifications in the construction of the model, which will be taken into account in the further development of the model.

scholarly journals CONTOUR METHOD FOR POSITIONING OBJECTS IN MOBILE COMPUTER VISION SYSTEMS

2021 ◽  
pp. 62-69
Author(s):  
Sergey Kondratyev ◽  
Vitaliy Kostenko ◽  
Marina Yadrova
Keyword(s):  
Computer Vision ◽  
Control Unit ◽  
Contour Method ◽  
Raspberry Pi ◽  
Closed Contour ◽  
Vision Systems ◽  
Mobile Computer ◽  
Video Cameras ◽  
Computer Vision Systems

The paper considers the possibility of solving the problem of improving the quality of technical vision using the contour method, which is used to position objects in mobile computer vision systems. The hardware part of the object positioning system includes two video cameras, a Raspberry Pi 3 microcomputer, a depth contour map screen, and a motor control unit. The codes of programs based on the OpenCV library, the algorithm of the system and examples of the implementation of the contour method are given. The algorithm of the developed positioning technique includes the selection of the contours of objects on the frames of a stereopair, removal of all open contours, calculation of the moment (center of mass) of each closed contour, determination of the displacement along the x-axis of the moments of the corresponding contours, filling each closed contour with points with a brightness inversely proportional to the displacement of the moments. The presence of two video cameras, a Raspberry Pi 3 microcomputer, a contour depth map screen provides stereoscopic and panoramic "vision", that is, the ability to determine the presence of objects and their distance, as well as to get an overall picture in the "field of view" of the system. The engine control unit allows mobile devices to avoid obstacles. Based on the analysis of the research results, it was found that the proposed system provides an increase in the quality of positioning of objects and a decrease in the required computing resource, which gives a significant decrease in power consumption and ensures the autonomy of the system.

scholarly journals Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1492
Author(s):  
Guoming Li ◽  
Yanbo Huang ◽  
Zhiqian Chen ◽  
Gary D. Chesser ◽  
Joseph L. Purswell ◽  
...  
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Convolutional Neural Network ◽  
Graphics Processing Units ◽  
Animal Species ◽  
Farm Animals ◽  
Future Research ◽  
Vision Systems ◽  
Animal Farming ◽  
Computer Vision Systems

Convolutional neural network (CNN)-based computer vision systems have been increasingly applied in animal farming to improve animal management, but current knowledge, practices, limitations, and solutions of the applications remain to be expanded and explored. The objective of this study is to systematically review applications of CNN-based computer vision systems on animal farming in terms of the five deep learning computer vision tasks: image classification, object detection, semantic/instance segmentation, pose estimation, and tracking. Cattle, sheep/goats, pigs, and poultry were the major farm animal species of concern. In this research, preparations for system development, including camera settings, inclusion of variations for data recordings, choices of graphics processing units, image preprocessing, and data labeling were summarized. CNN architectures were reviewed based on the computer vision tasks in animal farming. Strategies of algorithm development included distribution of development data, data augmentation, hyperparameter tuning, and selection of evaluation metrics. Judgment of model performance and performance based on architectures were discussed. Besides practices in optimizing CNN-based computer vision systems, system applications were also organized based on year, country, animal species, and purposes. Finally, recommendations on future research were provided to develop and improve CNN-based computer vision systems for improved welfare, environment, engineering, genetics, and management of farm animals.

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

ON METHODS OF OBJECT DETECTION IN VIDEO STREAMS

2017 ◽  
Vol 2 (1) ◽  
pp. 80-87
Author(s):  
Puyda V. ◽  
◽  
Stoian. A.
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Open Source ◽  
Feature Detection ◽  
Video Stream ◽  
Object Identification ◽  
Vision Systems ◽  
Modern Computer ◽  
Computer Vision Systems ◽  
Open Source Hardware

Detecting objects in a video stream is a typical problem in modern computer vision systems that are used in multiple areas. Object detection can be done on both static images and on frames of a video stream. Essentially, object detection means finding color and intensity non-uniformities which can be treated as physical objects. Beside that, the operations of finding coordinates, size and other characteristics of these non-uniformities that can be used to solve other computer vision related problems like object identification can be executed. In this paper, we study three algorithms which can be used to detect objects of different nature and are based on different approaches: detection of color non-uniformities, frame difference and feature detection. As the input data, we use a video stream which is obtained from a video camera or from an mp4 video file. Simulations and testing of the algoritms were done on a universal computer based on an open-source hardware, built on the Broadcom BCM2711, quad-core Cortex-A72 (ARM v8) 64-bit SoC processor with frequency 1,5GHz. The software was created in Visual Studio 2019 using OpenCV 4 on Windows 10 and on a universal computer operated under Linux (Raspbian Buster OS) for an open-source hardware. In the paper, the methods under consideration are compared. The results of the paper can be used in research and development of modern computer vision systems used for different purposes. Keywords: object detection, feature points, keypoints, ORB detector, computer vision, motion detection, HSV model color

scholarly journals Improving skeleton algorithm for helping Caenorhabditis elegans trackers

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Pablo E. Layana Castro ◽  
Joan Carles Puchalt ◽  
Antonio-José Sánchez-Salmerón
Keyword(s):  
Computer Vision ◽  
Caenorhabditis Elegans ◽  
Transformation Function ◽  
Simple Solution ◽  
New Method ◽  
Vision Systems ◽  
Distance Transformation ◽  
Automatic Computer ◽  
C Elegans ◽  
Computer Vision Systems

AbstractOne of the main problems when monitoring Caenorhabditis elegans nematodes (C. elegans) is tracking their poses by automatic computer vision systems. This is a challenge given the marked flexibility that their bodies present and the different poses that can be performed during their behaviour individually, which become even more complicated when worms aggregate with others while moving. This work proposes a simple solution by combining some computer vision techniques to help to determine certain worm poses and to identify each one during aggregation or in coiled shapes. This new method is based on the distance transformation function to obtain better worm skeletons. Experiments were performed with 205 plates, each with 10, 15, 30, 60 or 100 worms, which totals 100,000 worm poses approximately. A comparison of the proposed method was made to a classic skeletonisation method to find that 2196 problematic poses had improved by between 22% and 1% on average in the pose predictions of each worm.

Computer Vision Tagging the Metropolitan Museum of Art's Collection

2021 ◽  
Vol 14 (3) ◽  
pp. 1-17
Author(s):  
Elena Villaespesa ◽  
Seth Crider
Keyword(s):  
Computer Vision ◽  
Selection Criteria ◽  
Object Image ◽  
Metropolitan Museum ◽  
Museum Collections ◽  
Vision Systems ◽  
Computer Vision Systems ◽  
Similarities And Differences ◽  
Ibm Watson

Computer vision algorithms are increasingly being applied to museum collections to identify patterns, colors, and subjects by generating tags for each object image. There are multiple off-the-shelf systems that offer an accessible and rapid way to undertake this process. Based on the highlights of the Metropolitan Museum of Art's collection, this article examines the similarities and differences between the tags generated by three well-known computer vision systems (Google Cloud Vision, Amazon Rekognition, and IBM Watson). The results provide insights into the characteristics of these taxonomies in terms of the volume of tags generated for each object, their diversity, typology, and accuracy. In consequence, this article discusses the need for museums to define their own subject tagging strategy and selection criteria of computer vision tools based on their type of collection and tags needed to complement their metadata.

Sign in / Sign up

Export Citation Format

Share Document