Enhanced flyby science with onboard computer vision: Tracking and surface feature detection at small bodies

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

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Recognition of Birds in Blurred and Illumination Images by Local Features

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i7/0128 ◽

2017 ◽

Vol 7 (7) ◽

pp. 243

Author(s):

Suresha .M ◽

. Sandeep

Keyword(s):

Computer Vision ◽

Time Complexity ◽

Feature Detection ◽

Feature Matching ◽

Fast Algorithms ◽

Local Features ◽

Experimental Demonstration ◽

Harris Corner ◽

Blurred Image ◽

Blurred Images

Local features are of great importance in computer vision. It performs feature detection and feature matching are two important tasks. In this paper concentrates on the problem of recognition of birds using local features. Investigation summarizes the local features SURF, FAST and HARRIS against blurred and illumination images. FAST and Harris corner algorithm have given less accuracy for blurred images. The SURF algorithm gives best result for blurred image because its identify strongest local features and time complexity is less and experimental demonstration shows that SURF algorithm is robust for blurred images and the FAST algorithms is suitable for images with illumination.

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A computer vision approach to identifying the manufacturer and model of anterior cervical spinal hardware

Journal of Neurosurgery Spine ◽

10.3171/2019.6.spine19463 ◽

2019 ◽

Vol 31 (6) ◽

pp. 844-850 ◽

Cited By ~ 1

Author(s):

Kevin T. Huang ◽

Michael A. Silva ◽

Alfred P. See ◽

Kyle C. Wu ◽

Troy Gallerani ◽

...

Keyword(s):

Computer Vision ◽

Feature Detection ◽

High Accuracy ◽

Detection Accuracy ◽

Data Sets ◽

Visual Words ◽

Fusion Systems ◽

Kaze Feature ◽

Applications Of Machine Learning ◽

Cervical Plating

OBJECTIVERecent advances in computer vision have revolutionized many aspects of society but have yet to find significant penetrance in neurosurgery. One proposed use for this technology is to aid in the identification of implanted spinal hardware. In revision operations, knowing the manufacturer and model of previously implanted fusion systems upfront can facilitate a faster and safer procedure, but this information is frequently unavailable or incomplete. The authors present one approach for the automated, high-accuracy classification of anterior cervical hardware fusion systems using computer vision.METHODSPatient records were searched for those who underwent anterior-posterior (AP) cervical radiography following anterior cervical discectomy and fusion (ACDF) at the authors’ institution over a 10-year period (2008–2018). These images were then cropped and windowed to include just the cervical plating system. Images were then labeled with the appropriate manufacturer and system according to the operative record. A computer vision classifier was then constructed using the bag-of-visual-words technique and KAZE feature detection. Accuracy and validity were tested using an 80%/20% training/testing pseudorandom split over 100 iterations.RESULTSA total of 321 total images were isolated containing 9 different ACDF systems from 5 different companies. The correct system was identified as the top choice in 91.5% ± 3.8% of the cases and one of the top 2 or 3 choices in 97.1% ± 2.0% and 98.4 ± 13% of the cases, respectively. Performance persisted despite the inclusion of variable sizes of hardware (i.e., 1-level, 2-level, and 3-level plates). Stratification by the size of hardware did not improve performance.CONCLUSIONSA computer vision algorithm was trained to classify at least 9 different types of anterior cervical fusion systems using relatively sparse data sets and was demonstrated to perform with high accuracy. This represents one of many potential clinical applications of machine learning and computer vision in neurosurgical practice.

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Computer Vision Techniques in Manufacturing

10.36227/techrxiv.17125652.v1 ◽

2021 ◽

Author(s):

Longfei Zhou ◽

Lin Zhang ◽

Nicholas Konz

Keyword(s):

Computer Vision ◽

Product Life Cycle ◽

Manufacturing Systems ◽

Feature Detection ◽

Rapid Development ◽

Planning And Scheduling ◽

System A ◽

Control Assembly ◽

Implementation Data ◽

Algorithm Implementation

Computer vision techniques have played an important role in promoting the informatization, digitization and intelligence of industrial manufacturing systems. Considering the rapid development of computer vision techniques, we present a comprehensive review of the state-of-the-art of these techniques and their applications in manufacturing industries. We survey the most common methods, including feature detection, recognition, segmentation and 3D modeling. A system framework of computer vision in the manufacturing environment is proposed, consisting of a lighting module, a manufacturing system, a sensing module, computer vision algorithms, a decision-making module, and an actuator. Applications of computer vision to different stages of the entire product life cycle are then explored, including product design, modeling and simulation, planning and scheduling, the production process, inspection and quality control, assembly, transportation, and disassembly. Challenges include algorithm implementation, data pre-processing, data labeling, and benchmarks. Future directions include building benchmarks, developing methods for non-annotated data processing, developing effective data pre-processing mechanisms, customizing computer vision models, and opportunities aroused by 5G.

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Facial Image Processing in Computer Vision

Image Processing ◽

10.4018/978-1-4666-3994-2.ch056 ◽

2013 ◽

pp. 1111-1123

Author(s):

Moi Hoon Yap ◽

Hassan Ugail

Keyword(s):

Image Processing ◽

Computer Vision ◽

Computer Animation ◽

Feature Detection ◽

Research Field ◽

Future Research ◽

Expression Recognition ◽

Facial Image ◽

Detection Techniques ◽

Future Challenges

The application of computer vision in face processing remains an important research field. The aim of this chapter is to provide an up-to-date review of research efforts of computer vision scientist in facial image processing, especially in the areas of entertainment industry, surveillance, and other human computer interaction applications. To be more specific, this chapter reviews and demonstrates the techniques of visible facial analysis, regardless of specific application areas. First, the chapter makes a thorough survey and comparison of face detection techniques. It provides some demonstrations on the effect of computer vision algorithms and colour segmentation on face images. Then, it reviews the facial expression recognition from the psychological aspect (Facial Action Coding System, FACS) and from the computer animation aspect (MPEG-4 Standard). The chapter also discusses two popular existing facial feature detection techniques: Gabor feature based boosted classifiers and Active Appearance Models, and demonstrate the performance on our in-house dataset. Finally, the chapter concludes with the future challenges and future research direction of facial image processing.

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An approach to apple surface feature detection by machine vision

Computers and Electronics in Agriculture ◽

10.1016/0168-1699(94)90012-4 ◽

1994 ◽

Vol 11 (2-3) ◽

pp. 249-264 ◽

Cited By ~ 43

Author(s):

Q. Yang

Keyword(s):

Machine Vision ◽

Feature Detection ◽

Surface Feature

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A featurebased approach to fractal image compression

Connectivity ◽

10.31673/2412-9070.2020.052226 ◽

2020 ◽

Vol 146 (5) ◽

Author(s):

G. Ya. Kis ◽

Keyword(s):

Computer Vision ◽

Pattern Recognition ◽

Video Compression ◽

Feature Detection ◽

Lipschitz Constant ◽

Block Matching ◽

Matching Task ◽

Feature Points ◽

Fractal Compression ◽

Salient Points

Recently a number of researches have demonstrated performance improvement in the video fractal compression compared to the current video transmission standards (MPEG, H.263, H.264). This article describes a current problem of relatively low fractal encoding speed. Indeed, high computational complexity is a sore point of fractal compression approach. It seems almost every paper on this subject touches the problem of encoding speed. Productive ideas and algorithms can be borrowed from the pattern recognition problem. In the course of recent decades feature points approach in computer vision has been demonstrating good performance in SLAM and pattern recognition. Technology of feature detection, description and tracking is being developed successfully and has effective applications in augmented reality like Android ARCore and IOS ARKit frameworks that are real-time engines. Similarities among parts of video frames are analyzed and used for both image registration and visual scene tracking therefore it fits highly to block matching task. Statistic properties for domain/range blocks matching has been analyzed on the basis of previous investigation for fractal compression. As a result, a simple algorithm is proposed based on computer vision approach. The approach includes a visual feature points extraction, feature descriptors calculation and fast NN-search in descriptor space. The key idea of the proposed approach is as follows. Only a limited number of domain blocks around the most salient points are subject to selection. Other blocks are not essential for matching as transforms would have big Lipschitz constant and will have worse contractive properties. Salient points should be unique as well. Further the descriptors for feature point are calculated. The algorithm has O(N log N) complexity for pixel number in the frame image, however if the number of domain blocks is limited the complexity could be almost linear. Python program for the algorithm test has been developed and shows that reconstruction result is acceptable in terms of encoding speed (< 2 s on 2 GHz CPU) and quality (PSNR) ~25 dB. The result of the proposed approach could be interesting for further improvement both for image and video compression. Further steps for quality increasing are also described.

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