Automated Design of Image Operators that Detect Interest Points

2008 ◽  
Vol 16 (4) ◽  
pp. 483-507 ◽  
Author(s):  
Leonardo Trujillo ◽  
Gustavo Olague
Keyword(s):  
Computer Vision ◽  
Search Space ◽  
Search Problem ◽  
Interest Points ◽  
Fitness Evaluation ◽  
Modern Computer ◽  
New Perspective ◽  
Processing Techniques ◽  
Point Detection ◽  
Optimization Search

This work describes how evolutionary computation can be used to synthesize low-level image operators that detect interesting points on digital images. Interest point detection is an essential part of many modern computer vision systems that solve tasks such as object recognition, stereo correspondence, and image indexing, to name but a few. The design of the specialized operators is posed as an optimization/search problem that is solved with genetic programming (GP), a strategy still mostly unexplored by the computer vision community. The proposed approach automatically synthesizes operators that are competitive with state-of-the-art designs, taking into account an operator's geometric stability and the global separability of detected points during fitness evaluation. The GP search space is defined using simple primitive operations that are commonly found in point detectors proposed by the vision community. The experiments described in this paper extend previous results (Trujillo and Olague, 2006a,b) by presenting 15 new operators that were synthesized through the GP-based search. Some of the synthesized operators can be regarded as improved manmade designs because they employ well-known image processing techniques and achieve highly competitive performance. On the other hand, since the GP search also generates what can be considered as unconventional operators for point detection, these results provide a new perspective to feature extraction research.

Interest Point Detection Based on Monogenic Signal Theory

2014 ◽  
Vol 926-930 ◽  
pp. 3451-3454
Author(s):  
Li Juan Wang ◽  
Chang Sheng Zhang
Keyword(s):  
Computer Vision ◽  
Signal Theory ◽  
Good Precision ◽  
Interest Point ◽  
Phase Congruency ◽  
Interest Points ◽  
Monogenic Signal ◽  
Modern Computer ◽  
Interest Point Detection ◽  
Point Detection

A new algorithm is proposed for interest point detection based on monogenic signal theory in this paper. The detection of stable and informative image points is one of the most important problems in modern computer vision. Phase congruency is a dimensionless measure that remains invariant to changes in image illumination and contrast. A monogenic phase congruency function is constructed using the characteristics to detect interest points in image. The experiment results indicate that different kinds of interest points can be detected and located with good precision, thus the proposed method can be applied over wide classes of images.

scholarly journals A HIGH-ACCURACY METHOD TO MAKE MEASUREMENTS ALONG A LINE USING COMPUTER VISION AND LASER DISTANCE SENSORS

2017 ◽  
Vol 3 (2) ◽  
pp. 135-153
Author(s):  
Mariana Mascarenhas De Carvalho ◽  
Rodrigo Varejão Andreão ◽  
João Marques Salomão
Keyword(s):  
Computer Vision ◽  
Real World ◽  
Image Plane ◽  
Industrial Applications ◽  
Distance Measures ◽  
Interest Points ◽  
Image Processing Techniques ◽  
Computer Vision Applications ◽  
Processing Techniques ◽  
Potential Use

This paper presents an efficient method to measure real-world distance between interest points of a 3-D objectusing a camera, two laser distance sensors and Image Processing techniques. This method consists of calculating the distance between interest points of an object in the image plane. Two laser distance sensors placed next to the camera measure accurately the distance between the camera and the object. Moreover, the laser dots generated by the distance sensors are visible in the image, acting as a reference along with the distance measures in the calculations. As a result, thecalculation of the interest point's coordinate on the camera 3-D space is transformed in a real-world distance between interest points. Through a set of experiments, we show that the proposed system measures distancesup to 70 mm with sub-mm accuracy. The results suggest a potential use of our method in many other Computer Vision applications, including industrial applications.

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

Water Engineering in the Ancient World

2009 ◽  
Author(s):  
Charles R. Ortloff
Keyword(s):  
Distribution Systems ◽  
Survival Strategies ◽  
Economic Stability ◽  
Supply And Distribution ◽  
Modern Computer ◽  
Agricultural Water Supply ◽  
Ancient Times ◽  
Ancient Civilizations ◽  
New Perspective ◽  
Current Concerns

Charles Ortloff provides a new perspective on archaeological studies of the urban and agricultural water supply and distribution systems of the major ancient civilizations of South America, the Middle East, and South-East Asia, by using modern computer analysis methods to extract the true hydraulic/hydrological knowledge base available to these peoples. His many new revelations about the capabilities and innovations of ancient water engineers force us to re-evaluate what was known and practised in the hydraulic sciences in ancient times. Given our current concerns about global warming and its effect on economic stability, it is fascinating to observe how some ancient civilizations successfully coped with major climate change events by devising defensive agricultural survival strategies, while others, which did not innovate, failed to survive.

scholarly journals Clay-Based Brick Porosity Estimation Using Image Processing Techniques

2019 ◽  
Vol 29 (1) ◽  
pp. 1226-1234
Author(s):  
Safa Jida ◽  
Hassan Ouallal ◽  
Brahim Aksasse ◽  
Mohammed Ouanan ◽  
Mohamed El Amraoui ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Image Processing ◽  
Scanning Electron Microscopy ◽  
Computer Vision ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Porosity Estimation ◽  
Microscopy Images ◽  
Scanning Electron

Abstract This work intends to apprehend and emphasize the contribution of image-processing techniques and computer vision in the treatment of clay-based material known in Meknes region. One of the various characteristics used to describe clay in a qualitative manner is porosity, as it is considered one of the properties that with “kill or cure” effectiveness. For this purpose, we use scanning electron microscopy images, as they are considered the most powerful tool for characterising the quality of the microscopic pore structure of porous materials. We present various existing methods of segmentation, as we are interested only in pore regions. The results show good matching between physical estimation and Voronoi diagram-based porosity estimation.

A Comprehensive Survey of Neural Architecture Search

2021 ◽  
Vol 54 (4) ◽  
pp. 1-34
Author(s):  
Pengzhen Ren ◽  
Yun Xiao ◽  
Xiaojun Chang ◽  
Po-yao Huang ◽  
Zhihui Li ◽  
...  
Keyword(s):  
Research Work ◽  
Search Space ◽  
Feature Representation ◽  
Future Research ◽  
Related Research ◽  
Neural Architecture ◽  
Comprehensive Survey ◽  
Future Research Directions ◽  
Intuitive Idea ◽  
New Perspective

Deep learning has made substantial breakthroughs in many fields due to its powerful automatic representation capabilities. It has been proven that neural architecture design is crucial to the feature representation of data and the final performance. However, the design of the neural architecture heavily relies on the researchers’ prior knowledge and experience. And due to the limitations of humans’ inherent knowledge, it is difficult for people to jump out of their original thinking paradigm and design an optimal model. Therefore, an intuitive idea would be to reduce human intervention as much as possible and let the algorithm automatically design the neural architecture. Neural Architecture Search ( NAS ) is just such a revolutionary algorithm, and the related research work is complicated and rich. Therefore, a comprehensive and systematic survey on the NAS is essential. Previously related surveys have begun to classify existing work mainly based on the key components of NAS: search space, search strategy, and evaluation strategy. While this classification method is more intuitive, it is difficult for readers to grasp the challenges and the landmark work involved. Therefore, in this survey, we provide a new perspective: beginning with an overview of the characteristics of the earliest NAS algorithms, summarizing the problems in these early NAS algorithms, and then providing solutions for subsequent related research work. In addition, we conduct a detailed and comprehensive analysis, comparison, and summary of these works. Finally, we provide some possible future research directions.

scholarly journals Deep learning-enabled medical computer vision

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Andre Esteva ◽  
Katherine Chou ◽  
Serena Yeung ◽  
Nikhil Naik ◽  
Ali Madani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Medical Imaging ◽  
Real World ◽  
Recent Progress ◽  
Medical Applications ◽  
Modern Computer ◽  
Medical Computer

AbstractA decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

scholarly journals Survey of rice seed quality analysis for varietal purity estimation by using image processing techniques

2018 ◽  
Vol 7 (1.7) ◽  
pp. 34
Author(s):  
S. Durai ◽  
C. Mahesh ◽  
T. Sujithra ◽  
A. Suresh
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
South India ◽  
Seed Quality ◽  
Quality Analysis ◽  
Rice Seed ◽  
Two Factors ◽  
Computer Vision Technology ◽  
Processing Techniques ◽  
Varietal Purity

 In south India rice is the major food source and in agriculture, rice production covers more than 70 percentages of entire forming. But in recent the production only from south India not enough to satisfy the need of all, such a huge demand is there. The better production comes from the selection of good seeds. Up to now formers depend on two factors for selecting better seeds, One is the brand which is approved by some quality standards and second one is analyzed manually by experienced people. Both are risky one, we are not pretty much sure the accuracy of analyze. The second one is seeing and feeling. The inspection is not consistent also very time consuming. In the other way we can use computer vision technology to analyze the quality of the seeds. In recent years many of the big industries they are using computer vision technology with Digital Image Processing for many of the applications. In this Paper we are going to discuss the different seed quality analyzing methods and accuracy of result also. Moreover there are different factors and features are there for it, here we are going to study about varietal purity estimation by different methods.

scholarly journals AN EVOLUTIONARY ALGORITHM FOR FAST INTENSITY BASED IMAGE MATCHING BETWEEN OPTICAL AND SAR SATELLITE IMAGERY

2018 ◽  
Vol IV-3 ◽  
pp. 83-90 ◽  
Author(s):  
Peter Fischer ◽  
Philipp Schuegraf ◽  
Nina Merkle ◽  
Tobias Storch
Keyword(s):  
Evolutionary Algorithm ◽  
Satellite Imagery ◽  
Image Matching ◽  
Optimal Solution ◽  
Search Problem ◽  
Computational Costs ◽  
Radar Sensors ◽  
Function Calls ◽  
Point Detection ◽  
Very High

This paper presents a hybrid evolutionary algorithm for fast intensity based matching between satellite imagery from SAR and very high-resolution (VHR) optical sensor systems. The precise and accurate co-registration of image time series and images of different sensors is a key task in multi-sensor image processing scenarios. The necessary preprocessing step of image matching and tie-point detection is divided into a search problem and a similarity measurement. Within this paper we evaluate the use of an evolutionary search strategy for establishing the spatial correspondence between satellite imagery of optical and radar sensors. The aim of the proposed algorithm is to decrease the computational costs during the search process by formulating the search as an optimization problem. Based upon the canonical evolutionary algorithm, the proposed algorithm is adapted for SAR/optical imagery intensity based matching. Extensions are drawn using techniques like hybridization (e.g. local search) and others to lower the number of objective function calls and refine the result. The algorithm significantely decreases the computational costs whilst finding the optimal solution in a reliable way.

Interest Points Guided Mesh Simplification

2012 ◽  
Vol 263-266 ◽  
pp. 2320-2323 ◽  
Author(s):  
Ying Gao ◽  
Rui Zhao Wang ◽  
Jue Yuan
Keyword(s):  
Mesh Simplification ◽  
Experimental Results ◽  
Interest Point ◽  
Interest Points ◽  
Interest Point Detection ◽  
Feature Preserving ◽  
Error Metric ◽  
Point Detection ◽  
Quadric Error Metric

Based on interest point detection, a feature preserving mesh simplification algorithm is proposed. The Harris operator values of all vertices in the mesh were computed firstly. On the base of Garland’s simplification algorithm, we combine the Harris operator value with quadric error metric and change the order of edge collapsing in the simplification. The experimental results show that the proposed algorithm is effective and feature preserving.

Sign in / Sign up

Export Citation Format

Share Document