6,411 research outputs found
Large scale evaluation of local image feature detectors on homography datasets
We present a large scale benchmark for the evaluation of local feature
detectors. Our key innovation is the introduction of a new evaluation protocol
which extends and improves the standard detection repeatability measure. The
new protocol is better for assessment on a large number of images and reduces
the dependency of the results on unwanted distractors such as the number of
detected features and the feature magnification factor. Additionally, our
protocol provides a comprehensive assessment of the expected performance of
detectors under several practical scenarios. Using images from the
recently-introduced HPatches dataset, we evaluate a range of state-of-the-art
local feature detectors on two main tasks: viewpoint and illumination invariant
detection. Contrary to previous detector evaluations, our study contains an
order of magnitude more image sequences, resulting in a quantitative evaluation
significantly more robust to over-fitting. We also show that traditional
detectors are still very competitive when compared to recent deep-learning
alternatives.Comment: Accepted to BMVC 201
A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends
Computer vision (CV) is a big and important field
in artificial intelligence covering a wide range of applications.
Image analysis is a major task in CV aiming to extract, analyse
and understand the visual content of images. However, imagerelated
tasks are very challenging due to many factors, e.g., high
variations across images, high dimensionality, domain expertise
requirement, and image distortions. Evolutionary computation
(EC) approaches have been widely used for image analysis with
significant achievement. However, there is no comprehensive
survey of existing EC approaches to image analysis. To fill
this gap, this paper provides a comprehensive survey covering
all essential EC approaches to important image analysis tasks
including edge detection, image segmentation, image feature
analysis, image classification, object detection, and others. This
survey aims to provide a better understanding of evolutionary
computer vision (ECV) by discussing the contributions of different
approaches and exploring how and why EC is used for
CV and image analysis. The applications, challenges, issues, and
trends associated to this research field are also discussed and
summarised to provide further guidelines and opportunities for
future research
Indian Monuments Classification using Support Vector Machine
Recently, Content-Based Image Retrieval is a widely popular and efficient searching and indexing approach used by knowledge seekers. Use of images by e-commerce sites, by product and by service industries is not new nowadays. Travel and tourism are the largest service industries in India. Every year people visit tourist places and upload pictures of their visit on social networking sites or share via the mobile device with friends and relatives. Classification of the monuments is helpful to hoteliers for the development of a new hotel with state of the art amenities, to travel service providers, to restaurant owners, to government agencies for security, etc.. The proposed system had extracted features and classified the Indian monuments visited by the tourists based on the linear Support Vector Machine (SVM). The proposed system was divided into 3 main phases: preprocessing, feature vector creation and classification. The extracted features are based on Local Binary Pattern, Histogram, Co-occurrence Matrix and Canny Edge Detection methods. Once the feature vector had been constructed, classification was  performed using Linear SVM. The Database of 10 popular Indian monuments was generated with 50 images for each class. The proposed system is implemented in MATLAB and achieves very high accuracy. The proposed system was also tested on other popular benchmark databases
Genetic Programming for Object Detection : a Two-Phase Approach with an Improved Fitness Function
This paper describes two innovations that improve the efficiency and effectiveness of a genetic programming approach to object detection problems. The approach uses genetic programming to construct object detection programs that are applied, in a moving window fashion, to the large images to locate the objects of interest. The first innovation is to break the GP search into two phases with the first phase applied to a selected subset of the training data, and a simplified fitness function. The second phase is initialised with the programs from the first phase, and uses the full set of training data with a complete fitness function to construct the final detection programs. The second innovation is to add a program size component to the fitness function. This approach is examined and compared with a neural network approach on three object detection problems of increasing difficulty. The results suggest that the innovations increase both the effectiveness and the efficiency of the genetic programming search, and also that the genetic programming approach outperforms a neural network approach for the most difficult data set in terms of the object detection accuracy
Image similarity in medical images
Recent experiments have indicated a strong influence of the substrate grain orientation on the self-ordering in anodic porous alumina. Anodic porous alumina with straight pore channels grown in a stable, self-ordered manner is formed on (001) oriented Al grain, while disordered porous pattern is formed on (101) oriented Al grain with tilted pore channels growing in an unstable manner. In this work, numerical simulation of the pore growth process is carried out to understand this phenomenon. The rate-determining step of the oxide growth is assumed to be the Cabrera-Mott barrier at the oxide/electrolyte (o/e) interface, while the substrate is assumed to determine the ratio β between the ionization and oxidation reactions at the metal/oxide (m/o) interface. By numerically solving the electric field inside a growing porous alumina during anodization, the migration rates of the ions and hence the evolution of the o/e and m/o interfaces are computed. The simulated results show that pore growth is more stable when β is higher. A higher β corresponds to more Al ionized and migrating away from the m/o interface rather than being oxidized, and hence a higher retained O:Al ratio in the oxide. Experimentally measured oxygen content in the self-ordered porous alumina on (001) Al is indeed found to be about 3% higher than that in the disordered alumina on (101) Al, in agreement with the theoretical prediction. The results, therefore, suggest that ionization on (001) Al substrate is relatively easier than on (101) Al, and this leads to the more stable growth of the pore channels on (001) Al
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