Standard test image

About: Standard test image is a research topic. Over the lifetime, 5217 publications have been published within this topic receiving 98486 citations.

Digital camera and method for processing image

Abstract: PROBLEM TO BE SOLVED: To provide a digital camera and an image processing method by which a synthesized image obtained by compositing objects, especially a synthesized print can easily be generated and a stereoscopic photo can easily be photographed. SOLUTION: The digital camera is provided with an image pickup means that picks up an object to obtain digital image data, an image storage means that stores the digital image data, an image display means that displays an image, a reference image designation means that designates at least one area or over in the image referenced for synthesis as a reference image area, and an image synthesis means that displays the reference image in the reference image area onto the image display means while overlapping the reference image on the photographing object image that is photographed at present and the image data of the synthesized image resulting from compositing the reference images into the image of a photographed frame are generated on the basis of 1st and 2nd identification information and the designated area information of the reference image obtained as above to solve the tasks above.

Unsupervised Surface Defect Detection Using Deep Autoencoders and Data Augmentation

Abstract: Surface level defect detection, such as detecting missing components, misalignments and physical damages, is an important step in any manufacturing process. In this paper, similarity matching techniques for manufacturing defect detection are discussed. We are proposing an algorithm which detects surface level defects without relying on the availability of defect samples for training. Furthermore, we are also proposing a method which works when only one or a few reference images are available. It implements a deep autoencoder network and trains input reference image(s) along with various copies automatically generated by data augmentation. The trained network is then able to generate a descriptor—a unique signature of the reference image. After training, a test image of the same product is sent to the trained network to generate a test image descriptor. By matching the reference and test descriptors, a similarity score is generated which indicates if a defect is found. Our experiments show that this approach is more generic than traditional hand-engineered feature extraction methods and it can be applied to detect multiple type of defects.

On modeling variations for face authentication

Abstract: We present a scheme for face authentication in the presence of variations. To deal with variations, such as facial expressions and registration errors, with which traditional appearance-based methods do not perform well, we propose the eigenflow approach. In this approach, the optical flow and the optical flow residue between a test image and a training image are computed first. The optical flow is then fitted to a model that is pre-trained by applying principal component analysis (PCA) to optical flows resulting from variations caused by facial expressions and registration errors. The eigenflow residue, optimally combined with the optical flow residue using linear discriminant analysis (LDA), determines the authenticity of the test image. Experimental results show that the proposed scheme outperforms the traditional methods in the presence of expression variations and registration errors. The approach can be extended to model lighting and pose variations as well.

A computational method for the design of 2-D nonlinear Volterra filters

Abstract: A class of nonlinear 2-D filters based on the truncated discrete Volterra series is considered, together with a suited matrix notation. An optimization method is developed for the design of the filters according to the required input/output relation. The performance of different optimization algorithms is studied, specifically, the method of steepest descent, Powell's conjugate directions algorithm, and simulated annealing. It is found that the Powell technique is the most suited to the problem. A design example, together with the results obtained after processing a test image by the proposed filters and other standard techniques, is used to compare the performance of the three methods. >

Quantum Image Searching Based on Probability Distributions

Abstract: A quantum image searching method is proposed based on the probability distributions of the readouts from the quantum measurements. It is achieved by using low computational resources which are only a single Hadamard gate combined with m + 1 quantum measurement operations. To validate the proposed method, a simulation experiment is used where the image with the highest similarity value of 0.93 to the particular test image is retrieved as the search result from 4 × 4 binary image database. The proposal provides a basic step for designing a search engine on quantum computing devices where the image in the database is retrieved based on its similarity to the test image.