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.

Method and system for identifying defects in radiographic image data corresponding to a scanned object

Abstract: A method for identifying defects in radiographic image data corresponding to a scanned object is provided. The method includes acquiring radiographic image data corresponding to a scanned object. In one embodiment, the radiographic image data includes an inspection test image and a reference image corresponding to the scanned object. The method includes identifying one or more regions of interest in the reference image and aligning the inspection test image with the regions of interest identified in the reference image, to obtain a residual image. The method further includes identifying one or more defects in the inspection test image based upon the residual image and one or more defect probability values computed for one or more pixels in the residual image.

Automatic Detection and Classification of Knee Osteoarthritis Using Hu's Invariant Moments

Abstract: Significant information extraction from the images that are geometrically distorted or transformed is mainstream procedure in image processing. It becomes difficult to retrieve the relevant region when the images get distorted by some geometric deformation. Hu's moments are helpful in extracting information from such distorted images due to their unique invariance property. This work focuses on early detection and gradation of Knee Osteoarthritis utilizing Hu's invariant moments to understand the geometric transformation of the cartilage region in Knee X-ray images. The seven invariant moments are computed for the rotated version of the test image. The results demonstrated are found to be more competitive and promising, which are validated by ortho surgeons and rheumatologists.

System and method for predicting iconicity of an image

Abstract: A system and method for evaluating iconicity of an image are provided. In the method, at least one test image is received, each test image including an object in a selected class. Properties related to iconicity are computed for each test image. The properties may include one or more of: a) a direct measure of iconicity, which is computed with a direct iconicity prediction model which has been learned on a set of training images, each training image labeled with an iconicity score; b) one or more class-independent properties; and c) one or more class-dependent properties. A measure of iconicity of each of the test images is computed, based on the computed properties. By combining a set of complementary properties, an iconicity measure which shows good agreement with human evaluations of iconicity can be obtained.

Determining image-based product from digital image collection

Abstract: A method of making an image-based product includes providing an image collection having a plurality of digital images, each digital image having an image type; providing one or more image-type distributions, each image-type distribution corresponding to a theme and including a distribution of image types related to the theme; using a processor to automatically compare the image types of the digital images in the image collection to the image types in the image-type distribution; using the processor to automatically determine a match between the image types in the image collection and the image types in the image-type distribution; and selecting a group of digital images from the image collection having a distribution of image types specified by the determined matching image-type distribution. The method further includes assembling the digital images in the selected group of images into an image-based product and causing the construction of the image-based product.

Autoencoder-Based Fabric Defect Detection with Cross- Patch Similarity

Abstract: Fabric quality inspection plays an important role in the textile industry. As an effective approach to learn data representations, autoencoder has been adopted for defect detection. With the basic idea that the defect area cannot be recovered by the model trained on non-defective image patches, the residual is often used as an indication for defect judgement. However, usually the texture (non-defect) area in a defective patch also cannot be well reconstructed, which makes the pixel-wise detection inaccurate. In this paper, by exploring similarities between different patches in the whole test image, a novel autoencoder-based fabric defect detection method is proposed. In order to maintain the texture area in the reconstructed patch, the original encoded latent variable is modified, and the cross-patch similarity is introduced for determining the modification function. The whole algorithm is conducted in an iterative way, and the detection results will become better and better. Experimental results on the benchmark datasets demonstrate the effectiveness of our proposal.