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.

Natural image utility assessment using image contours

Abstract: In the quality assessment task, observers evaluate a natural image based on its perceptual resemblance to a reference. For the utility assessment task, observers evaluate the usefulness of a natural image as a surrogate for a reference. Humans generally use the information captured by an imaging system and tolerate distortions as long as the underlying task is performed reliably. Conventional notions of perceived quality cannot generally predict the perceived utility of a natural image. This paper examines variations to basic components of a recently introduced utility assessment algorithm that compares the contours of a reference and test image, referred to as the natural image contour evaluation (NICE), in terms of their capability to improve the prediction of perceived utility scores. Results show that classical edge-detection algorithms incorporated into NICE provide statistically equivalent performance to other, more complex edge-detection algorithms.

Stereoscopic panoramic image synthesis device, image capturing device, stereoscopic panoramic image synthesis method, recording medium, and computer program

Abstract: A plurality of images are acquired by panning an image capturing device (10) and performing high-speed continuous shooting. The optical flow between a reference image (previously captured image) and a currently captured image, in the chronologically captured images, is calculated, and images in an overlap area (area having disparity) between the images are clipped, on the basis of the calculated optical flow. A pair of clipped images is stored in a memory (48) as a left image and a right image, respectively. Thereafter, a left panoramic image is synthesized from a plurality of left images stored in the memory (48), and similarly, a right panoramic image is synthesized from a plurality of right images.

No-reference blur index using blur comparisons

Abstract: Presented is a new no-reference blur index for still images that is based on the observation that it can be difficult to perceive between versions of an image blurred to different degrees. A `re-blurred` image is produced by intentionally blurring the test image. Local sample statistics are computed in the vicinity of detected edges of the original and re-blurred images, respectively. These are differenced and normalised to construct a new blur index. Experimental results on four simulated blur databases and on the Real Blur Image Database show that the proposed method obtains high correlations with test subjective quality evaluations.

Face attribute recognition method based on multi-task deep learning

Abstract: The invention provides a face attribute recognition method based on the multi-task deep learning and relates to the face attribute recognition technique in the field of computer vision. The method comprises the steps of preparing an image data set; subjecting each image in the image data set to face detection one by one; detecting face key points in all detected faces; aligning each face with a standard face image according to the face alignment method based on detected face key points to form a face image training set; calculating an average face image in the training set; constructing a multi-task deep convolutional neural network, and training network parameters after subtracting the average face image from each face image in the face image training set so as to obtain a convolutional neural network model; detecting faces and face key points in a to-be-recognized test image, and aligning each face in the above image with the standard face image based on the face key points; placing the standard face image into the constructed convolutional neural network model after subtracting the average face image from the standard face image and conducting the feedforward arithmetic operation so as to obtain a result.