Learn More
This paper presents an algorithm designed to measure the local perceived sharpness in an image. Our method utilizes both spectral and spatial properties of the image: For each block, we measure the slope of the magnitude spectrum and the total spatial variation. These measures are then adjusted to account for visual perception, and then, the adjusted(More)
This paper presents an algorithm for video quality assessment, spatiotemporal MAD (ST-MAD), which extends our previous image-based algorithm (MAD [1]) to take into account visual perception of motion artifacts. ST-MAD employs spatiotemporal “images” (STS images [2]) created by taking time-based slices of the original and distorted videos.(More)
This paper presents the results of a computational experiment designed to investigate the extent to which metrics of image fidelity can be improved through knowledge of where humans tend to fixate in images. Five common metrics of image fidelity were augmented using two sets of fixation data, one set obtained under task-free viewing conditions and another(More)
This paper presents a block-based algorithm designed to measure the local perceived sharpness in an image. Our method utilizes both spectral and spatial properties of the image: For each block, we measure the slope of the magnitude spectrum and the total spatial variation. These measures are then adjusted to account for visual perception, and then the(More)
Most methods of image quality assessment (QA) have been designed for QA of degraded images. This paper presents the results of a study designed to investigate whether existing QA methods can be adapted to succeed on enhanced images. We developed a database containing digitally enhanced images and associated subjective quality ratings. Next, we analyzed the(More)
In this paper we present an algorithm which uses adaptive selection of low-level features for main subject detection. The algorithm first computes low-level features such as contrast and sharpness, each computed in a block-based fashion. Next, the algorithm quantifies the usefulness of each feature by using both statistical and geometric information(More)
Main subject detection (MSD) refers to the task of determining which spatial regions in an image correspond to the most visually relevant or scene-defining object(s) for general viewing purposes. This task, while trivial for a human, remains extremely challenging for a computer. Here, we present an algorithm for MSD which operates by adaptively refining(More)
In this paper, a new framework is proposed for autonomous universal surveillance based on video and audio data. “Universal” indicates no specification of targets of interest. Instead, regions of importance (ROIs) in a scene should be detected. Specifically, in the video domain, a frame-based main subject detection is proposed based on adaptive(More)
This paper presents an algorithm for detecting multiple salient objects in images. The algorithm extends our previous algorithm which was designed to detect only a single salient object. The new algorithm employs five feature maps (lightness distance, color distance, contrast, sharpness, and edge strength), along with a new image-adaptive technique for(More)
In 1979 74 CT head scans were obtained on 57 AGA (31 male, 26 female), >2500-gm, 28 days-of-age newborns with suspected CNS disease in the NSUH NICU. Indications for CT scan included: CNS bleed - 2; cyanotic episode - 3; myelomeningocele - 3; seizure - 34; macro/microcephalus - 10; miscellaneous - 6. Eight scans were normal; 4 showed CNS bleed; 4 showed(More)
  • 1