4,034 research outputs found
Distributed Representation of Geometrically Correlated Images with Compressed Linear Measurements
This paper addresses the problem of distributed coding of images whose
correlation is driven by the motion of objects or positioning of the vision
sensors. It concentrates on the problem where images are encoded with
compressed linear measurements. We propose a geometry-based correlation model
in order to describe the common information in pairs of images. We assume that
the constitutive components of natural images can be captured by visual
features that undergo local transformations (e.g., translation) in different
images. We first identify prominent visual features by computing a sparse
approximation of a reference image with a dictionary of geometric basis
functions. We then pose a regularized optimization problem to estimate the
corresponding features in correlated images given by quantized linear
measurements. The estimated features have to comply with the compressed
information and to represent consistent transformation between images. The
correlation model is given by the relative geometric transformations between
corresponding features. We then propose an efficient joint decoding algorithm
that estimates the compressed images such that they stay consistent with both
the quantized measurements and the correlation model. Experimental results show
that the proposed algorithm effectively estimates the correlation between
images in multi-view datasets. In addition, the proposed algorithm provides
effective decoding performance that compares advantageously to independent
coding solutions as well as state-of-the-art distributed coding schemes based
on disparity learning
A Perceptually Based Comparison of Image Similarity Metrics
The assessment of how well one image matches another forms a critical component both of models of human visual processing and of many image analysis systems. Two of the most commonly used norms for quantifying image similarity are L1 and L2, which are specific instances of the Minkowski metric. However, there is often not a principled reason for selecting one norm over the other. One way to address this problem is by examining whether one metric, better than the other, captures the perceptual notion of image similarity. This can be used to derive inferences regarding similarity criteria the human visual system uses, as well as to evaluate and design metrics for use in image-analysis applications. With this goal, we examined perceptual preferences for images retrieved on the basis of the L1 versus the L2 norm. These images were either small fragments without recognizable content, or larger patterns with recognizable content created by vector quantization. In both conditions the participants showed a small but consistent preference for images matched with the L1 metric. These results suggest that, in the domain of natural images of the kind we have used, the L1 metric may better capture human notions of image similarity
Disparity map generation based on trapezoidal camera architecture for multiview video
Visual content acquisition is a strategic functional block of any visual system. Despite its wide possibilities,
the arrangement of cameras for the acquisition of good quality visual content for use in multi-view video
remains a huge challenge. This paper presents the mathematical description of trapezoidal camera
architecture and relationships which facilitate the determination of camera position for visual content
acquisition in multi-view video, and depth map generation. The strong point of Trapezoidal Camera
Architecture is that it allows for adaptive camera topology by which points within the scene, especially the
occluded ones can be optically and geometrically viewed from several different viewpoints either on the
edge of the trapezoid or inside it. The concept of maximum independent set, trapezoid characteristics, and
the fact that the positions of cameras (with the exception of few) differ in their vertical coordinate
description could very well be used to address the issue of occlusion which continues to be a major
problem in computer vision with regards to the generation of depth map
A hybrid technique for face detection in color images
In this paper, a hybrid technique for face detection in color images is presented. The proposed technique combines three analysis models, namely skin detection, automatic eye localization, and appearance-based face/nonface classification. Using a robust histogram-based skin detection model, skin-like pixels are first identified in the RGB color space. Based on this, face bounding-boxes are extracted from the image. On detecting a face bounding-box, approximate positions of the candidate mouth feature points are identified using the redness property of image pixels. A region-based eye localization step, based on the detected mouth feature points, is then applied to face bounding-boxes to locate possible eye feature points in the image. Based on the distance between the detected eye feature points, face/non-face classification is performed over a normalized search area using the Bayesian discriminating feature (BDF) analysis method. Some subjective evaluation results are presented on images taken using digital cameras and a Webcam, representing both indoor and outdoor scenes
Offshore stereo measurements of gravity waves
Stereo video techniques are effective for estimating the space-time wave dynamics over an area of the ocean. Indeed, a stereo camera view allows retrieval of both spatial and temporal data whose statistical content is richer than that of time series data retrieved from point wave probes. To prove this, we consider an application of the Wave Acquisition Stereo System (WASS) for the analysis of offshore video measurements of gravity waves in the Northern Adriatic Sea. In particular, we deployed WASS at the oceanographic platform Acqua Alta, off the Venice coast, Italy. Three experimental studies were performed, and the overlapping field of view of the acquired stereo images covered an area of approximately 1100 m2. Analysis of the WASS measurements show that the sea surface can be accurately estimated in space and time together, yielding associated directional spectra and wave statistics that agree well with theoretical models. From the observed wavenumber-frequency spectrum one can also predict the vertical profile of the current flow underneath the wave surface. Finally, future improvements of WASS and applications are discussed
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