When a picture is shot all the information about the distance between the object and the camera gets lost. Depth estimation from a single image is a notable issue in computer vision. In this work we present a hardware and software framework to accomplish the task of 3D measurement through structured light. This technique allows to estimate the depth of the objects, by projecting specific light patterns on the measuring scene. The potentialities of the structured light are well-known in both scientific and industrial contexts. Our framework uses a picoprojector module provided by STMicroelectronics, driven by the designed software projecting time- multiplexing Gray code light patterns. The Gray code is an alternative method to represent binary numbers, ensuring that the hamming distance between two consecutive numbers is always one. Because of this property, this binary coding gives better results for depth estimation task. Many patterns are projected at different time instants, obtaining a dense coding for each pixel. This information is then used to compute the depth for each point in the image. In order to achieve better results, we integrate the depth estimation with the inverted Gray code patterns as well, to compensate projector-camera synchronization problems as well as noise in the scene. Even though our framework is designed for laser picoprojectors, it can be used with conventional image projectors and we present the results for this case too.
In this paper, we propose a low complexity smile detection technique, able to detect smiles in a variety of light conditions, face positions, image resolutions. The proposed approach firstly detects the faces in the image, then applies almost cost-free mouth detection, extracts features from this region and finally classifies between smiling and nonsmiling stages. In this paper different feature extraction methods and classification techniques are analyzed from both the performance and computational complexity standpoints. The best compromise between performances and complexity is represented by a combined approach which exploits both a shape feature and a texture feature and uses the Mahalanobis distance based classifier. This solution achieves good performances with very low complexity, being suitable for an implementation on mobile devices.
Color interpolation solutions drastically influence the quality of the whole image generation pipeline, so they must guarantee the rendering of high quality pictures by avoiding typical artifacts such as blurring, zipper effects, and false colors. Moreover, demosaicing should avoid emphasizing typical artifacts of real sensors data, such as noise and green imbalance effect, which would be further accentuated by the subsequent steps of the processing pipeline. We propose a new adaptive algorithm that decides the interpolation technique to apply to each pixel, according to its neighborhood analysis. Edges are effectively interpolated through a directional filtering approach that interpolates the missing colors, selecting the suitable filter depending on edge orientation. Regions close to edges are interpolated through a simpler demosaicing approach. Thus flat regions are identified and low-pass filtered to eliminate some residual noise and to minimize the annoying green imbalance effect. Finally, an effective false color removal algorithm is used as a postprocessing step to eliminate residual color errors. The experimental results show how sharp edges are preserved, whereas undesired zipper effects are reduced, improving the edge resolution itself and obtaining superior image quality.
The high level context image analysis regards many fields as face recognition, smile detection, automatic red eye removal,
iris recognition, fingerprint verification, etc. Techniques involved in these fields need to be supported by more powerful
and accurate routines. The aim of the proposed algorithm is to detect elliptical shapes from digital input images. It can
be successfully applied in topics as signal detection or red eye removal, where the elliptical shape degree assessment can
improve performances. The method has been designed to handle low resolution and partial occlusions. The algorithm is
based on the signature contour analysis and exploits some geometrical properties of elliptical points. The proposed method
is structured in two parts: firstly, the best ellipse which approximates the object shape is estimated; then, through the
analysis and the comparison between the reference ellipse signature and the object signature, the algorithm establishes if
the object is elliptical or not. The first part is based on symmetrical properties of the points belonging to the ellipse, while
the second part is based on the signature operator which is a functional representation of a contour. A set of real images
has been tested and results point out the effectiveness of the algorithm in terms of accuracy and in terms of execution time.
The present work concerns the development of a no-reference demosaicing quality metric. The demosaicing
operation converts a raw image acquired with a single sensor array, overlaid with a color filter array, into a
full-color image. The most prominent artifact generated by demosaicing algorithms is called zipper. In this work
we propose an algorithm to identify these patterns and measure their visibility in order to estimate the perceived
quality of rendered images. We have conducted extensive subjective experiments, and we have determined the
relationships between subjective scores and the proposed measure to obtain a reliable no-reference metric.
Post-processing algorithms are usually placed in the pipeline of imaging devices to remove residual color artifacts
introduced by the demosaicing step. Although demosaicing solutions aim to eliminate, limit or correct false colors and
other impairments caused by a non ideal sampling, post-processing techniques are usually more powerful in achieving
this purpose. This is mainly because the input of post-processing algorithms is a fully restored RGB color image.
Moreover, post-processing can be applied more than once, in order to meet some quality criteria. In this paper we
propose an effective technique for reducing the color artifacts generated by conventional color interpolation algorithms,
in YCrCb color space. This solution efficiently removes false colors and can be executed while performing the edge
emphasis process.
This paper presents an efficient solution for digital images sharpening, the Adaptive Directional Sharpening with
Overshoot Control (ADSOC), a method based on a high-pass filter able to perform a stronger sharpening in the detailed
zones of the image, preserving the homogeneous regions. The basic objective of this approach is to reduce the undesired
effects. The sharpening introduced along strong edges or into uniform regions could provide unpleased ringing artifacts
and noise amplification, which are the most common drawbacks of the sharpening algorithms. The ADSOC allows to the
user to choose the ringing intensity and it doesn't increase the isolated noisy pixel luminance value. Moreover, the
ADSOC works the orthogonally respect to the direction of the edges in the blurred image, in order to yield a more
effective contrast enhancement. The experiments showed good algorithm performances in terms of booth visual quality
and computational complexity.
This paper proposes a projective image registration algorithm, oriented to consumer devices. It exploits a “multi-resolution feature based method” for estimating the projective parameters through a 2D Daubechies Discrete Wavelet Transform (DWT). The algorithm has been fully tested with real image sequences acquired by CMOS sensors and compared to other registration techniques. The obtained results highlight the accuracy of the registration parameters.
An objective image quality metric can be used to compare the output of different image processing algorithms, but objective measures are not always well correlated with subjective image quality assessment; the latter implies the use of human observers, thus objective methods able to emulate the Human Visual System (HVS) better than the classical measures are preferred. In this paper a full reference objective metric, based on perceptual criteria and oriented to demosaiced images is proposed.
The basic idea is to model the main artifacts produced by the interpolation process, taking into account the HVS sensibility to the typical aliasing and the zipper defects. The proposed technique has been compared to the DE94 CIELAB metric. Furthermore, two subjective tests have been performed; one relative to the color aliasing artifact and one to the zipper effect. The experimental results highlight that the quality scores obtained by the proposed measures have a similar trend to the DE94 CIELAB metric. Moreover, subjective tests are in accordance with the obtained results.
This technique is useful to evaluate the quality of the interpolation techniques implemented in the image processing pipeline of different digital still cameras.
The Bluetooth specifications currently include many application profiles that define the requirements for some usage cases. In this paper the attention is focused on the 'Basic Imaging Profile' BIP. The BIP could add important features to classic Digital Still Cameras, such as the possibility to send images to a Bluetooth Printer, or to send the image on Internet with a Bluetooth cellular phone.
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