Mirko Guarnera

MANET: possible applications with PDA in wireless imaging environment

Wireless communication systems are becoming the new enabling technology for accessing, sharing and processing data. It is easy to foresee that in the near future users will access the Internet through wireless PDA, while roaming from one place to another. Mobile ad-hoc networks (MANET) are peer-to-peer wireless networks that do not rely on the presence of a wired interconnection infrastructure. Apart from more traditional applications in the military field as well as in disaster recovery and emergency situations, MANET may represent an interesting possibility for personal communications and interaction among mobile users. We identify some possible fields of application for such wireless systems, identifying advantages and drawbacks. We also present a prototype application where MANETs are used in order to provide a distributed image processing service.

Psychovisual and statistical optimization of quantization tables for DCT compression engines

The paper presents a new and statistically robust algorithm able to improve the performance of the standard DCT compression algorithm for both perceived quality and compression size. The approach proposed combines together an information theoretical/statistical approach with HVS (human visual system) response functions. The methodology applied permits us to obtain a suitable quantization table for specific classes of images and specific viewing conditions. The paper presents a case study where the right parameters are learned after an extensive experimental phase, for three specific classes: document, landscape and portrait. The results show both perceptive and measured (in term of PSNR) improvement. A further application shows how it is possible obtain significant improvement profiling the relative DCT error inside the pipeline of images acquired by typical digital sensors.

A global enhancement pipeline for low-cost imaging devices

The paper describes a suitable algorithms pipelineable to enhance the picture quality in terms of both measured and perceived quality. The overall pipeline is mainly devoted to improve image acquired by low cost imaging sensors, typically present in consumer devices (i.e. mobile phone, web-cams, PDA, etc). A series of ad-hoc heuristics and techniques have been applied, taking into account main compression artifacts, chromatic and geometric distortions, etc.. Experimental results show effectiveness of the proposed pipeline.

Recent Patents on Color Demosaicing

Single-sensor technology is a popular imaging approach used in image-enabled consumer electronic devices such as digital still and video cameras, mobile phones, personal digital assistants, and visual sensors for surveillance and automotive applications. Cameras make use of an electronic sensor (Charge Coupled Device - CCD - or Complementary Metal-Oxide-Semiconductor - CMOS) to acquire the spatial variations in light intensity and then use image processing algorithms to reconstruct a color picture from the data provided by the sensor. Acquisition of color images requires the presence of different sensors for different color channels. Manufacturers reduce the cost and complexity by placing a color filter array (CFA) on top of a single image sensor, which is basically a monochromatic device, to acquire color information of the true visual scene. Typical imaging pipelines implemented in single-sensor cameras are usually designed to find a trade-off between sub-optimal solutions (devoted to solve imaging acquisition) and technological problems (e.g., color balancing, thermal noise, etc.) in a context of limited hardware resources. In this paper we review the existing patent solutions devoted to demosaicing and able to generate a color image from a single-sensor reading. Demosaicing solutions can be basically divided into four main categories: inter-channel (spectral) correlation, edge based, pattern based and iterative together with alternative techniques also present in literature. Discussion about pro and cons of each technique will be briefly reported.

Semantic segmentation of images exploiting DCT based features and random forest

This paper presents an approach for generating class-specific image segmentation. We introduce two novel features that use the quantized data of the Discrete Cosine Transform (DCT) in a Semantic Texton Forest based framework (STF), by combining together colour and texture information for semantic segmentation purpose. The combination of multiple features in a segmentation system is not a straightforward process. The proposed system is designed to exploit complementary features in a computationally efficient manner. Our DCT based features describe complex textures represented in the frequency domain and not just simple textures obtained using differences between intensity of pixels as in the classic STF approach. Differently than existing methods (e.g., filter bank) just a limited amount of resources is required. The proposed method has been tested on two popular databases: CamVid and MSRC-v2. Comparison with respect to recent state-of-the-art methods shows improvement in terms of semantic segmentation accuracy. HighlightsA method for semantic image segmentation based on random forests.Novel texture features based on Discrete Cosine Transform to describe image regions.The method uses a limited amount of resources and works in realtime.The approach shows good performance overcoming other state of the art.The system obtains a better accuracy on small classes (i.e., Pedestrians).

Red-eyes removal through cluster based Linear Discriminant Analysis

Red-eye artifact is a well-known problem in digital photography. Since the large diffusion of mobile devices with embedded camera and flashgun, automatic detection and correction of red-eyes have become an important task. In this paper we describe a technique that makes use of three steps to identify and correct red-eyes. First, red-eye candidates are extracted from the input image by using simple color segmentation coupled with geometrical constraints. A set of linear discriminant classifiers is then learned on the clustered patches space, and hence employed to distinguish between eyes and non-eyes patches. The proposed cluster-based Linear Discriminant Analysis is used to deal with the multi-modally nature of the input space. The third step of the pipeline is devoted to artifacts correction through de-saturation and brightness reduction. Experimental results on a large dataset of images demonstrate the effectiveness of the pro- posed pipeline that outperforms other existing solutions in terms of hit rates maximization, false positives reduction and ad-hoc quality measure.

Adaptive color demosaicing and false color removal

Color interpolation solutions drastically influence the quality of the whole image generation pipeline, so they must guar- antee the rendering of high quality pictures by avoiding typical arti- facts such as blurring, zipper effects, and false colors. Moreover, demosaicing should avoid emphasizing typical artifacts of real sen- sors 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 neigh- borhood analysis. Edges are effectively interpolated through a direc- tional filtering approach that interpolates the missing colors, select- ing 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 experi- mental results show how sharp edges are preserved, whereas un- desired zipper effects are reduced, improving the edge resolution itself and obtaining superior image quality.

False colors removal on the YCr-Cb color space

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.

Boosting Gray Codes for Red Eyes Removal

Since the large diffusion of digital camera and mobile devices with embedded camera and flashgun, the red-eyes artifacts have de-facto become a critical problem. The technique herein described makes use of three main steps to identify and remove red-eyes. First, red eyes candidates are extracted from the input image by using an image filtering pipeline. A set of classifiers is then learned on gray code features extracted in the clustered patches space, and hence employed to distinguish between eyes and non-eyes patches. Once red-eyes are detected, artifacts are removed through desaturation and brightness reduction. The proposed method has been tested on large dataset of images achieving effective results in terms of hit rates maximization, false positives reduction and quality measure.

Red-eyes removal through cluster-based boosting on gray codes

Since the large diffusion of digital camera and mobile devices with embedded camera and flashgun, the redeyes artifacts have de facto become a critical problem. The technique herein described makes use of three main steps to identify and remove red eyes. First, red-eye candidates are extracted from the input image by using an image filtering pipeline. A set of classifiers is then learned on gray code features extracted in the clustered patches space and hence employed to distinguish between eyes and non-eyes patches. Specifically, for each cluster the gray code of the red-eyes candidate is computed and some discriminative gray code bits are selected employing a boosting approach. The selected gray code bits are used during the classification to discriminate between eye versus non-eye patches. Once red-eyes are detected, artifacts are removed through desaturation and brightness reduction. Experimental results on a large dataset of images demonstrate the effectiveness of the proposed pipeline that outperforms other existing solutions in terms of hit rates maximization, false positives reduction, and quality measure.