Design of algorithms that are able to estimate video quality as perceived by human observers is of interest for a number of applications. Depending on the video content, the artifacts introduced by the coding process can be more or less pronounced and diversely affect the quality of videos, as estimated by humans. While it is well understood that motion affects both human attention and coding quality, this relationship has only recently started gaining attention among the research community, when video quality assessment (VQA) is concerned. In this paper, the effect of calculating several objective measure features, related to video coding artifacts, separately for salient motion and other regions of the frames of the sequence is examined. In addition, we propose a new scheme for quality assessment of coded video streams, which takes into account salient motion. Standardized procedure has been used to calculate the Mean Opinion Score (MOS), based on experiments conducted with a group of non-expert observers viewing standard definition (SD) sequences. MOS measurements were taken for nine different SD sequences, coded using MPEG-2 at five different bit-rates. Eighteen different published approaches related to measuring the amount of coding artifacts objectively on a single-frame basis were implemented. Additional features describing the intensity of salient motion in the frames, as well as the intensity of coding artifacts in the salient motion regions were proposed. Automatic feature selection was performed to determine the subset of features most correlated to video quality. The results show that salient-motion-related features enhance prediction and indicate that the presence of blocking effect artifacts and blurring in the salient regions and variance and intensity of temporal changes in non-salient regions influence the perceived video quality.

Salient Motion Features for Video Quality Assessment

Existing object recognition techniques often rely on human labeled data conducting to severe limitations to design a fully autonomous machine vision system. In this work, we present an intelligent machine vision system able to learn autonomously individual objects present in real environment. This system relies on salient object detection. In its design, we were inspired by early processing stages of human visual system. In this context we suggest a novel fast algorithm for visually salient object detection, robust to real-world illumination conditions. Then we use it to extract salient objects which can be efficiently used for training the machine learning-based object detection and recognition unit of the proposed system. We provide results of our salient object detection algorithm on MSRA Salient Object Database benchmark comparing its quality with other state-of-the-art approaches. The proposed system has been implemented on a humanoid robot, increasing its autonomy in learning and interaction with humans. We report and discuss the obtained results, validating the proposed concepts.

A machine learning based intelligent vision system for autonomous object detection and recognition

Proposed skin detection method. The proposed method is a hybrid method that combines the neural networks and k-means.Modelling skin with different color-texture descriptors was investigated. High accuracy of F1=87.82% can be achieved based on images from the ECU database.Study showed that YIQ color space gives the highest separability in skin detection. Human skin detection is an essential step in most human detection applications, such as face detection. The performance of any skin detection system depends on assessment of two components: feature extraction and detection method. Skin color is a robust cue used for human skin detection. However, the performance of color-based detection methods is constrained by the overlapping color spaces of skin and non-skin pixels. To increase the accuracy of skin detection, texture features can be exploited as additional cues. In this paper, we propose a hybrid skin detection method based on YIQ color space and the statistical features of skin. A Multilayer Perceptron artificial neural network, which is a universal classifier, is combined with the k-means clustering method to accurately detect skin. The experimental results show that the proposed method can achieve high accuracy with an F1-measure of 87.82% based on images from the ECU database.

Hybrid Human Skin Detection Using Neural Network and K-Means Clustering Technique

The motivation of this paper is to provide an overview of the most recent trends and of the future research directions in color image and video processing. Rather than covering all aspects of the domain this survey covers issues related to the most active research areas in the last two years. It presents the most recent trends as well as the state-of-the-art, with a broad survey of the relevant literature, in the main active research areas in color imaging. It also focuses on the most promising research areas in color imaging science. This survey gives an overview about the issues, controversies, and problems of color image science. It focuses on human color vision, perception, and interpretation. It focuses also on acquisition systems, consumer imaging applications, and medical imaging applications. Next it gives a brief overview about the solutions, recommendations, most recent trends, and future trends of color image science. It focuses on color space, appearance models, color difference metrics, and color saliency. It focuses also on color features, color-based object tracking, scene illuminant estimation and color constancy, quality assessment and fidelity assessment, color characterization and calibration of a display device. It focuses on quantization, filtering and enhancement, segmentation, coding and compression, watermarking, and lastly on multispectral color image processing. Lastly, it addresses the research areas which still need addressing and which are the next and future perspectives of color in image and video processing.

/pdf/color-in-image-and-video-processing-most-recent-trends-and-3asac15kmf.pdf

Color in image and video processing: most recent trends and future research directions

Salient object detection is a basic technique for many computer vision applications. In this paper, we propose an adaptive computational model to detect the salient object in color images. Firstly, three human observation behaviors and scalable subtractive clustering techniques are used to construct attention Gaussian mixture model (AGMM) and background Gaussian mixture model (BGMM). Secondly, the Bayesian framework is employed to classify each pixel into salient object or background object. Thirdly, expectation-maximization (EM) algorithm is utilized to update the parameters of AGMM, BGMM, and Bayesian framework based on the detection results. Finally, the classification and update procedures are repeated until the detection results evolve to a steady state. Experiments on a variety of images demonstrate the robustness of the proposed method. Extensive quantitative evaluations and comparisons demonstrate that the proposed method significantly outperforms state-of-the-art methods.

An Adaptive Computational Model for Salient Object Detection

Recent research on computational modeling of visual attention has demonstrated that a bottom-up approach to identifying salient regions within an image can be applied to diverse and practical problems for which conventional machine vision techniques have not succeeded in producing robust solutions. This paper proposes a new method for extracting regions of interest (ROIs) from images using models of visual attention. It is presented in the context of improving content-based image retrieval (CBIR) solutions by implementing a biologically-motivated, unsupervised technique of grouping together images whose salient ROIs are perceptually similar. In this paper we focus on the process of extracting the salient regions of an image. The excellent results obtained with the proposed method have demonstrated that the ROIs of the images can be independently indexed for comparison against other regions on the basis of similarity for use in a CBIR solution.

/pdf/using-visual-attention-to-extract-regions-of-interest-in-the-52ytve0v71.pdf

Using visual attention to extract regions of interest in the context of image retrieval

Recent work in the computational modeling of visual attention has demonstrated that a purely bottom-up approach to identifying salient regions within an image can be successfully applied to diverse and practical problems from target recognition to the placement of advertisement This paper proposes an application of a combination of computational models of visual attention to the image retrieval problem We demonstrate that certain shortcomings of existing content-based image retrieval solutions can be addressed by implementing a biologically motivated, unsupervised way of grouping together images whose salient regions of interest (ROIs) are perceptually similar regardless of the visual contents of other (less relevant) parts of the image We propose a model in which only the salient regions of an image are encoded as ROIs whose features are then compared against previously seen ROIs and assigned cluster membership accordingly Experimental results show that the proposed approach works well for several combinations of feature extraction techniques and clustering algorithms, suggesting a promising avenue for future improvements, such as the addition of a top-down component and the inclusion of a relevance feedback mechanism

https://link.springer.com/content/pdf/10.1155%2F2007%2F43450.pdf

An attention-driven model for grouping similar images with image retrieval applications

We have developed a biologically-motivated, unsupervised way of grouping together images whose salient regions of interest (ROIs) are perceptually similar regardless of the visual contents of other (less relevant) parts of the image. In the implemented model cluster membership is assigned based on feature vectors extracted from salient ROIs. This paper focuses on the experimental evaluation of the proposed approach for several combinations of feature extraction techniques and unsupervised clustering algorithms. The results reported here show that this is a valid approach and encourage further research.

/pdf/an-unsupervised-method-for-clustering-images-based-on-their-2uidtsrdkh.pdf

An unsupervised method for clustering images based on their salient regions of interest

This work aims at investigating the influence of luminance information and environment illumination on skin classification We explore Bayesian approaches to perform automatic classification of human skin pixels on digital images, using color features as input Two probabilistic skin color models were built on different color spaces (RGB, normalized RG, HSI, HS, YCbCr and CbCr) and tested in a task of automatic pixel classification into skin and non-skin Analyses of classification performance were done by presenting an illumination controlled image database containing images acquired in four different illumination conditions (shadow, sun, incandescent and fluorescent lights) to these classifiers Our experiments show that building probabilistic skin color models using the CbCr color space generally improves performance of the classifiers and that best performance is achieved in shadow illumination

/pdf/a-study-of-the-effect-of-illumination-conditions-and-color-95iwej8iay.pdf

A Study of the Effect of Illumination Conditions and Color Spaces on Skin Segmentation

Background: The results of an in vivo study on the “ratio method” used in electronic foramen locators (EFL) are presented. EFLs are becoming widely used in the determination of the working length (WL) during the root canal treatment. The WL is the distance from a coronal reference point to the point at which canal preparation and filling should terminate. The “ratio method” was assessed by many clinicians with the aim of determining its ability to locate the apical foramen (AF). Nevertheless, in vivo studies to assess the method itself and to explain why the “ratio method” is able to locate the apical foramen and is unable to determine intermediate distances were not published so far. Methods: A developed apparatus applies an electrical current signal with constant amplitude of 10 μARMS through the endodontic file within the root canal. The applied current signal is composed by summing six sine waves, from 250 Hz to 8 kHz. Data were acquired with the endodontic file tip at 7 different positions within root canals. In the frequency domain the quotients between the amplitude of a reference frequency and the amplitudes of the other frequencies components were calculated. Twenty one root canals were analyzed in vivo, during the endodontic treatment of twelve teeth of different patients, with age between 20 to 55 years. Results: For the range of frequencies used in the commercial EFLs and for distances ranging from -3 mm to -1 mm of the AF, the impedance of the root canal is mainly resistive. However, when the file tip gets closer to AF, the root canal electrical impedance starts to change from a mainly resistive to a complex impedance. This change in the measured root canal impedance starts when the file tip is near -1.0 mm from the AF, getting stronger as the file tip gets closer to the AF. This change in the impedance behavior affects the ratio (quotient) of the impedance measured at different frequencies. Through graphic analysis it is demonstrated why EFLs based on the ratio method are unable to accurately measure any distances between - 3.0 and -0.5 mm from the apical foramen. The only reliable measurement is the 0 mm distance, which is when the file tip is at the AF. Conclusions: The electrical impedance values of 21 root canals were in vivo studied. The results confirm the ability of EFLs that are based on the ratio method to accurately locate the AF position and explain why they are unable to determine the file tip position along the root canal.

/pdf/in-vivo-assessment-of-the-impedance-ratio-method-used-in-1lm30tc6ep.pdf

Gustavo B. Borba

Papers

Using visual attention to extract regions of interest in the context of image retrieval

An attention-driven model for grouping similar images with image retrieval applications

An unsupervised method for clustering images based on their salient regions of interest

A Study of the Effect of Illumination Conditions and Color Spaces on Skin Segmentation

In vivo assessment of the impedance ratio method used in electronic foramen locators