Frontal-view Face Detection in The Presence of Skin-Tone Regions Using a New Symmetry Approach

In this paper, an efficient algorithm for detecting frontalview faces in color images is proposed. The proposed algorithm has a special task; it detects faces in the presence of skin-tone regions such as human body, clothes, and background. Firstly, a pixel based color classifier is applied to segment the skin pixels from background. Next, a hybrid cluster algorithm is applied to partition the skin region. It is well known that the frontal face is symmetrical; therefore we introduce a new symmetry approach, which is the main distinguishing feature of the proposed algorithm. It measures a symmetrical value, searches for the real center of the region, and then removes the extra unsymmetrical skin pixels. The cost functions are adopted to locate the real two eyes of the candidate face region. Finally, a template matching process is preformed between an aligning frontal face model and the candidate face region as a verification step. We have tested our algorithm on 200 images from different sets. Experimental results reveal that our algorithm can perform the detection of faces successfully under wide variations of captured images.Facultad de Informátic

Generic Symbolic Music Labeling Pipeline

The availability of large datasets is an essential key factor for machine learning success. However, for symbolic music datasets, while there are many symbolic music files available, labeled datasets are scarce in many applications. In this paper, we propose a general pipeline for symbolic music labeling. The input to the pipeline is unlabeled midi files without particular constraints. Firstly, the pipeline filters the input and splits it into time-limited musical segments. Secondly, the pipeline generates intermediate labels using multiple pre-trained models, neural networks, and heuristics. Finally, multiple methods are used to combine intermediate labels to generate final labels. A label is accepted only if it exceeds a certain confidence level. To test the pipeline, we apply it to label a new piano difficulty dataset, “PianoDiff”. We provide a thorough analysis to facilitate its usage in piano difficulty estimation for classification and generation using machine learning approaches. We test our pipeline on a dataset with manual labels. A random forest model trained on the weakly labeled dataset achieves an F1 score with a relative improvement of 13 percentage points compared to the same model trained on a smaller manually labeled dataset

Solution of fractional Volterra–Fredholm integro-differential equations under mixed boundary conditions by using the HOBW method

Abstract A new approximate technique is introduced to find a solution of FVFIDE with mixed boundary conditions. This paper started from the meaning of Caputo fractional differential operator. The fractional derivatives are replaced by the Caputo operator, and the solution is demonstrated by the hybrid orthonormal Bernstein and block-pulse functions wavelet method (HOBW). We demonstrate the convergence analysis for this technique to emphasize its reliability. The applicability of the HOBW is demonstrated using three examples. The approximate results of this technique are compared with the correct solutions, which shows that this technique has approval with the correct solutions to the problems

Diffusion weighted MRI and transient elastography assessment of liver fibrosis in hepatitis C patients: Validity of non invasive imaging techniques

Objective: Treatment for hepatitis C infection and monitoring of progression were based on degree of fibrosis, which were traditionally diagnosed by liver biopsy but it has many limitations. We aim to evaluate noninvasive imaging methods, so-called diffusion-weighted MRI (DW MRI) and transient elastography [(TE), fibroscan] in diagnosing liver fibrosis in hepatitis C (HCV) patients. Patients: The Study included 102 hepatitis C patients (62 male) with mean age of 38 ± 5. For all patients liver biopsy was done followed by DW MRI and TE. METAVIR classification system was used for staging liver fibrosis. Data obtained were collected and results of DW MRI and TE were compared with those of histopathology. The diagnostic performance of ADC and TE was determined using areas under receiver operating characteristic (AUROC) curves for significant fibrosis ⩾F3. Results: Measuring ADC at different b-values had a significant negative correlation with stage of fibrosis P = 0.001, the best negative correlation at b-value of 700 mm2/s. TE had a significant positive correlation with stage of fibrosis P = 0.005. Both examination showed a significant difference between fibrosis stage <F3 and stages ⩾F3 with P < 0.00 for ADC measure at each b-value and TE respectively. Conclusion: This study suggests that DW MRI and TE had favorable comparable results with liver biopsy for the diagnosis of significant liver fibrosis