New approach to calculating the fundamental matrix

The estimation of the fundamental matrix (F) is to determine the epipolar geometry and to establish a geometrical relation between two images of the same scene or elaborate video frames. In the literature, we find many techniques that have been proposed for robust estimations such as RANSAC (random sample consensus), least-squares median (LMeds), and M estimators as exhaustive. This article presents a comparison between the different detectors that are (Harris, FAST, SIFT, and SURF) in terms of detected points number, the number of correct matches and the computation speed of the ‘F’. Our method based first on the extraction of descriptors by the algorithm (SURF) was used in comparison to the other one because of its robustness, then set the threshold of uniqueness to obtain the best points and also normalize these points and rank it according to the weighting function of the different regions at the end of the estimation of the matrix''F'' by the technique of the M-estimator at eight points, to calculate the average error and the speed of the calculation ''F''. The results of the experimental simulation were applied to the real images with different changes of viewpoints, for example (rotation, lighting, and moving object), give a good agreement in terms of the counting speed of the fundamental matrix and the acceptable average error. The results of the simulation show this technique of use in real-time application

New approach to the identification of the easy expression recognition system by robust techniques (SIFT, PCA-SIFT, ASIFT and SURF)

In recent years, facial recognition has been a major problem in the field of computer vision, which has attracted lots of interest in previous years because of its use in different applications by different domains and image analysis. Which is based on the extraction of facial descriptors, it is a very important step in facial recognition. In this article, we compared robust methods (SIFT, PCA-SIFT, ASIFT and SURF) to extract relevant facial information with different facial posture variations (open and unopened mouth, glasses and no glasses, open and closed eyes). The simulation results show that the detector (SURF) is better than others at finding the similarity descriptor and calculation time. Our method is based on the normalization of vector descriptors and combined with the RANSAC algorithm to cancel outliers in order to calculate the Hessian matrix with the objective of reducing the calculation time. To validate our experience, we tested four facial images databases containing several modifications. The results of the simulation show that our method is more efficient than other detectors in terms of speed of recognition and determination of similar points between two images of the same face, one belonging to the base of the text and the other one to the base driven by different modifications. This method, which can be applied on a mobile platform to analyze the content of simple images, for example, to detect driver fatigue, human-machine interaction, human-robot. Using descriptors with properties important for good accuracy and real-time response

A combined method based on CNN architecture for variation-resistant facial recognition

Identifying individuals from a facial image is a technique that forms part of computer vision and is used in various fields such as security, digital biometrics, smartphones, and banking. However, it can prove difficult due to the complexity of facial structure and the presence of variations that can affect the results. To overcome this difficulty, in this paper, we propose a combined approach that aims to improve the accuracy and robustness of facial recognition in the presence of variations. To this end, two datasets (ORL and UMIST) are used to train our model. We then began with the image pre-processing phase, which consists in applying a histogram equalization operation to adjust the gray levels over the entire image surface to improve quality and enhance the detection of features in each image. Next, the least important features are eliminated from the images using the Principal Component Analysis (PCA) method. Finally, the pre-processed images are subjected to a neural network architecture (CNN) consisting of multiple convolution layers and fully connected layers. Our simulation results show a high performance of our approach, with accuracy rates of up to 99.50% for the ORL dataset and 100% for the UMIST dataset

Method of optimization of the fundamental matrix by technique speeded up robust features application of different stress images

The purpose of determining the fundamental matrix (F) is to define the epipolar geometry and to relate two 2D images of the same scene or video series to find the 3D scenes. The problem we address in this work is the estimation of the localization error and the processing time. We start by comparing the following feature extraction techniques: Harris, features from accelerated segment test (FAST), scale invariant feature transform (SIFT) and speed-up robust features (SURF) with respect to the number of detected points and correct matches by different changes in images. Then, we merged the best chosen by the objective function, which groups the descriptors by different regions in order to calculate ‘F’. Then, we applied the standardized eight-point algorithm which also automatically eliminates the outliers to find the optimal solution ‘F’. The test of our optimization approach is applied on the real images with different scene variations. Our simulation results provided good results in terms of accuracy and the computation time of ‘F’ does not exceed 900 ms, as well as the projection error of maximum 1 pixel, regardless of the modification

Interferon Lambda: A New Sword in Cancer Immunotherapy

The discovery of the interferon-lambda (IFN-λ) family has considerably contributed to our understanding of the role of interferon not only in viral infections but also in cancer. IFN-λ proteins belong to the new type III IFN group. Type III IFN is structurally similar to type II IFN (IFN-γ) but functionally identical to type I IFN (IFN-α/β). However, in contrast to type I or type II IFNs, the response to type III IFN is highly cell-type specific. Only epithelial-like cells and to a lesser extent some immune cells respond to IFN-λ. This particular pattern of response is controlled by the differential expression of the IFN-λ receptor, which, in contrast to IFN-α, should result in limited side effects in patients. Recently, we and other groups have shown in several animal models a potent antitumor role of IFN-λ that will open a new challenging era for the current IFN therapy

Role of RUNX2 in Melanoma: A New Player in Tumor Progression and Resistance to Therapy

RUNX2, a transcription factor, initially known for its indispensable role in skeletal development. RUNX2 is essential for osteoblast differentiation and the maintain of the osteocyte balance. RUNX2 acts directly on osteoblasts via Fgf pathway or on mesenchymal progenitors through Hedgehog, Wnt, Pthlh and DLX5. Currently, many reports point its critical role in the progression and metastasis of several cancer types. RUNX2 is involved in EMT process, invasion and metastasis through the modulation of important oncogenic pathways, including Wnt, FAK/PTK and AKT. In melanoma, RUNX2 is a key player in mediating intrinsic RTK-associated pro-oncogenic properties. We have showed a dramatic up regulation of RUNX2 expression with concomitant up-regulation of EGFR, IGF-1R and AXL, in melanoma cells rendered resistant to BRAF mutant inhibitors. Approximately half of melanomas carry BRAF mutations which enhance tumor invasion and metastasis. In this chapter, we describe the potential mechanisms, leading to the upregulation of RUNX2 in melanoma with BRAF mutations. We also highlight the critical role of PI3K/AKT in the expression and activation of RUNX2, and its consequences on the regulation of many critical factors, controlling cancer invasion and metastasis