Cervical cancer is a disease affecting a worrisomely large number of women worldwide. If not treated in a timely fashion, this disease can lead to death. Due to this problematic, this research employed the LBP, OC_LBP, CS-LTP, ICS-TS, and CCR texture descriptors for the characteristic extractions of 60 selected carcinogenic images classified as Types 1, 2, and 3 according to a database; afterward, a statistical multi-class classifier and an NN were used for image classification. The resulting characteristic vectors of all five descriptors were implemented in four tests to identify the images by type. The statistical multi-class combination and classification of all images achieved a classification efficiency of 83–100%. On the other hand, with the NN, the LBP, OC_LBP, and CCR descriptors presented a classification efficiency of between 81.6 and 98.3%, differing from that of ICS_TS and CS_LTP, which ranged from 36.6 to 55%. Based on the tests performed with regard to ablation, ROC curves, and confusion matrix, we consider that an efficient expert system can be developed with the objective of detecting cervical cancer at early stages.

Safeguarding the identity of people in photographs or videos published through social networks or television is of great importance to those who do not wish to be recognized. In this paper, a face detecting and coding system is designed with the goal of solving this problem. Mathematical models to generate chaotic orbits are deployed. One of them applies the diffusion technique to scramble the pixels of each face while another implements the confusion technique to alter the relation between plain text and ciphered text. Afterward, another two orbits are utilized for the steganography technique to modify the least significant bit (LSB) to conceal data that would allow authorized users to decipher the faces. To verify the robustness of the proposed encryption algorithm, different tests are performed with the Lena standard image, such as correlation diagrams, histograms, and entropy. In addition, occlusion, noise, and plain image attacks are performed. The results are compared with those of other works, and the proposed system provided high sensitivity at secret key and a large space for the encryption keys, good speed for ciphering, disorder in the cryptogram, security, data integrity, and robustness against different attacks.

In this article, a safe communication system is proposed that implements one or more portable devices denominated SBC (single-board computers), with which photographs are taken and that later utilizes the OpenCV Library for the detection and identification of the faces that appear in them. Subsequently, it consults the information in a stored database, whether locally in SBC or in a remote server, to verify that the faces should be coded, and it encrypts these, implementing a new cryptosystem that executes mathematical models to generate chaotic orbits, one of which is used for application on two occasions the technique of diffusion with the purpose of carrying out a small change in one of the pixels of the image, generating very different cryptograms. In addition, in order to make a safer system, it implements other chaotic orbits during the technique of confusion. With the purpose of verifying the robustness of the encryption algorithm, a statistical analysis is performed employing histograms, horizontal, vertical, and diagonal correlation diagrams, entropy, number of pixel change rate (NPCR), unified average change intensity (UACI), sensitivity of the key, encryption quality analysis, and the avalanche effect. The cryptosystem is very robust in that it generates highly disordered cryptograms, supports differential attacks, and in addition is highly sensitive to changes in the pixels as well as in the encrypted keys.

This paper proposes the transformation $\mathit{S}\to \overrightarrow{\mathit{C}}$, where S is a digital gray-level image and $\overrightarrow{\mathit{C}}$ is a vector expressed through the textural space. The proposed transformation is denominated Vectorial Image Representation on the Texture Space (VIR-TS), given that the digital image S is represented by the textural vector $\overrightarrow{\mathit{C}}$. This vector $\overrightarrow{\mathit{C}}$ contains all of the local texture characteristics in the image of interest, and the texture unit $\overrightarrow{\mathit{T}}$ entertains a vectorial character, since it is defined through the resolution of a homogeneous equation system. For the application of this transformation, a new classifier for multiple classes is proposed in the texture space, where the vector $\overrightarrow{\mathit{C}}$ is employed as a characteristics vector. To verify its efficiency, it was experimentally deployed for the recognition of digital images of tree barks, obtaining an effective performance. In these experiments, the parametric value λ employed to solve the homogeneous equation system does not affect the results of the image classification. The VIR-TS transform possesses potential applications in specific tasks, such as locating missing persons, and the analysis and classification of diagnostic and medical images.

Facial recognition is widely employed in identification systems where access restriction is a desirable quality, providing greater security and preventing the access of unauthorized parties. With this problem in mind, this paper proposes a new texture descriptor that works in the RGB, YCbCr and HSV color modes. It obtains perceptual descriptive data from the photographs; the descriptor considers similarities between neighboring pixels and diminishing noise interference, making it robust to minor disturbances and bringing greater stability between similar images. Furthermore, the descriptor achieves good classification performance when used with both a neural network (NN) and the statistical classifier Support Vector Machine (SVM) on color images. A performance comparison of the proposed descriptor against LBP and OC_LBP employing three different face databases is presented to demonstrate that the proposed descriptor can work with the RGB, YCbCr and HSV color modes, presenting the best results when employing a neural network as a classifier with color image databases in the three studied color modes.

In this paper, an RGB image with $\mathit{S}$ is separated by its channels, obtaining an image in each color channel ${\mathit{S}}_R$, ${\mathit{S}}_G$ and ${\mathit{S}}_B$. The Vectorial Image Representation on the Texture Space (VIR-TS) transform is calculated for each channel; ergo, each image is represented with a given vector, ${\mathit{S}}_R\to {\overrightarrow{\mathit{C}}}_{\mathit{R}}$, ${\mathit{S}}_G\to {\overrightarrow{\mathit{C}}}_{\mathit{G}}$ and ${\mathit{S}}_B\to {\overrightarrow{\mathit{C}}}_{\mathit{B}}$. Employing the ${\overrightarrow{\mathit{C}}}_{\mathit{R}}$, ${\overrightarrow{\mathit{C}}}_{\mathit{G}}$, and ${\overrightarrow{\mathit{C}}}_{\mathit{B}}$ vectors in a multi-class classifier, a database of RGB images was identified with the aim of verifying the classification efficiency of the VIR-TS transform. Based on the experimental results, the VIR-TS technique presents high efficiency when the noise is not added to the class and when the signal-to-noise ratio is high. For both instances, the efficiency presented is 100%. Nonetheless, if the class noise is high, the efficiency diminishes from 100%, to 95%, to 90% until it decreases to 10%. Based on the results obtained, the VIR-TS transform can be efficiently applied for the development of security systems and access control and can also be implemented in computer vision systems for medical diagnosis, drones, etc.