Brazil

One of the most used statistical approaches in the
literature for computing texture information is the Gray Level Cooccurrence Matrix (GLCM). The use of GLCM and textures descriptors calculated from there, allows, among other applications, the detection of the main direction of the elements of texture. This is possible essentially because the GLCM uses different angles to compute the relationship between the gray
tones in the image. An alternative to computing this relationship is the Sum of Difference Histograms (SDH). In contrast to GLCM, which is a bidimensional structure, SDHs are onedimensional. Once there is a relationship between the data stored in both structures, the behavior of the descriptors is similar, and the SDH have a lower computational cost being, for this, more efficient in a number of applications. In this sense, this work leads to a series of investigations regarding the correspondence between
these two approaches. Moreover, it investigates which texture descriptors, calculated from the SDH, can collaborate in the process of identifying the predominant direction of a texture.

IWSSIP 2020

Determining the Direction of Images Using Sum and Difference Histograms and Texture Descriptors

image orientation.

gray level cooccurrence matrix (glcm)

sum and difference histograms (sdh)

texture descriptors

The 27th International Conference on Systems, Signals and Image Processing.

technical paper

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ROI Extraction in Thermographic Breast Images Using Genetic Algorithms

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Hybrid Capsule Network Architecture Estimation for Melanoma Detection

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SpaceYNet: A Novel Approach to Pose and Depth-Scene Regression Simultaneously

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Ocular Recognition Using Deep Features for Identity Authentication

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A Survey on Performance Metrics for Object-Detection Algorithms

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Addressing Class Imbalance in Image-Based Plant Disease Detection: Deep Generative vs. Sampling-Based Approaches

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Analysis of Features for Breast Cancer Recognition in Different Magnifications of Histopathological Images

Borehole stability is one of the main factors influencing the success of an oil well drilling process. Fractures and beddings (sedimentary layers) are important stability features commonly identified in borehole imaging tools. Their visual presentation in the 2D Ultrasonic Borehole Imager (UBI) consists of sinusoidal curves, in which automatic detection is not trivial. Several previous works proposed different solutions, most of them based on the Hough transform (HT). Nevertheless, HT based solutions have a substantial computational cost. In this work, we propose an innovative approach to identify the harmonic curves related to fractures and sedimentary layers in borehole images based in the Iterative Local Search heuristic, reducing the computational cost of the task significantly.

Automatic Sinusoidal Curves Detection in Borehole Images Using the Iterated Local Search Algorithm

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Graphical Oracles to Assess Computer-Aided Diagnosis Systems: A Case Study in Mammogram Masses and Calcifications Detection

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