Sonar image processing for underwater object detection in matlab

Sonar image obtained from such sonar instruments are used in different fields to realize seafloor task, such as navigation, seabed mapping, fishing, ocean drilling barrier, oil exploration, mines’ detection, and so on
Because of the particular optical properties of light in water and the presence of suspended particles, sonar images are very noisy, the lighting is not uniform, the colors are muted and the contrast is low. Most methods dedicated to reduce the noise apply different filtering and are often classified in two categories: the methods acting in the spatial domain and those acting in the transformed domain. Stéphane Bazeille and Isabelle Quidu [6] proposed an algorithm for image preprocessing sonar which allows correction of lighting, noise and color and requires no user intervention or a priori information on the acquisition conditions. This algorithm is a combination of four different filters each of which aims to revise a special defect. To eliminate the defects of non-uniformity of illumination, the Homomorphic filtering is used.

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Background Subtraction in MATLAB

Background subtraction, also known as Foreground Detection, is a technique in the fields of image processing and computer vision wherein an image's foreground is extracted for further processing (object recognition etc.). Generally an image's regions of interest are objects (humans, cars, text etc.) in its foreground. After the stage of image preprocessing (which may include image denoising, post processing like morphology etc.) object localisation is required which may make use of this technique. Background subtraction is a widely used approach for detecting moving objects in videos from static cameras. The rationale in the approach is that of detecting the moving objects from the difference between the current frame and a reference frame, often called “background image”, or “background model”. Background subtraction is mostly done if the image in question is a part of a video stream. Background subtraction provides important cues for numerous applications in computer vision, for example surveillance tracking or human poses estimation. However, background subtraction is generally based on a static background hypothesis which is often not applicable in real environments. With indoor scenes, reflections or animated images on screens lead to background changes. In a same way, due to wind, rain or illumination changes brought by weather, static backgrounds methods have difficulties with outdoor scenes.

• Background subtraction (BS) is a common and widely used technique for generating a foreground mask (namely, a binary image containing the pixels belonging to moving objects in the scene) by using static cameras.
• As the name suggests, BS calculates the foreground mask performing a subtraction between the current frame and a background model, containing the static part of the scene or, more in general, everything that can be considered as background given the characteristics of the observed scene.
  

• Background modeling consists of two main steps:
  1. Background Initialization;
  2. Background Update.

                             

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Brain signal data by EEG signal processing technique using MATLAB

 EEG is brain signal processing technique that allows gaining the understanding of the complex inner mechanisms of the brain and abnormal brain waves have shown to be associated with particular brain disorders. The analysis of brain waves plays an important role in diagnosis of different brain disorders. MATLAB provides an interactive graphic user interface (GUI) allowing users to flexibly and interactively process their high-density EEG dataset and other brain signal data different techniques

Results After Compressive Sensing:-

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