Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> A. MAIN FEATURES OF DEPENDENT MOTION SEGMENTATION <s> Segmentation of objects in image sequences is very important in many aspects of multimedia applications. In second-generation image/video coding, images are segmented into objects to achieve efficient compression by coding the contour and texture separately. As the purpose is to achieve high compression performance, the objects segmented may not be semantically meaningful to human observers. The more recent applications, such as content-based image/video retrieval and image/video composition, require that the segmented objects be semantically meaningful. Indeed, the recent multimedia standard MPEG-4 specifies that a video is composed of meaningful video objects. Although many segmentation techniques have been proposed in the literature, fully automatic segmentation tools for general applications are currently not achievable. This paper provides a review of this important and challenging area of segmentation of moving objects. We describe common approaches including temporal segmentation, spatial segmentation, and the combination of temporal-spatial segmentation. As an example, a complete segmentation scheme, which is an informative part of MPEG-4, is summarized. <s> BIB001 </s> Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> A. MAIN FEATURES OF DEPENDENT MOTION SEGMENTATION <s> The quality of underwater image is poor due to the properties of water and its impurities. The properties of water cause attenuation of light travels through the water medium, resulting in low contrast, blur, inhomogeneous lighting, and color diminishing of the underwater images. This paper proposes a method of enhancing the quality of underwater image. The proposed method consists of two stages. At the first stage, the contrast correction technique is applied to the image, where the image is applied with the modified Von Kries hypothesis and stretching the image into two different intensity images at the average value with respects to Rayleigh distribution. At the second stage, the color correction technique is applied to the image where the image is first converted into hue-saturation-value (HSV) color model. The modification of the color component increases the image color performance. Qualitative and quantitative analyses indicate that the proposed method outperforms other state-of-the-art methods in terms of contrast, details, and noise reduction. <s> BIB002
To develop an effective dependent motion segmentation algorithm which can work for real videos (or real image sequences), several issues need to be considered. Among them, several issues are considered in most existing methods, including: the starting point (camera motion oriented or object motion oriented), effecviness in dealing with depth-dependency, requirment of prior knowledge on the camera model and object motions, robustness to missing data and noise, etc. As aforementioned, the objective of dependent motion segmentation depends on applications. For many applications, our aim is to estimate camera motions, while for others we just focus on the object motions. Thus, we first need to consider about the key point when designing motion segmentation algorithms. The second issue is also very important. The image plane motion is obtained by projecting 3D motions on to the 2D image field. A major drawback using 2D image plane motion for dependent motion segmentation is that the projected motions of an object on the image plane depends on its distance from the camera. Two objects which have the same real-world motion may have different image plane motions because of their different depths. This can cause a motion segmentation algorithm fails to segment between two objects at different depths even though they have the same real-world motion. Apart from the first two issues, we also need to consider about the prior knowledge of the camera model and object motions, which is not always known beforehand. For example, the camera model can be ranged from a simple translation model (with two parameters) to a more complex perspective model with eight parameters BIB001 . How to choose an appropriate camera motion model is still a challengeing issue to existing motion segmentation methods. Moreover, the object motions can be single or multiple, rigid or non-rigid. This kind of prior knowledge is very difficult to be obtained. Finally, the performance of motion segmentation methods may be influenced significantly by the presence of noise and occurance of missing data. For many applications in complex environments such as moving object detection in underwater imaging systems, the noise presence can be a critical problem. The quality of underwater images degrades due to many factors, such as the ocean flow, the water turbidity, strong reflections, back-scattering, rapid light attenuation, dynamic lighting and non-uniform lighting, etc. BIB002 . Moreover, some parts of image data may be missing because of the blurring and occlusion caused by motions of objects. Beyond the above mentioned main features of dependent motion segmentation, several other issues need to be considered. The main attributes of a dependent motion segmentation algorithm can be summarized as below: -Camera motion first vs. Object motion first: Some works segment object motion directly from the moving camera videos. These algorithms refer to object motion orientation ones. Other algorithms calculate camera motions first. These methods refer to camera motion orientation algorithms. -Feature based vs. Dense motion based: In feature based methods, objects are represented by a limited number of key feature points such as salient points or corners, while dense motion based methods generate a dense pixel-wise motion field like image plane motion or optical flow. -2D vs. 3D: Motion segmentation can be performed in the two-dimensional spaces (e.g. using image plane motion representation) or three-dimensional spaces (e.g. using structure from motion). They refer to 2D based methods and 3D based methods, respectively. -Rigid vs. Nonrigid: The object motions can be rigid or non-rigid. This shows the ability to deal with different kinds of object motions. -Single vs. Multiple: The objects can be single or multiple in the scene. This shows the ability to segment more than one objects from the scene. -Occlusions or not: The moving objects can cause occlusions. Even worse, the whole object may disappear and reappear at different frames. This shows the ability to deal with occlusions. 