Automatic object extraction and reconstruction in active video

Abstract

A new method of video object extraction is proposed to automatically extract the object of interest from actively acquired videos. Traditional video object extraction techniques often operate under the assumption of homogeneous object motion and extract various parts of the video that are motion consistent as objects. In contrast, the proposed active video object extraction (AVOE) approach assumes that the object of interest is being actively tracked by a non-calibrated camera under general motion and classifies the possible movements of the camera that result in the 2D motion patterns as recovered from the image sequence. Consequently, the AVOE method is able to extract the single object of interest from the active video. We formalize the AVOE process using notions from Gestalt psychology. We define a new Gestalt factor called "shift and hold" and present 2D object extraction algorithms. Moreover, since an active video sequence naturally contains multiple views of the object of interest, we demonstrate that these views can be combined to form a single 3D object regardless of whether the object is static or moving in the video.

Video object extraction using optimized smoothed dirichlet process multi-view learning with improved adaptive modified Markova random field

“…However, the more iteration was required to extract possible video objects. An object extraction method [15] was designed to extract the required motion pattern from the vide-os which consist of different motion patterns. The incorporation of color and texture features which were semantically relevant to objects through multiple views achieved various motion pattern based object extraction.…”

Automatic Detection of Object of Interest and Tracking in Active Video

“…Typical examples will be filming, professional video cameramen covering sporting events, or an amateur shooting at a tourist scene. As introduced by Lu and Li in [26], this type of videos can be characterized as Active Video. They are very much object-centered and often exhibit prominent catching and holding behaviors of the video camera operators.…”

Seguimiento de artefactos navales

“…Índice de Histograma Semántica del bin H [1] Magenta H [2] Blue H [3] Cyan H [4] Green H [5] Yellow H [6] Red H [7] Black H [8] Dark Gray H [9] Gray H [10] Light Gray H [11] White H [12] Vertical Edge H [13] Horizontal Edge H [14] Complex Edge Fuente: D. K.…”

“…El trabajo en imágenes está siendo estudiado para múltiples usos, es así como google, se encuentra diseñando un buscador de imágenes para clasificarlas 5 . Se trabaja en reconstrucción de imágenes [5] y [6], en navegación de vehículos autónomos [7].…”

“…El trabajo en imágenes está siendo estudiado para múltiples usos, es así como google, se encuentra diseñando un buscador de imágenes para clasificarlas 5 . Se trabaja en reconstrucción de imágenes [5] y [6], en navegación de vehículos autónomos [7]. en reconocimiento de rostros [8] y [9], en extracción de datos de documentos manuscritos [10] en clasificación de suelos [11].…”