Appearance Of New Objects Research Articles

Automatic moving object extraction toward compact video representation

An automatic object-oriented video segmentation and representation algorithm is proposed, where the local variance contrast and the frame difference contrast are jointly exploited for meaningful moving object extraction because these two visual features can indicate the spatial homogeneity of the gray levels and the temporal coherence of the motion fields efficiently. The 2-D entropic thresholding technique and the watershed transformation method are further developed to determine the global feature thresholds adaptively according to the variation of the video components. The obtained video components are first represented by a group of 4x4 blocks coarsely, and then the meaningful moving objects are generated by an iterative region-merging procedure according to the spatiotemporal similarity measure. The temporal tracking procedure is further proposed to obtain more semantic moving objects and to establish the correspondence of the moving objects among frames. Therefore, the proposed automatic moving object extraction algorithm can detect the appearance of new objects as well as the disappearance of existing objects efficiently because the correspondence of the video objects among frames is also established. Moreover, an object-oriented video representation and indexing approach is suggested, where both the operation of the camera (i.e., change of the viewpoint) and the birth or death of the individual objects are exploited to detect the breakpoints of the video data and to select the key frames adaptively.

Image registration and object recognition using affine invariants and convex hulls

This paper is concerned with the problem of feature point registration and scene recognition from images under weak perspective transformations which are well approximated by affine transformations and under possible occlusion and/or appearance of new objects. It presents a set of local absolute affine invariants derived from the convex hull of scattered feature points (e.g., fiducial or marking points, corner points, inflection points, etc.) extracted from the image. The affine invariants are constructed from the areas of the triangles formed by connecting three vertices among a set of four consecutive vertices (quadruplets) of the convex hull, and hence do make direct use of the area invariance property associated with the affine transformation. Because they are locally constructed, they are very well suited to handle the occlusion and/or appearance of new objects. These invariants are used to establish the correspondences between the convex hull vertices of a test image with a reference image in order to undo the affine transformation between them. A point matching approach for recognition follows this. The time complexity for registering L feature points on the test image with N feature points of the reference image is of order O(N x L). The method has been tested on real indoor and outdoor images and performs well.

The Control of the Attentional Focus

We conducted three experiments to examine the characteristics of focal attention. Experiments 1A, 1B and 1C confirmed previous results showing an inverse relation between size of the attentional focus and efficiency of processing. However, we also found attentional benefits with a comparatively short stimulus onset asynchrony. In addition, our results suggested that focusing attention is an exogenously driven reaction: Given a proper stimulus, the attentional system automatically produces a focusing response. Experiments 2 and 3 suggested that the appearance of new objects is a critical factor to capture attention. When the size of the cue that was used for focusing attention was kept constant across trials, attentional benefits disappeared. These results were interpreted as supporting the hypothesis that attentional capture is contingent on an attentional control setting.

Direct incremental model-based image motion segmentation for video analysis

Dynamic analysis of image sequences is an important task in object-oriented video applications. It often relies on the segmentation of each image of the sequence into region entities of apparent homogeneous motion. In this paper, we present an original motion segmentation algorithm based on 2D polynomial motion models, a multiresolution robust estimator to compute these motion models, and appropriate local observations supplying both motion relevant information and their reliability. Motion segmentation is formulated as a contextual statistical labeling problem exploiting multiscale Markov random field (MRF) models. One of its main features is that it avoids time consuming alternate iterations between motion model estimation and spatial support identification. An original detection step allows us to estimate and to update the number of required motion models, and thus to handle the appearance of new objects. Numerous experiments performed with real indoor and outdoor image sequences demonstrate the efficiency of the method.

Unsupervised video segmentation based on watersheds and temporal tracking

This paper presents a technique for unsupervised video segmentation. This technique consists of two phases: initial segmentation and temporal tracking, similar to a number of existing techniques. However, new algorithms for spatial segmentation, marker extraction, and modified watershed transformation are proposed for the present technique. The new algorithms make this technique differ from existing techniques by the following features: (1) it can effectively track fast moving objects, (2) it can detect the appearance of new objects as well as the disappearance of existing objects, and (3) it is computationally efficient because of the use of watershed transformations and a fast motion estimation algorithm. Simulation results demonstrate that the proposed technique can efficiently segment video sequences with fast moving, newly appearing, or disappearing objects in the scene.