4791841
Salient Boundary Detection using Ratio Contour
This paper presents a novel graph-theoretic approach, named ratio contour, to extract perceptually salient boundaries from a set of noisy boundary fragments detected in real images. The boundary saliency is defined using the Gestalt laws of closure, proximity, and continuity. This paper first constructs an undirected graph with two different sets of edges: solid edges and dashed edges. The weights of solid and dashed edges measure the local saliency in and between boundary fragments, respectively. Then the most salient boundary is detected by searching for an optimal cycle in this graph with minimum average weight. The proposed approach guarantees the global optimality without introducing any biases related to region area or boundary length. We collect a variety of images for testing the proposed approach with encouraging results.
4765426
Salient closed boundary extraction with ratio contour
We present ratio contour, a novel graph-based method for extracting salient closed boundaries from noisy images. This method operates on a set of boundary fragments that are produced by edge detection. Boundary extraction identifies a subset of these fragments and connects them sequentially to form a closed boundary with the largest saliency. We encode the Gestalt laws of proximity and continuity in a novel boundary-saliency measure based on the relative gap length and average curvature when connecting fragments to form a closed boundary. This new measure attempts to remove a possible bias toward short boundaries. We present a polynomial-time algorithm for finding the most-salient closed boundary. We also present supplementary preprocessing steps that facilitate the application of ratio contour to real images. We compare ratio contour to two closely related methods for extracting closed boundaries: Elder and Zucker's method based on the shortest-path algorithm and Williams and Thornber's method based on spectral analysis and a strongly-connected-components algorithm. This comparison involves both theoretic analysis and experimental evaluation on both synthesized data and real images.
554747
Image-Segmentation Evaluation From the Perspective of Salient Object Extraction
Image segmentation and its performance evaluation are very difficult but important problems in computer vision. A major challenge in segmentation evaluation comes from the fundamental conflict between generality and objectivity: For general-purpose segmentation, the ground truth and segmentation accuracy may not be well defined, while embedding the evaluation in a specific application, the evaluation results may not be extended to other applications. We present in this paper a new benchmark for evaluating image segmentation. Specifically, we formulate image segmentation as identifying the single most perceptually salient structure from an image. We collect a large variety of test images that conforms to this specific formulation, construct unambiguous ground truth for each image, and define a reliable way to measure the segmentation accuracy. We then present two special strategies to further address two important issues: (a) the most salient structures in some real images may not be unique or unambiguously defined, and (b) many available image-segmentation methods are not developed to directly extract a single salient structure. Finally, we apply this benchmark to evaluate and compare the performance of several state-of-the-art image-segmentation methods, including the normalized-cut method, the level-set method, the efficient graph-based method, the mean-shift method, and the ratio-contour method.
123309619
From fragments to salient closed boundaries: an in-depth study
This paper conducts an in-depth study on a classical perceptual-organization problem: finding salient closed boundaries from a set of boundary fragments detected in a noisy image. In this problem, a saliency boundary is formed by identifying and connecting a subset of fragments according to the simple Gestalt laws of closure, continuity, and proximity. Our specific interest is focused on the methods that aim to achieve boundary closure, an important global property of perceptual salient boundaries. In this paper, we analyze and compare three such methods that are developed in recent years: (a) Elder and Zucker's method based on the shortest-path algorithm, (b) Williams and Thornber's method combining the spectral-analysis and the strongly-connected-component algorithms, and (c) Wang, Kubota, and Siskind's method based on ratio-contour algorithm. Both theoretic analysis and experimental study show that, with a unified setting of fragment saliency, Wang, Kubota and Siskind's method more appropriately constrains the search space for the closed boundaries, and usually produces better performance than or at least comparable performance as the other two methods. Particularly, Wang, Kubota, and Siksind's method can always guarantee the boundary closure and simplicity, which may not be always hold in the other two methods. We construct and collect a variety of synthesized and real images for this comparison.
4791224
Image Segmentation with Minimum Mean Cut
We introduce a new graph-theoretic approach to image segmentation based on minimizing a novel class of ‘mean cut’ cost functions. Minimizing these cost functions corre sponds to finding a cut with minimum mean edge weight in a connected planar graph. This approach has several advantages over prior approaches to image segmentation. First, it allows cuts with both open and closed boundaries. Second, it guarantees that the partitions are connected. Third , the cost function does not introduce an explicit bias, such as a preference for large-area foregrounds, smooth or short boundaries, or similar-weight partitions. This lack of bia s allows it to produce segmentations that are better aligned with image edges, even in the presence of long thin regions. Finally, the global minimum of this cost function is largely insensitive to the precise choice of edge-weight function. In particular, we show that the global minimum is invariant under a linear transformation of the edge weights and thus insensitive to image contrast. Building on algorithms by Ahuja, Magnanti, and Orlin (1993), we present a polynomial-time algorithm for finding a global minimum of the mean-cut cost function and illustrate the results of applying that algorithm to several synthetic and real images.
122573148
Shape correspondence through landmark sliding
Motivated by improving statistical shape analysis, this paper presents a novel landmark-based method for accurate shape correspondence, where the general goal is to align multiple shape instances by corresponding a set of given landmark points along those shapes. Different from previous methods, we consider both global shape deformation and local geometric features in defining the shape-correspondence cost function to achieve a consistency between the landmark correspondence and the underlying shape correspondence. According to this cost function, we develop a novel landmark-sliding algorithm to achieve optimal landmark-based shape correspondence with preserved shape topology. The proposed method can be applied to correspond various 2D shapes in the forms of single closed curves, single open curves, self-crossing curves, and multiple curves. We also discuss the practical issue of landmark initialization. The proposed method has been tested on various biological shapes arising from medical image analysis and validated in constructing statistical shape models.
14774181
Nondifferentiable multiobjective programming under generalized d
Abstract In this paper, we are concerned with a nondifferentiable multiobjective programming problem with inequality constraints. We introduce four new classes of generalized convex functions by combining the concepts of weak strictly pseudoinvex, strong pseudoinvex, weak quasi invex, weak pseudoinvex and strong quasi invex functions in Aghezzaf and Hachimi [Numer. Funct. Anal. Optim. 22 (2001) 775], d -invex functions in Antczak [Europ. J. Oper. Res. 137 (2002) 28] and univex functions in Bector et al. [Univex functions and univex nonlinear programming, Proc. Admin. Sci. Assoc. Canada, 1992, p. 115]. By utilizing the new concepts, we derive a Karush–Kuhn–Tucker sufficient optimality condition and establish Mond–Weir type and general Mond–Weir type duality results for the nondifferentiable multiobjective programming problem.
16758055
Clustering in Knowledge Embedded Space
Cluster analysis is a fundamental technique in pattern recognition. It is difficult to cluster data on complex data sets. This paper presents a new algorithm for clustering. There are three key ideas in the algorithm: using mutual neighborhood graphs to discover knowledge and cluster data; using eigenvalues of local covariance matrixes to express knowledge and form a knowledge embedded space; and using a denoising trick in knowledge embedded space to implement clustering. Essentially, it learns a new distance metric by knowledge embedding and makes clustering become easier under this distance metric. The experiment results show that the algorithm can construct a quality neighborhood graph from a complex and noisy data set and well solve clustering problems.
33546649
Distinguishing between overlapping components in mixture models
10710388
CAPTCHA Challenge Tradeoffs: Familiarity of Strings versus Degradation of Images
It is a well documented fact that, for human readers, familiar text is more legible than unfamiliar text. Current-generation computer vision systems also are able to exploit some kinds of prior knowledge of linguistic context: for example, many OCR systems can use known lexica (word-lists, such as of commonly occurring English words) to disambiguate interpretations. It is interesting that human readers can exploit various degrees of familiarity; for example, strings of characters which, while not found in dictionaries, are similar to spelled words: e.g. "pronounceable" strings, or strings made up of frequently occurring character n-grams. In contrast to this, computer vision technologies for exploiting such poorly characterized constraints (absent an explicit, complete lexicon) are not yet well developed. This gap in ability may allow us to design stronger CAPTCHAs. We measure the familiarity of challenge strings generated by four methods (described by Bentley and Mallows) and we use the ScatterType CAPTCHA to degrade challenge images. We report the results of a human legibility trial which supports the hypothesis that more familiar strings are indeed more legible in CAPTCHAs. Our measurements may enable engineering CAPTCHAs with a more uniform distribution of difficulty by balancing image degradations against familiarity
14614060
A feature-based scalable codec for image compression
This paper presents a feature-based scheme for image compression. In the proposed method, the features in an image are recorded by three-dimensional verge points, which are defined as high curvature points on the image surface. The verge point representation can be further condensed using B-spline control point approximation. By encoding and transmitting these control points, the features in an image could be revealed at the decoder side. Furthermore, by selecting different coding modes, the proposed method can offer diversified scalabilities, including SNR scalability, spatial scalability, and shape scalability. In addition, this coding technique is suitable for region of interest selection and transmission. Experimental results are provided to present the scalability and interactivity of the proposed method.