Stylized rendering is the process of generating images or videos that can have the visual appeal of pieces of art, expressing the visual and emotional characteristics of artistic styles. A major problem in stylizing videos is the absence of temporal coherence, something that results in flickering of the structural drawing elements (such as brush strokes or curves), also known as swimming. This article describes the AnimVideo rendering tool that was developed for stylizing videos with temporal coherence. The temporal coherence is achieved by first fully segmenting the input video with a fast fuzzy segmentation algorithm that uses hybrid color spaces and motion information. The result of the segmentation algorithm is used to constrain the result of an optical flow algorithm, given as dense optical flow maps that are then used to correctly move, remove, or add structural drawing elements. The combination of these two methods is referred to as constrained optical flow, and we also provide the option of initializing the optical flow computation with displacement maps computed by homographies that map objects in adjacent frames. Also, we briefly describe some stylized rendering methods that were implemented in the tool. Finally, experimental results are shown, including snapshots of the tool's interface and illustrative examples of the produced renderings that validates the proposed techniques. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 100–110, 2009.

http://www.heathershrewsbury.com/dreu2010/wp-content/uploads/2010/07/ProducingSytlizedVideosUsingTheAnimVideoRenderingTool.pdf

Producing stylized videos using the AnimVideo rendering tool

We present a supervised classification method which represents each class by one or more optimum-path trees rooted at some key samples, called prototypes. The training samples are nodes of a complete graph, whose arcs are weighted by the distances between the feature vectors of their nodes. Prototypes are identified in all classes and the minimization of a connectivity function by dynamic programming assigns to each training sample a minimum-cost path from its most strongly connected prototype. This competition among prototypes partitions the graph into an optimum-path forest rooted at them. The class of the samples in an optimum-path tree is assumed to be the same of its root. A test sample is classified similarly, by identifying which tree would contain it, if the sample were part of the training set. By choice of the graph model and connectivity function, one can devise other optimum-path forest classifiers. We present one of them, which is fast, simple, multiclass, parameter independent, does not make any assumption about the shapes of the classes, and can handle some degree of overlapping between classes. We also propose a general algorithm to learn from errors on an evaluation set without increasing the training set, and show the advantages of our method with respect to SVM, ANN-MLP, and k-NN classifiers in several experiments with datasets of various types. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 120–131, 2009.

A preliminary version of the paper was presented at the 12th International Workshop on Combinatorial Image Analysis (Papa et al.,2008a).

/pdf/supervised-pattern-classification-based-on-optimum-path-1447ii27co.pdf

Supervised pattern classification based on optimum-path forest

Neighbor finding is an important and a basic part of image processing in quadtrees. A constant time algorithm is proposed for neighbor finding in quadtrees in [1]. In this paper, empirical tests are given for the constant time algorithm in comparison with usual neighbor finding algorithm using quadtrees [2] and another constant time algorithm using linear quadtree [3]. Experiments using synthetic images simulating worst case situations show that the proposed algorithm is in constant time complexity while others are not. Even for experiments using natural images, the proposed algorithm is more than twice as fast as algorithm using quadtrees and is slightly as fast as algorithm using linear quadtrees.

Constant time neighbor finding in quadtrees: An experimental result

Recently some CBIR approaches have shown the use of relevance feedback to train a pattern classifier to select relevant images for retrieval. This paper revisits this strategy by using an optimum-path forest (OPF) classifier. During relevance feedback iterations, the proposed method uses the OPF classifier to decide which database images are relevant or not. Images classified as relevant are sorted and presented to the user for a new iteration. Such images are ordered according to the normalized distance using relevant and irrelevant representative images, computed previously by the OPF classifier. Our experiments show that the proposed approach requires fewer iterations, being faster and more effective than methods based on SVM.

/pdf/a-new-cbir-approach-based-on-relevance-feedback-and-optimum-3m85wuveiu.pdf

A new CBIR approach based on relevance feedback and optimum-path forest classification

In this paper, we will focus on adaptive meshing and re-meshing. We present an original approach, based on Quadtree and Octree, to construct the initial mesh and refine it using mixed-elements. We propose a fast algorithm using a determined set of Patterns to handle transitions between fine and coarse regions, and to closely approximate surface boundaries. An optimized structure for storing edges permits to efficiently manage neighbor information, dramatically reducing the balancing time. Results, both in 2D and 3D, exhibit the multiple possibilities for local refinements. Comparisons in terms of accuracy, number of elements in the resulting mesh, and computation time, clearly position our method as a competitive alternative to generate a conform adapted mesh, which can be used with standard numerical methods.

Fast Quadtree/Octree adaptive meshing and re-meshing with linear mixed elements

Quadtrees and linear quadtrees are well-known hierarchical data structures to represent square images of size 2r times 2r. Finding the neighbors of a specific leaf node is a fundamental operation for many algorithms that manipulate quadtree data structures. In quadtrees, finding neighbors takes O(r) computational time for the worst case, where r is the resolution (or height) of a given quadtree. Schrack [1] proposed a constant-time algorithm for finding equal-sized neighbors in linear quadtrees. His algorithm calculates the location codes of equal-sized neighbors; it says nothing, however, about their existence. To ensure their existence, additional checking of the location codes is needed, which usually takes O(r) computational time. In this paper, a new algorithm to find the neighbors of a given leaf node in a quadtree is proposed which requires just O(1) (i.e., constant) computational time for the worst case. Moreover, the algorithm takes no notice of the existence or nonexistence of neighbors. Thus, no additional checking is needed. The new algorithm will greatly reduce the computational complexities of almost all algorithms based on quadtrees.

A Constant-Time Algorithm for Finding Neighbors in Quadtrees

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A Constant-Time Algorithm for Finding Neighbors in Quadtrees