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Gathering statistical and geometrical information by processing the shape contours is the common way of feature extraction on object detection and recognition studies. Compactness is an important shape descriptor which specifies the similarity between a shape and a circle. In this study, we propose a new compactness measure based on examining the distribution of the contour moments with respect to...
Human action recognition and interpretation constitutes an important part of the video understanding. In this work, a novel action recognition system is developed that uses edge features obtained from optical flow power shapes which is represented as sequential gradient histograms. The presented system can achieve equal results to the complicated top action recognition systems of nowadays. The system...
Plants play a crucial role in terms of the lives of human and other creatures since the existence of the universe. Despite the studies of plant scientists, there are many undiscovered and unidentified species in our environment. This paper is aimed to add the leaves, whose images have been clearly attained, to the system and to provide a proper analysis of those leaves. The images could be either...
Moment invariants have been a hot research topic for several decades already. Even though existing moment invariants are good for applications like pattern recognition, there is still a need to further improve the existing moment invariants published in the literature. In this paper, a new set of invariant moments is proposed by using the ridgelet function, which is good at capturing line features...
Zernike moments are widely used in shape retrieval, recognition and classification. The rotational invariance property of Zernike moments is very simple to achieve, due to their separable magnitude-phase property. However, Zernike moments are not directly invariant to scale and translation. Recently Cartesian Zernike moments invariants (CZMI) were introduced to directly make Zernike moments invariant...
In this paper, we present an approach that simultaneously clusters database members and learns dictionaries from the clusters. The method learns dictionaries in the Radon transform domain, while clustering in the image domain. Themain feature of the proposed approach is that it provides rotation invariant clustering which is useful in Content Based Image Retrieval (CBIR). We demonstrate through experimental...
The shape reconstruction problem is one of important and most applied problems in many branches of computer science. In this paper a new method is introduced which a graph as output and work on boundary samples as input, in the plan. The proposed method starts reconstruction from the random polygon production on input points and by calculating the Euclidean distance between points, it produces a set...
This paper presents the different steps for an automatic fluorescence-labelled cell classification method. First a data features study is discussed in order to describe cell texture by means of morphological and statistical texture descriptors. Then, results on supervised classification using logistic regression, random forest and neural networks, for both morphological and statistical descriptors,...
It is suggested how a Markov random field can be used for object tracking with context information. The tracking is formulated as a two layer process. In the first phase, the image is represented by a set of feature points which are tracked by a standard tracker. In the second phase, the proposed semi-supervised learning and labeling algorithm is used to label the points to three classes — object,...
While the skeleton of a 2D shape corresponds to a planar graph, its encoding by usual graph data structures does not allow to capture its planar properties. Graph kernels may be defined on graph's encoding of the skeleton in order to define a similarity measure between shapes. Such graph kernels are usually based on a decomposition of graphs into bags of walks or trails. These linear patterns do not...
In reconstructing 3-D shape from images based on feature points, we usually define a triangular mesh that has those feature points as vertices, and display the object as a polyhedron. If the object itself is a polyhedron, however, some of the displayed edges may be inconsistent with the true shape. For this problem, Nakatsuji et al. proposed a method that automatically eliminates such inconsistencies...
Scale invariance is a desirable property for many vision tasks such as image segmentation and classification. One way to achieve such invariance is to collect images containing objects of all scales and then train a classifie r. In practice, however, only a finite number of images at a finite number of scales can be collected, and this poses the problem of scale sampling. In this paper, we focus on...
An automated histology analysis is proposed for classification of local image patches of colon histopathology images into four principle classes: normal, cancer, adenomatous and inflamed classes. Shape features based on stroma, lumen and imperfectly segmented nuclei are combined with texture features for classification. The classification is analyzed under the three scenarios: normal vs. abnormal,...
The problem of simultaneously estimating affine deformations between multiple objects occur in many applications. Herein, a direct method is proposed which provides the result as a solution of a linear system of equations without establishing correspondences between the objects. The key idea is to construct enough linearly independent equations using covariant functions, and then finding the solution...
Vein image recognition based on modeling shape or geometrical layout of feature points is generative approach, and the performance is usually limited by segmentation error due to poor vein image quality. This paper instead proposes to model the discriminative appearance of local image patch using the vocabulary tree model. The discriminative approach is further extended to consider the geometrical...
Breast cancer grading of histological tissue samples by visual inspection is the standard clinical practice for the diagnosis and prognosis of cancer development. An important parameter for tumor prognosis is the number of mitotic cells present in histologically stained breast cancer tissue sections. We propose a hierarchical learning workflow for automated mitosis detection in breast cancer. From...
Shape analysis relies on using a finite number of points on the contour of an object to compare the shapes of objects. These points are called landmarks. Hence, when landmarks are not available for analysis, we must place some appropriately on the contour. In this paper, we describe a new method for placing landmarks well on the contours of objects in the same class. The landmarks located by our method...
The study of neurological processes and pharmaceutical effects often relies on the analysis of mice behaviour. Automatic tracking tools are widely employed for this purpose, however they are mainly limited to a single mouse. We propose a real time segmentation and tracking algorithm able to manage multiple interacting mice regardless of their fur colour or light settings via an infrared camera. The...
In this paper a novel 2D shape recognition approach is proposed. The main idea is to exploit in this context the huge amount of work carried out by bioinformati-cians in the biological sequence analysis research field. In the proposed approach, we encode shapes as biological sequences, employing standard and well established sequence alignment tools to devise a similarity score, finally used in a...
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