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We propose a new combination of deep belief networks and sparse manifold learning strategies for the 2D segmentation of non-rigid visual objects. With this novel combination, we aim to reduce the training and inference complexities while maintaining the accuracy of machine learning-based non-rigid segmentation methodologies. Typical non-rigid object segmentation methodologies divide the problem into...
Segmentation of biomedical images is a challenging task, especially when there is low quality or missing data. The use of prior information can provide significant assistance for obtaining more accurate results. In this paper we propose a new approach for dendritic spine segmentation from microscopic images over time, which is motivated by incorporating shape information from previous time points...
The current work proposes an approach for the recognition of plants from their digital leaf images using multiple visual features to handle heterogeneous plant types. Recognizing the fact that plant leaves can have a variety of recognizable features like color (green and non-green) and shape (simple and compound) and texture (vein structure patterns), a single set of features may not be efficient...
In this paper, we propose an approach for achieving generalized segmentation of microorganisms in microscopy images. It employs a pixel-wise classification strategy based on local features. Multilayer perceptrons are utilized for classification of the local features and is trained for each specific segmentation problem using supervised learning. This approach was tested on five different segmentation...
This paper investigates a novel solution for the recognition of objects of interest in aerial images. The solution builds on a combination of algorithms inspired from the human visual system with classical and modern algorithms. The goal is to achieve intelligent and powerful approaches that allow for fast and automatic treatment of complex images. The methodology that is proposed innovatively combines...
This paper proposes a cartilage thickness detection and visualization method that does not utilize a shape model. The proposed method consists of three parts: volume of interest (VOI) initialization, bone segmentation, and cartilage thickness visualization. For VOI initialization, a novel 3D U-shape cuboidal filter is proposed to detect individual bones such as the femur, tibia, and patella, and for...
Object recognition is one of the most classic problem in computer vision where several techniques in classification have been proposed. In this work, we investigate a new promising improvement on a classification problem using multiple feature types. We assume that different features are suitable for different objects and the occurrence of particular objects usually relate to each other via particular...
This paper proposes a novel pose-invariant segmentation approach for left ventricle in 3D CT images. The proposed formulation is modular with respect to the image support (i.e. landmarks, edges and regional statistics). The prior is represented as a third-order Markov Random Field (MRF) where triplets of points result to a low-rank statistical prior while inheriting invariance to global transformations...
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...
In this paper we present a method to combine the detection and segmentation of object categories from 3D scenes. In the process, we combine the top-down cues available from object detection technique of Implicit Shape Models and the bottom-up power of Markov Random Fields for the purpose of segmentation. While such approaches have been tried for the 2D image problem domain before, this is the first...
Partitioning polygonal object into meaningful parts is one of the key steps to recognize this kind of object. Psychologist thinks that when people recognize object, they first decompose object recursively into several parts to recognize, and then combine the parts as a whole to know what the object is. So how to partition object is an important research point in Pattern Recognition and Computer Graphics...
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