In this work, we deal with a brain tumor segmentation problem from magnetic resonance imaging (MRI), considered financially and time demanding when carrying out manually. To tackle this specific and complex domain problem, convolutional networks have proved competent due to significantly better performance than standard segmentation approaches. Therefore, within our research, we propose an approach which is dealing with tumor segmentation. During the elaboration, we propose multiple architectures, training phases and evaluation metrics in order to facilitate reliable and automatic delineation of tumorous tissues. For this purpose, we proposed a novel adaptation of the Tversky index loss formula to avoid label imbalance.
Many domain specific challenges for feature matching and similarity learning in computer vision have been relying on labelled data, either using heuristic or more recent approaches via deep learning. While aiming for precise solutions, we need to process larger number of features which may result in higher computational complexity. This paper proposes a novel method of similarity learning through two-part cost function as it could be done using heuristic approaches in original feature space in an unsupervised manner, while also reducing feature complexity. The features are encoded on the lower dimensionality manifold which preserve original structure of data. This approach takes advantage of siamese networks and autoencoders to obtain compressed features while maintaining same distance properties as in the original feature space. This is done by introducing new loss function with two terms, which aims for good reconstruction as well as learning the similar data point neighborhood from encoded and reconstructed feature space.
Computational models predicting stimulus-driven human visual attention usually incorporate simple visual features, such as intensity, color and orientation. However, saliency of shapes and their contour segments influence attention too. Therefore, we built 30 own shape saliency models based on existing shape representation and matching techniques and compared them with 5 existing saliency methods. Since available fixation datasets were usually recorded on natural scenes where various factors of attention are present, we performed a novel eye-tracking experiment that primarily focuses on shape and contour saliency. Fixations from 47 participants who looked at silhouettes of abstract and realworld objects were used to evaluate the accuracy of proposed saliency models and investigate which shape properties are most attentive. The results showed that visual attention integrates local contour saliency, saliency of global shape features and shape dissimilarities. Fixation data also showed that intensity and orientation contrasts play an important role in shape perception. We found that humans tend to fixate first irregular geometrical shapes and objects whose similarity to a circle is different from other objects.
In this paper, we propose an enhanced method of 3D object description and recognition based on local descriptors using RGB image and depth information (D) acquired by Kinect sensor. Our main contribution is focused on an extension of the SIFT feature vector by the 3D information derived from the depth map (SIFT-D). We also propose a novel local depth descriptor (DD) that includes a 3D description of the key point neighborhood. Thus defined the 3D descriptor can then enter the decision-making process. Two different approaches have been proposed, tested and evaluated in this paper. First approach deals with the object recognition system using the original SIFT descriptor in combination with our novel proposed 3D descriptor, where the proposed 3D descriptor is responsible for the pre-selection of the objects. Second approach demonstrates the object recognition using an extension of the SIFT feature vector by the local depth description. In this paper, we present the results of two experiments for the evaluation of the proposed depth descriptors. The results show an improvement in accuracy of the recognition system that includes the 3D local description compared with the same system without the 3D local description. Our experimental system of object recognition is working near real-time.
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