Active contour driven by scalable local regional information on expandable kernel

An active contour that uses the pixel’s intensity on a set of expandable kernels along the propagating contour for image segmentation is presented in this paper. The objective is this study is to employ the scalable kernels to attract the contour to meet the desired boundary. The key characteristics of this scheme is that the kernels gradually expand to find an object’s boundary. So this scheme could penetrate to the concave boundary more effective and efficient than some other schemes. If a Gaussian kernel is applied, it could trace the object with a blurred or smooth boundary. Moreover, the directional selectivity feature enables in capturing two edge’s types with just one initial position. Its performance showed more desirable segmentation outcomes compared to the other existing active contours using regional information when segmenting the noisy image and the non-uniform (or heterogeneous) textures. Meanwhile, the level set implementation enables topological flexibility to our active contour scheme.

Automated Detection of Brain Abnormalities Using Multi-Directional Features and Randomized Learning

Automated detection of brain abnormalities through magnetic resonance imaging (MRI) has made a significant stride in the past decade. The feature extractors exploited in the literature suffer from issues like limited directional selectivity and high dimensionality, and the classifiers used have critical drawbacks like slow learning speed, poor computational scalability, and trivial human intervention. The fast curvelet transform (FCT) and ripplet-II transform (R2T) provides improved discriminant ability and high directional selectivity. Extreme learning machine (ELM), a randomized learning algorithm for single layer feed-forward neural network, has received significant attention as it provides good generalization performance at much faster speed. In this chapter, the authors compare the effectiveness of two feature extractors based on FCT and R2T along with different ELM algorithms. These schemes have been evaluated on three brain MR datasets and comparative analyses have been made on several combinations of methods. Finally, the potential of the best scheme is compared to the state of the art.

The computation of directional selectivity in the Drosophila OFF motion pathway

In flies, the direction of moving ON and OFF features is computed separately. T4 (ON) and T5 (OFF) are the first neurons in their respective pathways to extract a directionally selective response from their non-selective inputs. Our recent study of T4 found that the integration of offset depolarizing and hyperpolarizing inputs is critical for the generation of directional selectivity. However, T5s lack small-field inhibitory inputs, suggesting they may use a different mechanism. Here we used whole-cell recordings of T5 neurons and found a similar receptive field structure: fast depolarization and persistent, spatially offset hyperpolarization. By assaying pairwise interactions of local stimulation across the receptive field, we found no amplifying responses, only suppressive responses to the non-preferred motion direction. We then evaluated passive, biophysical models and found that a model using direct inhibition, but not the removal of excitation, can accurately predict T5 responses to a range of moving stimuli.

IR and VI Fusion using Hybrid VSM and Swarm Optimization

In the current era, usage of Infrared (IR) sensors, in the real world has been increased because of decrease in their cost. Many recent applications of IR images are included in the field of defence, medicine, astronomy, meteorology, industry and science. One of the main applications in the field of defence is person detection. However, IR images have unique challenges. Image fusion based on DWT superior things because it is a multiresolution approach, it allows picture disintegration in various parameters and provides directional information. Most of the existing methods used two or three different quality measures to measure the performance of the fused system. The present study used the ten quality metrics on all derived approaches and noted down various qualitative conclusions. Proposed system the base layers obtained and Further the proposed work found that, the methods based on wavelet transforms have compactness, directional selectivity and orthogonality. Due to these, DWT based fusion methods are used in literature when compared to pyramid decomposition based fusion methods